{ "cells": [  {   "cell_type": "markdown",   "metadata": {},   "source": [    "Simple visualizer for log files written by the training loop"   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {},   "outputs": [],   "source": [    "import matplotlib.pyplot as plt\n",    "%matplotlib inline"   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {},   "outputs": [],   "source": [    "def parse_logfile(logfile):\n",    "    # so the tricky part we have to deal with in these log files\n",    "    # is that the job could crash and get restarted, which will\n",    "    # re-wind back and start re-logging older steps. So we keep\n",    "    # all the data as dictionary and over-write old data with new\n",    "    # and then at the end compile everything together\n",    "\n",    "    # read raw data\n",    "    streams = {} # stream:str -> {step: val}\n",    "    with open(logfile, \"r\") as f:\n",    "        for line in f:\n",    "            parts = line.split()\n",    "            assert len(parts) == 2\n",    "            step = int(parts[0].split(\":\")[1])\n",    "            stream = parts[1].split(\":\")[0]\n",    "            val = float(parts[1].split(\":\")[1])\n",    "            if not stream in streams:\n",    "                streams[stream] = {}\n",    "            d = streams[stream]\n",    "            d[step] = val\n",    "    # now re-represent as list of (step, val) tuples\n",    "    streams_xy = {}\n",    "    for k, v in streams.items():\n",    "        # get all (step, val) items, sort them\n",    "        xy = sorted(list(v.items()))\n",    "        # unpack the list of tuples to tuple of lists\n",    "        streams_xy[k] = zip(*xy)\n",    "    # return the xs, ys lists\n",    "    return streams_xy\n",    "\n",    "# parse_logfile(\"../log124M/main.log\")"   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {},   "outputs": [],   "source": [    "sz = \"350M\"\n",    "loss_baseline = {\n",    "    \"124M\": 3.424958,\n",    "    \"350M\": 3.083089,\n",    "    \"774M\": 3.000580,\n",    "    \"1558M\": 2.831273,\n",    "}[sz]\n",    "hella_baseline = {\n",    "    \"124M\": 0.294463,\n",    "    \"350M\": 0.375224,\n",    "    \"774M\": 0.431986,\n",    "    \"1558M\": 0.488946,\n",    "}[sz]\n",    "\n",    "# assumes each model run is stored in this way\n",    "logfile = f\"../log{sz}/main.log\"\n",    "streams = parse_logfile(logfile)\n",    "\n",    "plt.figure(figsize=(16, 6))\n",    "\n",    "# Panel 1: losses: both train and val\n",    "plt.subplot(121)\n",    "xs, ys = streams[\"trl\"] # training loss\n",    "plt.plot(xs, ys, label=f'llm.c ({sz}) train loss')\n",    "print(\"Min Train Loss:\", min(ys))\n",    "xs, ys = streams[\"tel\"] # validation loss\n",    "plt.plot(xs, ys, label=f'llm.c ({sz}) val loss')\n",    "# horizontal line at GPT-2 baseline\n",    "if loss_baseline is not None:\n",    "    plt.axhline(y=loss_baseline, color='r', linestyle='--', label=f\"OpenAI GPT-2 ({sz}) checkpoint val loss\")\n",    "plt.xlabel(\"steps\")\n",    "plt.ylabel(\"loss\")\n",    "plt.yscale('log')\n",    "plt.legend()\n",    "plt.title(\"Loss\")\n",    "print(\"Min Validation Loss:\", min(ys))\n",    "\n",    "# Panel 2: HellaSwag eval\n",    "plt.subplot(122)\n",    "xs, ys = streams[\"eval\"] # HellaSwag eval\n",    "plt.plot(xs, ys, label=f\"llm.c ({sz})\")\n",    "# horizontal line at GPT-2 baseline\n",    "if hella_baseline:\n",    "    plt.axhline(y=hella_baseline, color='r', linestyle='--', label=f\"OpenAI GPT-2 ({sz}) checkpoint\")\n",    "plt.xlabel(\"steps\")\n",    "plt.ylabel(\"accuracy\")\n",    "plt.legend()\n",    "plt.title(\"HellaSwag eval\")\n",    "print(\"Max Hellaswag eval:\", max(ys))"   ]  } ], "metadata": {  "kernelspec": {   "display_name": "pytorch3",   "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": "3.10.14"  } }, "nbformat": 4, "nbformat_minor": 2}