lengths.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eric/miniconda3/envs/dl3/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Found cached dataset json (/home/eric/.cache/huggingface/datasets/json/default-789f51900889f651/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n",
      "100%|██████████| 1/1 [00:00<00:00, 784.28it/s]\n",
      "Loading cached processed dataset at /home/eric/.cache/huggingface/datasets/json/default-789f51900889f651/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-f691ee34ec2034cb.arrow\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import LlamaTokenizer\n",
    "\n",
    "\n",
    "tokenizer = LlamaTokenizer.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\", add_eos_token=True\n",
    ")\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "\n",
    "data = load_dataset(\"json\", data_files=\"alpaca_data.json\")\n",
    "\n",
    "\n",
    "def generate_prompt(data_point):\n",
    "    # sorry about the formatting disaster gotta move fast\n",
    "    if data_point[\"input\"]:\n",
    "        return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{data_point[\"instruction\"]}\n",
    "\n",
    "### Input:\n",
    "{data_point[\"input\"]}\n",
    "\n",
    "### Response:\n",
    "{data_point[\"output\"]}\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{data_point[\"instruction\"]}\n",
    "\n",
    "### Response:\n",
    "{data_point[\"output\"]}\"\"\"\n",
    "\n",
    "\n",
    "data = data.map(\n",
    "    lambda data_point: {\"prompt\": tokenizer(generate_prompt(data_point))}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f6f1af20af0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "lens = [len(x[\"prompt\"][\"input_ids\"]) for x in data[\"train\"]]\n",
    "plt.hist(lens, bins=100)\n",
    "plt.title(\"Distribution of prompt lengths\")\n",
    "plt.axvline(256, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f6eef316ce0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([len([l for l in lens if l <= m]) for m in range(max(lens) + 1)])\n",
    "plt.title(\"Number of fully covered examples as a function of max length\")\n",
    "plt.axvline(x=256, color=\"red\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of tokens left out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f6eef392020>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([sum(min(l, m) for l in lens) for m in range(max(lens) + 1)])\n",
    "plt.title(\"Token coverage as a function of max length\")\n",
    "plt.axvline(x=256, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl3",
   "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": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "90bfda469df5ac7fed8d7e225d563f60a7a7aa420ccfadb091c914debf775e49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}