gpt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9948a78a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33b88394",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed: int):\n",
    "    import random, numpy as np, torch\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9486931",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "name = \"EleutherAI/gpt-neo-2.7B\"\n",
    "# get the model and tokenizer,\n",
    "# EleutherAI/gpt-neo-2.7B = 9.9GB compressed\n",
    "# gpt2-xl (1.5Bn Params) = 6.7GB compressed\n",
    "# always cache these models, reduces useless bandwidth.\n",
    "tokenizer = AutoTokenizer.from_pretrained(name, cache_dir = \"../hf-cache/\")\n",
    "model = AutoModelForCausalLM.from_pretrained(name, cache_dir = \"../hf-cache/\")\n",
    "print(\"Model loaded ..., moving to GPU\")\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else \"CPU\"\n",
    "model = model.to(device).eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61776ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa55b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gpt import GPT\n",
    "gpt = GPT(model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee5a26a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "gpt(\"Hello World!\", r = 4, n = 10, temp = 0.9, top_p = 1.0)"
   ]
  }
 ],
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