8_bit_quantization.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TrelisResearch/install-guides/blob/main/8_bit_quantization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "## Sharding of LLMs\n",
        "\n",
        "The free environment of colab without a GPU has a large amount of RAM (around 25GB), which means you can use it to load models with large weights that may not fit in an environment with a GPU.\n",
        "\n",
        "Built by Trelis. Find us on [HuggingFace](https://huggingface.co/Trelis)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eevFh-M7OoVT"
      },
      "source": [
        "### Login to Huggingface Hub\n",
        "\n",
        "You need to create token with write role [here](https://huggingface.co/settings/tokens) and input in cell below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383,
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        "id": "q8h9yB2oDRl1",
        "outputId": "3419f59e-73d5-442b-efee-b3bd2c6dbb29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.7.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5aeb060d6ac549bbb0e7e84bee1b4a9d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install huggingface_hub\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cache_dir=''"
      ],
      "metadata": {
        "id": "ggbKPp9SiXzT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J58MIrzoZOmJ"
      },
      "outputs": [],
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IV9jC4cPZSOj"
      },
      "outputs": [],
      "source": [
        "# import os\n",
        "# cache_dir = \"/content/drive/My Drive/huggingface_cache\"\n",
        "# os.makedirs(cache_dir, exist_ok=True) # Ensure the directory exists\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfBzP8gWzkpv"
      },
      "source": [
        "### Install requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RUKITZDmXA8U"
      },
      "outputs": [],
      "source": [
        "# https://stackoverflow.com/questions/56081324/why-are-google-colab-shell-commands-not-working\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "fefa3ce8-9ec9-46c3-b3eb-00dd35d7e154"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "# !pip install transformers==4.31 #temporary fix required owing to breaking changes on Aug 9th 2023\n",
        "\n",
        "# !pip install -q einops\n",
        "# !pip install -q sentencepiece\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show bitsandbytes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iP9fawkUx6gw",
        "outputId": "01f96491-6eaa-4429-bc65-4204b21d29a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: bitsandbytes\n",
            "Version: 0.41.1\n",
            "Summary: k-bit optimizers and matrix multiplication routines.\n",
            "Home-page: https://github.com/TimDettmers/bitsandbytes\n",
            "Author: Tim Dettmers\n",
            "Author-email: dettmers@cs.washington.edu\n",
            "License: MIT\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: \n",
            "Required-by: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U xformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gInKs2ps5HLp",
        "outputId": "b7923e63-a0b4-4830-eeed-ad8796716375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.1/109.1 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOtwYRI3zzXI"
      },
      "source": [
        "### Model loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "cg3fiQOvmI3Q",
        "outputId": "82b0dd8f-db98-4cd9-932f-cdc90263542e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44941f32a8c543d6b646f7f064322c53"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "## Full precision models\n",
        "# model_id = 'meta-llama/Llama-2-7b-chat-hf'\n",
        "# model_id = 'mosaicml/mpt-7b-instruct'\n",
        "# model_id = 'Trelis/Llama-2-7b-chat-hf-sharded-bf16'\n",
        "# model_id = 'Trelis/Llama-2-13b-chat-hf-function-calling'\n",
        "\n",
        "## 8-bit models\n",
        "model_id = 'Trelis/Llama-2-7b-chat-hf-hosted-inference-8bit'\n",
        "# model_id = 'Trelis/mpt-7b-instruct-hosted-inference'\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline, TextStreamer\n",
        "\n",
        "# # MPT Models\n",
        "# config = transformers.AutoConfig.from_pretrained(model_id, trust_remote_code=True)\n",
        "# config.init_device = 'cuda:0' # Unclear whether this really helps a lot or interacts with device_map.\n",
        "# config.max_seq_len = 512\n",
        "\n",
        "# model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True, config=config)\n",
        "\n",
        "# Llama 2 Models\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 145,
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        },
        "outputId": "22f366fe-08fc-4acc-f59a-266d9e2b4ee2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/725 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d114440068549769f45f961f056bb53"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "865f93ebf1ef45ec9f9bcf3ea420835d"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7b564eaf87e4ee19b3476ed969cef5e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ec12921106c4b50ade29165978e0215"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MPT Inference\n",
        "def stream(user_instruction):\n",
        "    INSTRUCTION_KEY = \"### Instruction:\"\n",
        "    RESPONSE_KEY = \"### Response:\"\n",
        "    INTRO_BLURB = \"Below is an instruction that describes a task. Write a response that appropriately completes the request.\"\n",
        "    PROMPT_FOR_GENERATION_FORMAT = \"\"\"{intro}\n",
        "    {instruction_key}\n",
        "    {instruction}\n",
        "    {response_key}\n",
        "    \"\"\".format(\n",
        "        intro=INTRO_BLURB,\n",
        "        instruction_key=INSTRUCTION_KEY,\n",
        "        instruction=\"{instruction}\",\n",
        "        response_key=RESPONSE_KEY,\n",
        "    )\n",
        "\n",
        "    prompt = PROMPT_FOR_GENERATION_FORMAT.format(instruction=user_instruction)\n",
        "\n",
        "    inputs = tokenizer([prompt], return_tensors=\"pt\").to(\"cuda:0\")\n",
        "    streamer = TextStreamer(tokenizer)\n",
        "\n",
        "    # Despite returning the usual output, the streamer will also print the generated text to stdout.\n",
        "    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500, eos_token_id=0, temperature=1)"
      ],
      "metadata": {
        "id": "CzfSHG70xQMY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Llama 2 Inference\n",
        "def stream(user_prompt):\n",
        "    system_prompt = 'You are a helpful assistant that provides accurate and concise responses'\n",
        "\n",
        "    B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
        "    B_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\"\n",
        "\n",
        "    prompt = f\"{B_INST} {B_SYS}{system_prompt.strip()}{E_SYS}{user_prompt.strip()} {E_INST}\\n\\n\"\n",
        "\n",
        "    inputs = tokenizer([prompt], return_tensors=\"pt\").to(\"cuda:0\")\n",
        "\n",
        "    streamer = TextStreamer(tokenizer)\n",
        "\n",
        "    # Despite returning the usual output, the streamer will also print the generated text to stdout.\n",
        "    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)"
      ],
      "metadata": {
        "id": "LNg9nucIxqVq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stream('Count to ten')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gviXllyEyyIt",
        "outputId": "ebebe129-69b9-49e0-b489-4b392c8117ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [INST] <<SYS>>\n",
            "You are a helpful assistant that provides accurate and concise responses\n",
            "<</SYS>>\n",
            "\n",
            "Count to ten [/INST]\n",
            "\n",
            "Of course! Here are the numbers from 1 to 10:\n",
            "\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10</s>\n"
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            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1708: UserWarning: You are calling `save_pretrained` to a 8-bit converted model you may likely encounter unexepected behaviors. If you want to save 8-bit models, make sure to have `bitsandbytes>0.37.2` installed.\n",
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