Gemma/Deploy_with_vLLM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### 版權所有 2024 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfsDR_omdNea"
      },
      "source": [
        "# Gemma - deploy with vLLM\n",
        "\n",
        "This notebook demonstrates how you can deploy a Gemma model with [vLLM](https://github.com/vllm-project/vllm) and query it. vLLM is a fast and easy-to-use library for LLM inference and serving, and has built-in support for Gemma deployment.\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/doggy8088/gemma-cookbook/blob/zh-tw-240628/Gemma/Deploy_with_vLLM.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwMiP7jDdAL1"
      },
      "source": [
        "## 設定\n",
        "\n",
        "### 選擇 Colab 執行環境\n",
        "要完成此指南，你需要有一個資源足夠的 Colab 執行環境來執行 Gemma 模型。在這種情況下，你可以使用 T4 GPU：\n",
        "\n",
        "1. 在 Colab 視窗的右上角，選擇 **▾ (Additional connection options)** 。\n",
        "2. 選擇 **Change runtime type** 。\n",
        "3. 在 **Hardware accelerator** 下，選擇 **T4 GPU** 。\n",
        "\n",
        "### 在 Hugging Face 上設定 Gemma\n",
        "vLLM 在底層使用 Hugging Face。所以你需要：\n",
        "\n",
        "* 通過接受 Hugging Face 特定模型頁面的 Gemma 許可來獲取 [huggingface.co](huggingface.co) 上的 Gemma 訪問權限，即 [Gemma 2B](https://huggingface.co/google/gemma-2b)。\n",
        "* 生成一個 [Hugging Face access token](https://huggingface.co/docs/hub/en/security-tokens) 並將其配置為 Colab 機密 'HF_TOKEN'。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJaQ-OVoPKCo"
      },
      "source": [
        "## 安裝\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHrOMaOAPSAM",
        "outputId": "28aeca99-71db-4f89-95c8-266f65309f16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "!pip install vllm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wn2lC5hVPUxy"
      },
      "source": [
        "## 推論\n",
        "\n",
        "### 匯入 vLLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q8o4A6QKPp3d"
      },
      "outputs": [],
      "source": [
        "from vllm import LLM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76mRtotdPu9N"
      },
      "source": [
        "實例化 vLLM 的引擎 (注意 Colab 的免費 T4 GPU 只支援 `float32`)。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
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              "config.json:   0%|          | 0.00/627 [00:00<?, ?B/s]"
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        {
          "name": "stdout",
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          "text": [
            "INFO 05-24 06:23:04 llm_engine.py:100] Initializing an LLM engine (v0.4.2) with config: model='google/gemma-2b', speculative_config=None, tokenizer='google/gemma-2b', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float32, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=google/gemma-2b)\n"
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              "tokenizer_config.json:   0%|          | 0.00/33.6k [00:00<?, ?B/s]"
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        {
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          "text": [
            "INFO 05-24 06:23:06 utils.py:660] Found nccl from library /root/.config/vllm/nccl/cu12/libnccl.so.2.18.1\n",
            "INFO 05-24 06:23:08 selector.py:69] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.\n",
            "INFO 05-24 06:23:08 selector.py:32] Using XFormers backend.\n",
            "WARNING 05-24 06:23:10 gemma.py:54] Gemma's activation function was incorrectly set to exact GeLU in the config JSON file when it was initially released. Changing the activation function to approximate GeLU (`gelu_pytorch_tanh`). If you want to use the legacy `gelu`, edit the config JSON to set `hidden_activation=gelu` instead of `hidden_act`. See https://github.com/huggingface/transformers/pull/29402 for more details.\n",
            "INFO 05-24 06:23:10 weight_utils.py:199] Using model weights format ['*.safetensors']\n"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO 05-24 06:24:08 model_runner.py:175] Loading model weights took 9.3440 GB\n",
            "INFO 05-24 06:24:16 gpu_executor.py:114] # GPU blocks: 1724, # CPU blocks: 7281\n",
            "INFO 05-24 06:24:19 model_runner.py:937] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n",
            "INFO 05-24 06:24:19 model_runner.py:941] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n",
            "INFO 05-24 06:24:29 model_runner.py:1017] Graph capturing finished in 11 secs.\n"
          ]
        }
      ],
      "source": [
        "llm = LLM(model=\"google/gemma-2b\", dtype=\"float32\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMg2unvIOtH4"
      },
      "source": [
        "執行推論與單一提示"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JihVtFsOwsn",
        "outputId": "7ebada7c-a08a-4c7f-f2e0-c64a8a2107b8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  1.42it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Prompt: The capital of USA is, Gemma output:  planning to invest in sustainable transportation and is ready to accept transformational advances in technology\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "prompt = \"The capital of USA is\"\n",
        "output = llm.generate(prompt)\n",
        "print()\n",
        "print(f\"Prompt: {prompt}, Gemma output: {output[0].outputs[0].text}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlLNziE93xdP"
      },
      "source": [
        "執行批次推論"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AW-ex-Fgs_La"
      },
      "outputs": [],
      "source": [
        "prompts = [\n",
        "    \"The best thing about California is\",\n",
        "    \"Physics studies\",\n",
        "    \"The best place for sightseeing in Japan is\",\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8MPTh5XtH0Z",
        "outputId": "a7df01c5-7c0c-4c1c-eb8e-b62f84a1f548"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Processed prompts: 100%|██████████| 3/3 [00:00<00:00,  3.59it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Prompt: The best thing about California is, Gemma output:  that there are always new ways to spend time and enjoy the magnificent state as a\n",
            "\n",
            "Prompt: Physics studies, Gemma output:  matter, its properties, how it interacts with other matter, and other fields of\n",
            "\n",
            "Prompt: The best place for sightseeing in Japan is, Gemma output:  Kyoto. It is the very first city you will visit in Japan when you get\n"
          ]
        },
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "outputs = llm.generate(prompts)\n",
        "\n",
        "for output in outputs:\n",
        "    prompt = output.prompt\n",
        "    generated_text = output.outputs[0].text\n",
        "    print()\n",
        "    print(f\"Prompt: {prompt}, Gemma output: {generated_text}\")"
      ]
    },
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