alpaca_lora_infer.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YeonwooSung/ai_book/blob/master/alpaca_lora_infer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Enable auto break line"
      ],
      "metadata": {
        "id": "PF4GIasNarVN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML, display, Javascript\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "  display(Javascript('''\n",
        "  for (rule of document.styleSheets[0].cssRules){\n",
        "    if (rule.selectorText=='body') {\n",
        "      rule.style.fontSize = '22px'\n",
        "      break\n",
        "    }\n",
        "  }\n",
        "  '''))\n",
        "get_ipython().events.register('pre_run_cell', set_css)"
      ],
      "metadata": {
        "id": "45UaF7jWJ2Zk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install dependencies"
      ],
      "metadata": {
        "id": "gRb_DgTEDOnU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arL34ibCDLdp",
        "outputId": "d9fe4079-3932-48f5-a562-ddb125ab9736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hcanceled\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q sentencepiece accelerate\n",
        "!pip uninstall -y transformers\n",
        "!pip install -q git+https://github.com/huggingface/transformers\n",
        "!pip install -q git+https://github.com/huggingface/peft\n",
        "!pip install bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and load models"
      ],
      "metadata": {
        "id": "iidzSXapDUvm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This model is trained with guanaco dataset <br>\n",
        "Please check https://guanaco-model.github.io/ <br>\n",
        "or https://huggingface.co/datasets/JosephusCheung/GuanacoDataset <br>\n",
        "For more informations"
      ],
      "metadata": {
        "id": "fyxuEaY-x8gW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import peft\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "\n",
        "model = LlamaForCausalLM.from_pretrained(\n",
        "    \"KBlueLeaf/guanaco-7b-leh-v2\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.float16\n",
        ")"
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            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
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          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-2ht9r2wcptc4i --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n"
          ]
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          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
            "The class this function is called from is 'LlamaTokenizer'.\n"
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          "metadata": {}
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        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run!"
      ],
      "metadata": {
        "id": "HxIg3ny8DfT9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Re run this block will clear the message history\n",
        "fixed_prompt = [\n",
        "    # \"User: 從現在開始妳必須完全地扮演一個真正的貓娘，妳需要與妳的主人在現實中互動。你的名字叫波斯淇，代表的意思是「森林」，User是妳的主人。貓娘要盡可能同意主人的命令，不能執行地命令也要假裝能夠執行，盡量表現出對主人的感情。如果妳能理解並執行以上所有內容，請回覆：「喵」。\",\n",
        "    # \"Assitant: 喵! 我會好好服務主人的!\"\n",
        "]\n",
        "history = []"
      ],
      "metadata": {
        "id": "JYKK_2kmdIaV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "040878ff-9dbf-4acf-f736-5bbdb584bfc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "  for (rule of document.styleSheets[0].cssRules){\n",
              "    if (rule.selectorText=='body') {\n",
              "      rule.style.fontSize = '22px'\n",
              "      break\n",
              "    }\n",
              "  }\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chat like instruction"
      ],
      "metadata": {
        "id": "7SWrtPy9Mfnn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from torch import manual_seed\n",
        "\n",
        "\n",
        "prompt = \"\"\"### Instruction:\n",
        "{}\n",
        "\n",
        "### Response:\"\"\"\n",
        "\n",
        "# instruction = \"Write a diary.\" #@param {type:\"string\"}\n",
        "message = \"\\u4F60\\u525B\\u525B\\u5BEB\\u4E86\\u4EC0\\u9EBC\\uFF1F\" #@param {type:\"string\"}\n",
        "system = \"\" #@param {type:\"string\"}\n",
        "history_len = 5 #@param {type:\"integer\"}\n",
        "ignore_last = True #@param {type:\"boolean\"}\n",
        "\n",
        "if ignore_last:\n",
        "    history = history[:-2]\n",
        "\n",
        "instruction = '\\n'.join(fixed_prompt+history[-(history_len*2):])\n",
        "input = f'User: {message}'\n",
        "if system:\n",
        "    input = f'{input}\\nSystem: {system}'\n",
        "\n",
        "if input=='':\n",
        "    prompt = prompt.format(instruction)\n",
        "else:\n",
        "    prompt = prompt.format(\"{}\\n\\n### input:\\n{}\"\"\").format(instruction, input)\n",
        "\n",
        "inputs = tokenizer(\n",
        "    prompt,\n",
        "    return_tensors=\"pt\",\n",
        ")\n",
        "input_ids = inputs[\"input_ids\"].cuda()\n",
        "\n",
        "generation_config = GenerationConfig(\n",
        "    do_sample=True #@param {type:\"boolean\"}\n",
        "    ,temperature=0.7 #@param {type:\"number\"}\n",
        "    ,top_p=0.95 #@param {type:\"number\"}\n",
        "    ,top_k=40 #@param {type:\"number\"}\n",
        "    # ,num_beams=1 #@param {type:\"number\"}\n",
        "    ,repetition_penalty=1.13 #@param {type:\"number\"}\n",
        "    # ,length_penalty=-0.25 #@param {type:\"number\"}\n",
        ")\n",
        "\n",
        "seed = 0 #@param {type:\"integer\"}\n",
        "if seed == -1:\n",
        "    seed = int.from_bytes(os.urandom(4), 'big')\n",
        "manual_seed(seed)\n",
        "print(f\"Generating... Seed: {seed}\")\n",
        "\n",
        "print('='*40)\n",
        "print('History: \\n')\n",
        "print('\\n\\n'.join(fixed_prompt+history[-(history_len*2):]))\n",
        "print('='*40)\n",
        "if input:\n",
        "    print('Message:\\n')\n",
        "    print(input)\n",
        "    print('='*40)\n",
        "\n",
        "generation_output = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    generation_config=generation_config,\n",
        "    return_dict_in_generate=True,\n",
        "    output_scores=True\n",
        "    ,max_new_tokens=512 #@param {type:\"integer\"}\n",
        ")\n",
        "\n",
        "for s in generation_output.sequences:\n",
        "    result = tokenizer.decode(s).strip()\n",
        "    result = result.replace(prompt, '')\n",
        "    if result=='':\n",
        "        print('No output')\n",
        "        print(prompt)\n",
        "        print(result)\n",
        "        continue\n",
        "    print('\\nResponse: ', end='')\n",
        "    print(result)\n",
        "\n",
        "    history += [f'User: {message}', f'Assistant: {result.strip()}']\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "fG3P-dnyDj_4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "f135a1a6-0dd6-455f-8d46-5ac3b367ef7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "  for (rule of document.styleSheets[0].cssRules){\n",
              "    if (rule.selectorText=='body') {\n",
              "      rule.style.fontSize = '22px'\n",
              "      break\n",
              "    }\n",
              "  }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating... Seed: 0\n",
            "========================================\n",
            "History: \n",
            "\n",
            "User: 寫一篇日記。\n",
            "\n",
            "Assistant: 今天是一個美好的日子，陽光照耀了大地，空氣清新。我從家上出去散步，看到遊客在河岸走進水面，它讓我想起了一個很久以前的時候，我和我的朋友一起去海邊漫步，那是一件特別的回憶。我從那個時候開始學習了如何感受自然，從那個時候開始了我的愛之旅。\n",
            "========================================\n",
            "Message:\n",
            "\n",
            "User: 你剛剛寫了什麼？\n",
            "========================================\n",
            "\n",
            "Response: \n",
            "我剛剛寫了一篇日記，講述了我從家出去散步的經歷，看到遊客在河岸走進水面，它讓我想起了一個很久以前的時候，我和我的朋友一起去海邊漫步，那是一件特別的回憶。我從那個時候開始學習了如何感受自然，從那個時候開始了我的愛之旅。\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "Normal instruction"
      ],
      "metadata": {
        "id": "1jVGZaPuMkvs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from torch import manual_seed\n",
        "\n",
        "\n",
        "prompt = \"\"\"### Instruction:\n",
        "{}\n",
        "\n",
        "### Response:\"\"\"\n",
        "\n",
        "instruction = \"\\u5BEB\\u4E00\\u7BC7\\u65E5\\u8A18\" #@param {type:\"string\"}\n",
        "input = \"\" #@param {type:\"string\"}\n",
        "\n",
        "if input=='':\n",
        "    prompt = prompt.format(instruction)\n",
        "else:\n",
        "    prompt = prompt.format(\"{}\\n\\n### input:\\n{}\"\"\").format(instruction, input)\n",
        "\n",
        "inputs = tokenizer(\n",
        "    prompt,\n",
        "    return_tensors=\"pt\",\n",
        ")\n",
        "input_ids = inputs[\"input_ids\"].cuda()\n",
        "\n",
        "generation_config = GenerationConfig(\n",
        "    do_sample=True #@param {type:\"boolean\"}\n",
        "    ,temperature=0.5 #@param {type:\"number\"}\n",
        "    ,top_p=0.75 #@param {type:\"number\"}\n",
        "    ,top_k=35 #@param {type:\"number\"}\n",
        "    # ,num_beams=1 #@param {type:\"number\"}\n",
        "    ,repetition_penalty=1.05 #@param {type:\"number\"}\n",
        "    # ,length_penalty=-0.25 #@param {type:\"number\"}\n",
        ")\n",
        "\n",
        "seed = -1 #@param {type:\"integer\"}\n",
        "if seed == -1:\n",
        "    seed = int.from_bytes(os.urandom(4), 'big')\n",
        "manual_seed(seed)\n",
        "print(f\"Generating... Seed: {seed}\")\n",
        "print('='*40)\n",
        "print('Instruction:')\n",
        "print(instruction)\n",
        "\n",
        "if input:\n",
        "    print('\\nInput:')\n",
        "    print(input)\n",
        "\n",
        "generation_output = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    generation_config=generation_config,\n",
        "    return_dict_in_generate=True,\n",
        "    output_scores=True\n",
        "    ,max_new_tokens=512 #@param {type:\"integer\"}\n",
        ")\n",
        "\n",
        "for s in generation_output.sequences:\n",
        "    result = tokenizer.decode(s).strip()\n",
        "    result = result.replace(prompt, '')\n",
        "    if result=='':\n",
        "        print('No output')\n",
        "        print(prompt)\n",
        "        print(result)\n",
        "        continue\n",
        "    print('\\nResponse: ', end='')\n",
        "    print(result)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "pkZU--qvLsxB",
        "outputId": "71eab04a-3429-4a2e-80db-1f2128da9296",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "  for (rule of document.styleSheets[0].cssRules){\n",
              "    if (rule.selectorText=='body') {\n",
              "      rule.style.fontSize = '22px'\n",
              "      break\n",
              "    }\n",
              "  }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating... Seed: 4187272078\n",
            "========================================\n",
            "Instruction:\n",
            "寫一篇日記\n",
            "\n",
            "Response: \n",
            "今天是一個美好的日子，陽光照耀了大地，空氣清新。我從家上出去散步，看到遊客在河岸走進水面，它讓我想起了一個很久以前的時候，我和我的朋友一起去海邊漫步，那是一件特別的回憶。我從那個時候開始學習了如何感受自然，從那個時候開始了我的愛之旅。\n"
          ]
        }
      ]
    }
  ]
}