JimmyLiao_Breeze_7B_FC.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/jimmyliao/llm-workshop/blob/main/JimmyLiao_Breeze_7B_FC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jInrBGaXMmSL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Remember to use A100 Runtime\n",
        "- Original Notebook: https://huggingface.co/MediaTek-Research/Breeze-7B-FC-v1_0\n",
        "- Modified by: Jimmy Liao <sjliao@gmail.com>"
      ],
      "metadata": {
        "id": "J_P7YeJKMbgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HciUksuF9SXV",
        "outputId": "c4dfeafe-7966-435d-909b-2a48d8b6bff2"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Sep 27 15:46:44 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0              44W / 350W |      2MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U vllm==0.4.3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lYdCJTQ29N6",
        "outputId": "02897bd5-c169-4d0b-cf6a-3ee082fda27b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting vllm==0.4.3\n",
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            "  Downloading ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl.metadata (5.3 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading lark-1.2.2-py3-none-any.whl (111 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.0/111.0 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading uvicorn-0.30.6-py3-none-any.whl (62 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-ml-py, ninja, websockets, uvloop, triton, python-dotenv, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lark, jiter, interegular, httptools, h11, diskcache, watchfiles, uvicorn, tiktoken, starlette, nvidia-cusolver-cu12, nvidia-cudnn-cu12, httpcore, torch, prometheus-fastapi-instrumentator, lm-format-enforcer, httpx, fastapi, xformers, vllm-flash-attn, ray, openai, outlines, vllm\n",
            "  Attempting uninstall: nvidia-nccl-cu12\n",
            "    Found existing installation: nvidia-nccl-cu12 2.23.4\n",
            "    Uninstalling nvidia-nccl-cu12-2.23.4:\n",
            "      Successfully uninstalled nvidia-nccl-cu12-2.23.4\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.3.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.3.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.3.3\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.6.59\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.6.59:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.6.59\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.6.68\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.6.68:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.68\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.6.68\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.6.68:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.68\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.6.1.4\n",
            "    Uninstalling nvidia-cublas-cu12-12.6.1.4:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.6.1.4\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.4.69\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.4.69:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.4.69\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.4.0.58\n",
            "    Uninstalling nvidia-cudnn-cu12-9.4.0.58:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.4.0.58\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.4.1+cu121\n",
            "    Uninstalling torch-2.4.1+cu121:\n",
            "      Successfully uninstalled torch-2.4.1+cu121\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.4.1+cu121 requires torch==2.4.1, but you have torch 2.3.0 which is incompatible.\n",
            "torchvision 0.19.1+cu121 requires torch==2.4.1, but you have torch 2.3.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed diskcache-5.6.3 fastapi-0.115.0 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.2 interegular-0.3.3 jiter-0.5.0 lark-1.2.2 lm-format-enforcer-0.10.1 ninja-1.11.1.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.560.30 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 openai-1.50.1 outlines-0.0.34 prometheus-fastapi-instrumentator-7.0.0 python-dotenv-1.0.1 ray-2.37.0 starlette-0.38.6 tiktoken-0.7.0 torch-2.3.0 triton-2.3.0 uvicorn-0.30.6 uvloop-0.20.0 vllm-0.4.3 vllm-flash-attn-2.5.8.post2 watchfiles-0.24.0 websockets-13.1 xformers-0.0.26.post1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVS9NOc12wlM",
        "outputId": "ed746cc3-ff67-4e4d-ba2a-1fb329623a19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'mtkresearch'...\n",
            "remote: Enumerating objects: 58, done.\u001b[K\n",
            "remote: Counting objects: 100% (58/58), done.\u001b[K\n",
            "remote: Compressing objects: 100% (48/48), done.\u001b[K\n",
            "remote: Total 58 (delta 20), reused 30 (delta 8), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (58/58), 20.91 KiB | 20.91 MiB/s, done.\n",
            "Resolving deltas: 100% (20/20), done.\n",
            "/content/mtkresearch\n",
            "Processing /content/mtkresearch\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: mtkresearch\n",
            "  Building wheel for mtkresearch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mtkresearch: filename=mtkresearch-0.0.6-py3-none-any.whl size=6142 sha256=6333c3ea6fb3766c72f561930bae277a784a2aac8bd974fc2ac88bb3c4a59ca3\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-wdfq43r4/wheels/12/82/1a/965f8030402f7056ec00e919cb17fc03c9ff2c52a22f925ac3\n",
            "Successfully built mtkresearch\n",
            "Installing collected packages: mtkresearch\n",
            "Successfully installed mtkresearch-0.0.6\n",
            "Name: mtkresearch\n",
            "Version: 0.0.6\n",
            "Summary: mtkresearch\n",
            "Home-page: \n",
            "Author: \n",
            "Author-email: \n",
            "License: Apache License 2.0\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: \n",
            "Required-by: \n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/mtkresearch/mtkresearch.git\n",
        "%cd mtkresearch\n",
        "!pip install .\n",
        "!pip show mtkresearch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !nvidia-smi --query-gpu=name --format=csv,noheader | wc -l\n",
        "# !nvidia-smi --query-gpu=name --format=csv,noheader"
      ],
      "metadata": {
        "id": "UlYhioV6-CdG"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()\n",
        "print(f\"Free GPU memory: {free_gpu_memory / 1024 ** 2:.2f} MB\")\n",
        "print(f\"Total GPU memory: {total_gpu_memory / 1024 ** 2:.2f} MB\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJu0k0lnD0S5",
        "outputId": "06336268-e39b-41b8-dc1f-a08eeba52cd8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Free GPU memory: 40090.81 MB\n",
            "Total GPU memory: 40513.81 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hosting by VLLM"
      ],
      "metadata": {
        "id": "r89ZG60lL150"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from vllm import LLM, SamplingParams\n",
        "\n",
        "llm = LLM(\n",
        "    model='MediaTek-Research/Breeze-7B-FC-v1_0',\n",
        "    # tensor_parallel_size=num_gpu, # number of gpus\n",
        "    tensor_parallel_size=1,\n",
        "    gpu_memory_utilization=0.7\n",
        ")\n",
        "\n",
        "turn_end_token_id = 61876 # <|im_end|>\n",
        "params = SamplingParams(\n",
        "    temperature=0.01,\n",
        "    top_p=0.01,\n",
        "    max_tokens=4096,\n",
        "    repetition_penalty=1.1,\n",
        "    stop_token_ids=[turn_end_token_id]\n",
        ")\n",
        "\n",
        "def _inference(prompt, llm, params):\n",
        "    return llm.generate(prompt, params)[0].outputs[0].text\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553,
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        },
        "id": "nkFlkFaW76tT",
        "outputId": "eb475451-c770-4b01-fe50-9a64004145ba"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/650 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de6df9fa805b4ae5860f5d9fe957e7d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 09-27 15:50:08 llm_engine.py:161] Initializing an LLM engine (v0.4.3) with config: model='MediaTek-Research/Breeze-7B-FC-v1_0', speculative_config=None, tokenizer='MediaTek-Research/Breeze-7B-FC-v1_0', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, 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=MediaTek-Research/Breeze-7B-FC-v1_0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.41k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97b727fdea3641d2ab22537237fa0d04"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/911k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eb1733022ba446cbd72998064b2199d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/197 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "223a5568273b430096f8af1202adb239"
            }
          },
          "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": "7f6e5cc75b4648ada79c4c0ccd6c8092"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75204b0c214846fca6ff358dd56dee7e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 09-27 15:50:12 selector.py:139] Cannot use FlashAttention-2 backend due to sliding window.\n",
            "INFO 09-27 15:50:12 selector.py:51] Using XFormers backend.\n",
            "INFO 09-27 15:50:15 selector.py:139] Cannot use FlashAttention-2 backend due to sliding window.\n",
            "INFO 09-27 15:50:15 selector.py:51] Using XFormers backend.\n",
            "INFO 09-27 15:50:15 weight_utils.py:207] Using model weights format ['*.bin']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00003-of-00004.bin:   0%|          | 0.00/4.60G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eac548e5f6d40dbbcb00b76974e944e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00004-of-00004.bin:   0%|          | 0.00/508M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a2cf9db2c2649f2ab42e5a4675843d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00004.bin:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63d8509937fb45a2a93bddaac2189c09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00002-of-00004.bin:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b10173d40834446c806077b94380be68"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 09-27 15:51:18 model_runner.py:146] Loading model weights took 13.9536 GB\n",
            "INFO 09-27 15:51:20 gpu_executor.py:83] # GPU blocks: 4856, # CPU blocks: 2048\n",
            "INFO 09-27 15:51:23 model_runner.py:854] 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 09-27 15:51:23 model_runner.py:858] 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 09-27 15:51:29 model_runner.py:924] Graph capturing finished in 6 secs.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Instruction following"
      ],
      "metadata": {
        "id": "8a-glsA9Lx9O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mtkresearch.llm.prompt import MRPromptV2\n",
        "\n",
        "sys_prompt = ('You are a helpful AI assistant built by MediaTek Research. '\n",
        "  'The user you are helping speaks Traditional Chinese and comes from Taiwan.')\n",
        "\n",
        "prompt_engine = MRPromptV2()\n",
        "\n",
        "conversations = [\n",
        "    {\"role\": \"system\", \"content\": sys_prompt},\n",
        "    {\"role\": \"user\", \"content\": \"請問什麼是深度學習？\"},\n",
        "]\n",
        "\n",
        "prompt = prompt_engine.get_prompt(conversations)\n",
        "\n",
        "\n",
        "output_str = _inference(prompt, llm, params)\n",
        "result = prompt_engine.parse_generated_str(output_str)\n",
        "\n",
        "print(result)\n",
        "# {'role': 'assistant',\n",
        "#  'content': '深度學習（Deep Learning）是一種機器學習方法，它模仿人類大腦的神經網路結構來\n",
        "#              處理複雜的數據和任務。在深度學習中，模型由多層人工神經元組成，每個神經元之間有\n",
        "#              權重連接，並通過非線性轉換進行計算。這些層與層之間的相互作用使模型能夠學習複雜\n",
        "#              的函數關係或模式，從而解決各種問題，如圖像識別、自然語言理解、語音辨識等。深度\n",
        "#              學習通常需要大量的數據和強大的計算能力，因此經常使用圖形處理器（GPU）或特殊的\n",
        "#              加速器來執行。'}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZRlW6CK-JoI",
        "outputId": "f723c45b-1131-42f8-8b40-af5960f97aa7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processed prompts: 100%|██████████| 1/1 [00:01<00:00,  1.93s/it, Generation Speed: 65.18 toks/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'role': 'assistant', 'content': '深度學習（Deep Learning）是一種機器學習方法，它模仿人類大腦的神經網路結構來處理複雜的數據和任務。在深度學習中，模型由多層人工神經元組成，每個神經元之間有權重連接，並通過非線性轉換進行計算。這些層與層之間的相互作用使模型能夠學習複雜的函數關係或模式，從而解決各種問題，如圖像識別、自然語言理解、語音辨識等。深度學習通常需要大量的數據和強大的計算能力，因此經常使用圖形處理器（GPU）或特殊的加速器來執行。'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function Calling"
      ],
      "metadata": {
        "id": "KcaezDOKL9od"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "from mtkresearch.llm.prompt import MRPromptV2\n",
        "\n",
        "functions = [\n",
        "    {\n",
        "      \"name\": \"get_current_weather\",\n",
        "      \"description\": \"Get the current weather in a given location\",\n",
        "      \"parameters\": {\n",
        "        \"type\": \"object\",\n",
        "        \"properties\": {\n",
        "          \"location\": {\n",
        "            \"type\": \"string\",\n",
        "            \"description\": \"The city and state, e.g. San Francisco, CA\"\n",
        "          },\n",
        "          \"unit\": {\n",
        "            \"type\": \"string\",\n",
        "            \"enum\": [\"celsius\", \"fahrenheit\"]\n",
        "          }\n",
        "        },\n",
        "        \"required\": [\"location\"]\n",
        "      }\n",
        "    }\n",
        "]\n",
        "\n",
        "def fake_get_current_weather(location, unit=None):\n",
        "    return {'temperature': 30}\n",
        "\n",
        "mapping = {\n",
        "    'get_current_weather': fake_get_current_weather\n",
        "}\n",
        "\n",
        "prompt_engine = MRPromptV2()\n",
        "\n",
        "# stage 1: query\n",
        "conversations = [\n",
        "    {\"role\": \"user\", \"content\": \"請問台北目前溫度是攝氏幾度？\"},\n",
        "]\n",
        "\n",
        "prompt = prompt_engine.get_prompt(conversations, functions=functions)\n",
        "\n",
        "output_str = _inference(prompt, llm, params)\n",
        "result = prompt_engine.parse_generated_str(output_str)\n",
        "\n",
        "print(result)\n",
        "# {'role': 'assistant',\n",
        "#  'tool_calls': [\n",
        "#    {'id': 'call_U9bYCBRAbF639uUqfwehwSbw', 'type': 'function',\n",
        "#     'function': {'name': 'get_current_weather', 'arguments': '{\"location\": \"台北, 台灣\", \"unit\": \"celsius\"}'}}]}\n",
        "\n",
        "# stage 2: execute called functions\n",
        "conversations.append(result)\n",
        "\n",
        "tool_call = result['tool_calls'][0]\n",
        "func_name = tool_call['function']['name']\n",
        "func = mapping[func_name]\n",
        "arguments = json.loads(tool_call['function']['arguments'])\n",
        "called_result = func(**arguments)\n",
        "\n",
        "# stage 3: put executed results\n",
        "conversations.append(\n",
        "    {\n",
        "        'role': 'tool',\n",
        "        'tool_call_id': tool_call['id'],\n",
        "        'name': func_name,\n",
        "        'content': json.dumps(called_result)\n",
        "    }\n",
        ")\n",
        "\n",
        "prompt = prompt_engine.get_prompt(conversations, functions=functions)\n",
        "\n",
        "output_str2 = _inference(prompt, llm, params)\n",
        "result2 = prompt_engine.parse_generated_str(output_str2)\n",
        "print(result2)\n",
        "# {'role': 'assistant', 'content': '台北目前的溫度是攝氏30度'}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDPdLEyuL7Ss",
        "outputId": "6007da40-2a89-4fa8-b2e3-8ee2e3bcf229"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  1.77it/s, Generation Speed: 65.51 toks/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'role': 'assistant', 'tool_calls': [{'id': 'call_YCBRAbF639uUqfwehwSbwwla', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{\"location\": \"台北, 台灣\", \"unit\": \"攝氏\"}'}}]}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  4.90it/s, Generation Speed: 64.09 toks/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'role': 'assistant', 'content': '台北目前的溫度是攝氏30度。'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LssCGFXEMB88"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}