chatglm-tuning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 前置动作\n",
    "1. 克隆`https://github.com/unit-mesh/unit-minions`项目\n",
    "1. 将`userstory_detail_double_clean_cn.jsonl`数据集放到`input1`或者修改下面的数据集路径\n",
    "1. 将`ChatGLM-6b`的模型文件准备到`input2`或者修改下面模型引用的路径。或者直接指定为`THUDM/chatglm-6b`\n",
    "1. 如果不是用的`openbayes`平台，记得修改下方的模型输出路径，并提前创建好路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AgrFiDyijmzT",
    "outputId": "cd41cc3d-7473-4de8-d3c0-787b2cd7358f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/mymusise/ChatGLM-Tuning.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T10:36:24.933679Z",
     "iopub.status.busy": "2023-04-10T10:36:24.933085Z",
     "iopub.status.idle": "2023-04-10T10:38:03.389723Z",
     "shell.execute_reply": "2023-04-10T10:38:03.387921Z",
     "shell.execute_reply.started": "2023-04-10T10:36:24.933641Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.8/site-packages (from pandas->datasets==2.10.1->-r requirements.txt (line 14)) (2.8.2)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/site-packages (from torchvision->icetk->-r requirements.txt (line 8)) (9.4.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->-r requirements.txt (line 11)) (3.11.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->-r requirements.txt (line 11)) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->-r requirements.txt (line 11)) (3.2.2)\n",
      "Building wheels for collected packages: peft\n",
      "  Building wheel for peft (pyproject.toml) ... \u001B[?25ldone\n",
      "\u001B[?25h  Created wheel for peft: filename=peft-0.3.0.dev0-py3-none-any.whl size=49840 sha256=7c43951977e86496ab34272d91a6e84e85cf529d4fd5e935e6f1db1266c67f7e\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-62jm2rma/wheels/95/fe/57/a484616f9bd99820cb946c7c3d2b1b492423b504356b0797dd\n",
      "Successfully built peft\n",
      "Installing collected packages: tokenizers, sentencepiece, cpm_kernels, bitsandbytes, xxhash, regex, pyarrow, protobuf, dill, responses, multiprocess, accelerate, transformers, icetk, peft, datasets\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 3.20.3\n",
      "    Uninstalling protobuf-3.20.3:\n",
      "      Successfully uninstalled protobuf-3.20.3\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",
      "onnx 1.13.0 requires protobuf<4,>=3.20.2, but you have protobuf 3.20.0 which is incompatible.\u001B[0m\u001B[31m\n",
      "\u001B[0mSuccessfully installed accelerate-0.17.1 bitsandbytes-0.37.1 cpm_kernels-1.0.11 datasets-2.10.1 dill-0.3.6 icetk-0.0.4 multiprocess-0.70.14 peft-0.3.0.dev0 protobuf-3.20.0 pyarrow-11.0.0 regex-2023.3.23 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.3 transformers-4.27.1 xxhash-3.2.0\n",
      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
      "\u001B[0m"
     ]
    }
   ],
   "source": [
    "%cd ChatGLM-Tuning\n",
    "pip install -r requirements.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2023-04-10T02:21:36.213334Z",
     "iopub.status.busy": "2023-04-10T02:21:36.213177Z",
     "iopub.status.idle": "2023-04-10T02:21:36.513696Z",
     "shell.execute_reply": "2023-04-10T02:21:36.513026Z",
     "shell.execute_reply.started": "2023-04-10T02:21:36.213317Z"
    },
    "id": "6ojreD5AjmzV",
    "outputId": "ba9497f7-62a5-49af-8b9c-8663b3587041",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "formatting..: 100%|██████████████████████| 2462/2462 [00:00<00:00, 72707.65it/s]\n"
     ]
    }
   ],
   "source": [
    "!python cover_alpaca2jsonl.py \\\n",
    "    --data_path /openbayes/input/input0/userstory_detail_double_clean_cn.jsonl \\\n",
    "    --save_path /output/dataset/userstory_detail_double_clean_cn_alpaca.jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2023-04-10T02:21:36.514480Z",
     "iopub.status.busy": "2023-04-10T02:21:36.514335Z",
     "iopub.status.idle": "2023-04-10T02:21:37.564892Z",
     "shell.execute_reply": "2023-04-10T02:21:37.564454Z",
     "shell.execute_reply.started": "2023-04-10T02:21:36.514466Z"
    },
    "id": "sTe5STs8jmzW",
    "outputId": "f820a47a-9d4d-4f9d-d3cf-5075ceea09fe",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found cached dataset generator (/root/.cache/huggingface/datasets/generator/default-0133ef0c8080d865/0.0.0)\n",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "!python tokenize_dataset_rows.py \\\n",
    "    --jsonl_path /openbayes/home/dataset/userstory_detail_double_clean_cn_alpaca.jsonl \\\n",
    "    --save_path /openbayes/home/dataset \\\n",
    "    --max_seq_length 380 \\\n",
    "    --model_name /openbayes/input/input1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T02:21:37.566370Z",
     "iopub.status.busy": "2023-04-10T02:21:37.566172Z",
     "iopub.status.idle": "2023-04-10T02:21:37.569121Z",
     "shell.execute_reply": "2023-04-10T02:21:37.568573Z",
     "shell.execute_reply.started": "2023-04-10T02:21:37.566355Z"
    },
    "id": "H6uEl46RjmzW",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
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    "execution": {
     "iopub.execute_input": "2023-04-10T02:21:37.570018Z",
     "iopub.status.busy": "2023-04-10T02:21:37.569822Z",
     "iopub.status.idle": "2023-04-10T02:21:51.336272Z",
     "shell.execute_reply": "2023-04-10T02:21:51.335739Z",
     "shell.execute_reply.started": "2023-04-10T02:21:37.570003Z"
    },
    "id": "AuNItamXjmzW",
    "outputId": "88fc0657-8fd3-45b2-9243-4315869f3d08",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib'), PosixPath('/usr/local/nvidia/lib64')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain libcudart.so as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('tcp'), PosixPath('443'), PosixPath('//10.111.0.1')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//alchemist-experience'), PosixPath('7890')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('localhost,127.0.0.1,openbayes-server-svc,openbayes-storage-server-svc,10.0.0.0/8')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('kdump/jobs/lkydyl2p0529')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/output/.torch')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//openbayes-server-svc/api/users/kdump/jobs/lkydyl2p0529')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('home/4b90ac3c-c89e-4b7b-b1ae-4eb9abb83678')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-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('//matplotlib_inline.backend_inline')}\n",
      "  warn(msg)\n",
      "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.\n",
      "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.\n",
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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: 8.6\n",
      "CUDA SETUP: Detected CUDA version 117\n",
      "CUDA SETUP: Loading binary /usr/local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "980999abad044d71a71dae95a50db914",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModel, TrainingArguments, AutoConfig\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from peft import get_peft_model, LoraConfig, TaskType\n",
    "\n",
    "\n",
    "class CastOutputToFloat(nn.Sequential):\n",
    "    def forward(self, x): return super().forward(x).to(torch.float32)\n",
    "\n",
    "model = AutoModel.from_pretrained(\"/openbayes/input/input1\", load_in_8bit=True, trust_remote_code=True, device_map='auto')\n",
    "model.supports_gradient_checkpointing = True\n",
    "model.gradient_checkpointing_enable()\n",
    "model.enable_input_require_grads()\n",
    "model.lm_head = CastOutputToFloat(model.lm_head)\n",
    "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
    "\n",
    "model.is_parallelizable = True\n",
    "model.model_parallel = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2023-04-10T02:21:51.337063Z",
     "iopub.status.busy": "2023-04-10T02:21:51.336881Z",
     "iopub.status.idle": "2023-04-10T02:21:51.563773Z",
     "shell.execute_reply": "2023-04-10T02:21:51.563270Z",
     "shell.execute_reply.started": "2023-04-10T02:21:51.337049Z"
    },
    "id": "FE4b35mhjmzX",
    "outputId": "72202622-939c-406b-f03c-191ef92cd891",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"/openbayes/input/input1\", trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b8o3iIw8jmzX",
    "tags": []
   },
   "source": [
    "## Test before finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2023-04-10T02:21:51.564648Z",
     "iopub.status.busy": "2023-04-10T02:21:51.564486Z",
     "iopub.status.idle": "2023-04-10T02:22:24.700769Z",
     "shell.execute_reply": "2023-04-10T02:22:24.700099Z",
     "shell.execute_reply.started": "2023-04-10T02:21:51.564632Z"
    },
    "id": "Bd9CtPzDjmzY",
    "outputId": "5f8fd6d9-421e-4bc5-8e5a-ae74b631fea4",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/transformers/generation/utils.py:1201: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/site-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n",
      "The dtype of attention mask (torch.int64) is not bool\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instruction: create agile user story for following topic\n",
      "Input: animation and comics:browse and search for animations and comics\n",
      "Answer: User Story:\n",
      "\n",
      "Title: browse and search for animations and Comics\n",
      "\n",
      "Objective: Develop a user-friendly interface for browsing and searching for animations and Comics\n",
      "\n",
      "Context: The company is looking to create a new user interface for their animation and漫画 collection. The user interface will be used to browse and search for animations and漫画, as well as to access specific titles and章节.\n",
      "\n",
      "Action steps:\n",
      "\n",
      "1.  Create a user manual and a test user interface to evaluate the effectiveness of the interface.\n",
      "2.  Implement the user manual and test the interface with a small\n",
      "### 1.Answer:\n",
      " 用户故事：可以浏览和搜索动画和漫画\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想要浏览和搜索动画和漫画\n",
      "以便于我能够找到我喜欢的动画和漫画\n",
      "\n",
      "ac 1: 用户可以浏览和搜索动画和漫画\n",
      "假设 用户打开 animation and comics 应用\n",
      "当 用户点击浏览和搜索动画和漫画按钮\n",
      "于是 用户可以看到动画和漫画的列表，并可以搜索特定的动画和漫画 \n",
      "\n",
      "\n",
      "Instruction: create agile user story for following topic\n",
      "Input: animation and comics:participate in online forums and discussions\n",
      "Answer: User Story:\n",
      "\n",
      "Title: Participating in Online forums and Discussions for Animation and Comics\n",
      "\n",
      "Objective: To improve participation in online forums and discussions for animation and Comics by providing a platform for users to share and discuss their works.\n",
      "\n",
      "Key Results:\n",
      "\n",
      "-   A higher number of users participating in online forums and discussions related to animation and Comics\n",
      "-   A more active and engaging community with a greater sense of community spirit\n",
      "-   A greater understanding and appreciation of animation and Comics by the community\n",
      "\n",
      "User Story Explanation:\n",
      "\n",
      "Online forums and discussions are a popular\n",
      "### 2.Answer:\n",
      " 用户故事：可以参与在线论坛和讨论\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想参与在线论坛和讨论\n",
      "以便于我可以与其他爱好者分享我的想法，观点和经验。\n",
      "\n",
      "ac 1: 用户可以参与在线论坛和讨论\n",
      "假设 用户已经登录到 animation and comics 应用\n",
      "当 用户点击论坛按钮\n",
      "于是 用户可以参与在线论坛和讨论 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from cover_alpaca2jsonl import format_example\n",
    "import json\n",
    "\n",
    "\n",
    "with open(\"/openbayes/input/input0/userstory_detail_double_clean_cn.jsonl\", encoding=\"utf-8\") as f:\n",
    "    examples = list(f)\n",
    "    examplesSample = examples[:2]\n",
    "\n",
    "# instructions = json.load(open(\"data/alpaca_data.json\"))\n",
    "\n",
    "\n",
    "with torch.no_grad():\n",
    "    for idx, item in enumerate(examplesSample):\n",
    "        item = json.loads(item)\n",
    "        feature = format_example(item)\n",
    "        input_text = feature[\"context\"]\n",
    "        input_ids = tokenizer.encode(input_text, return_tensors='pt')\n",
    "        out = model.generate(\n",
    "            input_ids=input_ids,\n",
    "            max_length=150,\n",
    "            temperature=0\n",
    "        )\n",
    "        answer = tokenizer.decode(out[0])\n",
    "        print(answer)\n",
    "        item['infer_answer'] = answer\n",
    "        print(f\"### {idx+1}.Answer:\\n\", item.get('output'), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T02:22:24.701811Z",
     "iopub.status.busy": "2023-04-10T02:22:24.701633Z",
     "iopub.status.idle": "2023-04-10T02:22:32.010781Z",
     "shell.execute_reply": "2023-04-10T02:22:32.010174Z",
     "shell.execute_reply.started": "2023-04-10T02:22:24.701794Z"
    },
    "id": "gLAH_7SEjmzZ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "peft_config = LoraConfig(\n",
    "    task_type=TaskType.CAUSAL_LM, inference_mode=False,\n",
    "    r=8,\n",
    "    lora_alpha=32, lora_dropout=0.1,\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, peft_config)\n",
    "model.is_parallelizable = True\n",
    "model.model_parallel = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T02:22:32.011667Z",
     "iopub.status.busy": "2023-04-10T02:22:32.011505Z",
     "iopub.status.idle": "2023-04-10T02:22:32.487819Z",
     "shell.execute_reply": "2023-04-10T02:22:32.487302Z",
     "shell.execute_reply.started": "2023-04-10T02:22:32.011652Z"
    },
    "id": "dfhAAHJ7jmzZ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import datasets\n",
    "\n",
    "dataset_path = \"/openbayes/home/dataset\"\n",
    "\n",
    "dataset = datasets.load_from_disk(dataset_path)\n",
    "\n",
    "# train_num = 2000\n",
    "\n",
    "# mini_train_dataset = datasets.Dataset.from_dict(dataset[:train_num])\n",
    "mini_train_dataset = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T02:22:32.488665Z",
     "iopub.status.busy": "2023-04-10T02:22:32.488442Z",
     "iopub.status.idle": "2023-04-10T02:22:32.774339Z",
     "shell.execute_reply": "2023-04-10T02:22:32.773926Z",
     "shell.execute_reply.started": "2023-04-10T02:22:32.488649Z"
    },
    "id": "L2TUEnw0jmzZ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import Trainer, HfArgumentParser\n",
    "import os\n",
    "\n",
    "\n",
    "def data_collator(features: list) -> dict:\n",
    "    len_ids = [len(feature[\"input_ids\"]) for feature in features]\n",
    "    longest = max(len_ids)\n",
    "    input_ids = []\n",
    "    labels_list = []\n",
    "    for ids_l, feature in sorted(zip(len_ids, features), key=lambda x: -x[0]):\n",
    "        ids = feature[\"input_ids\"]\n",
    "        seq_len = feature[\"seq_len\"]\n",
    "        labels = (\n",
    "            [-100] * (seq_len - 1) + ids[(seq_len - 1) :] + [-100] * (longest - ids_l)\n",
    "        )\n",
    "        ids = ids + [tokenizer.pad_token_id] * (longest - ids_l)\n",
    "        _ids = torch.LongTensor(ids)\n",
    "        labels_list.append(torch.LongTensor(labels))\n",
    "        input_ids.append(_ids)\n",
    "    input_ids = torch.stack(input_ids)\n",
    "    labels = torch.stack(labels_list)\n",
    "    return {\n",
    "        \"input_ids\": input_ids,\n",
    "        \"labels\": labels,\n",
    "    }\n",
    "\n",
    "class ModifiedTrainer(Trainer):\n",
    "\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        return model(\n",
    "            input_ids=inputs[\"input_ids\"],\n",
    "            labels=inputs[\"labels\"],\n",
    "        ).loss\n",
    "    \n",
    "    def save_model(self, output_dir=None, _internal_call=False):\n",
    "        from transformers.trainer import TRAINING_ARGS_NAME\n",
    "\n",
    "        os.makedirs(output_dir, exist_ok=True)\n",
    "        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n",
    "        saved_params = {\n",
    "            k: v.to(\"cpu\") for k, v in self.model.named_parameters() if v.requires_grad\n",
    "        }\n",
    "        torch.save(saved_params, os.path.join(output_dir, \"adapter_model.bin\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "execution": {
     "iopub.execute_input": "2023-04-10T02:22:32.775099Z",
     "iopub.status.busy": "2023-04-10T02:22:32.774915Z",
     "iopub.status.idle": "2023-04-10T03:55:19.126542Z",
     "shell.execute_reply": "2023-04-10T03:55:19.126029Z",
     "shell.execute_reply.started": "2023-04-10T02:22:32.775084Z"
    },
    "id": "KrfU9gATjmza",
    "outputId": "674645bc-8e0b-4e6b-944c-a8b2616a8da6",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='3000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3000/3000 1:32:43, Epoch 9/10]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.986000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>0.572900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.532600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.470700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.453300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.434100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>0.388400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.372100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.384300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.336400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>0.322000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.333700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>0.295400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1400</td>\n",
       "      <td>0.279100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>0.284400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1600</td>\n",
       "      <td>0.254500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1700</td>\n",
       "      <td>0.236400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1800</td>\n",
       "      <td>0.245300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1900</td>\n",
       "      <td>0.221300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2100</td>\n",
       "      <td>0.209500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2200</td>\n",
       "      <td>0.190100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2300</td>\n",
       "      <td>0.176300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2400</td>\n",
       "      <td>0.178100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>0.168900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2600</td>\n",
       "      <td>0.152200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2700</td>\n",
       "      <td>0.155200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2800</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2900</td>\n",
       "      <td>0.138600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.140200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=3000, training_loss=0.30902166016896565, metrics={'train_runtime': 5566.3363, 'train_samples_per_second': 4.312, 'train_steps_per_second': 0.539, 'total_flos': 1.516394609347461e+17, 'train_loss': 0.30902166016896565, 'epoch': 9.74})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    \"output\",\n",
    "    fp16 =True,\n",
    "    gradient_accumulation_steps=1,\n",
    "    per_device_train_batch_size = 8,\n",
    "    learning_rate = 1e-4,\n",
    "    max_steps=3000,\n",
    "    save_steps=1000,\n",
    "    logging_steps=100,\n",
    "    remove_unused_columns=False,\n",
    "    seed=0,\n",
    "    data_seed=0,\n",
    "    group_by_length=False,\n",
    ")\n",
    "\n",
    "\n",
    "trainer = ModifiedTrainer(\n",
    "    model=model,\n",
    "    train_dataset=mini_train_dataset,\n",
    "    args=training_args,\n",
    "    data_collator=data_collator,\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XBRIo1ERjmza"
   },
   "source": [
    "## Test After finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2023-04-10T03:55:19.127441Z",
     "iopub.status.busy": "2023-04-10T03:55:19.127283Z",
     "iopub.status.idle": "2023-04-10T03:55:43.983528Z",
     "shell.execute_reply": "2023-04-10T03:55:43.982977Z",
     "shell.execute_reply.started": "2023-04-10T03:55:19.127425Z"
    },
    "id": "vO-yxceQjmza",
    "outputId": "b074e527-35dc-4309-c0a1-48622cd45d51",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instruction: create agile user story for following topic\n",
      "Input: animation and comics:browse and search for animations and comics\n",
      "Answer: 用户故事:可以浏览和搜索动画和漫画\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想要浏览和搜索动画和漫画\n",
      "以便于我能够找到我喜欢的动画和漫画\n",
      "\n",
      "ac 1: 用户可以浏览和搜索动画和漫画\n",
      "假设 用户打开 animation and comics 应用\n",
      "当 用户点击浏览和搜索动画和漫画按钮\n",
      "于是 用户可以看到动画和漫画的列表,并可以搜索特定的动画和漫画\n",
      "### 1.Answer:\n",
      " 用户故事：可以浏览和搜索动画和漫画\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想要浏览和搜索动画和漫画\n",
      "以便于我能够找到我喜欢的动画和漫画\n",
      "\n",
      "ac 1: 用户可以浏览和搜索动画和漫画\n",
      "假设 用户打开 animation and comics 应用\n",
      "当 用户点击浏览和搜索动画和漫画按钮\n",
      "于是 用户可以看到动画和漫画的列表，并可以搜索特定的动画和漫画 \n",
      "\n",
      "\n",
      "Instruction: create agile user story for following topic\n",
      "Input: animation and comics:participate in online forums and discussions\n",
      "Answer: 用户故事:可以参与在线论坛和讨论\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想参与在线论坛和讨论\n",
      "以便于我可以与其他用户分享我的作品,讨论漫画,以及讨论其他主题。\n",
      "\n",
      "ac 1: 用户可以参与在线论坛和讨论\n",
      "ac 2: 用户可以在论坛和讨论中发表作品\n",
      "ac 3: 用户可以在论坛和讨论中讨论漫画,讨论其他主题,以及分享其他内容\n",
      "### 2.Answer:\n",
      " 用户故事：可以参与在线论坛和讨论\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想参与在线论坛和讨论\n",
      "以便于我可以与其他爱好者分享我的想法，观点和经验。\n",
      "\n",
      "ac 1: 用户可以参与在线论坛和讨论\n",
      "假设 用户已经登录到 animation and comics 应用\n",
      "当 用户点击论坛按钮\n",
      "于是 用户可以参与在线论坛和讨论 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from cover_alpaca2jsonl import format_example\n",
    "import json\n",
    "\n",
    "\n",
    "# instructions = json.load(open(\"data/alpaca_data.json\"))\n",
    "\n",
    "\n",
    "# with torch.no_grad():\n",
    "#     for idx, item in enumerate(instructions[:5]):\n",
    "#         feature = format_example(item)\n",
    "#         input_text = feature[\"context\"]\n",
    "#         input_ids = tokenizer.encode(input_text, return_tensors='pt')\n",
    "#         out = model.generate(\n",
    "#             input_ids=input_ids,\n",
    "#             max_length=150,\n",
    "#             temperature=0\n",
    "#         )\n",
    "#         answer = tokenizer.decode(out[0])\n",
    "#         print(answer)\n",
    "#         item['infer_answer'] = answer\n",
    "#         print(f\"### {idx+1}.Answer:\\n\", item.get('output'), '\\n\\n')\n",
    "\n",
    "# from cover_alpaca2jsonl import format_example\n",
    "# import json\n",
    "\n",
    "\n",
    "with open(\"/openbayes/input/input0/userstory_detail_double_clean_cn.jsonl\", encoding=\"utf-8\") as f:\n",
    "    examples = list(f)\n",
    "    examplesSample = examples[:2]\n",
    "    \n",
    "    \n",
    "\n",
    "# instructions = json.load(open(\"data/alpaca_data.json\"))\n",
    "\n",
    "\n",
    "with torch.no_grad():\n",
    "    for idx, item in enumerate(examplesSample):\n",
    "        item = json.loads(item)\n",
    "        feature = format_example(item)\n",
    "        input_text = feature[\"context\"]\n",
    "        input_ids = tokenizer.encode(input_text, return_tensors='pt')\n",
    "        out = model.generate(\n",
    "            input_ids=input_ids,\n",
    "            max_length=150,\n",
    "            temperature=0\n",
    "        )\n",
    "        answer = tokenizer.decode(out[0])\n",
    "        print(answer)\n",
    "        item['infer_answer'] = answer\n",
    "        print(f\"### {idx+1}.Answer:\\n\", item.get('output'), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kh5GyEwMfhVl"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T03:55:43.985203Z",
     "iopub.status.busy": "2023-04-10T03:55:43.985040Z",
     "iopub.status.idle": "2023-04-10T03:55:44.044008Z",
     "shell.execute_reply": "2023-04-10T03:55:44.043493Z",
     "shell.execute_reply.started": "2023-04-10T03:55:43.985187Z"
    },
    "id": "RkwLnnDDjmza",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "model.save_pretrained(\"/output/models/lora/chatglm/userstory_detail\")\n",
    "\n",
    "def save_tunable_parameters(model, path):\n",
    "    saved_params = {\n",
    "        k: v.to(\"cpu\")\n",
    "        for k, v in model.named_parameters()\n",
    "        if v.requires_grad\n",
    "    }\n",
    "    torch.save(saved_params, path)\n",
    "save_tunable_parameters(model, os.path.join(\"/output/models/lora/chatglm/userstory_detail\", \"chatglm-lora-userstory_detail.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T10:35:41.925636Z",
     "iopub.status.busy": "2023-04-10T10:35:41.925297Z",
     "iopub.status.idle": "2023-04-10T10:35:48.861352Z",
     "shell.execute_reply": "2023-04-10T10:35:48.860470Z",
     "shell.execute_reply.started": "2023-04-10T10:35:41.925609Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Building wheels for collected packages: ffmpy\n",
      "  Building wheel for ffmpy (setup.py) ... \u001B[?25ldone\n",
      "\u001B[?25h  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4693 sha256=d084ac641d620a95ea8da67b1bf5b7892741577c49c86199dd1eb73fb5eea3f7\n",
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      "Installing collected packages: rfc3986, pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, pydantic, orjson, multidict, mdurl, fsspec, frozenlist, filelock, async-timeout, aiofiles, yarl, starlette, markdown-it-py, linkify-it-py, huggingface-hub, httpcore, aiosignal, mdit-py-plugins, httpx, gradio-client, fastapi, aiohttp, gradio\n",
      "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.0 ffmpy-0.3.0 filelock-3.11.0 frozenlist-1.3.3 fsspec-2023.4.0 gradio-3.24.1 gradio-client-0.0.8 httpcore-0.16.3 httpx-0.23.3 huggingface-hub-0.13.4 linkify-it-py-2.0.0 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 multidict-6.0.4 orjson-3.8.10 pydantic-1.10.7 pydub-0.25.1 python-multipart-0.0.6 rfc3986-1.5.0 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.1 websockets-11.0.1 yarl-1.8.2\n",
      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
      "\u001B[0m"
     ]
    }
   ],
   "source": [
    "%cd ..\n",
    "pip install -r unit-minions/apps/userstory/chatglm/chatglm-web/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-10T11:08:03.453611Z",
     "iopub.status.busy": "2023-04-10T11:08:03.453307Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib64'), PosixPath('/usr/local/nvidia/lib')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain libcudart.so as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('home/fadbaa56-d92a-4147-b576-6b27bfacd360')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('tcp'), PosixPath('443'), PosixPath('//10.111.0.1')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//alchemist-experience'), PosixPath('7890')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('localhost,127.0.0.1,openbayes-server-svc,openbayes-storage-server-svc,10.0.0.0/8')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('kdump/jobs/lkydyl2p0529')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-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('//matplotlib_inline.backend_inline')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/output/.torch')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//openbayes-server-svc/api/users/kdump/jobs/lkydyl2p0529')}\n",
      "  warn(msg)\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: 8.6\n",
      "CUDA SETUP: Detected CUDA version 117\n",
      "CUDA SETUP: Loading binary /usr/local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n",
      "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.\n",
      "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.\n",
      "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.\n",
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n",
      "Loading checkpoint shards: 100%|██████████████████| 8/8 [00:06<00:00,  1.15it/s]\n",
      "/usr/local/lib/python3.8/site-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "/usr/local/lib/python3.8/site-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "Running on local URL:  http://0.0.0.0:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n",
      "The dtype of attention mask (torch.int64) is not bool\n",
      "answer: Instruction: create agile user story for following topic\n",
      "Input: animation and comics:browse and search for animations and comics\n",
      "Answer: 用户故事:可以浏览和搜索动画和漫画\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想要浏览和搜索动画和漫画\n",
      "以便于我能够找到我喜欢的动画和漫画\n",
      "\n",
      "ac 1: 用户可以浏览和搜索动画和漫画\n",
      "假设 用户打开 animation and comics 应用\n",
      "当 用户点击浏览和搜索动画和漫画按钮\n",
      "于是 用户可以看到动画和漫画的列表,并可以搜索特定的动画和漫画\n",
      "answer: Instruction: create agile user story for following topic\n",
      "Input: animation and comics:browse and search for animations and comics\n",
      "Answer: 用户故事:可以浏览和搜索动画和漫画\n",
      "作为一个 animation and comics 应用的用户\n",
      "我想要浏览和搜索动画和漫画\n",
      "以便于我能够找到我喜欢的动画和漫画\n",
      "\n",
      "ac 1: 用户可以浏览和搜索动画和漫画\n",
      "假设 用户打开 animation and comics 应用\n",
      "当 用户点击浏览和搜索动画和漫画按钮\n",
      "于是 用户可以看到动画和漫画的列表,并可以搜索特定的动画和漫画\n",
      "answer: Instruction: create agile user story for following topic\n",
      "Input: 电商平台:购买商品\n",
      "Answer: 用户故事:可以在电商平台购买商品\n",
      "作为 一个消费者\n",
      "我想 在电商平台购买商品\n",
      "以便于 我能够购买到我想要的商品\n",
      "\n",
      "ac 1:  消费者可以在电商平台购买商品\n",
      "假设 消费者已经登录电商平台\n",
      "当 消费者点击购买商品按钮\n",
      "于是 消费者可以看到商品的详细信息,并可以选择购买\n"
     ]
    }
   ],
   "source": [
    "!python unit-minions/apps/userstory/chatglm/chatglm-web/app.py"
   ]
  }
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