Finetuning_LLama_2_0_on_Colab_with_1_GPU.ipynb

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            "Installing collected packages: tokenizers, safetensors, transformers\n",
            "Successfully installed safetensors-0.3.3 tokenizers-0.13.3 transformers-4.31.0\n",
            "Collecting accelerate==0.21.0\n",
            "  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
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            "\u001b[?25hCollecting peft==0.4.0\n",
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            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets->trl==0.4.7)\n",
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            "Installing collected packages: bitsandbytes, xxhash, dill, multiprocess, datasets, accelerate, trl, peft\n",
            "Successfully installed accelerate-0.21.0 bitsandbytes-0.40.2 datasets-2.14.4 dill-0.3.7 multiprocess-0.70.15 peft-0.4.0 trl-0.4.7 xxhash-3.3.0\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub\n",
        "!pip install transformers==4.31.0\n",
        "!pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 trl==0.4.7\n",
        "!pip install sentencepiece\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "colab上每次登录，GPU上有一些基础的python库，但是有些没有，需要安装，比如Huggingface_hub。"
      ],
      "metadata": {
        "id": "W23fXvs-hTcS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "6d3dbb12c726473d93c2aa664dbe31bf",
            "c043c90ae32744fead7e774c81d57869",
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            "17cc4b908f894d119a166331bc63da09",
            "f0337d5101f241e8bc15a7bc7ae03efe",
            "31790e97877e498194e6e929dee9a026"
          ]
        },
        "id": "7whM1udHipes",
        "outputId": "3ad6ab23-ec98-4666-b678-9c83b7b8d4a9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae17faef73d748ffb6ebe542b28ccc5c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "如果使用私有数据，需要登录Huggingface账号，开源的数据集也可以不登录。"
      ],
      "metadata": {
        "id": "K_lzqE3hMjv3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Load tokenizer and model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "# Activate nested quantization for 4-bit base models (double quantization)\n",
        "use_nested_quant = False\n",
        "\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        ")\n",
        "\n",
        "model_name = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "#Load LLaMA tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "# needed for llama tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "####Below is for mlabonne/guanaco-llama2-1k dataset\n",
        "#tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
        "\n",
        "\n",
        "#Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}\n",
        "\n",
        "#Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
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            "0cdedfc4f5bf4382bf6d7184f7f7f47d"
          ]
        },
        "id": "5-EgoB5ShWrl",
        "outputId": "3e3c61f1-2561-4a1f-afca-7e1b8a1dbe4a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
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          "metadata": {}
        },
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          "metadata": {}
        },
        {
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        {
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "导入预训练模型. 使用transformers库的AutoTokenizer类和 AutoModelForCausalLM 类自动下载和创建模型实例. The BitsAndBytesConfig类用于模型的量化参数设置，比如4-bit是量化位数，torch.bfloat16是微调时用的数据类型。"
      ],
      "metadata": {
        "id": "BjVYByv8lYpy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "NfwVQCwnlaUz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "从peft库导入prepare_model_for_kbit_training函数，并使用该函数进行k-bit量化前准备. gradient_checkpointing_enable() 函数是能了在训练阶段可以降低内存使用的梯度 checkpointing特性。"
      ],
      "metadata": {
        "id": "F0QPHAa1lgmZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def print_trainable_parameters(model):\n",
        "  \"\"\"\n",
        "\n",
        "  Prints the number of trainable parameters in the model.\n",
        "\n",
        "  \"\"\"\n",
        "  trainable_params = 0\n",
        "  all_param = 0\n",
        "  for _, param in model.named_parameters():\n",
        "    all_param += param.numel()\n",
        "    if param.requires_grad:\n",
        "      trainable_params += param.numel()\n",
        "      print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "      )\n",
        "\n",
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "# LoRA attention dimension 64， 8\n",
        "lora_r = 8\n",
        "\n",
        "# Alpha parameter for LoRA scaling 16,32\n",
        "lora_alpha = 32\n",
        "\n",
        "# Dropout probability for LoRA layers 0.1 0.05\n",
        "lora_dropout = 0.1\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "  r=lora_r,\n",
        "  lora_alpha=lora_alpha,\n",
        "  target_modules=[\"q_proj\",\"v_proj\"],\n",
        "  lora_dropout=lora_dropout,\n",
        "  bias=\"none\",\n",
        "  task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, peft_config)\n",
        "print_trainable_parameters(model)\n",
        "\n"
      ],
      "metadata": {
        "id": "Ghvw4DqSlfD9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5439db72-7946-418c-8c60-6ee953684068"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 32768 || all params: 139493376 || trainable%: 0.02349072116513977\n",
            "trainable params: 65536 || all params: 139526144 || trainable%: 0.04697040864255519\n",
            "trainable params: 98304 || all params: 156336128 || trainable%: 0.06287989939216097\n",
            "trainable params: 131072 || all params: 156368896 || trainable%: 0.08382229673093043\n",
            "trainable params: 163840 || all params: 240820224 || trainable%: 0.06803415314487873\n",
            "trainable params: 196608 || all params: 240852992 || trainable%: 0.08162987653481174\n",
            "trainable params: 229376 || all params: 257662976 || trainable%: 0.08902171493975138\n",
            "trainable params: 262144 || all params: 257695744 || trainable%: 0.10172616587722923\n",
            "trainable params: 294912 || all params: 342147072 || trainable%: 0.086194512282718\n",
            "trainable params: 327680 || all params: 342179840 || trainable%: 0.09576250897773522\n",
            "trainable params: 360448 || all params: 358989824 || trainable%: 0.10040618867235634\n",
            "trainable params: 393216 || all params: 359022592 || trainable%: 0.10952402683338658\n",
            "trainable params: 425984 || all params: 443473920 || trainable%: 0.09605615590652997\n",
            "trainable params: 458752 || all params: 443506688 || trainable%: 0.10343744805038882\n",
            "trainable params: 491520 || all params: 460316672 || trainable%: 0.1067786656226086\n",
            "trainable params: 524288 || all params: 460349440 || trainable%: 0.11388913604413203\n",
            "trainable params: 557056 || all params: 544800768 || trainable%: 0.10224948875255624\n",
            "trainable params: 589824 || all params: 544833536 || trainable%: 0.10825765321465088\n",
            "trainable params: 622592 || all params: 561643520 || trainable%: 0.11085180863477247\n",
            "trainable params: 655360 || all params: 561676288 || trainable%: 0.11667930692491686\n",
            "trainable params: 688128 || all params: 646127616 || trainable%: 0.10650032330455289\n",
            "trainable params: 720896 || all params: 646160384 || trainable%: 0.11156610925871926\n",
            "trainable params: 753664 || all params: 662970368 || trainable%: 0.11367989225123257\n",
            "trainable params: 786432 || all params: 663003136 || trainable%: 0.11861663351167015\n",
            "trainable params: 819200 || all params: 747454464 || trainable%: 0.10959864974463515\n",
            "trainable params: 851968 || all params: 747487232 || trainable%: 0.11397759901803915\n",
            "trainable params: 884736 || all params: 764297216 || trainable%: 0.11575810842676156\n",
            "trainable params: 917504 || all params: 764329984 || trainable%: 0.1200402992433174\n",
            "trainable params: 950272 || all params: 848781312 || trainable%: 0.11195722461900763\n",
            "trainable params: 983040 || all params: 848814080 || trainable%: 0.11581334748829802\n",
            "trainable params: 1015808 || all params: 865624064 || trainable%: 0.11734978754010239\n",
            "trainable params: 1048576 || all params: 865656832 || trainable%: 0.12113067918350351\n",
            "trainable params: 1081344 || all params: 950108160 || trainable%: 0.11381272633212623\n",
            "trainable params: 1114112 || all params: 950140928 || trainable%: 0.11725755276589875\n",
            "trainable params: 1146880 || all params: 966950912 || trainable%: 0.11860788234098071\n",
            "trainable params: 1179648 || all params: 966983680 || trainable%: 0.12199254490003389\n",
            "trainable params: 1212416 || all params: 1051435008 || trainable%: 0.11531059844642343\n",
            "trainable params: 1245184 || all params: 1051467776 || trainable%: 0.11842341043840035\n",
            "trainable params: 1277952 || all params: 1068277760 || trainable%: 0.11962731490356965\n",
            "trainable params: 1310720 || all params: 1068310528 || trainable%: 0.12269091857157098\n",
            "trainable params: 1343488 || all params: 1152761856 || trainable%: 0.11654514703165196\n",
            "trainable params: 1376256 || all params: 1152794624 || trainable%: 0.11938431801708332\n",
            "trainable params: 1409024 || all params: 1169604608 || trainable%: 0.12047011360611876\n",
            "trainable params: 1441792 || all params: 1169637376 || trainable%: 0.12326829063301069\n",
            "trainable params: 1474560 || all params: 1254088704 || trainable%: 0.11758019949440514\n",
            "trainable params: 1507328 || all params: 1254121472 || trainable%: 0.12018995238126343\n",
            "trainable params: 1540096 || all params: 1270931456 || trainable%: 0.12117852561830053\n",
            "trainable params: 1572864 || all params: 1270964224 || trainable%: 0.12375360142316641\n",
            "trainable params: 1605632 || all params: 1355415552 || trainable%: 0.11846049705057538\n",
            "trainable params: 1638400 || all params: 1355448320 || trainable%: 0.12087513598452798\n",
            "trainable params: 1671168 || all params: 1372258304 || trainable%: 0.12178232007259182\n",
            "trainable params: 1703936 || all params: 1372291072 || trainable%: 0.12416724372597245\n",
            "trainable params: 1736704 || all params: 1456742400 || trainable%: 0.11921833263039505\n",
            "trainable params: 1769472 || all params: 1456775168 || trainable%: 0.12146500289604058\n",
            "trainable params: 1802240 || all params: 1473585152 || trainable%: 0.1223030781461077\n",
            "trainable params: 1835008 || all params: 1473617920 || trainable%: 0.12452400144536788\n",
            "trainable params: 1867776 || all params: 1558069248 || trainable%: 0.11987759866241837\n",
            "trainable params: 1900544 || all params: 1558102016 || trainable%: 0.12197814908674119\n",
            "trainable params: 1933312 || all params: 1574912000 || trainable%: 0.12275682704811443\n",
            "trainable params: 1966080 || all params: 1574944768 || trainable%: 0.12483485389120642\n",
            "trainable params: 1998848 || all params: 1659396096 || trainable%: 0.12045635185102906\n",
            "trainable params: 2031616 || all params: 1659428864 || trainable%: 0.1224286285525283\n",
            "trainable params: 2064384 || all params: 1676238848 || trainable%: 0.1231557186771512\n",
            "trainable params: 2097152 || all params: 1676271616 || trainable%: 0.12510812567502186\n",
            "trainable params: 2129920 || all params: 1760722944 || trainable%: 0.12096849236037445\n",
            "trainable params: 2162688 || all params: 1760755712 || trainable%: 0.12282726020768973\n",
            "trainable params: 2195456 || all params: 1777565696 || trainable%: 0.12350913414566704\n",
            "trainable params: 2228224 || all params: 1777598464 || trainable%: 0.12535024332694292\n",
            "trainable params: 2260992 || all params: 1862049792 || trainable%: 0.12142489474309395\n",
            "trainable params: 2293760 || all params: 1862082560 || trainable%: 0.12318250808385209\n",
            "trainable params: 2326528 || all params: 1878892544 || trainable%: 0.12382443090901957\n",
            "trainable params: 2359296 || all params: 1878925312 || trainable%: 0.12556624709519054\n",
            "trainable params: 2392064 || all params: 1963376640 || trainable%: 0.1218341886760963\n",
            "trainable params: 2424832 || all params: 1963409408 || trainable%: 0.12350108897919675\n",
            "trainable params: 2457600 || all params: 1980219392 || trainable%: 0.12410746051314298\n",
            "trainable params: 2490368 || all params: 1980252160 || trainable%: 0.1257601456170107\n",
            "trainable params: 2523136 || all params: 2064703488 || trainable%: 0.12220330980522856\n",
            "trainable params: 2555904 || all params: 2064736256 || trainable%: 0.123788401185512\n",
            "trainable params: 2588672 || all params: 2081546240 || trainable%: 0.12436293512269034\n",
            "trainable params: 2621440 || all params: 2081579008 || trainable%: 0.12593516700183788\n",
            "trainable params: 2654208 || all params: 2166030336 || trainable%: 0.12253789597894163\n",
            "trainable params: 2686976 || all params: 2166063104 || trainable%: 0.12404883288201746\n",
            "trainable params: 2719744 || all params: 2182873088 || trainable%: 0.12459469196589408\n",
            "trainable params: 2752512 || all params: 2182905856 || trainable%: 0.12609393998528903\n",
            "trainable params: 2785280 || all params: 2267357184 || trainable%: 0.12284257723727043\n",
            "trainable params: 2818048 || all params: 2267389952 || trainable%: 0.1242859878387606\n",
            "trainable params: 2850816 || all params: 2284199936 || trainable%: 0.12480588739496401\n",
            "trainable params: 2883584 || all params: 2284232704 || trainable%: 0.12623862686802684\n",
            "trainable params: 2916352 || all params: 2368684032 || trainable%: 0.12312119137044954\n",
            "trainable params: 2949120 || all params: 2368716800 || trainable%: 0.1245028531903856\n",
            "trainable params: 2981888 || all params: 2385526784 || trainable%: 0.12499914148941285\n",
            "trainable params: 3014656 || all params: 2385559552 || trainable%: 0.1263710225750843\n",
            "trainable params: 3047424 || all params: 2470010880 || trainable%: 0.12337694642057609\n",
            "trainable params: 3080192 || all params: 2470043648 || trainable%: 0.12470192591511646\n",
            "trainable params: 3112960 || all params: 2486853632 || trainable%: 0.12517664730820796\n",
            "trainable params: 3145728 || all params: 2486886400 || trainable%: 0.12649262949847648\n",
            "trainable params: 3178496 || all params: 2571337728 || trainable%: 0.12361254476175912\n",
            "trainable params: 3211264 || all params: 2571370496 || trainable%: 0.12488530940972577\n",
            "trainable params: 3244032 || all params: 2588180480 || trainable%: 0.12534025447869848\n",
            "trainable params: 3276800 || all params: 2588213248 || trainable%: 0.12660471475957766\n",
            "trainable params: 3309568 || all params: 2672664576 || trainable%: 0.1238302789552893\n",
            "trainable params: 3342336 || all params: 2672697344 || trainable%: 0.1250547880965006\n",
            "trainable params: 3375104 || all params: 2689507328 || trainable%: 0.12549153389032894\n",
            "trainable params: 3407872 || all params: 2689540096 || trainable%: 0.12670835452753926\n",
            "trainable params: 3440640 || all params: 2773991424 || trainable%: 0.12403210659673618\n",
            "trainable params: 3473408 || all params: 2774024192 || trainable%: 0.1252118856791859\n",
            "trainable params: 3506176 || all params: 2790834176 || trainable%: 0.12563182829534048\n",
            "trainable params: 3538944 || all params: 2790866944 || trainable%: 0.12680446868340564\n",
            "trainable params: 3571712 || all params: 2875318272 || trainable%: 0.12421970933727645\n",
            "trainable params: 3604480 || all params: 2875351040 || trainable%: 0.12535791108135444\n",
            "trainable params: 3637248 || all params: 2892161024 || trainable%: 0.12576229227270025\n",
            "trainable params: 3670016 || all params: 2892193792 || trainable%: 0.1268938481975692\n",
            "trainable params: 3702784 || all params: 2976645120 || trainable%: 0.12439453985028621\n",
            "trainable params: 3735552 || all params: 2976677888 || trainable%: 0.1254939950022567\n",
            "trainable params: 3768320 || all params: 2993487872 || trainable%: 0.12588392407557414\n",
            "trainable params: 3801088 || all params: 2993520640 || trainable%: 0.1269771769470746\n",
            "trainable params: 3833856 || all params: 3077971968 || trainable%: 0.124557859521091\n",
            "trainable params: 3866624 || all params: 3078004736 || trainable%: 0.12562111925223496\n",
            "trainable params: 3899392 || all params: 3094814720 || trainable%: 0.12599759122252074\n",
            "trainable params: 3932160 || all params: 3094847488 || trainable%: 0.12705504924706648\n",
            "trainable params: 3964928 || all params: 3179298816 || trainable%: 0.12471076892949719\n",
            "trainable params: 3997696 || all params: 3179331584 || trainable%: 0.125740140478534\n",
            "trainable params: 4030464 || all params: 3196141568 || trainable%: 0.12610405122079998\n",
            "trainable params: 4063232 || all params: 3196174336 || trainable%: 0.12712798404748846\n",
            "trainable params: 4096000 || all params: 3280625664 || trainable%: 0.1248542326833422\n",
            "trainable params: 4128768 || all params: 3280658432 || trainable%: 0.12585180949432043\n",
            "trainable params: 4161536 || all params: 3297468416 || trainable%: 0.12620396846888252\n",
            "trainable params: 4194304 || all params: 3297501184 || trainable%: 0.12719643651233334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`print_trainable_parameters`函数用于打印模型可训练参数. 从peft库导入 `LoraConfig` 和 `get_peft_model`函数。`LoraConfig`用于配置缩减训练参数的LORA (Low Rank Approximation)方法。`get_peft_model`将LORA方法应用于模型. 打印的是模型可训练参数的情况。\n",
        "\n",
        "从终端输出可以看到使用LORA方法后约11%的参数才会被微调时更新， 这大大降低了内存，不同的LORA参数会需要不同的内存，下图中的两种配置，分别对应了训练的时候需要内存情况。"
      ],
      "metadata": {
        "id": "L0EzlDvomyOV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"Abirate/english_quotes\")\n",
        "dataset = dataset.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)"
      ],
      "metadata": {
        "id": "eBoIH77gmzYS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
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        },
        "outputId": "23f08722-9a46-4895-e95c-8f1ba69d9b6b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a91fc34772db4221be2d77591ab8d8ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6399f99ba124aaa92f5ece0194a2479"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/647k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6240778cff314da7b83f3ce453f102bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c69e4891d3ef47d58062982b48c06ad1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8d8abeb5a4e4e5ba0112bd1dfb1c4ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc63e016ed2d4c5bb62748ff2bf881ab"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "从Huggingface的`datasets`库导入`load_dataset`函数， 用其加载`\"Abirate/english_quotes\"`数据集中的`“quotes”`字段，然后使用LLaMA tokenizer对其tokenize化。"
      ],
      "metadata": {
        "id": "7gbtz19PnEDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "################################################################################\n",
        "# TrainingArguments parameters\n",
        "################################################################################\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 1\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 4\n",
        "\n",
        "# Batch size per GPU for evaluation\n",
        "per_device_eval_batch_size = 4\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use, paged_adamw_8bit paged_adamw_32bit etc...\n",
        "optim = \"paged_adamw_8bit\"\n",
        "\n",
        "# Learning rate schedule\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 0\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 25\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"llama-2-7b-shichaog\"\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = transformers.TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "## needed for llama tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    # args=transformers.TrainingArguments(\n",
        "    #     per_device_train_batch_size=1,\n",
        "    #     gradient_accumulation_steps=4,\n",
        "    #     warmup_steps=2,\n",
        "    #     max_steps=10,\n",
        "    #     learning_rate=2e-4,\n",
        "    #     fp16=True,\n",
        "    #     logging_steps=1,\n",
        "    #     output_dir=\"outputs\",\n",
        "    #     optim=\"paged_adamw_8bit\"\n",
        "    #     ),\n",
        "    args=training_arguments,\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "\n",
        "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()\n",
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "KTVD0tHYnFKG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "outputId": "2ca504dd-f1e5-44e2-e9d8-3488517652c4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='111' max='627' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [111/627 01:56 < 09:09, 0.94 it/s, Epoch 0.18/1]\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>25</td>\n",
              "      <td>1.098700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.917400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.998600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.923200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-c5f97d21c6c7>\u001b[0m in \u001b[0;36m<cell line: 105>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;31m# silence the warnings. Please re-enable for inference!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1537\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1538\u001b[0m         )\n\u001b[0;32m-> 1539\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1540\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1541\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1808\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1809\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1811\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2663\u001b[0m                 \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2664\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2665\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2667\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_accumulation_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   1849\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1851\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1852\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[0;32m--> 487\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    488\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "这里使用了transformers库中的Trainer类，使用模型, 训练数据集, 以及训练参数对Trainer实例化，训练数据集设置了训练时的各种参数，比如 batch size, learning rate, and 优化算法 (paged_adamw_8bit)。  `DataCollatorForLanguageModeling` 用于整理和批处理（batch）标记化数据。 最终调用trainer.train()方法开启微调训练。在后文又给了基于trl库的更简单的接口。"
      ],
      "metadata": {
        "id": "1R4uZt6UnfDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "################################################################################\n",
        "# SFT parameters\n",
        "################################################################################\n",
        "from trl import SFTTrainer\n",
        "# Maximum sequence length to use\n",
        "max_seq_length = None\n",
        "\n",
        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
        "packing = False\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}\n",
        "\n",
        "# Set supervised fine-tuning parameters from trl library\n",
        "trainer2 = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"quote\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer2.train()\n",
        "\n",
        "# Save trained model\n",
        "trainer2.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "yjmr671AsffP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637,
          "referenced_widgets": [
            "49dc531bf6474e25885cf74e23a52363",
            "383d1a2da1dd413cae7e44826f2430c6",
            "5723e1e4fba54850882c9bfc81edc185",
            "e2c394eaae564118a882cb69407def5c",
            "8b4955da940e4a64b54a734b44a054f5",
            "660cd44ada104ff1924115d5bef48048",
            "f3464301ab9b490fb1434d6bec938107",
            "ccc1d4a892a44a649108537259342fbb",
            "b4957f5671fd4eb6a6edcf283bd35c53",
            "d60a660c85bb4088bb2013c89eb3b7c5",
            "1bf5d83c44fa46f59376b14f48a5d07b"
          ]
        },
        "outputId": "0b7119ba-849c-4e76-899d-df14ff99f657"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49dc531bf6474e25885cf74e23a52363"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='117' max='627' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [117/627 02:02 < 09:02, 0.94 it/s, Epoch 0.19/1]\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>25</td>\n",
              "      <td>1.351300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.389100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.366300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.425100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-e8b6e30656c7>\u001b[0m in \u001b[0;36m<cell line: 27>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m# Train model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mtrainer2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;31m# Save trained model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1537\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1538\u001b[0m         )\n\u001b[0;32m-> 1539\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1540\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1541\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1808\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1809\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1811\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2663\u001b[0m                 \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2664\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2665\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2667\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_accumulation_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   1849\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1851\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1852\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[0;32m--> 487\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    488\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "这段代码和上一段使用transformers库的Trainer是一样的意义和作用，这里的SFTTrainer是对上面Trainer的封装，参数的意义都是一样的。因为trl库支持了PPO之类的RLHF，所以把SFT也支持了会使trl库更完备一些。"
      ],
      "metadata": {
        "id": "2PUXdgP6Pi4C"
      }
    }
  ]
}