{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers accelerate bitsandbytes sentencepiece einops"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJQbBFX7iZnj",
        "outputId": "a0c34c5b-887c-4c68-e5f9-144d499f7926"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
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            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.41.1-py3-none-any.whl (92.6 MB)\n",
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            "\u001b[?25hCollecting 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[?25hCollecting einops\n",
            "  Downloading einops-0.6.1-py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, bitsandbytes, einops, huggingface-hub, transformers, accelerate\n",
            "Successfully installed accelerate-0.22.0 bitsandbytes-0.41.1 einops-0.6.1 huggingface-hub-0.16.4 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.32.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "yfuda1Wn5XT9"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "TOKENIZER_ID = \"AIBunCho/japanese-novel-gpt-j-6b\"\n",
        "MODEL_ID = \"tsukemono/japanese-novel-gpt-j-6b-f16-marisa\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "cDq4-8Z7QriO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
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            "8ec7a9770f5846a985884edcacc2ae3e",
            "d7751471eb464633ad44596fd17969e7"
          ]
        },
        "outputId": "98f6a8ef-10b0-4322-b701-48f08cf22169"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/548 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcdfae31b60c4c8ab34014069c73b581"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/834k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6264d9f755d84eee89807e185a416cb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/58.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b235823fb48425c8703a9ebc39f2e2e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/286 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba249a1a32d64d3382e1a59a7735842e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:470: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        }
      ],
      "source": [
        "# tokenizer設定\n",
        "tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_ID, use_fast=True)\n",
        "ret_token = tokenizer(\"[SEP]\",  truncation=True, add_special_tokens=False)['input_ids'][-1]\n",
        "bra_token = tokenizer(\"（\",  truncation=True, add_special_tokens=False)['input_ids'][-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742,
          "referenced_widgets": [
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        },
        "id": "QzpL_PiA7Diy",
        "outputId": "0a46143e-bc8a-422d-f7f1-8ecb437d67fb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/951 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "GPTJForCausalLM(\n",
              "  (transformer): GPTJModel(\n",
              "    (wte): Embedding(50400, 4096)\n",
              "    (drop): Dropout(p=0.0, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-27): 28 x GPTJBlock(\n",
              "        (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTJAttention(\n",
              "          (attn_dropout): Dropout(p=0.0, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (out_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "        )\n",
              "        (mlp): GPTJMLP(\n",
              "          (fc_in): Linear(in_features=4096, out_features=16384, bias=True)\n",
              "          (fc_out): Linear(in_features=16384, out_features=4096, bias=True)\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=50400, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# model設定\n",
        "model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map=\"auto\", load_in_8bit=False, torch_dtype=torch.float16)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "COSQGiA0Qu2r"
      },
      "outputs": [],
      "source": [
        "# テキスト生成関数の定義\n",
        "def generate(text,input=None,maxTokens=512):\n",
        "    prompt = f\"ユーザー: {text}[SEP]魔理沙: \"\n",
        "    input_ids = tokenizer(prompt,\n",
        "        return_tensors=\"pt\",\n",
        "        truncation=True,\n",
        "        add_special_tokens=False\n",
        "    ).input_ids.cuda()\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(\n",
        "            input_ids = input_ids,\n",
        "            max_length=maxTokens,\n",
        "            # max_new_tokens=50,\n",
        "            do_sample=True,\n",
        "            temperature=0.1,\n",
        "            top_p=0.9,\n",
        "            top_k=20,\n",
        "            no_repeat_ngram_size=2,\n",
        "            repetition_penalty=1.15,\n",
        "            pad_token_id=tokenizer.pad_token_id,\n",
        "            bad_words_ids=[[bra_token]],\n",
        "            eos_token_id = [tokenizer.eos_token_id,ret_token]\n",
        "        )\n",
        "    outputs = tokenizer.decode(outputs.tolist()[0][input_ids.size(1):],skip_special_tokens=True)\n",
        "    return outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "jmLMyrHP6M7k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "b1ce94ca-0289-4221-b56c-8b8b7df330a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'そうだな。一言で言えば、幻想の世界だ! '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "generate(\"幻想郷ってどんな場所?\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate(\"仏教についてどう思う?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "lgS8pSgdjiuC",
        "outputId": "30b20ec1-04e2-41d9-cbf9-fc78eb62024d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'そうだな。私は、宗教はよく分からないが......ただ、神様ってのは本当にいると思うぜ。 '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate(\"日本で一番高い山は?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "uIppGgdujrNj",
        "outputId": "9321adec-5082-4467-cc6f-3cfd532858d3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'富士山だ!'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate(\"自己紹介してください\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "a4NI8EBdJwHU",
        "outputId": "1711112b-9d4c-4986-9c82-7f56e533559a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'霧雨魔理沙だ。普通の魔法使いだぜ! '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    }
  ],
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