Chapter_8_2022_04_15.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "## - - 이전 코드 시작 - -"
      ],
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          "text": [
            "Collecting ratsnlp\n",
            "  Downloading ratsnlp-1.0.1-py3-none-any.whl (42 kB)\n",
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            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
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            "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, PyYAML, pyDeprecate, fsspec, aiohttp, xlrd, torchmetrics, tokenizers, sacremoses, huggingface-hub, future, dataclasses, transformers, pytorch-lightning, Korpora, flask-ngrok, flask-cors, ratsnlp\n",
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            "    Found existing installation: PyYAML 3.13\n",
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            "      Successfully uninstalled future-0.16.0\n",
            "Successfully installed Korpora-0.2.0 PyYAML-5.4.1 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 dataclasses-0.6 flask-cors-3.0.10 flask-ngrok-0.0.25 frozenlist-1.3.0 fsspec-2022.3.0 future-0.18.2 huggingface-hub-0.5.1 multidict-6.0.2 pyDeprecate-0.3.0 pytorch-lightning-1.3.4 ratsnlp-1.0.1 sacremoses-0.0.49 tokenizers-0.10.3 torchmetrics-0.7.3 transformers-4.10.0 xlrd-2.0.1 yarl-1.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#체크포인트 로드\n",
        "from transformers import GPT2LMHeadModel\n",
        "model = GPT2LMHeadModel.from_pretrained(\n",
        "    \"skt/kogpt2-base-v2\",\n",
        ")\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "72146d6fed3f482a9f842eeed45c3277",
            "1f8a330267f34240a92d0c8bb9eda3ac",
            "be73435fd63b49fa95c4d349aeac3eda",
            "707ccadf1a5b46e6a7753d2531e810cf",
            "bcae3329da7c413fb0dca5b25bc1c5b3",
            "671765d59f974ede91b49ccc91a26b0e",
            "c407d0ea383f4258a1076e54e80c695d",
            "a313f495fb8f491c9dc7189321855929",
            "022992edc7d64d9699dc440dcc2977c7",
            "8033f65c092c4254ae34ceed807edaae",
            "f8265f1baf8445338bd31dd8745c04fe",
            "a30b5aa5f433403aa8e1c7e9c529de5e",
            "ae2617965d854116a5e4cfcbd1871482",
            "457500188f6f4f7a8388fbddb6129bde",
            "13a6abbc20f640e981b603c5a986dfef",
            "d687b0c1e0d94624a14e8426d949bdcf",
            "8d581ff27e334727991a36174a30082a",
            "5e94042a1c954ff49c1dcad8a8172020",
            "9fce666954554c5dafc7154fbc4ac7bd",
            "4f1c1b0246764528acde4b769c6c4a65",
            "b69cf4b0ef164c088232028336f1ad38",
            "c6e7d76b5e4d45edb392e8c09f721dfd"
          ]
        },
        "id": "wKQr6cbu_G3E",
        "outputId": "86a5ce5f-00a6-4d60-988a-71b654f78641"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72146d6fed3f482a9f842eeed45c3277"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/513M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a30b5aa5f433403aa8e1c7e9c529de5e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(51200, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=51200, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#토크나이저 로드\n",
        "from transformers import PreTrainedTokenizerFast\n",
        "tokenizer = PreTrainedTokenizerFast.from_pretrained(\n",
        "    \"skt/kogpt2-base-v2\",\n",
        "    eos_token=\"</s>\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102,
          "referenced_widgets": [
            "92fa6caec03c4c6388133fea2b0e0f34",
            "6add4a0d26644f23815416422f6699fa",
            "d1694bbdeb3b4a1cb7befb826b91280c",
            "43e10bf65c8644088766946108fa5dbc",
            "5e5b9b26ee394e8089281dbc78f1270a",
            "11d89d779eed4e8495507e32dffebb2a",
            "a1b0a3aff7864541a224a2f41e3a2536",
            "6e968bce170b4fee9a66af6e6567c679",
            "2c0e23f2554d45638235d4a34050b4b7",
            "26d565ba6f364b1390e15400622263d7",
            "e77251bf39654b7b8c9a44318f791e8c"
          ]
        },
        "id": "-kYfdfVK_G5i",
        "outputId": "8ffc574c-0989-41ad-b72c-644f2a4d8130"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.83M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92fa6caec03c4c6388133fea2b0e0f34"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'GPT2Tokenizer'. \n",
            "The class this function is called from is 'PreTrainedTokenizerFast'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#모델 입력값 만들기\n",
        "input_ids = tokenizer.encode(\"안녕하세요\", return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "yWBqbpkV_G7u"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0agaNZeA_G-D",
        "outputId": "d4d70868-4eac-4185-a789-29666764737c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[25906,  8702,  7801,  8084]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#그리드 서치\n",
        "import torch\n",
        "with torch.no_grad():\n",
        "  generated_ids = model.generate(\n",
        "      input_ids,\n",
        "      do_sample=False,\n",
        "      min_length=10,\n",
        "      max_length=50,\n",
        "  )"
      ],
      "metadata": {
        "id": "59EC9h_Y_HAO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## - - 이전 코드 끝 - -"
      ],
      "metadata": {
        "id": "zcaDfkfC_CIF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "앞의 코드에서 그리드 서치를 수행한 결과인 `generated_ids`는 토큰 인덱스 시퀀스여서 사람이 알아보기 어렵습니다.   \n",
        "다음 코드를 실행하면 토크나이저가 `generated_ids`를 문장(문자열)으로 변환해 줍니다."
      ],
      "metadata": {
        "id": "q62ekSRf_CMu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰 인덱스를 문장으로 복원하기\n",
        "print(tokenizer.decode([el.item() for el in generated_ids[0]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTE6bXeJ_4Co",
        "outputId": "c42fdcda-9788-419e-fddb-2a026cd1cda9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "안녕하세요?\"\n",
            "\"그럼, 그건 뭐예요?\"\n",
            "\"그럼, 그건 뭐예요?\"\n",
            "\"그럼, 그건 뭐예요?\"\n",
            "\"그럼, 그건 뭐예요?\"\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "여기서 \"그럼, 그건 뭐예요?\"는 모델이 생성한 문장입니다. 그리드 서치는 최고 확률을 내는 단어 시퀀스를 찾는 방법이므로 컨텍스트(`안녕하세요`)가 동일하다면 코드를 반복해서 실행해도 결과가 바뀌진 않습니다."
      ],
      "metadata": {
        "id": "G_JoxNdc_CPh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<**3단계**> **빔 서치하기**   \n",
        "\n",
        "하지만 그리드 서치도 완벽한 대안은 아닙니다. 순간의 최선이 항상 전체의 최선이 될 수는 없기 때문입니다. 이번에 소개할 ***빔 서치(beam search)*** 는 빔 크기만큼의 선택지를 계산 범위에 넣습니다. 다음 그림은 빔 크기가 2인 빔 서치의 예시입니다."
      ],
      "metadata": {
        "id": "yqBIpns1_CSC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<center><그림 - 빔 서치></center>\n",
        "\n",
        "<p align=\"center\"><img src=\"https://i.imgur.com/UCkkvzH.jpg\">\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "tBPJYrLzAdbd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "모델은 `그` 다음에 올 단어로 `책`(0.5), `집`(0.4), `사람`(0.1) 순으로 예측했습니다. 우리는 빔 크기를 2로 설정해 두었으므로 예측 결과에서 확률이 높은 2개(`책, 집`)만 탐색 대상으로 남겨 두고, `사람`은 제거합니다. 그리고 모델에 `그 책`을 입력해 다음 토큰 등장 확률을 계산합니다.  \n",
        "계산 결과는 다음과 같습니다."
      ],
      "metadata": {
        "id": "Qk26VLF0Addv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|토큰 시퀀스|등장 확률|\n",
        "|:---:|:---:|\n",
        "|**그 책이**|**0.5 $\\times$ 0.4 = 0.2**|\n",
        "|그 책을|0.5 $\\times$ 0.3 = 0.15|\n",
        "|그 책 읽|0.5 $\\times$ 0.3 = 0.15|\n"
      ],
      "metadata": {
        "id": "v9UTt-EvAdgF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "모델에 `그 집`을 입력해 다음 단어 시퀀스 확률을 계산합니다.  \n",
        "계산 결과는 다음과 같습니다."
      ],
      "metadata": {
        "id": "YJ8DLiXOAdiB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|토큰 시퀀스|등장 확률|\n",
        "|:---:|:---:|\n",
        "|**그 집에**|**0.4 $\\times$ 0.7 = 0.28**|\n",
        "|그 집은|0.4 $\\times$ 0.2 = 0.08|\n",
        "|그 집 사|0.4 $\\times$ 0.1 = 0.04|"
      ],
      "metadata": {
        "id": "56P0QNszAdkB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "우리는 빔 크기를 2로 설정해 두었으므로 위 6가지 경우의 수에서 가장 확률이 높은 시퀀스 2개만을 남겨 둡니다. `그 집에`(0.28), `그 책이`(0.2)가 바로 그것입니다. 만일 빔 서치를 여기에서 그만둔다면 이 둘 가운데 확률값이 조금이라도 높은 `그 집에`가 최종 생성 결과가 됩니다."
      ],
      "metadata": {
        "id": "mevoGa_2Ad-G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "빔 서치는 그리디 서치보다 계산량이 많은 편입니다. 그리디 서치가 매 순간 최고 확률을 내는 한 가지 경우의 수만 선택한다면, 빔 서치는 빔 크기만큼의 경우의 수를 고려하기 때문입니다. 하지만 빔 서치는 그리디 서치보다 조금이라도 더 높은 확률을 내는 문장을 생성할 수 있습니다. 앞에서 그리디 서치로 찾은 단어 시퀀스 확률은 0.2(`그 책이`)인 반면, 빔 서치는 이보다 약간 높은 0.28(`그 집에`)인 것을 확인할 수 있습니다."
      ],
      "metadata": {
        "id": "PU22NdufDDPx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "진행 중인 실습에서 다음 코드를 실행하면 빔 서치를 수행합니다. 핵심 인자는 `do_sample=False`, `num_beams=3`입니다. `num_beams`는 빔 크기를 의미합니다. `num_beams=1`로 설정한다면 매 순간 최고 확률을 내는 단어만 선택한다는 뜻이므로 정확히 그리디 서치로 동작하게 됩니다."
      ],
      "metadata": {
        "id": "U3L358sfDDSP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uT4EjCP-7pz",
        "outputId": "04712601-3355-4f9f-a48f-3a9634bf0b03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "안녕하세요?\"\n",
            "\"그렇지 않습니다.\"\n",
            "\"그렇지 않습니다.\"\n",
            "\"그렇지 않습니다.\"\n",
            "\"그렇지 않습니다.\"\n",
            "\"그렇지 않습니다.\"\n",
            "\"그렇지 않습니다.\"\n",
            "\"그\n"
          ]
        }
      ],
      "source": [
        "#빔 서치\n",
        "with torch.no_grad():\n",
        "    generated_ids = model.generate(\n",
        "        input_ids,\n",
        "        do_sample=False,\n",
        "        mix_length=10,\n",
        "        max_length=50,\n",
        "        num_beams=3,\n",
        "    )\n",
        "    print(tokenizer.decode([el.item() for el in generated_ids[0]]))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<**4단계**> **반복되는 표현 줄이기**   \n",
        "\n",
        "그리디 서치(`\"그럼, 그건 뭐예요?\"`)나 빔 서치(`\"그렇지 않습니다.\"`) 모두 특정 표현이 반복되고 있음을 확인할 수 있습니다. 이럴 때 다음 코드를 실행하면 토큰이 n-gram 단위로 반복될 경우 모델이 계산한 결과를 무시하고 해당 n-gram의 마지막 토큰 등장 확률을 0으로 만들어 생성에서 배제하게 됩니다. 이 코드에서 핵심 인자는 `no_repeat_ngram_size=3`입니다.   \n",
        "3개 이상의 토큰이 반복될 경우, 세 번째 토큰의 등장 확률을 0으로 만듭니다.  \n",
        "\n",
        "cf) n-gram이란 n개의 연속적인 단어(또는 토큰)의 나열을 가리킵니다."
      ],
      "metadata": {
        "id": "57tSKP1ZFIo0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#반복 줄이기(1)\n",
        "with torch.no_grad():\n",
        "    generated_ids = model.generate(\n",
        "        input_ids,\n",
        "        do_sample=False,\n",
        "        min_length=10,\n",
        "        max_length=50,\n",
        "        no_repeat_ngram_size=3,\n",
        "    )\n",
        "    print(tokenizer.decode([el.item() for el in generated_ids[0]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKf7hHIsE4G2",
        "outputId": "a4e71cf3-2cdb-4343-ec85-3f0e7e0d84b7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "안녕하세요?\"\n",
            "\"그럼, 그건 뭐예요?\" 하고 나는 물었다.\n",
            "\"그건 뭐죠?\" 나는 물었다.\n",
            "나는 대답하지 않았다.\n",
            "\"그런데 왜 그걸 물어요? 그건 무슨 뜻이에요?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과는 위와 같은데요, 반복되는 n-gram 등장 확률을 인위적으로 조정할 뿐 최고 확률을 내는 토큰 시퀀스를 찾는다(`do_sample=False`)는 본질은 변함이 없으므로 코드를 반복해서 실행해도 생성 결과가 바뀌진 않습니다."
      ],
      "metadata": {
        "id": "XdglzLhXGZ1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "49s-CyYZGWOB"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}