Text_Correction.ipynb

{
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        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/damilojohn/Text-Descrambling/blob/main/Text_Correction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0gqgg9KtpRtT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d13e48a1-43be-454a-c6be-cce7db4b25e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -qU huggingface_hub wandb transformers[torch] datasets nvidia-ml-py3 accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU transformers wandb datasets"
      ],
      "metadata": {
        "id": "ToqObKfsEDUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GX69H6upfR2M"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import wandb\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import inspect\n",
        "from tqdm import tqdm\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import AdamW, get_linear_schedule_with_warmup\n",
        "from torch.utils.data import Dataset, DataLoader,random_split, RandomSampler, SequentialSampler\n",
        "import torch.nn.functional as F\n",
        "from datasets import load_dataset\n",
        "from transformers import Seq2SeqTrainingArguments,Seq2SeqTrainer,DataCollatorForSeq2Seq\n",
        "from transformers import  TrainingArguments, Trainer\n",
        "import torch.nn as nn\n"
      ],
      "metadata": {
        "id": "HVxTog_hFwPW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NOJvSpodmUqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0514ec93-793c-4673-e2b5-b661e7f48766"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ],
      "source": [
        "!wandb login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ox64IIRrTwH7"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FsqicXtbmq2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "0ba62fd5-106e-4ad1-f256-41b59e41fe58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mdamilojohn\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.6"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240408_142110-s5c53bx0</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/damilojohn/Text%20Descrambling/runs/s5c53bx0' target=\"_blank\">gpt2-medium-new strategy</a></strong> to <a href='https://wandb.ai/damilojohn/Text%20Descrambling' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/damilojohn/Text%20Descrambling' target=\"_blank\">https://wandb.ai/damilojohn/Text%20Descrambling</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/damilojohn/Text%20Descrambling/runs/s5c53bx0' target=\"_blank\">https://wandb.ai/damilojohn/Text%20Descrambling/runs/s5c53bx0</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: WANDB_LOG_MODEL='end'\n"
          ]
        }
      ],
      "source": [
        "run = wandb.init(project='Text Descrambling',\n",
        "           entity='damilojohn',\n",
        "           name = 'gpt2-medium-new strategy',\n",
        "           config=config\n",
        ")\n",
        "%env WANDB_LOG_MODEL='end'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EVTqs1jQm1yn"
      },
      "outputs": [],
      "source": [
        "config = {'model name':'GPT2-medium',\n",
        "          'learning_rate':'5e-5',\n",
        "          'architecture' : 'decoder-only',\n",
        "          'context_length':'not set',\n",
        "          'training_strategy':'sliding window with input tokens set to 101 during loss calculations'\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [],
      "source": []
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      "outputs": [
        {
          "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": "242327121af442f6bf1ec2cb0989f4eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6bea8dddc03c4a86a2e6b90c8532a664"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating val split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "406e2580e724435ab32c53edabb0bdb7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = load_dataset('damilojohn/text-descrambling')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vC3Px5qvrM0D"
      },
      "outputs": [],
      "source": [
        "# artifact = wandb.Artifact('word-deshuffling',type='dataset')\n",
        "# artifact.add_dir('/content/drive/MyDrive/Colab datasets/Text-Deshuffling')\n",
        "# run.log_artifact(artifact)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"damilojohn/Text-Descrambling\")"
      ],
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            "322f36f7b5c64482a1eb13aafb357722",
            "511fec11b6ef40d0a06f746660dcd30e",
            "0dc02a630a2444ecbce61f212aabf0d0"
          ]
        },
        "id": "jNVGpo47VWRk",
        "outputId": "c0943934-1662-46e3-fbc3-f5c5fc16f049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/169 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "807f24f580404c0199eb4812d896d9be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/7.23M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdd4778ffbda496fad0278ae7c2fd989"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/733k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24e0cc0b37534f8986a5a9b7138d20db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3dbc9ea69639450b80a5707751346d8c"
            }
          },
          "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": "1b4acf6ea92f47a1a081939ab3770910"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "23ad699ec5ca493b832ce959c26b61f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec01e60f07cd43c89897a9d3899fb3bd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda'if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "mjuhYbrBGBcg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6whw5qIy2s4h"
      },
      "source": [
        "# Finetuning T5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pq0I6drdFodR"
      },
      "outputs": [],
      "source": [
        "model.to('cuda')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JHe6ZixbMVDu"
      },
      "outputs": [],
      "source": [
        "#testing out the model\n",
        "text = 'translate english to german: the dichotomy of man'\n",
        "input = tokenizer(text,return_tensors='pt').to('cuda')\n",
        "out = model.generate(**input,)\n",
        "out = tokenizer.decode(out[0])\n",
        "out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bJztR-q3cW2E"
      },
      "outputs": [],
      "source": [
        "def add_prefix(row):\n",
        "  '''adding t5 task definition prompt'''\n",
        "  row['text'] = f\"Reorder: {row['text']}\"\n",
        "  return row\n",
        "dataset = dataset.map(add_prefix,batched=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o8L91Nb2nkhL"
      },
      "outputs": [],
      "source": [
        "max_text_length = 64\n",
        "max_label_length = 64\n",
        "prefix = 'Reorder: '\n",
        "def preprocess_function(sample):\n",
        "    # Getting text and label\n",
        "    inputs = sample['text']\n",
        "    label = sample[\"label\"]\n",
        "    # Tokenizing the text and label\n",
        "    inputs = tokenizer(inputs, padding=\"max_length\", truncation=True, max_length=max_text_length,)\n",
        "    # with tokenizer.as_target_tokenizer:\n",
        "    #   outputs = tokenizer(label,padding='max_length',truncation=True,max_length=max_label_length)\n",
        "\n",
        "    outputs = tokenizer(label, padding=\"max_length\", truncation=True, max_length=max_label_length,)\n",
        "    sample[\"input_ids\"] = inputs.input_ids\n",
        "    sample[\"attention_mask\"] = inputs.attention_mask\n",
        "    sample[\"decoder_input_ids\"] = outputs.input_ids\n",
        "    sample[\"decoder_attention_mask\"] = outputs.attention_mask\n",
        "    sample[\"labels\"] = outputs.input_ids\n",
        "    # The labels are used to calcuate the loss while training, and because we added padding to make all tokens to be of same size,\n",
        "    # we also need to convert the padding number ( 0 ) to ( -100 ), so that we can tell huggingface that these number can be ignorned while calcuating loss.\n",
        "    # Why specifically -100 ? It's simply an arbitrary number, again so that huggingface can ignore this number while calcuating loss\n",
        "    sample[\"labels\"] = [[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in sample[\"labels\"]]\n",
        "\n",
        "    return sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ix0nUAKUm6Ii"
      },
      "outputs": [],
      "source": [
        "def WER(predictions):\n",
        "  labels_ids = predictions.label_ids\n",
        "  pred_ids = predictions.predictions\n",
        "\n",
        "  pred = tokenizer.batch_decode(pred_ids,skip_special_tokens=True)\n",
        "  labels_ids[labels_ids==-100] = tokenizer.pad_token_id\n",
        "  labels = tokenizer.batch_decode(labels_ids,skip_special_tokens=True)\n",
        "  error_rate = wer.compute(predictions=pred,references=labels,)\n",
        "\n",
        "  return {\n",
        "      'word_error_rate': error_rate\n",
        "  }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Py65sCproZb9"
      },
      "outputs": [],
      "source": [
        "batch_size = 128\n",
        "tokenized_dataset = dataset.map(preprocess_function,batched=True,batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-MMfcMDIoa0L"
      },
      "outputs": [],
      "source": [
        "tokenized_dataset.set_format(\n",
        "    type=\"torch\", columns=[\"input_ids\", \"attention_mask\",\"decoder_input_ids\",\"decoder_attention_mask\",\"labels\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-xSFqCIeomm7"
      },
      "outputs": [],
      "source": [
        "N_epochs = 4\n",
        "BATCH_SIZE = 8\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    \"Text Correction\",\n",
        "    fp16=False,\n",
        "    learning_rate=1e-3,\n",
        "    do_train=True,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    weight_decay=0.01,\n",
        "    warmup_steps=500,\n",
        "    save_strategy='epoch',\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=BATCH_SIZE,\n",
        "    num_train_epochs=N_epochs,\n",
        "    report_to ='wandb',\n",
        "    load_best_model_at_end=True,\n",
        "    save_total_limit=5,\n",
        "    predict_with_generate=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pKJWIt2Z6m23"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer,model='t5-large',pad_to_multiple_of=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-erdcmrYo1Tz"
      },
      "outputs": [],
      "source": [
        "trainer = Seq2SeqTrainer(model = model,\n",
        "                         args = args,\n",
        "                         train_dataset = tokenized_dataset['train'],\n",
        "                         eval_dataset = tokenized_dataset['val'],\n",
        "                         tokenizer=tokenizer,\n",
        "                         data_collator=data_collator\n",
        "                         )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gGvkSrLspX4Q"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eYpCzoo4f6P5"
      },
      "outputs": [],
      "source": [
        "def descramble(text):\n",
        "  model.eval()\n",
        "  input = tokenizer(text,truncation=True,padding='max_length',max_length=max_text_length,return_tensors='pt',add_special_tokens=True).to('cuda')\n",
        "  print(text)\n",
        "  print(input.input_ids)\n",
        "  out = model.generate(**input,\n",
        "                       num_beams=5, # num_beams = 1 for temperature sampling\n",
        "                       early_stopping=True)\n",
        "  out = tokenizer.decode(out[0],add_special_tokens=False)\n",
        "  return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LoaBSIco_1m"
      },
      "outputs": [],
      "source": [
        "text = dataset['train']['text'][9]\n",
        "text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BSCkjzKApGNQ"
      },
      "outputs": [],
      "source": [
        "descramble(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TuvR5Sbhgk_z"
      },
      "outputs": [],
      "source": [
        "model.eval()\n",
        "input = tokenizer(text,truncation=True,padding='max_length',max_length=max_text_length,return_tensors='pt',add_special_tokens=True).to('cuda')\n",
        "out = model.generate(**input,temperature=1)\n",
        "out = tokenizer.decode(out[0])\n",
        "out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wKHtufVGJ7yX"
      },
      "outputs": [],
      "source": [
        "wandb.finish()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G8mpsAV_phJP"
      },
      "outputs": [],
      "source": [
        "model_artifact_name = 'T5-EFFICIENT-MINI'\n",
        "art = wandb.Artifact(model_artifact_name,type='model')\n",
        "art.add_dir(local_path='/content/Text Correction/checkpoint-5008')\n",
        "wandb.init()\n",
        "wandb.log_artifact(art)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vwg1ryDJEOze"
      },
      "outputs": [],
      "source": [
        "#testing out the model on a sample instance\n",
        "model.eval()\n",
        "test_text = dataset['train']['text'][14]\n",
        "label = dataset['train']['label'][14]\n",
        "input = tokenizer(test_text,return_tensors= 'pt',padding='max_length',max_length=max_text_length,truncation=True,).to('cuda')\n",
        "input_id = input.input_ids\n",
        "attention_mask = input.attention_mask\n",
        "output = model(input_ids=input_id,decoder_input_ids=input_id)\n",
        "output = tokenizer.decode(output[0],skip_special_tokens=True)\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SkCTA1ufrA1T"
      },
      "outputs": [],
      "source": [
        "def generate_predictions(batch):\n",
        "\n",
        "    # Tokenizing the test\n",
        "    inputs = tokenizer(batch[\"text\"], padding=\"max_length\", truncation=True, max_length=max_text_length, return_tensors=\"pt\")\n",
        "\n",
        "    # Sending the tensors to GPU\n",
        "    input_ids = inputs.input_ids.to(\"cuda\")\n",
        "    attention_mask = inputs.attention_mask.to(\"cuda\")\n",
        "\n",
        "    # Generating the predicted tokens ids\n",
        "    outputs = model.generate(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    # Converting the token ids to sentence\n",
        "    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    batch[\"predictions\"] = output_str\n",
        "\n",
        "    return batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2oXJd9LdrhyH"
      },
      "outputs": [],
      "source": [
        "pred = dataset['train'].map(generate_predictions,batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6ltipDMrnTg"
      },
      "outputs": [],
      "source": [
        "from pynvml import *\n",
        "\n",
        "def print_gpu_utilization():\n",
        "    nvmlInit()\n",
        "    handle = nvmlDeviceGetHandleByIndex(0)\n",
        "    info = nvmlDeviceGetMemoryInfo(handle)\n",
        "    print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n",
        "print_gpu_utilization()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UEcCpJeMnCih"
      },
      "outputs": [],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dph_SHqXnMQp"
      },
      "outputs": [],
      "source": [
        "import torch, gc\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vEQcmrtMpp_U"
      },
      "outputs": [],
      "source": [
        "with wandb.init(project=\"Text Descrambling\") as run:\n",
        "\n",
        "  # Connect an Artifact to the run\n",
        "  my_model_name = \"\"\n",
        "  my_model_artifact = run.use_artifact(my_model_name)\n",
        "\n",
        "  # Download model weights to a folder and return the path\n",
        "  model_dir = my_model_artifact.download()\n",
        "\n",
        "  # Load your Hugging Face model from that folder\n",
        "  #  using the same model class\n",
        "  model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "      model_dir, )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbUxT7C5Ob5w"
      },
      "source": [
        "# GPT2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jj8ntxzIS2An"
      },
      "outputs": [],
      "source": [
        "# tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium',bos_token='<|startoftext|>', eos_token='<|endoftext|>',padding_side='right')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "Oi5qk8HXIEmz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8aa0f28-aa16-46f2-a9ea-e8142386bdd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'id'],\n",
              "        num_rows: 40001\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label', 'id'],\n",
              "        num_rows: 4001\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'id'],\n",
              "        num_rows: 10000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VwuD2DaHvUB4",
        "colab": {
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          "height": 301,
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            "d655e870a65a426692e60404cee01ba8",
            "0f4c01e0e22141f292f6680b0978316b",
            "aab515be23994ca4a1435017d779695b",
            "dcfc80438e1f49d8a1427e876bb62e69"
          ]
        },
        "outputId": "693ab8e1-ab64-4566-8aa7-2895ee2b90fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f82e732b506a444db67c716eed359f39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8778b96a6cd842d69d209fc7d3d1bd5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1da68bd6a8994830a3f1c007f218a235"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee4d8cc891cd4b26bb0ba73449b02ce2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/718 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68bdd85034884695be2a0b5ca61bcac4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium',padding_side='right')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vjM60pXeXavT"
      },
      "outputs": [],
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.pad_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "N6SfdGtFG8hP",
        "outputId": "7320ec45-9ce0-4f03-d60b-fc2c2a63001f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|endoftext|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zop02E2WWyV1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "e7aa41dd86b14d54abcc9da39dd21c5c",
            "796086b50bd7449b996e10a7aa807751",
            "0fb453714d894894af9f85aec9316899",
            "b4ffdb342beb4ee69546d368e2f9333f",
            "656ffd0613fe4f46a9005668f7fe2099",
            "248606c8c6a849e69a4e8a1403e6e449",
            "593d54d7da5f43728096dc1146cd661c",
            "5552e383021f45caba574249ab2c5c03",
            "184907158053459fa7014c6986d77659",
            "dfba7f394cf3485b8098a279f5d2cef8",
            "24c2157c0abb4f3db659c406228fdd58",
            "c74428c375ac43068a43e23d5dc6fb6c",
            "899d87143eff414085e75d72a387986d",
            "3cd45ac1c4b0435caaf072c6d5357c38",
            "6901d10cb20643d5be5c42dab2404c62",
            "c3c33b2cda364740816768e1a0bb86f7",
            "878f3e8b580e455787fbe9ccea29535f",
            "b2c9e05b74644c4d8658997f127c4878",
            "ce8a665a7f8f4e7fa11ad4b067eb473b",
            "771be56576f54b58bc51b77811dc43cf",
            "b29d17f9ed244227ab052fd779f8a452",
            "b89789c022de4a038d3fa4fe1aff9666"
          ]
        },
        "outputId": "d22f7f0c-e82f-4c7d-ac9b-20a109f0b961"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7aa41dd86b14d54abcc9da39dd21c5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c74428c375ac43068a43e23d5dc6fb6c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = GPT2LMHeadModel.from_pretrained('gpt2-medium')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ABm37SWjQfny"
      },
      "outputs": [],
      "source": [
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def test_model(text):\n",
        "  input = tokenizer(text,return_tensors='pt',padding='max_length',truncation=True,max_length=64).to(device)\n",
        "  input_ids = input.input_ids\n",
        "  attention_mask = input.attention_mask\n",
        "  out = model.generate(input_ids=input_ids,attention_mask=attention_mask,max_new_tokens=60,do_sample=True,top_p=0.96)\n",
        "  out = tokenizer.decode(out[0],skip_special_tokens=True)\n"
      ],
      "metadata": {
        "id": "p1Idju_3J7Lg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input = [f\"wrong sentence: {sentence} correct sentence:\" for sentence in sentences]\n",
        "input_tokens = tokenizer(input, return_tensors='pt',\n",
        "                                      padding='max_length', truncation=True,\n",
        "                                      max_length=128)\n",
        "out = model.generate(**input_tokens,\n",
        "                          max_new_tokens=128,# do_sample=True,\n",
        "                          num_beams=5)\n",
        "print(out)\n",
        "# for i, output in enumerate(out):\n",
        "#   decoded_output = tokenizer.decode(out, skip_special_tokens=True)\n",
        "#   print(f\"Input: {input[i]}\")\n",
        "#   print(f\"Output: {decoded_output}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFNM73osmDuR",
        "outputId": "a4b91f6c-01b0-453e-88c4-b38f9ede7423"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " test_model(dataset['train']['text'][5])"
      ],
      "metadata": {
        "id": "cUdq596wMCvs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uukRsq5AS19p"
      },
      "outputs": [],
      "source": [
        "print('the max model length for this token is {}'.format(tokenizer.model_max_length))\n",
        "print(f'this model has {sum([p.numel() for p in model.parameters()])//10**6:.1f}M parameters')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Wrong Training Strategy"
      ],
      "metadata": {
        "id": "xGL5zNqRDMs6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5VrsQXz6-w_x"
      },
      "outputs": [],
      "source": [
        "# Define a function to preprocess the data\n",
        "def preprocess_data(row):\n",
        "    input_text = row['text']\n",
        "    target_text = row['label']\n",
        "    prefix_text = [' '.join(_text.split(' ')[:-1]) + ' [sep] 'for _text in input_text]\n",
        "    input = tokenizer(prefix_text, padding='max_length',truncation=True, max_length=32,return_tensors ='pt').to(device)\n",
        "    input_ids,attention_mask = input.input_ids,input.attention_mask\n",
        "    labels = tokenizer(target_text,padding='max_length',truncation=True, max_length=32, return_tensors='pt').to(device)\n",
        "    labels = labels.input_ids\n",
        "    labels = [[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in labels]\n",
        "\n",
        "    return {'input_ids': input_ids,'attention_mask':attention_mask,'labels': labels,'prompt':prefix_text}\n",
        "\n",
        "\n",
        "processed_data = dataset.map(preprocess_data,batched=True,batch_size=256)\n",
        "processed_data.set_format(type='torch',columns=['input_ids','attention_mask','labels'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data['train']['input_ids'][5]"
      ],
      "metadata": {
        "id": "3MWGrxxgjJ3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ArKtN_81CRMF"
      },
      "outputs": [],
      "source": [
        "f\"model input : {processed_data['train']['text'][6]} {processed_data['train']['labels'][6]}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ftsTkyWlcZN_"
      },
      "outputs": [],
      "source": [
        "class Gpt2Dataset(Dataset):\n",
        "  def __init__(self,data,max_length=64):\n",
        "    self.input_ids = data['input_ids'].to(device)\n",
        "    self.attention_mask = data['attention_mask'].to(device)\n",
        "    self.labels = data['labels'].to(device)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    return dict(input_ids=self.input_ids[idx],attention_mask=self.attention_mask[idx],labels=self.labels[idx])\n",
        "\n",
        "  def __len__(self,):\n",
        "    return len(self.input_ids)\n",
        "  def __str__(self):\n",
        "    return 'A dataset object that holds a list of input ids and target ids for gpt2 to be passed to a dataloader'\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5wwIEKIaPwOx"
      },
      "outputs": [],
      "source": [
        "train_dataset = Gpt2Dataset(data=processed_data['train'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1hxmb7SNePUp"
      },
      "outputs": [],
      "source": [
        "val_dataset = Gpt2Dataset(data=processed_data['val'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[5]['labels']"
      ],
      "metadata": {
        "id": "_PGa_o_t1njq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q8eWP4KgcZe5"
      },
      "outputs": [],
      "source": [
        "seed_val = 12\n",
        "random.seed(seed_val)\n",
        "np.random.seed(seed_val)\n",
        "torch.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed_all(seed_val)\n",
        "batch_size = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7dtzcCk9du2Q"
      },
      "outputs": [],
      "source": [
        "train_dataloader = DataLoader(\n",
        "            train_dataset,  # The training samples.\n",
        "            sampler = RandomSampler(train_dataset), # Select batches randomly\n",
        "            batch_size = batch_size # Trains with this batch size.\n",
        "        )\n",
        "\n",
        "# For validation the order doesn't matter, so we'll just read them sequentially.\n",
        "val_dataloader = DataLoader(\n",
        "            val_dataset, # The validation samples.\n",
        "            sampler = SequentialSampler(val_dataset), # Pull out batches sequentially.\n",
        "            batch_size = batch_size # Evaluate with this batch size.\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zpK8nwpIcZVh"
      },
      "outputs": [],
      "source": [
        "learning_rate = 1e-5\n",
        "epochs = 5\n",
        "# warmup_steps = 1e2\n",
        "# epsilon = 1e-8\n",
        "sample_every = 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z9h9ddogcZTH"
      },
      "outputs": [],
      "source": [
        "optimizer = AdamW(\n",
        "    model.parameters(),\n",
        "    lr= learning_rate,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#training loop\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  model.train()\n",
        "  print(\"\")\n",
        "  print('======== Epoch {:} / {:} ========'.format(epoch + 1, epochs))\n",
        "  print('Training...')\n",
        "  total_train_loss = 0\n",
        "  for step,batch in enumerate(train_dataloader):\n",
        "    optimizer.zero_grad()\n",
        "    input_ids = batch['input_ids']\n",
        "    attention_mask = batch['attention_mask']\n",
        "    labels = batch['labels']\n",
        "    output = model(input_ids=input_ids,\n",
        "                   attention_mask=attention_mask,\n",
        "                   labels = labels\n",
        "                   )\n",
        "    loss = output.loss\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    total_train_loss += loss\n",
        "  avg_train_loss = total_train_loss / len(train_dataloader)\n",
        "  print(('Epoch loss {:.4f}'.format(avg_train_loss)))\n",
        "  # at the end of each epoch, see what generation is like\n",
        "  idx = random.randint(0,len(val_dataset))\n",
        "  test_text = processed_data['val']['prompt'][idx]\n",
        "  print(f'test input {test_text}')\n",
        "  print(f'model_output: {test_model(test_text)}')"
      ],
      "metadata": {
        "id": "NNbu2CBrVulI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_pred(batch):\n",
        "  model.eval()\n",
        "  # Tokenizing the test\n",
        "  with torch.no_grad():\n",
        "    # Sending the tensors to GPU\n",
        "    input_ids = batch['input_ids'].to(device)\n",
        "    attention_mask = batch['attention_mask'].to(device)\n",
        "\n",
        "    # Generating the predicted tokens ids\n",
        "    outputs = model.generate(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    # Converting the token ids to sentence\n",
        "    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    batch[\"predictions\"] = output_str\n",
        "\n",
        "    return batch"
      ],
      "metadata": {
        "id": "SMwLh1yuben4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_model(processed_data['val']['prompt'][5])"
      ],
      "metadata": {
        "id": "GURWk6Hv9OWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_pred(processed_dataset['val'])\n",
        "print(val_dataset['predictions'][5])"
      ],
      "metadata": {
        "id": "szzCppeodL69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.save('gpt2_hope.pt')"
      ],
      "metadata": {
        "id": "igcUUw9wjWww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## New"
      ],
      "metadata": {
        "id": "ghk9T_yrDTX0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def format_text(data,split='train'):\n",
        "  text = []\n",
        "  for row in tqdm(data):\n",
        "    txt = f'''wrong sentence: {row['text']} correct sentence: {row['label']} <|endoftext|>\\n'''\n",
        "    text.append(txt)\n",
        "  with open(f'text-descrambling-{split}.txt','w') as f:\n",
        "    f.writelines(text)"
      ],
      "metadata": {
        "id": "1GY_KJNKWNqR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "format_text(dataset['train'],split='train')\n",
        "format_text(dataset['val'],split='val')"
      ],
      "metadata": {
        "id": "JD4xE1rpYjMC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41561536-ba84-4748-fd53-c645ff29cf9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 40001/40001 [00:02<00:00, 13945.28it/s]\n",
            "100%|██████████| 4001/4001 [00:00<00:00, 14431.33it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5C84Bfvskmbq"
      },
      "outputs": [],
      "source": [
        "# Define a function to preprocess the data\n",
        "def preprocess_data(row):\n",
        "    target_text = row['label']\n",
        "    #add prompt to every row in the dataset\n",
        "    input_text = f'''wrong sentence: {row['text']} correct sentence:'''\n",
        "    #find the length of the input prompt\n",
        "    prompt_len = len(tokenizer(input_text).input_ids)\n",
        "    input = tokenizer(f'{input_text} {target_text} <|endoftext|>', padding='max_length', truncation=True, max_length=128, return_tensors ='pt').to(device)\n",
        "    input_ids,attention_mask = input.input_ids,input.attention_mask\n",
        "    #turn all of the tokens before the actual correct sentence to -100 so loss is only calculated for generation after 'correct sentence:'\n",
        "    labels = input_ids.clone()\n",
        "    labels[:, :prompt_len] = -100\n",
        "    # Turn all pad tokens to -100\n",
        "    labels[labels == tokenizer.pad_token_id] = -100\n",
        "    assert (labels == -100).sum() > len(labels), f\"Labels are all -100, something wrong.\"\n",
        "    # if (labels == -100).sum() == len(labels) - 1:\n",
        "    #         raise\n",
        "    return {'input_ids': input_ids.squeeze(),'attention_mask':attention_mask.squeeze(),'labels': labels.squeeze(),'prompt':input_text}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = preprocess_data(dataset['train'][5])\n",
        "out['labels']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WABL4thdUeIz",
        "outputId": "2f1642b7-edf0-4adb-9a0c-0affbfbe26a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "        47183,    11,   262,  6801,   318, 14162,   351,   281, 19898, 13688,\n",
              "          286,   262, 41968,    13,   220,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
              "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data = dataset.map(preprocess_data)\n",
        "processed_data.set_format(type='torch',columns=['input_ids','attention_mask','labels'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "a97916dd88844122be77059dbc90b2b9",
            "8f715004ed5f48acabda3982a162d42b",
            "3abb0dd628f64fe5a972b0fc9a4ecb97",
            "cf36180c4eca4eb99ee8e63f090dd3e5",
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            "e11194863bef413db2dbe0c9657b03e5",
            "e1ccdc88a74543b5bf6c88d22ffe5a58",
            "5adabf1f68024e4299f053dfd79f80b6",
            "b58b665321c54aaab6e422ec8c185432",
            "1881b937898f438998694038c6eddd4d",
            "7a69fa2361a24ffaa28daf2187f1e831",
            "af144c141ced4551878bacfb10a8b269",
            "1222da8ab3d44b34b85b49cc32257a1c",
            "2a2fed92c30f4ab1a33316ee99ff8d9c",
            "6d4b4bb312c0470c97db28d9eb5c0109",
            "3405b8e008ac442d98f1768f4d589016",
            "097c76968736497d9924b252ce575d86",
            "044e6b7b7f9b4a4ab77e416e94a090ec",
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            "104c3b77bb64431ba6c2cc87703baf1a",
            "61f143ca72fe4e5f9e7e782788428d9b",
            "92d4fd061b0646d2854818c54b1e8318",
            "6b673dc92e5a4c64b27d94616c4a332a",
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            "75df36929f3e487281de9269e9932bda",
            "964b1fececbf41559ae4f0814129ae00",
            "6d64ca75aa9240c5b874cdc14dea9172",
            "0213669fd40f41248ffdcf2b20afac52",
            "d9b29b80fbed4824bb9a8466d480fc4a",
            "2cefee04fcf14e5e97cd4275f1502c6b"
          ]
        },
        "id": "vAeiT6DHOzKR",
        "outputId": "2886e8c1-b253-4b5f-c8f3-6074ab0a5a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/40001 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a97916dd88844122be77059dbc90b2b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4001 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af144c141ced4551878bacfb10a8b269"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61f143ca72fe4e5f9e7e782788428d9b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data['validation']['prompt'][9]"
      ],
      "metadata": {
        "id": "_fpcHbrNWWEL",
        "outputId": "0c96718d-9c1a-4671-e869-6bcccaf619fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'wrong sentence: by tracking selectively paying features. robust facilitate temporal attention visual to maps Attention correct sentence:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "id": "JyatJLdvpqkz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##inference\n",
        "idx = random.randint(range(0,len(val_df)))\n",
        "test_text = processed_data['val']['prompt'][idx]\n",
        "print(f'model input ----> {test_text}')\n",
        "input = tokenizer(test_text,padding='max_length',truncation=True,max_length=32,return_tensors='pt').to(device)\n",
        "input_ids,attention_mask = input.input_ids,input.attention_mask\n",
        "out = model.generate(input_ids,attention_mask,num_beams=5)\n",
        "out = tokenizer.decode(out[0])\n",
        "print(f'model output ---> {out}')"
      ],
      "metadata": {
        "id": "65Pu0ff4tk0u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_inputs(config, tokenizer, examples):\n",
        "    max_length = config[\"max_length\"]\n",
        "    cut_off = 0.8\n",
        "    is_long  = False\n",
        "    # hacky backward compatible\n",
        "    different_eos = tokenizer.eos_token != \"</s>\"\n",
        "    out = {\"labels\": [], \"input_ids\": []}\n",
        "    for prompt, response in zip(examples[\"input\"], examples[\"label\"]):\n",
        "        if different_eos:\n",
        "            if response.count(\"</s> \\n\") > 0:\n",
        "                response = response.replace(\"</s> \\n\", f\"{tokenizer.eos_token} \\n\")\n",
        "\n",
        "        prompt_len = len(tokenizer(prompt + \"\\n\", return_tensors=\"pt\")[\"input_ids\"][0])\n",
        "\n",
        "        # hack if our prompt is super long\n",
        "        # we need to include some labels so we arbitrarily trunacate at max_input_length\n",
        "        # if the length is too long\n",
        "        max_input_length = int(max_length * cut_off)\n",
        "        if prompt_len >= max_input_length:\n",
        "            is_long = True\n",
        "            # if prompt is too long, truncate\n",
        "            # but make sure to truncate to at max 1024 tokens\n",
        "            new_len = min(max_input_length, int(len(prompt) * cut_off))\n",
        "            #print(\"before: \", prompt)\n",
        "            prompt = prompt[:new_len]\n",
        "            #print(\"after: \", prompt)\n",
        "            # get new prompt length\n",
        "            prompt_len = tokenizer(prompt + \"\\n\", return_tensors=\"pt\", max_length=max_input_length, truncation=True).input_ids.ne(tokenizer.pad_token_id).sum().item()\n",
        "\n",
        "        assert prompt_len <= max_input_length, f\"prompt length {prompt_len} exceeds max length {max_length}\"\n",
        "\n",
        "        #print(\"Raw sample: \", tokenizer(prompt + \"\\n\" + response + tokenizer.eos_token, max_length=max_length, return_tensors=\"pt\")[\"input_ids\"].squeeze())\n",
        "        input_tokens = tokenizer(prompt + \"\\n\" + response + tokenizer.eos_token,\n",
        "                                 truncation=True, max_length=max_length, return_tensors=\"pt\")[\"input_ids\"].squeeze()\n",
        "        #print(\"Truncated: \", input_tokens)\n",
        "\n",
        "        labels = input_tokens.clone()\n",
        "        labels[:prompt_len] = -100\n",
        "        if len(labels) < max_length:\n",
        "            # pad to max_length with -100\n",
        "            labels = torch.cat([labels, torch.full((max_length - len(labels),), -100)])\n",
        "\n",
        "        assert (labels == -100).sum() < len(labels), f\"Labels are all -100, something wrong. prompt length {prompt_len} exceeds max length {max_length}\"\n",
        "\n",
        "        if (labels == -100).sum() == len(labels) - 1:\n",
        "            print(prompt)\n",
        "            print(response)\n",
        "            raise\n",
        "\n",
        "        input_tokens = tokenizer.pad({\"input_ids\": input_tokens}, padding=\"max_length\", max_length=max_length)[\"input_ids\"]\n",
        "        # if is_long:\n",
        "        #   print(input_tokens)\n",
        "        #   print(prompt)\n",
        "\n",
        "        out[\"labels\"].append(labels)\n",
        "        out[\"input_ids\"].append(input_tokens)\n",
        "\n",
        "    out = {k: torch.stack(v) if isinstance(v, list) else v for k, v in out.items()}\n",
        "\n",
        "    return out\n",
        "\n",
        "def load_data(config, tokenizer, split=\"train\"):\n",
        "    if split == 'train':\n",
        "      dataset_path = config[\"train_dataset_path\"]\n",
        "    else:\n",
        "      dataset_path= config[\"test_dataset_path\"]\n",
        "\n",
        "    if os.path.exists(dataset_path):\n",
        "        if os.path.isdir(dataset_path):\n",
        "            files = glob.glob(os.path.join(dataset_path, \"*_clean.jsonl\"))\n",
        "        else:\n",
        "            files = [dataset_path]\n",
        "\n",
        "        print(f\"Reading files {files}\")\n",
        "\n",
        "        dataset = load_dataset(\"json\", data_files=files, split=\"train\") #,field=\"data\")\n",
        "\n",
        "    else:\n",
        "        dataset = load_dataset(dataset_path, split=\"train\", revision=config[\"revision\"] if \"revision\" in config else None)\n",
        "\n",
        "    #dataset = dataset.train_test_split(test_size=.01, seed=config[\"seed\"])\n",
        "\n",
        "    #dataset = dataset[\"train\"]\n",
        "\n",
        "    if config[\"streaming\"] is False:\n",
        "        kwargs = {\"num_proc\": config[\"num_proc\"]}\n",
        "    else:\n",
        "        kwargs = {}\n",
        "\n",
        "    cols_to_keep = [\"input_ids\", \"labels\", \"attention_mask\"]\n",
        "    # tokenize inputs and return labels and attention mask\n",
        "    dataset = dataset.map(\n",
        "        lambda ele: tokenize_inputs(config, tokenizer, ele),\n",
        "        batched=True,\n",
        "        **kwargs\n",
        "    )\n",
        "    remove_cols = [col for col in dataset.column_names if col not in cols_to_keep]\n",
        "    dataset = dataset.remove_columns(remove_cols)\n",
        "\n",
        "\n",
        "    dataset = dataset.with_format(\"torch\")\n",
        "\n",
        "\n",
        "    # create dataloader with default data collator since we already have labels\n",
        "    # if split == 'train':\n",
        "    #   dataloader = DataLoader(\n",
        "    #       dataset,\n",
        "    #       collate_fn=DefaultDataCollator(),\n",
        "    #       batch_size=config[\"batch_size\"],iiii\n",
        "    #       shuffle=True,\n",
        "    #   )\n",
        "\n",
        "    # else:\n",
        "    #   dataloader = DataLoader(\n",
        "    #       dataset,\n",
        "    #       collate_fn=DefaultDataCollator(),\n",
        "    #       batch_size=config[\"eval_batch_size\"],\n",
        "    #       shuffle=True,\n",
        "    #   )\n",
        "\n",
        "\n",
        "    return dataset #, dataloader"
      ],
      "metadata": {
        "id": "IhAptQpt3Gb4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, AdamW\n",
        "from transformers import TrainingArguments, Trainer\n",
        "import os\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler\n",
        "import torch\n",
        "from torch.optim import AdamW\n",
        "from argparse import ArgumentParser\n",
        "from torchmetrics import MeanMetric\n",
        "from tqdm import tqdm\n",
        "import wandb\n",
        "# from apex import amp\n",
        "\n",
        "# Initialize tokenizer and model\n",
        "#tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-medium\")\n",
        "model = GPT2LMHeadModel.from_pretrained(\"BeardedMonster/traffic\")\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "# Training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"/content/drive/MyDrive/Fiverr/almajar/gpt2-medium-finetuned\",\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    gradient_accumulation_steps=128,\n",
        "    save_steps=32,\n",
        "    save_total_limit=3,\n",
        "    logging_steps=16,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=32,\n",
        "    learning_rate=5e-5,\n",
        "    warmup_steps=0,\n",
        "    fp16=True,\n",
        "    fp16_full_eval=True,\n",
        "    logging_dir='./logs',\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_loss\",\n",
        "    greater_is_better=False,\n",
        "    report_to=None,\n",
        "    run_name=\"gpt2-medium-finetuned\",\n",
        "    hub_model_id = \"BeardedMonster/traffic\",\n",
        "    hub_strategy =\"checkpoint\",\n",
        "    hub_token = \"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\",\n",
        "    hub_private_repo =True\n",
        ")\n",
        "\n",
        "\n",
        "# optimizer_cls = (\n",
        "#     AdamW\n",
        "#     if accelerator.state.deepspeed_plugin is None\n",
        "#     or \"optimizer\" not in accelerator.state.deepspeed_plugin.deepspeed_config\n",
        "#     else DummyOptim\n",
        "# )\n",
        "\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=config[\"lr\"], weight_decay=config[\"weight_decay\"])\n",
        "\n",
        "# decay to min_lr instead of 0\n",
        "lr_ratio = config[\"min_lr\"] / config[\"lr\"]\n",
        "\n",
        "# Creates Dummy Scheduler if `scheduler` was specified in the config file else creates `args.lr_scheduler_type` Scheduler\n",
        "\n",
        "scheduler = get_scheduler(\n",
        "    name=\"cosine\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=config[\"warmup_steps\"] ,\n",
        "    num_training_steps=250000,\n",
        ")\n",
        "\n",
        "\n",
        "# Enable mixed precision training\n",
        "# model, optimizer = amp.initialize(model, optimizer, opt_level=\"O2\")\n",
        "\n",
        "# Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    data_collator=DefaultDataCollator(),\n",
        "    tokenizer=tokenizer,\n",
        "    optimizers=(optimizer, scheduler),\n",
        "\n",
        ")\n",
        "\n",
        "# Start training\n",
        "trainer.train()\n",
        "#3d4f49c65c423034b92482c50338953c67f62254   wandb"
      ],
      "metadata": {
        "id": "GttlvCEIecGv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4CjiUq1secEQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L7m_IOSBecB7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R6fTFxZ2eb_w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Optimization"
      ],
      "metadata": {
        "id": "O4qC8stAtZVH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.cuda.amp import autocast"
      ],
      "metadata": {
        "id": "-7VVESnvvQou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_init():\n",
        "  model = GPT2LMHeadModel.from_pretrained('gpt2-large')\n",
        "  tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')\n",
        "  model.lm_head = nn.Linear(model.config.n_embd,model.config.vocab_size)\n",
        "  return model"
      ],
      "metadata": {
        "id": "IuoWiGjGtjy7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomTrainer(Trainer):\n",
        "  def __init__(self, *args, **kwargs):\n",
        "    super().__init__(self,*args,*kwargs)\n",
        "    self.scaler = torch.cuda.amp.GradScaler()\n",
        "\n",
        "  @autocast()\n",
        "  def training_step(self,model,inputs):\n",
        "    out = model(**inputs,use_cache=True)\n",
        "    loss = out.loss\n",
        "    return loss\n",
        "\n",
        "  def backward(self,loss,model,optimizer):\n",
        "    return self.scaler.scale(loss).backward\n",
        "\n",
        "  def update_params(self,optimizer.scheduler):\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3MoTxt9btjuv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final"
      ],
      "metadata": {
        "id": "zGwYOZDWeaWM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def format_text(data,split='train'):\n",
        "  text = []\n",
        "  for row in tqdm(data):\n",
        "    txt = f'''wrong sentence: {row['text']} correct sentence: {row['label']}\\n'''\n",
        "    text.append(txt)\n",
        "  with open(f'text-descrambling-{split}.txt','w') as f:\n",
        "    f.writelines(text)"
      ],
      "metadata": {
        "id": "g4MH3RregZ3b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "format_text(dataset['train'],split='train')\n",
        "format_text(dataset['val'],split='val')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKU6eaxhgZCl",
        "outputId": "a69f8632-4a1f-4311-8b5e-ac90f2589160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 40001/40001 [00:03<00:00, 11491.32it/s]\n",
            "100%|██████████| 4001/4001 [00:00<00:00, 14174.70it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel,TrainingArguments, Trainer, DataCollatorWithPadding\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "## GPT-2 Small ('gpt2'): 124 million parameters.\n",
        "## GPT-2 Medium ('gpt2-medium'): 345 million parameters.\n",
        "## GPT-2 Large ('gpt2-large'): 774 million parameters.\n",
        "## GPT-2 XL ('gpt2-xl'): 1.5 billion parameters.\n",
        "\n",
        "\n",
        "# Load pre-trained GPT-2 tokenizer and model\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-medium\")\n",
        "model = GPT2LMHeadModel.from_pretrained(\"gpt2-medium\")\n",
        "\n",
        "# Set padding token\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Your custom dataset\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, tokenizer, file_path, block_size):\n",
        "        self.tokenizer = tokenizer\n",
        "        with open(file_path, \"r\") as f:\n",
        "            self.text = f.read().splitlines()\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "    def __getitem__(self, idx):\n",
        "        tokenized_inputs = self.tokenizer(\n",
        "            self.text[idx],\n",
        "            truncation=True,\n",
        "            padding=\"max_length\",\n",
        "            max_length=128,\n",
        "            return_tensors=\"pt\")\n",
        "        tokenized_inputs[\"labels\"] = tokenized_inputs[\"input_ids\"]\n",
        "        return tokenized_inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
          "referenced_widgets": [
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            "da9a8b8a05c742a0be07497b6441bb02"
          ]
        },
        "id": "heaFU34WecKG",
        "outputId": "b56c3e59-2d52-4da3-eaa0-08de161d2ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c592990406f54f9a81fb2c0adb2b327c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a43eaa21c23484e9f60203b1f96b275"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d252f98310a4ae4922cd0b4865e2bc6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a483b6454414aca9390f893f6a1181a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/718 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18dc797f5f5b45e4ab01aa76cfe815b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "975a4b7cf5f84e549de4c15e08d84f9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "273cf92761b7405c91a7c657dbc6e262"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "train_data = CustomDataset(tokenizer, \"text-descrambling-train.txt\", 128)\n",
        "val_data = CustomDataset(tokenizer,'text-descrambling-val.txt',128)\n",
        "\n",
        "# Create a data collator that will dynamically pad the sequences\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "rNuhQ19miIRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.text[6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "rx0dpmSaiqab",
        "outputId": "2d4a69bf-b735-4508-fb56-143e431601b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'wrong sentence: largely on the configuration framework. CLab formalization The is based correct sentence: The formalization is largely based on the CLab configuration framework.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "??TrainingArguments"
      ],
      "metadata": {
        "id": "AYEs-ihR8P2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training arguments and Trainer\n",
        "training_args = TrainingArguments(\n",
        "    per_device_train_batch_size=16,\n",
        "    num_train_epochs=5, # Increse for more training from the fine-tuning data\n",
        "    learning_rate=5e-5,  # Decrease the learning rate for smaller fine-tuning data\n",
        "    output_dir='./results',\n",
        "    logging_dir='./logs',\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    save_strategy='epoch',\n",
        "    evaluation_strategy='epoch',\n",
        "    remove_unused_columns=False,\n",
        "    push_to_hub=False,\n",
        "    report_to='wandb',\n",
        "    fp16=True\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=val_data,  # You can specify an evaluation dataset here\n",
        "    data_collator=data_collator,  # Add the data collator here\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "EG0iOtQLtjom",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "489b8798-5708-451e-9674-242229aee8db"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "      \n",
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              "      [ 7858/12505 1:26:29 < 51:09, 1.51 it/s, Epoch 3.14/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.776100</td>\n",
              "      <td>0.780853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.748700</td>\n",
              "      <td>0.759563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.720100</td>\n",
              "      <td>0.754352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
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        },
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='8364' max='12505' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 8364/12505 1:31:48 < 45:27, 1.52 it/s, Epoch 3.34/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.776100</td>\n",
              "      <td>0.780853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.748700</td>\n",
              "      <td>0.759563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.720100</td>\n",
              "      <td>0.754352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install modal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYRLKEFsUg8c",
        "outputId": "8f06155e-17b4-4a75-f24b-cf3844b0c53c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting modal\n",
            "  Downloading modal-0.62.47-py3-none-any.whl (443 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/443.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/443.6 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m440.3/443.6 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m443.6/443.6 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from modal) (3.9.3)\n",
            "Collecting aiostream~=0.5.2 (from modal)\n",
            "  Downloading aiostream-0.5.2-py3-none-any.whl (39 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from modal) (2024.2.2)\n",
            "Requirement already satisfied: click>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from modal) (8.1.7)\n",
            "Collecting fastapi (from modal)\n",
            "  Downloading fastapi-0.110.1-py3-none-any.whl (91 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.9/91.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting grpclib==0.4.7 (from modal)\n",
            "  Downloading grpclib-0.4.7.tar.gz (61 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.3/61.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: protobuf!=4.24.0,<6.0,>=3.19 in /usr/local/lib/python3.10/dist-packages (from modal) (3.20.3)\n",
            "Requirement already satisfied: rich>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from modal) (13.7.1)\n",
            "Collecting synchronicity~=0.6.5 (from modal)\n",
            "  Downloading synchronicity-0.6.6-py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.10/dist-packages (from modal) (0.10.2)\n",
            "Requirement already satisfied: typer~=0.9.0 in /usr/local/lib/python3.10/dist-packages (from modal) (0.9.4)\n",
            "Collecting types-certifi (from modal)\n",
            "  Downloading types_certifi-2021.10.8.3-py3-none-any.whl (2.1 kB)\n",
            "Collecting types-toml (from modal)\n",
            "  Downloading types_toml-0.10.8.20240310-py3-none-any.whl (4.8 kB)\n",
            "Collecting watchfiles (from modal)\n",
            "  Downloading watchfiles-0.21.0-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[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions~=4.6 in /usr/local/lib/python3.10/dist-packages (from modal) (4.10.0)\n",
            "Collecting h2<5,>=3.1.0 (from grpclib==0.4.7->modal)\n",
            "  Downloading h2-4.1.0-py3-none-any.whl (57 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: multidict in /usr/local/lib/python3.10/dist-packages (from grpclib==0.4.7->modal) (6.0.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=12.0.0->modal) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=12.0.0->modal) (2.16.1)\n",
            "Collecting sigtools==4.0.1 (from synchronicity~=0.6.5->modal)\n",
            "  Downloading sigtools-4.0.1-py2.py3-none-any.whl (76 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from sigtools==4.0.1->synchronicity~=0.6.5->modal) (23.2.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->modal) (1.3.1)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->modal) (1.4.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->modal) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->modal) (4.0.3)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from fastapi->modal) (2.6.4)\n",
            "Collecting starlette<0.38.0,>=0.37.2 (from fastapi->modal)\n",
            "  Downloading starlette-0.37.2-py3-none-any.whl (71 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: anyio>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from watchfiles->modal) (3.7.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio>=3.0.0->watchfiles->modal) (3.6)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio>=3.0.0->watchfiles->modal) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio>=3.0.0->watchfiles->modal) (1.2.0)\n",
            "Collecting hyperframe<7,>=6.0 (from h2<5,>=3.1.0->grpclib==0.4.7->modal)\n",
            "  Downloading hyperframe-6.0.1-py3-none-any.whl (12 kB)\n",
            "Collecting hpack<5,>=4.0 (from h2<5,>=3.1.0->grpclib==0.4.7->modal)\n",
            "  Downloading hpack-4.0.0-py3-none-any.whl (32 kB)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=12.0.0->modal) (0.1.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi->modal) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi->modal) (2.16.3)\n",
            "Building wheels for collected packages: grpclib\n",
            "  Building wheel for grpclib (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for grpclib: filename=grpclib-0.4.7-py3-none-any.whl size=76220 sha256=d7ad92b6b35e77e893d10eb2c76bcd0143790b91962a261668504b6675082f80\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/c0/1c/3d807409d0c67efeab2949832ba409205b1b6fe03f739ae4c1\n",
            "Successfully built grpclib\n",
            "Installing collected packages: types-certifi, types-toml, sigtools, hyperframe, hpack, aiostream, watchfiles, synchronicity, starlette, h2, grpclib, fastapi, modal\n",
            "Successfully installed aiostream-0.5.2 fastapi-0.110.1 grpclib-0.4.7 h2-4.1.0 hpack-4.0.0 hyperframe-6.0.1 modal-0.62.47 sigtools-4.0.1 starlette-0.37.2 synchronicity-0.6.6 types-certifi-2021.10.8.3 types-toml-0.10.8.20240310 watchfiles-0.21.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!modal token set --token-id ak-y8rPUo61iJXeF0QvmMJTyC --token-secret as-vlj12qKdkFqqTbEd1OabJr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n04qtUHqUjfx",
        "outputId": "bbb126d8-0ca8-4754-e86d-aaa2b1e619d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Verifying token against \u001b[4;34mhttps://api.modal.com\u001b[0m\n",
            "\u001b[32mToken verified successfully!\u001b[0m\n",
            "\u001b[?25l\u001b[32m⠋\u001b[0m Storing token\n",
            "\u001b[1A\u001b[2K\u001b[32mToken written to \u001b[0m\u001b[35m/root/\u001b[0m\u001b[35m.modal.toml\u001b[0m\u001b[32m in profile \u001b[0m\u001b[35mdamilojohn\u001b[0m\u001b[32m.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!modal volume get 'finetune-text-descrambler' 'model' ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4kiDk4lVWWq",
        "outputId": "1af0dca7-c537-454f-b66c-e864cad3fe31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2;36m[10:14:52]\u001b[0m\u001b[2;36m \u001b[0mRequesting model                                                            \u001b]8;id=683968;file:///usr/local/lib/python3.10/dist-packages/modal/volume.py\u001b\\\u001b[2mvolume.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=507181;file:///usr/local/lib/python3.10/dist-packages/modal/volume.py#349\u001b\\\u001b[2m349\u001b[0m\u001b]8;;\u001b\\\n",
            "\u001b[33mUsage: \u001b[0mmodal volume get [OPTIONS] VOLUME_NAME REMOTE_PATH [LOCAL_DESTINATION]\n",
            "\u001b[2mTry \u001b[0m\u001b[2;34m'modal volume get \u001b[0m\u001b[1;2;34m-\u001b[0m\u001b[1;2;34m-help\u001b[0m\u001b[2;34m'\u001b[0m\u001b[2m for help.\u001b[0m\n",
            "\u001b[31m╭─\u001b[0m\u001b[31m Error \u001b[0m\u001b[31m─────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
            "\u001b[31m│\u001b[0m Path is a not a regular file: b'model'                                                           \u001b[31m│\u001b[0m\n",
            "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!modal volume get --help"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WBHTvQyV2Ze",
        "outputId": "aa412c82-ef76-40c9-ff80-b7b153672f6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m                                                                                                    \u001b[0m\n",
            "\u001b[1m \u001b[0m\u001b[1;33mUsage: \u001b[0m\u001b[1mmodal volume get [OPTIONS] VOLUME_NAME REMOTE_PATH [LOCAL_DESTINATION]\u001b[0m\u001b[1m                     \u001b[0m\u001b[1m \u001b[0m\n",
            "\u001b[1m                                                                                                    \u001b[0m\n",
            " Download files from a modal.Volume.                                                                \n",
            " \u001b[2mSpecifying a glob pattern (using any \u001b[0m\u001b[1;2;36;40m*\u001b[0m\u001b[2m or \u001b[0m\u001b[1;2;36;40m**\u001b[0m\u001b[2m patterns) as the \u001b[0m\u001b[1;2;36;40mremote_path\u001b[0m\u001b[2m will download all \u001b[0m       \n",
            " \u001b[2mmatching \u001b[0m\u001b[2;3mfiles\u001b[0m\u001b[2m, preserving the source directory structure for the matched files.\u001b[0m\u001b[2m \u001b[0m\u001b[1;2mExample\u001b[0m\u001b[2m \u001b[0m\u001b[1;2;36;40mbash \u001b[0m     \n",
            " \u001b[1;2;36;40mmodal volume get <volume-name> logs/april-12-1.txt . modal volume get <volume-name> \"**\" \u001b[0m          \n",
            " \u001b[1;2;36;40mdump_volume \u001b[0m\u001b[2m \u001b[0m\u001b[2mUse \"-\" (a hyphen) as LOCAL_DESTINATION to write contents of file to stdout (only for\u001b[0m \n",
            " \u001b[2mnon-glob paths).\u001b[0m                                                                                   \n",
            "                                                                                                    \n",
            "\u001b[2m╭─\u001b[0m\u001b[2m Arguments \u001b[0m\u001b[2m─────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[2m─╮\u001b[0m\n",
            "\u001b[2m│\u001b[0m \u001b[31m*\u001b[0m    volume_name            \u001b[1;33mTEXT               \u001b[0m  \u001b[2m[default: None]\u001b[0m \u001b[2;31m[required]\u001b[0m                      \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m \u001b[31m*\u001b[0m    remote_path            \u001b[1;33mTEXT               \u001b[0m  \u001b[2m[default: None]\u001b[0m \u001b[2;31m[required]\u001b[0m                      \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m      local_destination      \u001b[1;2;33m[\u001b[0m\u001b[1;33mLOCAL_DESTINATION\u001b[0m\u001b[1;2;33m]\u001b[0m  \u001b[2m[default: .]\u001b[0m                                    \u001b[2m│\u001b[0m\n",
            "\u001b[2m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
            "\u001b[2m╭─\u001b[0m\u001b[2m Options \u001b[0m\u001b[2m───────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[2m─╮\u001b[0m\n",
            "\u001b[2m│\u001b[0m \u001b[1;36m-\u001b[0m\u001b[1;36m-force\u001b[0m    \u001b[1;35m-\u001b[0m\u001b[1;35m-no\u001b[0m\u001b[1;35m-force\u001b[0m    \u001b[1;33m    \u001b[0m  \u001b[2m[default: no-force]\u001b[0m                                               \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m \u001b[1;36m-\u001b[0m\u001b[1;36m-env\u001b[0m                    \u001b[1;33mTEXT\u001b[0m  Environment to interact with. If not specified, Modal will use    \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m                                the default environment of your current profile, or the           \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m                                \u001b[1;36;40mMODAL_ENVIRONMENT\u001b[0m variable. Otherwise, raises an error if the     \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m                                workspace has multiple environments.                              \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m                                \u001b[2m[default: None]                                                  \u001b[0m \u001b[2m│\u001b[0m\n",
            "\u001b[2m│\u001b[0m \u001b[1;36m-\u001b[0m\u001b[1;36m-help\u001b[0m                   \u001b[1;33m    \u001b[0m  Show this message and exit.                                       \u001b[2m│\u001b[0m\n",
            "\u001b[2m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yASliQDdYs1j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from modal import Stub, Volume, Image"
      ],
      "metadata": {
        "id": "KdQDUrwgW_UH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpt2_image = (\n",
        "    Image.debian_slim(python_version='3.10')\n",
        "    .pip_install(\n",
        "        'transformers[torch]',\n",
        "        'accelerate',\n",
        "        \"datasets\",\n",
        "        'wandb'\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "OMDTlgNgY8_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stub = Stub(image=gpt2_image)"
      ],
      "metadata": {
        "id": "5MezZ-6LW_Ql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volume = Volume.from_name('finetune-text-descrambler')"
      ],
      "metadata": {
        "id": "xqPEYIUDW_OR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@stub.function(volumes = {'/vol':volume})\n",
        "def inference(model_name):\n",
        "  model = GPT2LMHeadModel.from_pretrained('./vol/model')\n",
        "  print(model.config)\n"
      ],
      "metadata": {
        "id": "5GRHtwotW-v6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@stub.local_entrypoint()\n",
        "def main():\n",
        "  inference.remote()"
      ],
      "metadata": {
        "id": "CWD7vytzZOOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMng-fjA2-hP"
      },
      "source": [
        "# BERT2BERT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Crleiau7vMUN"
      },
      "outputs": [],
      "source": [
        "from transformers import EncoderDecoderModel\n",
        "from google.colab import drive\n",
        "from transformers import BertTokenizer\n",
        "import pandas as pd\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tqrdf2U_vMSQ"
      },
      "outputs": [],
      "source": [
        "model_path = '/content/drive/MyDrive/Models/Bert2BertForDescrambling'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "csZ3LJ-gvd92"
      },
      "outputs": [],
      "source": [
        "model = EncoderDecoderModel.from_pretrained(model_path).to('cuda')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kgt_mxuXw019"
      },
      "outputs": [],
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qlsg33ENwfUE"
      },
      "outputs": [],
      "source": [
        "val_path= '/content/drive/MyDrive/Colab datasets/Text-Deshuffling/val (1).csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B1qsQL9NwfJC"
      },
      "outputs": [],
      "source": [
        "val_df = pd.read_csv(val_path)\n",
        "train_path = '/content/drive/MyDrive/Colab datasets/Text-Deshuffling/train (8).csv'\n",
        "train_df = pd.read_csv(train_path)\n",
        "test_path = '/content/drive/MyDrive/Colab datasets/Text-Deshuffling/test (9).csv'\n",
        "test_df = pd.read_csv(test_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nUK-oeF-xSsl"
      },
      "outputs": [],
      "source": [
        "text = test_df['text'].iloc[600]\n",
        "label = test_df['label'].iloc[600]\n",
        "input = tokenizer(text,return_tensors='pt',add_special_tokens=True).to('cuda')\n",
        "input_id = input.input_ids\n",
        "attention_mask = input.attention_mask\n",
        "output = model.generate(input_ids=input_id,attention_mask=attention_mask,\n",
        "                        num_beams=5)\n",
        "output = tokenizer.decode(output[0],skip_special_tokens=True,\n",
        "                          early_stopping=True)\n",
        "print(text)\n",
        "print(label)\n",
        "print(output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_km5U4pcMAU"
      },
      "source": [
        "# LORA and QLORA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZFzM_DG3In6"
      },
      "outputs": [],
      "source": [
        "!pip install bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ItLPUyoG3F2r"
      },
      "outputs": [],
      "source": [
        "import bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2hwjeDFAcKKv"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjN-C0cM3LWE"
      },
      "source": [
        "# Trying it out !! 🤗"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fDyuvtqE3UcN"
      },
      "outputs": [],
      "source": [
        "import gradio as gr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LYqoXoCg332R"
      },
      "outputs": [],
      "source": [
        "def descramble(prompt):\n",
        "  input = tokenizer(prompt,return_tensors='pt')\n",
        "  input_id = input.input_ids\n",
        "  attention_mask = input.attention_mask\n",
        "  max_length = len(prompt.split(' '))\n",
        "  output = Model.generate(input_ids=input_id,attention_mask=attention_mask,)\n",
        "  output = tokenizer.decode(output[0],skip_special_tokens=True)\n",
        "  return gr.Textbox.update(value=output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mNWL-7LZ33z7"
      },
      "outputs": [],
      "source": [
        "def set_example(example):\n",
        "  return gr.TextArea.update(value=example[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aOvVNch433x0"
      },
      "outputs": [],
      "source": [
        "demo = gr.Blocks()\n",
        "with demo:\n",
        "    gr.Markdown(\n",
        "        '''\n",
        "        # A Text Descrambler 😎😎\n",
        "        Turn your Incoherent Sentences to Grammatically correct Sentences.\n",
        "        This was built using transformers and Gradio\n",
        "        ''')\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            gr.Markdown(\n",
        "                '''\n",
        "                Enter a meaningless sentence here\n",
        "\n",
        "                ''')\n",
        "            prompt = gr.TextArea(\n",
        "                value = examples[0][0],\n",
        "                placeholder = \"Enter A Text to see it's correct form \"\n",
        "            )\n",
        "            example_prompts = gr.Dataset(\n",
        "                components = [prompt],\n",
        "                samples = examples)\n",
        "        with gr.Column():\n",
        "            find_answer = gr.Button('Click here to generate your sentence 👀🤺').style(full_width=False)\n",
        "        with gr.Column():\n",
        "            answer = gr.Textbox(label='Answer',placeholder = \"Correct Form\")\n",
        "        with gr.Column():\n",
        "          gr.Markdown(\n",
        "              '''\n",
        "              ## Still Under Construction 💀🎃⏳\n",
        "              ''')\n",
        "    find_answer.click(\n",
        "        fn=descramble,\n",
        "        inputs=[prompt],\n",
        "        outputs=[answer]\n",
        "    )\n",
        "    example_prompts.click(\n",
        "        fn=set_example,\n",
        "        inputs=[example_prompts],\n",
        "        outputs=example_prompts.components,\n",
        "    )\n",
        "\n",
        "\n",
        "\n",
        "demo.launch(debug=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GPT2"
      ],
      "metadata": {
        "id": "2oj6PnlCAfwt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sao2xBNe33vg"
      },
      "outputs": [],
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "from datasets import load_dataset\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('damilojohn/text-descrambling')"
      ],
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      "cell_type": "code",
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        "model = GPT2LMHeadModel.from_pretrained('damilojohn/text-descrambling-gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('openai-community/gpt2-medium')"
      ],
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      },
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        "sentences = [\n",
        "    'the which wiring flow. propose to diagram, method network a reflects signal We visualize',\n",
        "    'the interaction networks. the gap Finally, analyze chemical the junction between synapse and we',\n",
        "    'the process The pseudorandom number illustrated in is Mathematica. generator using',\n",
        "    'in the of structure resulted decrease mutual signal in information. Introducing correlations input-output',\n",
        "    'statistical estimators functionals. of various of consistent We investigate existence the bounded-memory',\n",
        "    'rather negative sense. the question This in strong a is in resolved'\n",
        "]"
      ],
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      },
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      "outputs": []
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        "def generate(input):\n",
        "        model.to(device)\n",
        "        tokenizer.pad_token = tokenizer.eos_token\n",
        "        model.eval()\n",
        "        if isinstance(input,list):\n",
        "          input = [f\"wrong sentence: {sentence} correct sentence:\"\n",
        "                 for sentence in input]\n",
        "        else:\n",
        "          input = f'wrong sentence: {input} correct sentence:'\n",
        "        input_tokens = tokenizer(input, return_tensors='pt',\n",
        "                                      padding='max_length', truncation=True,\n",
        "                                      max_length=128).to(device)\n",
        "\n",
        "        out = model.generate(**input_tokens,\n",
        "                                  max_new_tokens=128,\n",
        "                                  do_sample=True,\n",
        "                                  num_beams=10\n",
        "                                )\n",
        "        ans = []\n",
        "        for i, out in enumerate(out):\n",
        "            decoded_output = tokenizer.decode(out,\n",
        "                                                   skip_special_tokens=True)\n",
        "            print(f\"Output: {decoded_output}\")"
      ],
      "metadata": {
        "id": "VlJSH2DeBhNL"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "generate(sentences)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLu9Zt7VCUpu",
        "outputId": "2cd4890f-47f7-44e9-980a-d462a4195f89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output: wrong sentence: the which wiring flow. propose to diagram, method network a reflects signal We visualize correct sentence:The diagram, which reflects the wiring network to propose a signal flow.                                                                                                                  \n",
            "Output: wrong sentence: the interaction networks. the gap Finally, analyze chemical the junction between synapse and we correct sentence:The gap junction and the chemical interaction between the synapse networks.                                                                                                                   \n",
            "Output: wrong sentence: the process The pseudorandom number illustrated in is Mathematica. generator using correct sentence:The pseudorandom number generator is illustrated using the process in Mathematica.                                                                                                                \n",
            "Output: wrong sentence: in the of structure resulted decrease mutual signal in information. Introducing correlations input-output correct sentence:This resulted in decrease of mutual correlations in the input-output structure of signal information.                                                                                                               \n",
            "Output: wrong sentence: statistical estimators functionals. of various of consistent We investigate existence the bounded-memory correct sentence:The existence of bounded-memory estimators consistent with various statistical functionals.                                                                                                                 \n",
            "Output: wrong sentence: rather negative sense. the question This in strong a is in resolved correct sentence:The question is resolved in a rather strong negative sense.                                                                                                                     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input = 'differential low-power The comparators. fully uses ADC clocked'\n",
        "generate(input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x39s6eAqCmYl",
        "outputId": "2cb33cc9-4612-4fd4-ec67-b372a3ab836d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output: wrong sentence: differential low-power The comparators. fully uses ADC clocked correct sentence:The ADC uses fully clocked low-power differential comparators.                                                                                                                   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "sCS6FX3ME67l"
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