BERT_from_scratch.ipynb

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          }
        },
        "e865b7162ab74fba8f8c920cf2f6d7f5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/subhajitchatterjee07/toy-BERT/blob/main/BERT_from_scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from dataclasses import dataclass"
      ],
      "metadata": {
        "id": "9_O7yQzKwAPY"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@dataclass\n",
        "class BERTConfig:\n",
        "  enc_layers = 6\n",
        "  head_count = 12\n",
        "  emb_size = 384 # from 12\n",
        "  seq_len = 36\n",
        "  drop_rate = 0.1"
      ],
      "metadata": {
        "id": "Qi2FtP0YvYW4"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#step 1 : get the data\n",
        "\n",
        "!wget -O input.txt https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfRkuI5OwZM0",
        "outputId": "aa32f6d4-bcd2-45cb-de0d-cae88853b66d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-01-05 08:56:02--  https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1115394 (1.1M) [text/plain]\n",
            "Saving to: ‘input.txt’\n",
            "\n",
            "\rinput.txt             0%[                    ]       0  --.-KB/s               \rinput.txt           100%[===================>]   1.06M  --.-KB/s    in 0.06s   \n",
            "\n",
            "2025-01-05 08:56:02 (17.2 MB/s) - ‘input.txt’ saved [1115394/1115394]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('input.txt', 'r') as f:\n",
        "  data = f.read()\n",
        "\n",
        "print(len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOtfp-fj0iz2",
        "outputId": "a233f3fa-e6fa-466c-8563-0ac40b56677c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1115394\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data[:1000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hB2nWsTZ0vb9",
        "outputId": "11017f70-5b1b-4b30-e9f2-01dc2e337cc4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First Citizen:\n",
            "Before we proceed any further, hear me speak.\n",
            "\n",
            "All:\n",
            "Speak, speak.\n",
            "\n",
            "First Citizen:\n",
            "You are all resolved rather to die than to famish?\n",
            "\n",
            "All:\n",
            "Resolved. resolved.\n",
            "\n",
            "First Citizen:\n",
            "First, you know Caius Marcius is chief enemy to the people.\n",
            "\n",
            "All:\n",
            "We know't, we know't.\n",
            "\n",
            "First Citizen:\n",
            "Let us kill him, and we'll have corn at our own price.\n",
            "Is't a verdict?\n",
            "\n",
            "All:\n",
            "No more talking on't; let it be done: away, away!\n",
            "\n",
            "Second Citizen:\n",
            "One word, good citizens.\n",
            "\n",
            "First Citizen:\n",
            "We are accounted poor citizens, the patricians good.\n",
            "What authority surfeits on would relieve us: if they\n",
            "would yield us but the superfluity, while it were\n",
            "wholesome, we might guess they relieved us humanely;\n",
            "but they think we are too dear: the leanness that\n",
            "afflicts us, the object of our misery, is as an\n",
            "inventory to particularise their abundance; our\n",
            "sufferance is a gain to them Let us revenge this with\n",
            "our pikes, ere we become rakes: for the gods know I\n",
            "speak this in hunger for bread, not in thirst for revenge.\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lines = data.splitlines()\n",
        "lines[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oObsY16s13-i",
        "outputId": "dca862cd-339e-4890-a4d2-1850c3b6ee91"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['First Citizen:',\n",
              " 'Before we proceed any further, hear me speak.',\n",
              " '',\n",
              " 'All:',\n",
              " 'Speak, speak.',\n",
              " '',\n",
              " 'First Citizen:',\n",
              " 'You are all resolved rather to die than to famish?',\n",
              " '',\n",
              " 'All:']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(lines))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nD2bao5Y2vpd",
        "outputId": "38c02edc-a42c-46fc-a7fe-b50846aa4f72"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for elem in lines:\n",
        "#   if elem == '':\n",
        "#     lines.remove(elem) NOT A GOOD PRACTICE TO CHANGE THE ARRAY YOU ARE ITSELF WORKING ON !!\n",
        "\n",
        "no_sp_lines = []\n",
        "for elem in lines:\n",
        "  if elem != '':\n",
        "    no_sp_lines.append(elem)\n",
        "  else:\n",
        "    continue"
      ],
      "metadata": {
        "id": "7_cNRCst0_qY"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(no_sp_lines))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2WAGchZ-23pG",
        "outputId": "3d45a5da-bc4d-4a7c-848b-144a881f5e86"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_sp_lines[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRyzl9G_27RZ",
        "outputId": "f4d8ed5f-8075-4352-a956-29cc4658596e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['First Citizen:',\n",
              " 'Before we proceed any further, hear me speak.',\n",
              " 'All:',\n",
              " 'Speak, speak.',\n",
              " 'First Citizen:']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cat_lines = []\n",
        "i = 0\n",
        "\n",
        "while i < len(no_sp_lines):\n",
        "    if no_sp_lines[i].endswith(':'):\n",
        "        x = no_sp_lines[i] + ' ' + no_sp_lines[i + 1]\n",
        "        cat_lines.append(x)\n",
        "        i += 2\n",
        "    else:\n",
        "        cat_lines.append(no_sp_lines[i])\n",
        "        i += 1"
      ],
      "metadata": {
        "id": "y9ZMX8Y_3Bv-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_lines[:30]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_Sfw8Q44ak9",
        "outputId": "87886b9a-a376-425d-cb8a-6a3fa9aa6623"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['First Citizen: Before we proceed any further, hear me speak.',\n",
              " 'All: Speak, speak.',\n",
              " 'First Citizen: You are all resolved rather to die than to famish?',\n",
              " 'All: Resolved. resolved.',\n",
              " 'First Citizen: First, you know Caius Marcius is chief enemy to the people.',\n",
              " \"All: We know't, we know't.\",\n",
              " \"First Citizen: Let us kill him, and we'll have corn at our own price.\",\n",
              " \"Is't a verdict?\",\n",
              " \"All: No more talking on't; let it be done: away, away!\",\n",
              " 'Second Citizen: One word, good citizens.',\n",
              " 'First Citizen: We are accounted poor citizens, the patricians good.',\n",
              " 'What authority surfeits on would relieve us: if they',\n",
              " 'would yield us but the superfluity, while it were',\n",
              " 'wholesome, we might guess they relieved us humanely;',\n",
              " 'but they think we are too dear: the leanness that',\n",
              " 'afflicts us, the object of our misery, is as an',\n",
              " 'inventory to particularise their abundance; our',\n",
              " 'sufferance is a gain to them Let us revenge this with',\n",
              " 'our pikes, ere we become rakes: for the gods know I',\n",
              " 'speak this in hunger for bread, not in thirst for revenge.',\n",
              " 'Second Citizen: Would you proceed especially against Caius Marcius?',\n",
              " \"All: Against him first: he's a very dog to the commonalty.\",\n",
              " 'Second Citizen: Consider you what services he has done for his country?',\n",
              " 'First Citizen: Very well; and could be content to give him good',\n",
              " 'report fort, but that he pays himself with being proud.',\n",
              " 'Second Citizen: Nay, but speak not maliciously.',\n",
              " 'First Citizen: I say unto you, what he hath done famously, he did',\n",
              " 'it to that end: though soft-conscienced men can be',\n",
              " 'content to say it was for his country he did it to',\n",
              " 'please his mother and to be partly proud; which he']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(cat_lines)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkTTwamD5VEX",
        "outputId": "1ba910a2-f114-426a-fa9b-ebaadc5d7af7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24618"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(max(cat_lines, key=len)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2RoUu6f3WMb",
        "outputId": "45181d5c-5ee4-4a91-fdd0-e361279f0566"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "113\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# WE GOTTA TOKENIZE\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "# Load the pretrained tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Special tokens\n",
        "print(\"Special Tokens:\")\n",
        "print(\"CLS:\", tokenizer.cls_token)\n",
        "print(\"SEP:\", tokenizer.sep_token)\n",
        "print(\"PAD:\", tokenizer.pad_token)\n",
        "print(\"MASK:\", tokenizer.mask_token)\n",
        "print(\"UNK:\", tokenizer.unk_token)\n",
        "\n",
        "\n",
        "sentence = \"To be, or not to be.\"\n",
        "encoded = tokenizer.encode(sentence, add_special_tokens=True)\n",
        "\n",
        "print(\"\\nEncoded with special tokens:\", encoded)\n",
        "print(\"Decoded back:\", tokenizer.decode(encoded))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
          "referenced_widgets": [
            "2b3c91cc8d74433b923b2a692aff6b2d",
            "481e6be357964976854fbf2fae4f873a",
            "587a0340ec7b4467a271384ebdbf539d",
            "aead3e855e124edb95dc15d0b6e8ce86",
            "bb5b6e43f0ff4be082fec0132c987ab3",
            "3e1bea69f27a4140822e5bd78da8c0b9",
            "aff790e65df8480f9beebd781a0041d7",
            "3773ee0c94d145a691f3dc7896f77806",
            "d939db2f5a5a43dc9799e899cae882e3",
            "8569ca108ea844dcbe97586cd5c6d741",
            "3caefd1a727845e0b813f782330fd341",
            "b4f450cd402c4a03b65fba138e049dfc",
            "4d11cf117ff146ca8cbe7835ea11181f",
            "b10ddf6900ac45728546ee8a59e095ce",
            "f9bfae547bc945e68c8e6ad30772cdec",
            "b5fb01d4b63449b4af8deff6302514b7",
            "e548d8a7bab341d395132616f99ffc2f",
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            "95364392c049452bbd6ad348d3e38e1d",
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            "003d52d4216b49a0b8bf304bcbce1091",
            "80fb4b864bbc4b899400e463c80e7be1",
            "0b950d17955a4320b7120805467390c5",
            "255b450bdde24a41b5ed295ee9872411",
            "e865b7162ab74fba8f8c920cf2f6d7f5"
          ]
        },
        "id": "IaHMlqAt3hOy",
        "outputId": "db91d72c-8fac-42b3-830d-75c65e4ef4e0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b3c91cc8d74433b923b2a692aff6b2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b4f450cd402c4a03b65fba138e049dfc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88e66a3c43e549efbdd12ea888aeef1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdbc178f543d4089b1f983f360eaad35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Special Tokens:\n",
            "CLS: [CLS]\n",
            "SEP: [SEP]\n",
            "PAD: [PAD]\n",
            "MASK: [MASK]\n",
            "UNK: [UNK]\n",
            "\n",
            "Encoded with special tokens: [101, 2000, 2022, 1010, 2030, 2025, 2000, 2022, 1012, 102]\n",
            "Decoded back: [CLS] to be, or not to be. [SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_encoded = tokenizer(\n",
        "    cat_lines,\n",
        "    padding='max_length',\n",
        "    #truncation=True,\n",
        "    max_length=36,\n",
        "    return_tensors=\"pt\"\n",
        ")\n",
        "\n",
        "# for elem in test_encoded['input_ids']:\n",
        "#   print(elem)\n",
        "# for elem in test_encoded['attention_mask']:\n",
        "#   print(elem)"
      ],
      "metadata": {
        "id": "K30qEvhY7yUl"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for elem in test_encoded:\n",
        "  print(elem)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Y8UFlu688aO",
        "outputId": "9fecd759-217c-43ad-95e9-0f46403c2674"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_ids\n",
            "token_type_ids\n",
            "attention_mask\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.batch_decode(test_encoded['input_ids'][0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkbMSoGw8FX5",
        "outputId": "33d52302-1747-45a8-f927-0a4b05d1d376"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'first', 'citizen', ':', 'before', 'we', 'proceed', 'any', 'further', ',', 'hear', 'me', 'speak', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_encoded['input_ids'][0])"
      ],
      "metadata": {
        "id": "WqvPCgeR95pq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dad2bd3-4f41-45af-93c0-8df926e30453"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.model_max_length)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mvfHsA93-y6",
        "outputId": "9179e24b-f173-4332-e43e-01198564ca97"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print([len(seq) for seq in test_encoded['input_ids']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIr9kRyz4NK0",
        "outputId": "af0ccc9c-b877-450e-d901-8d88f59e2eed"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_encoded['input_ids'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gmSM7cKFrHC",
        "outputId": "72f84e85-05c9-4b8c-af94-67f93dcebfbb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([  101,  2034,  6926,  1024,  2077,  2057, 10838,  2151,  2582,  1010,\n",
              "         2963,  2033,  3713,  1012,   102,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, elem in enumerate(test_encoded['input_ids']):\n",
        "  if elem[-1] != 0:\n",
        "    print(elem, i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_XbkLJTDFzb6",
        "outputId": "cd854353-754c-409a-e58a-39d13ed191c4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([  101,  2033, 29049,  2015,  1012,  8040,  3126, 10736, 14161, 10696,\n",
            "          999, 11839,  2017,  1010,  2909,  1010,  1037,  2773,  1024,  1998,\n",
            "         2004,  1045,  2409,  2017,  1010,  2026,  2402,  3203,  2919,  2063,\n",
            "         2033,  1999, 15549,  2890,  2017,   102]) 10883\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for MLM\n",
        "# To create a dataset we know that 15% of the tokens in each sequence is masked at random\n",
        "# of these 15% tokens, 80% are masked using the mask token [MASK]\n",
        "# 10% are used in the same place unchanged, and rest 10% are replaced by a random word\n",
        "# The caveat is that I will need to store the correct data along with the masked data to compare their embeddings during loss calculation\n",
        "# So better prepare functions"
      ],
      "metadata": {
        "id": "Kh7-lrP2GMFb"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.all_special_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NN7MTaUDMAZ5",
        "outputId": "46e94816-6b0a-49f9-8dfc-4fe0b4f50b68"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[100, 102, 0, 101, 103]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.vocab_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNutPiWHNSXs",
        "outputId": "56396b4a-60cc-4191-d46e-95571abe0e13"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30522"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# This function is supposed to mask words for MLM\n",
        "def masker(encoded_text):\n",
        "    masking_prob = 0.15\n",
        "    masked_input_ids = encoded_text.clone()  # Make sure we don't modify the original tensor\n",
        "    labels = encoded_text.clone()  # Same for labels\n",
        "\n",
        "    for i in range(len(encoded_text)):\n",
        "        if random.random() < masking_prob and masked_input_ids[i] not in tokenizer.all_special_ids:\n",
        "            # Replace with [MASK] 80% of the time\n",
        "            if random.random() < 0.8:\n",
        "                masked_input_ids[i] = tokenizer.mask_token_id\n",
        "            # Replace with a random token 10% of the time\n",
        "            elif random.random() < 0.9:\n",
        "                masked_input_ids[i] = random.randint(0, tokenizer.vocab_size - 1)\n",
        "            # Keep the original word 10% of the time\n",
        "            else:\n",
        "                continue\n",
        "\n",
        "    # Set the labels to -100 for the positions that were masked\n",
        "    #labels[masked_input_ids == tokenizer.mask_token_id] = -100\n",
        "    return masked_input_ids, labels\n"
      ],
      "metadata": {
        "id": "L3iPKKqtKZI6"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating embeddings\n",
        "import torch\n",
        "#position_embeddings\n",
        "token_embedding_layer = torch.nn.Embedding(tokenizer.vocab_size, BERTConfig.emb_size)\n",
        "#print(embedding_layer(torch.tensor([3])))\n"
      ],
      "metadata": {
        "id": "zfO56K4QPehW"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(token_embedding_layer(test_encoded['input_ids'][0]))"
      ],
      "metadata": {
        "id": "yAZCOG1HuNAZ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(token_embedding_layer(test_encoded['input_ids'][0]).shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XPUepBgu8t4",
        "outputId": "c3f56468-553f-4e6b-b78e-5a386bcd21a0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([36, 384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_encoded['input_ids'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYu4j9TAusYN",
        "outputId": "835c3ff8-ae94-4c82-f390-6f6262822be7"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([  101,  2034,  6926,  1024,  2077,  2057, 10838,  2151,  2582,  1010,\n",
              "         2963,  2033,  3713,  1012,   102,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pos_embedding_layer = torch.nn.Embedding(BERTConfig.seq_len, BERTConfig.emb_size)\n",
        "print(pos_embedding_layer.weight.size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVRGQycwuzPG",
        "outputId": "55a92286-c312-4471-e52c-9197c15c6921"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([36, 384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seg_embedding_layer = torch.nn.Embedding(2, BERTConfig.emb_size)\n",
        "#print(seg_embedding_layer(torch.tensor([0])))"
      ],
      "metadata": {
        "id": "B2WcQsLwyL9V"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(seg_embedding_layer(torch.tensor([1])))"
      ],
      "metadata": {
        "id": "VCAfdKLizO6u"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Okay we got our embeddings, now we need to build Bidirectional Attention Mechanism\n",
        "\n",
        "class BidirectionalSelfAttention(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    # We begin by initializing trainable params for query, key, and value\n",
        "    self.W_query = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "    self.W_key = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "    self.W_value = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "\n",
        "  def forward(self, x):\n",
        "    query = self.W_query(x)\n",
        "    key = self.W_key(x)\n",
        "    value = self.W_value(x)\n",
        "    attention_scores = query @ key.T\n",
        "    #print(f\"attention scores size {attention_scores.shape}\")\n",
        "    attention_weights = torch.softmax(attention_scores / key.shape[-1]**0.5, dim=-1)\n",
        "    #print(f\"Attention weights size {attention_weights.shape}\")\n",
        "    context_vector = attention_weights @ value\n",
        "    #print(f\"Context vector size {context_vector.shape}\")\n",
        "    return context_vector\n"
      ],
      "metadata": {
        "id": "xDvjc3nwzkri"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MultiHeadAttention(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.num_heads = BERTConfig.head_count\n",
        "    assert BERTConfig.emb_size % self.num_heads == 0, \"emb_size must be divisible by num_head\"\n",
        "    self.head_dim = BERTConfig.emb_size // self.num_heads\n",
        "    self.W_query = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "    self.W_key = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "    self.W_value = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "    self.out_proj = torch.nn.Linear(BERTConfig.emb_size, BERTConfig.emb_size, bias=False)\n",
        "    self.dropout = torch.nn.Dropout(BERTConfig.drop_rate)\n",
        "\n",
        "  def forward(self, x):\n",
        "    b, num_tokens, d_in = x.shape\n",
        "    keys = self.W_key(x)\n",
        "    queries = self.W_query(x)\n",
        "    values = self.W_value(x)\n",
        "    keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)\n",
        "    values = values.view(b, num_tokens, self.num_heads, self.head_dim)\n",
        "    queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)\n",
        "    keys = keys.transpose(1, 2)\n",
        "    queries = queries.transpose(1, 2)\n",
        "    values = values.transpose(1, 2)\n",
        "    attn_scores = queries @ keys.transpose(2, 3)\n",
        "    attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
        "    attn_weights = self.dropout(attn_weights)\n",
        "    context_vec = (attn_weights @ values).transpose(1, 2)\n",
        "    context_vec = context_vec.contiguous().view(b, num_tokens, BERTConfig.emb_size)\n",
        "    context_vec = self.out_proj(context_vec) #K\n",
        "    return context_vec"
      ],
      "metadata": {
        "id": "xcZj6uRu4CDt"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get embeddings for both examples\n",
        "embedding_0 = token_embedding_layer(test_encoded['input_ids'][0])\n",
        "embedding_1 = token_embedding_layer(test_encoded['input_ids'][1])\n",
        "\n",
        "# Stack them along the first dimension (batch dimension)\n",
        "batch = torch.stack([embedding_0, embedding_1], dim=0)\n",
        "print(batch.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OELI5FpTXOwM",
        "outputId": "e3283ea3-bce7-45fe-a1c3-bb3fc2d97831"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 36, 384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "att = MultiHeadAttention()\n",
        "context_vector = att.forward(batch)\n",
        "print(context_vector.shape)\n",
        "# it works imo !!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXXGDgKuDQun",
        "outputId": "cdd9d7b9-ec05-489a-e4dc-c2cdc8a33285"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 36, 384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Adding a new dimension at the start (for batch size)\n",
        "# x = token_embedding_layer(test_encoded['input_ids'][0]).unsqueeze(0)\n",
        "# print(x.shape)\n"
      ],
      "metadata": {
        "id": "9XYO-lLGWkuc"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LayerNorm(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.eps = 1e-5\n",
        "    self.emb_dim = BERTConfig.emb_size\n",
        "    self.scale = torch.nn.Parameter(torch.ones(self.emb_dim))\n",
        "    self.shift = torch.nn.Parameter(torch.zeros(self.emb_dim))\n",
        "  def forward(self, x):\n",
        "    mean = x.mean(dim=-1, keepdim=True)\n",
        "    var = x.var(dim=-1, keepdim=True, unbiased=False)\n",
        "    norm_x = (x - mean) / torch.sqrt(var + self.eps)\n",
        "    return self.scale * norm_x + self.shift"
      ],
      "metadata": {
        "id": "Qip_oZpEDrcC"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class FeedForward(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.layers = torch.nn.Sequential(\n",
        "      torch.nn.Linear(BERTConfig.emb_size, 4 * BERTConfig.emb_size),\n",
        "      torch.nn.GELU(),\n",
        "      torch.nn.Linear(4 * BERTConfig.emb_size, BERTConfig.emb_size),\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    return self.layers(x)"
      ],
      "metadata": {
        "id": "FiPzfYciI-Nu"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TransformerBlock(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.att = MultiHeadAttention()\n",
        "    self.ff = FeedForward()\n",
        "    self.norm1 = LayerNorm()\n",
        "    self.norm2 = LayerNorm()\n",
        "    self.drop_resid = torch.nn.Dropout(BERTConfig.drop_rate)\n",
        "  def forward(self, x):\n",
        "    shortcut = x\n",
        "    x = self.norm1(x)\n",
        "    #print(\"x is LN'ed\")\n",
        "    #print(x.shape)\n",
        "    x = self.att(x)\n",
        "    #print(\"attention complete\")\n",
        "    #print(x.shape)\n",
        "    x = self.drop_resid(x)\n",
        "    #print(\"dropout complete\")\n",
        "    #print(x.shape)\n",
        "    x = x + shortcut\n",
        "    shortcut = x\n",
        "    x = self.norm2(x)\n",
        "    x = self.ff(x)\n",
        "    x = self.drop_resid(x)\n",
        "    x = x + shortcut\n",
        "    return x"
      ],
      "metadata": {
        "id": "EgBWiGnKLEUN"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "block = TransformerBlock()\n",
        "output = block(batch)"
      ],
      "metadata": {
        "id": "h7md6fAKMcOl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(output.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMMDc08WNARU",
        "outputId": "8cbbd981-6efc-4270-b23b-9aedffa88737"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 36, 384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTModel(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.tok_emb = torch.nn.Embedding(tokenizer.vocab_size, BERTConfig.emb_size)\n",
        "    self.pos_emb = torch.nn.Embedding(BERTConfig.seq_len, BERTConfig.emb_size)\n",
        "    self.seg_emb = torch.nn.Embedding(2, BERTConfig.emb_size)\n",
        "    self.drop_emb = torch.nn.Dropout(BERTConfig.drop_rate)\n",
        "    self.trf_blocks = torch.nn.Sequential(\n",
        "          *[TransformerBlock() for _ in range(BERTConfig.enc_layers)]\n",
        "    )\n",
        "\n",
        "  def forward(self, in_idx):\n",
        "    batch_size, seq_len = in_idx.shape\n",
        "    tok_embeds = self.tok_emb(in_idx)\n",
        "    #print(tok_embeds.shape)\n",
        "\n",
        "    pos_embeds = self.pos_emb(torch.arange(seq_len))\n",
        "    #print(pos_embeds.shape)\n",
        "    x = tok_embeds + pos_embeds\n",
        "    x = self.drop_emb(x)\n",
        "    logits = self.trf_blocks(x)\n",
        "    return logits\n"
      ],
      "metadata": {
        "id": "CGw5FJQJXfsG"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get embeddings for both examples\n",
        "emb_0 = test_encoded['input_ids'][0]\n",
        "emb_1 = test_encoded['input_ids'][1]\n",
        "\n",
        "# Stack them along the first dimension (batch dimension)\n",
        "baetch = torch.stack([emb_0, emb_1], dim=0)\n",
        "print(baetch.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAjeplwRcH0b",
        "outputId": "176bcd70-fb24-47e5-ac3b-7e0cebfa9620"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 36])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(emb_0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DywpM47Bc8Hj",
        "outputId": "cb37205f-c2da-476b-8384-e391ae1593c4"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([  101,  2034,  6926,  1024,  2077,  2057, 10838,  2151,  2582,  1010,\n",
            "         2963,  2033,  3713,  1012,   102,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert = BERTModel()\n",
        "op = bert.forward(baetch)"
      ],
      "metadata": {
        "id": "kh2EYJfbbnX4"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sample():\n",
        "  index1 = random.randint(0, len(test_encoded['input_ids']) - 1)\n",
        "  random_line1 = test_encoded['input_ids'][index1]\n",
        "  if random.random() < 0.5 and index1 < len(cat_lines)-1:\n",
        "    index2 = index1 + 1\n",
        "    random_line2 = test_encoded['input_ids'][index2]\n",
        "  else:\n",
        "    index2 = random.randint(0, len(test_encoded['input_ids']) - 1)\n",
        "    random_line2 = test_encoded['input_ids'][index2]\n",
        "  return random_line1, random_line2"
      ],
      "metadata": {
        "id": "LDsDegzK72eg"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_sample()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXtg8PXg8Wh7",
        "outputId": "dc45337c-589f-41db-b8ba-bd72efbc4d36"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([ 101, 2027, 2024, 2035, 5743, 1012, 2092, 1010, 1045, 2097, 3328, 2870,\n",
              "          102,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0]),\n",
              " tensor([  101,  1998, 15223, 21146,  7096,  2031,  4376,  4595,  3554,  2273,\n",
              "           999,   102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0]))"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def concatenate_and_adjust(tensor1, tensor2, target_size=36):\n",
        "    # Step 1: Strip zeros from the first tensor\n",
        "    tensor1_nonzero = tensor1[tensor1 != 0]\n",
        "\n",
        "    # Step 2: Concatenate the two tensors\n",
        "    concatenated = torch.cat((tensor1_nonzero, tensor2[1:]))\n",
        "\n",
        "    # Step 3: Trim or pad with zeros to match the target size\n",
        "    if len(concatenated) > target_size:\n",
        "        concatenated = concatenated[:target_size]  # Trim to target size\n",
        "    elif len(concatenated) < target_size:\n",
        "        padding = torch.zeros(target_size - len(concatenated), dtype=concatenated.dtype)\n",
        "        concatenated = torch.cat((concatenated, padding))  # Pad with zeros\n",
        "\n",
        "    return concatenated"
      ],
      "metadata": {
        "id": "nQz7uG7MGyDY"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "a, b = get_sample()\n",
        "c = concatenate_and_adjust(a, b)\n",
        "print(c)\n",
        "print(masker(c))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KE9f5l3rG0Jw",
        "outputId": "455a3bd7-7235-43c1-c256-19114d47b99b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([  101, 11089,  2232,  2293,  2026,  2684,  1998,  2016,  2293,  2705,\n",
            "         2032,  1010,   102,  2030,  2119,  4487, 11393, 19661,  6171,  2037,\n",
            "        12242,  2015,  1024,  1998,  3568,  1010,  2065,  2017,  2360,  2053,\n",
            "         2062,  2084,  2023,  1010,   102,     0])\n",
            "(tensor([  101, 11089,  2232,   103,   103,  2684,  1998,  2016,  2293,   103,\n",
            "          103, 18322,   102,  2030,  2119,   103, 11393,   103,  6171,   103,\n",
            "        12242,  2015,  1024,  1998,  3568,  1010,  2065,  2017,  2360,  2053,\n",
            "         2062,  2084,  2023,  1010,   102,     0]), tensor([  101, 11089,  2232,  2293,  2026,  2684,  1998,  2016,  2293,  2705,\n",
            "         2032,  1010,   102,  2030,  2119,  4487, 11393, 19661,  6171,  2037,\n",
            "        12242,  2015,  1024,  1998,  3568,  1010,  2065,  2017,  2360,  2053,\n",
            "         2062,  2084,  2023,  1010,   102,     0]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert = BERTModel()\n",
        "learning_rate = 3e-4\n",
        "max_iters = 500\n",
        "eval_interval = 10\n",
        "lossi = []\n",
        "m = bert.to('cpu')\n",
        "# Create the loss function once at the start of training\n",
        "criterion = torch.nn.MSELoss()\n",
        "\n",
        "# print the number of parameters in the model\n",
        "print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')\n",
        "optimizer = torch.optim.AdamW(bert.parameters(), lr=learning_rate)\n",
        "\n",
        "for iter in range(max_iters):\n",
        "\n",
        "    # sample a batch of data\n",
        "    random_line1, random_line2 = get_sample()\n",
        "\n",
        "    y = concatenate_and_adjust(random_line1, random_line2)\n",
        "\n",
        "\n",
        "    # During training:\n",
        "    x, mask = masker(y)\n",
        "    x_b = torch.unsqueeze(x, dim=0)\n",
        "    out = bert.forward(x_b)\n",
        "\n",
        "    # ready y_b for comparison\n",
        "    labels = token_embedding_layer(y) + pos_embedding_layer(torch.arange(BERTConfig.seq_len))\n",
        "    emb_labels = torch.unsqueeze(labels, dim=0)\n",
        "\n",
        "\n",
        "    # Calculate MSE loss\n",
        "    loss = criterion(out, emb_labels)\n",
        "    lossi.append(loss)\n",
        "    if iter % eval_interval == 0:\n",
        "      print(loss.item())\n",
        "    # Backprop\n",
        "    optimizer.zero_grad(set_to_none=True)\n",
        "    loss.backward()\n",
        "    optimizer.step()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "j6bVPrBJ7KcK",
        "outputId": "e3798fba-0329-42bc-f698-3a40749434aa"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22.372608 M parameters\n",
            "4.5362138748168945\n",
            "2.7630512714385986\n",
            "1.9280072450637817\n",
            "1.5701024532318115\n",
            "1.1709837913513184\n",
            "1.0874379873275757\n",
            "0.9303451180458069\n",
            "1.150646686553955\n",
            "1.303514838218689\n",
            "0.6886135339736938\n",
            "1.0360405445098877\n",
            "1.0891201496124268\n",
            "0.760444164276123\n",
            "0.8754635453224182\n",
            "0.7708002924919128\n",
            "0.6432527899742126\n",
            "0.6862133741378784\n",
            "0.8527920246124268\n",
            "0.7044945359230042\n",
            "0.5423007607460022\n",
            "0.633232831954956\n",
            "0.5492527484893799\n",
            "0.5232040882110596\n",
            "0.6800875067710876\n",
            "0.628070056438446\n",
            "0.8830176591873169\n",
            "0.6091949343681335\n",
            "0.39832228422164917\n",
            "0.7584078311920166\n",
            "0.708210289478302\n",
            "0.7026234865188599\n",
            "0.49344101548194885\n",
            "0.5862016081809998\n",
            "0.5580405592918396\n",
            "0.6154147982597351\n",
            "0.5999875664710999\n",
            "0.5718657374382019\n",
            "0.620936930179596\n",
            "0.40485212206840515\n",
            "0.4701393246650696\n",
            "0.5086435079574585\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-53-75f014d6412c>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset_to_none\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                             )\n\u001b[1;32m    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    488\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36m_use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"differentiable\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_break\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_break\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adamw.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    218\u001b[0m             )\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             adamw(\n\u001b[0m\u001b[1;32m    221\u001b[0m                 \u001b[0mparams_with_grad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mmaybe_fallback\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    152\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mdisabled_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_fallback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adamw.py\u001b[0m in \u001b[0;36madamw\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_single_tensor_adamw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m     func(\n\u001b[0m\u001b[1;32m    783\u001b[0m         \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m         \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adamw.py\u001b[0m in \u001b[0;36m_single_tensor_adamw\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable, has_complex)\u001b[0m\n\u001b[1;32m    425\u001b[0m                 \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_exp_avg_sqs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mbias_correction2_sqrt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m                 \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mbias_correction2_sqrt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcdiv_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_avg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdenom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstep_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_training_loss(lossi):\n",
        "    # Convert tensor losses to numpy array\n",
        "    losses = [loss.detach().numpy() for loss in lossi]\n",
        "\n",
        "    # Create figure and axis\n",
        "    plt.figure(figsize=(10, 6))\n",
        "\n",
        "    # Plot the loss values\n",
        "    plt.plot(range(len(losses)), losses, 'b-', alpha=0.8, label='Training Loss')\n",
        "\n",
        "    # Customize the plot\n",
        "    plt.title('Training Loss over Iterations', fontsize=14, pad=15)\n",
        "    plt.xlabel('Iteration', fontsize=12)\n",
        "    plt.ylabel('Loss', fontsize=12)\n",
        "    plt.grid(True, linestyle='--', alpha=0.7)\n",
        "    plt.legend()\n",
        "\n",
        "    # If you want to add a trend line (moving average)\n",
        "    window_size = min(100, len(losses)//10)  # Adjust window size based on data length\n",
        "    if window_size > 1:\n",
        "        moving_avg = [sum(losses[max(0, i-window_size):i])/min(i, window_size)\n",
        "                     for i in range(1, len(losses)+1)]\n",
        "        plt.plot(range(len(moving_avg)), moving_avg, 'r-', alpha=0.5,\n",
        "                label=f'Moving Average (window={window_size})')\n",
        "        plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Use the function\n",
        "plot_training_loss(lossi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "O7y___T2m2-b",
        "outputId": "4f98e7f2-23fb-47f2-c412-b85e7d8b6556"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SORT OF THE THING!!"
      ],
      "metadata": {
        "id": "GWta9uxWpqZd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}