BPE_Implementation.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "gO2lHkc6yMNQ"
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      "source": [
        "## Implementing the Byte-Pair encoding method (as opposed to single word encoding)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcav-bsDDYp9"
      },
      "source": [
        "For example in context - LLama uses vocabulary size of 32K Tokens, GPT3 478 base characters and 10 000 vocabulary merges."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzM8eE_bDxni"
      },
      "source": [
        "Depends on the structure of the language, bpe in English vs in Chinese would follow different rules - like a different take on spaces"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSTKd3WRs7rz"
      },
      "source": [
        "Byte-level BPE: base vocabulary constructed not with unicode characters but bytes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPRbeLKzt1Xo"
      },
      "source": [
        "### Tokenization"
      ]
    },
    {
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      "outputs": [
        {
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            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
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            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
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            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\") #gpt3 and 2 have the same encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l9BnPiEt7YfY"
      },
      "outputs": [],
      "source": [
        "!pip3 install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XoH-8OMI7cIy"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ds=load_dataset(\"tatsu-lab/alpaca\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qw-_ftn7oHm",
        "outputId": "f1b31660-a6b4-4981-9e77-ea6571ffff7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'instruction': 'Give three tips for staying healthy.'}\n"
          ]
        }
      ],
      "source": [
        "ds=ds.select_columns(\"instruction\")\n",
        "print(ds[0])\n",
        "ds=[d['instruction'] for d in ds]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPIFtnZ99HL-",
        "outputId": "8ab52536-2ecb-48f4-f382-bb05e8d1fb31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Give three tips for staying healthy.', 'What are the three primary colors?', 'Describe the structure of an atom.', 'How can we reduce air pollution?', 'Describe a time when you had to make a difficult decision.']\n"
          ]
        }
      ],
      "source": [
        "print(ds[:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ovommlsdx0Gc",
        "outputId": "b4db0cbb-61ae-4e32-bcc0-7db20ede87b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'Give': 932, 'Ġthree': 2135, 'Ġtips': 265, 'TRUNCATED':'OUTPUT'}\n"
          ]
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "word_freqs = defaultdict(int)\n",
        "\n",
        "for text in ds:\n",
        "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
        "    new_words = [word for word, offset in words_with_offsets]\n",
        "    for word in new_words:\n",
        "        word_freqs[word] += 1\n",
        "\n",
        "print(word_freqs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HwImIcEh4u0q"
      },
      "outputs": [],
      "source": [
        "chars = []\n",
        "\n",
        "for word in word_freqs.keys():\n",
        "    for letter in word:\n",
        "        if letter not in chars:\n",
        "            chars.append(letter)\n",
        "chars.sort()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yngGGiHb9i2d",
        "outputId": "cda50d89-2ddb-4110-b941-f38697342f5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['!', '\"', '#', '$', '%', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[', '\\\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', '¡', '¢', '£', '¥', '¦', '§', '¨', '©', 'ª', '°', '±', '³', '´', '·', '¸', '¹', '¼', '¿', 'Â', 'Ã', 'Ï', 'â', 'ê', 'ë', 'ì', 'ï', 'ð', 'Ċ', 'Ġ', 'Ģ', 'ģ', 'Ĩ', 'Ī', 'ĭ', 'ı', 'Ĵ', 'ĵ', 'Ķ', 'Ĺ', 'ĺ', 'Ļ', 'ļ', 'ľ', 'Ŀ', 'ŀ', 'Ł', 'ł', 'Ń']\n"
          ]
        }
      ],
      "source": [
        "print(chars)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "phU1V6RU9kFs"
      },
      "outputs": [],
      "source": [
        "# add special tokens\n",
        "chars.append(\"<|endoftext|>\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFdgIwiC-QTP"
      },
      "source": [
        "Split each word into characters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jFkv1Z0H-Emv"
      },
      "outputs": [],
      "source": [
        "wordsplits = {word: [c for c in word] for word in word_freqs.keys()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ppj2a5l1AEO1",
        "outputId": "628010f2-e262-4015-fbc6-36c44675d5e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'Give': ['G', 'i', 'v', 'e'], 'Ġthree': ['Ġ', 't', 'h', 'r', 'e', 'e'], 'Ġtips': ['Ġ', 't', 'i', 'p', 's']}\n"
          ]
        }
      ],
      "source": [
        "print(wordsplits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3g2Jr9J-Tw6"
      },
      "source": [
        "calculate the frequency of a pair in given set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eotA22H99txW"
      },
      "outputs": [],
      "source": [
        "def frequencepairs(splits):\n",
        "    pair_freqs = defaultdict(int)\n",
        "\n",
        "    for word, freq in word_freqs.items():\n",
        "        split = splits[word]\n",
        "        if len(split) == 1:\n",
        "            continue\n",
        "        for i in range(len(split) - 1):\n",
        "            pair = (split[i], split[i + 1])\n",
        "            pair_freqs[pair] += freq\n",
        "    return pair_freqs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U36_GLYu-1Pc"
      },
      "source": [
        "Let's see an example with our character split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKe84omd-pqK",
        "outputId": "7ff47936-5bd9-486c-d527-af25286763ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('G', 'i'): 3189\n",
            "('i', 'v'): 15517\n",
            "('v', 'e'): 22147\n",
            "('Ġ', 't'): 82754\n",
            "('t', 'h'): 64324\n",
            "('h', 'r'): 3536\n"
          ]
        }
      ],
      "source": [
        "freqpair = frequencepairs(wordsplits)\n",
        "\n",
        "for i, key in enumerate(freqpair.keys()):\n",
        "    print(f\"{key}: {freqpair[key]}\")\n",
        "    if i >= 5:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cr9I4aBC-5Xz"
      },
      "outputs": [],
      "source": [
        "def bestpair(freqspairs):\n",
        "  maxi=0\n",
        "  best=\"\"\n",
        "\n",
        "  for pair, freq in freqspairs.items():\n",
        "      if maxi < freq:\n",
        "          best = pair\n",
        "          maxi = freq\n",
        "\n",
        "  return(best, maxi)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zADZ9Kj3_i1F",
        "outputId": "6ac7d0ad-5268-45a4-e3d6-525fb833fcab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(('Ġ', 't'), 82754)\n"
          ]
        }
      ],
      "source": [
        "print(bestpair(freqpair))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twZkFqEU_33s"
      },
      "outputs": [],
      "source": [
        "merges ={}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLghI_nmAKT0"
      },
      "source": [
        "create a function to update the split of each word that contains this combination"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SxxdTYJE_99C"
      },
      "outputs": [],
      "source": [
        "def splitmerge(a, b, splits):\n",
        "\n",
        "    for word in splits:\n",
        "        decomposed = splits[word]\n",
        "        # decomposed is a list of symbols forming the word\n",
        "        if len(decomposed) == 1:\n",
        "            continue\n",
        "\n",
        "        i = 0\n",
        "        # loop over all possible pairs in a the split of that word\n",
        "        while i < len(decomposed) - 1:\n",
        "            if decomposed[i] == a and decomposed[i + 1] == b:\n",
        "                # replace two individual symbol by merged symbol\n",
        "                decomposed = decomposed[:i] + [a + b] + decomposed[i + 2 :]\n",
        "            else:\n",
        "                i += 1\n",
        "        splits[word] = decomposed\n",
        "\n",
        "\n",
        "    return splits"
      ]
    },
    {
      "cell_type": "code",
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        }
      ],
      "source": [
        "vocab=chars.copy()\n",
        "vocab_size = 1000\n",
        "# desired size of known vocabulary\n",
        "\n",
        "while len(vocab) < vocab_size:\n",
        "  print(len(vocab))\n",
        "  freqpair = frequencepairs(wordsplits)\n",
        "  best, maxfreq=bestpair(freqpair)\n",
        "\n",
        "  wordsplits = splitmerge(*best, wordsplits)\n",
        "\n",
        "  merges[best] = best[0] + best[1]\n",
        "  vocab.append(best[0] + best[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "SsuG1p4YC77D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "222eca20-62a7-4635-bae7-8aad20bf26be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Formulate', 'Ġaffect', 'Ġunderstand', 'eline', 'Ġteach', 'Ġment', 'to', 'face', 'Ġvi', 'aphrase', 'Ġbegin', 'anish', 'QL', 'Ġlearn', 'arth', 'Ġsummer', 'Ġchall', 'Ġvisual', 'Ġgener', 'Ġrate', 'ĠSpanish', 'Ġqual', 'Ġsingle', 'Ġplace', 'Ġtool', 'Ġant', 'Ġchanging', 'Ass', 'Ġsecond', 'Ġpres', 'Reword', 'ves', 'Ġadjective', 'Ġbeen', 'sert', '19', 'Ġdetail', 'Ġpublic', 'Ġland', 'Ġassociated', 'air', 'Ġskill', 'ough', 'Ġ=', 'Ġtra', 'Ġtag', 'Ġformat', 'Ġtakes', 'ets', 'sent', 'Ġyears', 'Ġsupport', 'Ġsummarize', 'Ġrain', 'Ġpunctuation', 'lock', 'Ġly', 'Ġconsider', 'Ġbreak', 'ely', 'Ġris', 'Ġconveys', 'Ġpassword', 'Paraphrase', 'Ġsurve', 'Ġlogical', 'ply', 'rib', 'Ġcommunication', 'ĠHTML', 'Ġright', 'Ġexerc', 'Ġcare', 'Ġdid', 'Ġx', 'Ġmax', 'ume', 'Ġfound', 'cessary', 'tual', 'Ġnouns', 'Ġmist', 'Propose', 'Ġpract', 'Ġadverb', 'Ġnames', 'Ġlow', 'Ġpand', 'Ġphysical', 'onym', 'Ġmot', 'sentence', 'ty', 'Ġ$', 'Ġtimeline', 'Ġsaf', 'St', 'Ġ12', 'Complete', 'atest']\n"
          ]
        }
      ],
      "source": [
        "print(vocab[900:])"
      ]
    }
  ],
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