create_daT5-base.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oh2xfITDhN2u"
      },
      "source": [
        "This notebook contains the code for creating a compressed version of mT5-base, containing only embeddings for the most used Danish and English vocabulary.\n",
        "\n",
        "Code adapted from https://gist.github.com/avidale/44cd35bfcdaf8bedf51d97c468cc8001."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BoiF06nfGvtW"
      },
      "outputs": [],
      "source": [
        "# installing modules\n",
        "!pip install torch transformers sentencepiece\n",
        "!sudo apt install git-lfs\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X99M7UWoHC9k"
      },
      "outputs": [],
      "source": [
        "# importing modules\n",
        "from transformers import T5Tokenizer, AutoModelForSeq2SeqLM\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcVexj3Ye6X3"
      },
      "source": [
        "# Removing unused vocabulary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "7OnBRq8pHFDN",
        "outputId": "c078cc6d-01b2-47f8-aa2f-ad34b9eb4c1b"
      },
      "outputs": [],
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(\"google/mt5-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkXHkM6OHJcH",
        "outputId": "e3a09b2d-82eb-4cfa-a6bb-0d37466ff821"
      },
      "outputs": [],
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained('google/mt5-base')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0vhvaP8HKm8",
        "outputId": "d48a92ad-0358-47bf-a5a0-583af98f08c6"
      },
      "outputs": [],
      "source": [
        "print(tokenizer.vocab_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMItls1shI3-"
      },
      "source": [
        "The mT5 tokeniser contains embeddings for 250K tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hz6Bv4tZIsX5",
        "outputId": "e40c6a94-c9d2-4839-b1dc-b386056dc1a0"
      },
      "outputs": [],
      "source": [
        "def msize(m):\n",
        "    return sum(p.numel() for p in m.parameters())\n",
        "\n",
        "original_size = msize(model)\n",
        "print(msize(model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hX8pzm4nhhMt"
      },
      "source": [
        "The model has 582M parameters. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmvmyYsyHh2s",
        "outputId": "aa72c8fb-e202-4460-efe9-fd28a821f1e2"
      },
      "outputs": [],
      "source": [
        "print(msize(model.shared) / msize(model))\n",
        "print(msize(model.lm_head) / msize(model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18ckhebWLLra"
      },
      "source": [
        "Input and output embeddings constitute 66% of the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amFXHV9OL9SU"
      },
      "source": [
        "# Determine the new tokens"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfeGCTv5Vvmu"
      },
      "source": [
        "Both English and Danish corpora are downloaded from the Leipzig Corpora Collection (https://wortschatz.uni-leipzig.de/en/download). The corpora contain 1 million sentences each."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XNHwPMCHiRhr"
      },
      "outputs": [],
      "source": [
        "!tar -xsvf dan-dk_web-public_2019_1M.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!tar -xsvf eng-com_web-public_2018_1M.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "IoJlXMw_M7pT",
        "outputId": "5d1ca6d0-4153-4290-db8e-5e0ec29e6f80"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "pd.options.display.max_colwidth = 300\n",
        "import csv\n",
        "fname = 'dan-dk_web-public_2019_1M/dan-dk_web-public_2019_1M-sentences.txt'\n",
        "df_da = pd.read_csv(fname, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n",
        "df_da.columns = ['idx', 'text']\n",
        "df_da.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "V-Uc7nbziyXp",
        "outputId": "c14d04a8-5b09-4977-aa10-9ed34a2ebfba"
      },
      "outputs": [],
      "source": [
        "fname = 'eng-com_web-public_2018_1M/eng-com_web-public_2018_1M-sentences.txt'\n",
        "df_en = pd.read_csv(fname, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n",
        "df_en.columns = ['idx', 'text']\n",
        "df_en.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "from tqdm.auto import tqdm, trange\n",
        "\n",
        "cnt_da = Counter()\n",
        "for text in tqdm(df_da.text):\n",
        "    cnt_da.update(tokenizer.encode(text))\n",
        "\n",
        "cnt_en = Counter()\n",
        "for text in tqdm(df_en.text):\n",
        "    cnt_en.update(tokenizer.encode(text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M07fj3z0NWiy",
        "outputId": "b335c276-1ab1-4465-cbb8-8a9fa5d08cff"
      },
      "outputs": [],
      "source": [
        "print('Tokenised Danish words:', len(cnt_da))\n",
        "print('Tokenised English words:', len(cnt_en))\n",
        "common = len(set(cnt_da.keys()).intersection(set(cnt_en.keys())))\n",
        "print('Common word between corpora:', common)\n",
        "print('Amount of Danish words that are also in English corpora:', common / len(cnt_da)*100)\n",
        "\n",
        "diff_en = len(set(cnt_en.keys()).difference(set(cnt_da.keys())))\n",
        "print('Words that are only in the English corpus:', diff_en)\n",
        "diff_da = len(set(cnt_da.keys()).difference(set(cnt_en.keys())))\n",
        "print('Words that are only in the Danish corpus:', diff_da)\n",
        "\n",
        "total = common + diff_en + diff_da\n",
        "print('Total number of tokenised words across corpora:', total)\n",
        "\n",
        "print('Percentage of total model vocabulary:', total/tokenizer.vocab_size*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNudkAe5NbKT",
        "outputId": "de2363c8-acec-4048-fc6a-5041e675ab13"
      },
      "outputs": [],
      "source": [
        "print('Danish top tokens')\n",
        "for top in 10_000, 20_000, 30_000:\n",
        "    print(top, sum(v for k, v in cnt_da.most_common(top)) / sum(cnt_da.values()))\n",
        "print('English top tokens')\n",
        "for top in 10_000, 20_000, 30_000:\n",
        "    print(top, sum(v for k, v in cnt_en.most_common(top)) / sum(cnt_en.values()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ULUmyllmNA0"
      },
      "source": [
        "For both English and Danish, the top 10K tokens cover about 95% of the tokenised vocabulary, while the top 20K tokens encompass about 98%."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9RzGibfZQbgP"
      },
      "outputs": [],
      "source": [
        "old_voc = tokenizer.get_vocab()\n",
        "old_inv_voc = {v: k for k, v in old_voc.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKwEQtbRljiC"
      },
      "source": [
        "The 30 most used tokens in both languages are mostly service words or prefixes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8oL4rL8QZ8f",
        "outputId": "a8c56a5f-d5ba-4da1-80fe-4038d86f9efe"
      },
      "outputs": [],
      "source": [
        "print('Danish:', tokenizer.convert_ids_to_tokens([k for k, v in cnt_da.most_common(30)]))\n",
        "print('English:', tokenizer.convert_ids_to_tokens([k for k, v in cnt_en.most_common(30)]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwwPWiO3Po1x"
      },
      "source": [
        "Composition of the new vocabulary:\n",
        "* Top 10K of the English vocabulary\n",
        "* Top 30K of the Danish vocabulary (or almost, to make the total number of tokens 30K)\n",
        "* 100 special tokens that T5 uses\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-aSMIB1Pxvh",
        "outputId": "ee53265d-6192-40df-e98c-67b3ca11b285"
      },
      "outputs": [],
      "source": [
        "new_tokens = set(range(1000))\n",
        "for i, (k, v) in enumerate(cnt_en.most_common(10_000)):\n",
        "    if k not in new_tokens:\n",
        "        new_tokens.add(k)\n",
        "for i, (k, v) in enumerate(cnt_da.most_common(25_000)):\n",
        "    if len(new_tokens) == 29_900:\n",
        "        print(i, 'Danish tokens are included')\n",
        "        break\n",
        "    if k not in new_tokens:\n",
        "        new_tokens.add(k)\n",
        "\n",
        "for t in range(tokenizer.vocab_size - 100, tokenizer.vocab_size):\n",
        "    new_tokens.add(t)\n",
        "\n",
        "print(len(new_tokens))\n",
        "kept_ids = sorted(new_tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q21bC7tpTyuW",
        "outputId": "bed03ca4-c652-4d9b-cb4e-d2caceecc51e"
      },
      "outputs": [],
      "source": [
        "len(kept_ids) / tokenizer.vocab_size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLAFLhrDoD4U"
      },
      "source": [
        "The new vocabulary is only 12% of the original one. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaaCyAPlomLt"
      },
      "source": [
        "### Update the embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P5033SckRzzo"
      },
      "outputs": [],
      "source": [
        "new_size = len(kept_ids)\n",
        "new_emb = torch.nn.Embedding(new_size, model.shared.embedding_dim)\n",
        "new_head = torch.nn.Linear(in_features=model.lm_head.in_features, out_features=new_size, bias=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CjD6LS_9fe_M"
      },
      "outputs": [],
      "source": [
        "for new_id, old_id in enumerate(kept_ids):\n",
        "    new_emb.weight.data[new_id] = model.shared.weight.data[old_id]\n",
        "    new_head.weight.data[new_id] = model.lm_head.weight.data[old_id]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vv7IuBORRseE"
      },
      "outputs": [],
      "source": [
        "model.shared.weight = new_emb.weight\n",
        "model.lm_head.weight = new_head.weight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_aPBQ20kvCB",
        "outputId": "d2289964-728d-45b2-afc9-e366e1d6b98b"
      },
      "outputs": [],
      "source": [
        "print(msize(model), msize(model) / original_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcIDtmymo56s"
      },
      "source": [
        "The new model has 244M parameters - 42% of the original size. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdKmFJY_k7xZ"
      },
      "source": [
        "### Update the tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-X25sG0jmc83"
      },
      "source": [
        "T5 uses Sentencepiece tokenizer, which is implemented in C and is opaque to Python. \n",
        "\n",
        "We can download the model and deploy it into Python using its Protobuf representation. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OpII_eX3mY80"
      },
      "outputs": [],
      "source": [
        "!wget https://raw.githubusercontent.com/google/sentencepiece/master/src/sentencepiece_model.proto"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGb1DiYmpnkr"
      },
      "source": [
        "We compile the protobuf description of the sentencepiece model in order to be able to modify it. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!sudo apt install protobuf-compiler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I6B0MA5DmaZM"
      },
      "outputs": [],
      "source": [
        "!protoc --python_out=. sentencepiece_model.proto"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJwHRRzbngJY"
      },
      "source": [
        "Now we can serialize the model used by the current tokenizer and open it as a protobuf class. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
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        },
        "id": "MdQM0L3lnybA",
        "outputId": "aa4db10b-f8c2-48fe-8e67-2afa6586550c"
      },
      "outputs": [],
      "source": [
        "import sentencepiece_model_pb2 as spmp\n",
        "smp = tokenizer.sp_model.serialized_model_proto()\n",
        "m = spmp.ModelProto()\n",
        "m.ParseFromString(smp)\n",
        "\n",
        "print('the loaded model has pieces:', len(m.pieces))\n",
        "new_pieces = [m.pieces[idx] for idx in kept_ids]\n",
        "print('the new pieces:', len(new_pieces))\n",
        "\n",
        "# replace the content of the first 30K pieces\n",
        "for i, p in enumerate(new_pieces):\n",
        "    m.pieces[i].piece = p.piece\n",
        "    m.pieces[i].score = p.score\n",
        "    m.pieces[i].type = p.type\n",
        "\n",
        "# drop the remaining pieces\n",
        "n = len(new_pieces)\n",
        "for i in trange(len(m.pieces) - n):\n",
        "    m.pieces.pop(len(m.pieces) - 1)\n",
        "\n",
        "print(len(m.pieces))\n",
        "with open('new_sp.model', 'wb') as f:\n",
        "    f.write(m.SerializeToString())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qWeP6N1sry93"
      },
      "outputs": [],
      "source": [
        "new_tokenizer = T5Tokenizer('new_sp.model', extra_ids=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czfXG1IqsDT4"
      },
      "source": [
        "### Save the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oanCNPiIsCdU",
        "outputId": "574a65e7-a1f4-465b-aa28-f6b2d9990200"
      },
      "outputs": [],
      "source": [
        "model.config.__dict__['vocab_size'] = new_size\n",
        "model.config.__dict__['_name_or_path'] = 'cointegrated/daT5-base'\n",
        "model.config"
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      "execution_count": null,
      "metadata": {
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        "new_tokenizer.save_pretrained('daT5-base')\n",
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        "The updated model and tokeniser can be loaded using the Huggingface API with the following commands:"
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    },
    {
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      "execution_count": null,
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      "outputs": [],
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        "AutoModelForSeq2SeqLM.from_pretrained('sarakolding/daT5-base')"
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