indictrans2-finetune.ipynb

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      "metadata": {
        "id": "A87wEtLrf-M9"
      },
      "source": [
        "## Setup"
      ]
    },
    {
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      "source": [
        "\n",
        "!pip install -q peft sacrebleu\n",
        "!git clone https://github.com/AI4Bharat/IndicTrans2\n",
        "%cd /content/IndicTrans2/huggingface_interface\n",
        "!bash install.sh\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ua5PNUgwz0TE"
      },
      "source": [
        "## Unzip Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1REpcnpyAzM",
        "outputId": "f54e98fc-850f-44ec-a080-d8d7a229a27a"
      },
      "outputs": [],
      "source": [
        "!unzip /content/en-indic-exp.zip -d /content/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Finetune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "XOZ2yNpByaWn",
        "outputId": "10c3280e-2219-47c9-c028-c4f44f9a3d89"
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      "outputs": [],
      "source": [
        "%cd /content/IndicTrans2/huggingface_interface\n",
        "\n",
        "fine_tuning_args = ' '.join([\n",
        "    '--model_name \"ai4bharat/indictrans2-en-indic-dist-200M\"',\n",
        "    '--direction \"en-indic\"',\n",
        "    '--src_lang_list \"eng_Latn\"',\n",
        "    '--tgt_lang_list \"hin_Deva\"',\n",
        "    '--data_dir \"/content/en-indic-exp\"', \n",
        "    '--output_dir \"output\"',\n",
        "    '--batch_size 4',\n",
        "    '--max_steps 2000',\n",
        "    '--num_workers 1',\n",
        "    '--lora_r 32',\n",
        "    '--lora_alpha 64',\n",
        "])\n",
        "\n",
        "!python train_lora.py {fine_tuning_args}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17ktzmh02G6c"
      },
      "source": [
        "## LoRA Upload to 🤗HUB\n",
        "\n",
        "Login to Huggingface Hub"
      ]
    },
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      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Saved checkpoint will be in `IndicTrans2/huggingface_interface/output/checkpoint-{steps}`"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "outputId": "4edcbc3e-bce0-409b-e5d1-f64d11d8a29f"
      },
      "outputs": [],
      "source": [
        "\n",
        "from transformers import AutoModelForSeq2SeqLM\n",
        "from peft import PeftModel\n",
        "\n",
        "\n",
        "base_ckpt_dir = \"ai4bharat/indictrans2-en-indic-dist-200M\"\n",
        "lora_identifier = 'indictrans2-conv'\n",
        "lora_ckpt_dir = ''\n",
        "\n",
        "base_model = AutoModelForSeq2SeqLM.from_pretrained(base_ckpt_dir, trust_remote_code=True)\n",
        "lora_model = PeftModel.from_pretrained(base_model, lora_ckpt_dir)\n",
        "lora_model.push_to_hub(repo_id=lora_identifier)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXbkHWQCjG6C"
      },
      "source": [
        "## Inference with LoRA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ukgkcp8kf58e"
      },
      "outputs": [],
      "source": [
        "\n",
        "!pip install peft\n",
        "!git clone https://github.com/VarunGumma/IndicTransTokenizer.git\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pa0YdkgHh-62"
      },
      "source": [
        "⚠️ *Now **Restart** the session*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import requests\n",
        "\n",
        "url = \"https://demo-api.models.ai4bharat.org/inference/translation/v2\"\n",
        "payload = {\n",
        "    \"controlConfig\": {\"dataTracking\": True},\n",
        "    \"input\": [],\n",
        "    \"config\": {\n",
        "        \"serviceId\": \"\",\n",
        "        \"language\": {\"sourceLanguage\": \"en\", \"targetLanguage\": \"hi\"},\n",
        "    },\n",
        "}\n",
        "\n",
        "\n",
        "def indictrans2_api(inputs):\n",
        "    inputs = [{\"source\": i} for i in inputs]\n",
        "    payload[\"input\"] = inputs\n",
        "\n",
        "    while True:\n",
        "        response = requests.post(url, json=payload)\n",
        "        resp_data = response.json()\n",
        "        if \"output\" in resp_data:\n",
        "            break\n",
        "\n",
        "    results = []\n",
        "    for output in resp_data[\"output\"]:\n",
        "        results.append(output[\"target\"])\n",
        "\n",
        "    return results\n"
      ]
    },
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        "id": "tlzU77Bl-i1B"
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      "source": [
        "import sys\n",
        "import torch\n",
        "from transformers import AutoModelForSeq2SeqLM, BitsAndBytesConfig\n",
        "from IndicTransTokenizer.IndicTransTokenizer.utils import IndicProcessor\n",
        "from IndicTransTokenizer.IndicTransTokenizer.tokenizer import IndicTransTokenizer\n",
        "from peft import PeftModel\n",
        "\n",
        "\n",
        "en_indic_ckpt_dir =  \"ai4bharat/indictrans2-en-indic-dist-200M\"\n",
        "lora_ckpt_dir = 'sam749/indictrans2-conv'\n",
        "BATCH_SIZE = 8\n",
        "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "HALF = True if torch.cuda.is_available() else False\n",
        "\n",
        "quantization = None\n",
        "\n",
        "\n",
        "def initialize_model_and_tokenizer(ckpt_dir, direction, quantization):\n",
        "    if quantization == \"4-bit\":\n",
        "        qconfig = BitsAndBytesConfig(\n",
        "            load_in_4bit=True,\n",
        "            bnb_4bit_use_double_quant=True,\n",
        "            bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "        )\n",
        "    elif quantization == \"8-bit\":\n",
        "        qconfig = BitsAndBytesConfig(\n",
        "            load_in_8bit=True,\n",
        "            bnb_8bit_use_double_quant=True,\n",
        "            bnb_8bit_compute_dtype=torch.bfloat16,\n",
        "        )\n",
        "    else:\n",
        "        qconfig = None\n",
        "\n",
        "    tokenizer = IndicTransTokenizer(direction=direction)\n",
        "    model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "        ckpt_dir,\n",
        "        trust_remote_code=True,\n",
        "        low_cpu_mem_usage=True,\n",
        "        quantization_config=qconfig,\n",
        "    )\n",
        "\n",
        "\n",
        "    if qconfig == None:\n",
        "        model = model.to(DEVICE)\n",
        "    if HALF:\n",
        "        model.half()\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    if lora_ckpt_dir:\n",
        "      lora_model = PeftModel.from_pretrained(model, lora_ckpt_dir)\n",
        "      return tokenizer, lora_model\n",
        "\n",
        "    return tokenizer, model\n",
        "\n",
        "\n",
        "def batch_translate(input_sentences, src_lang, tgt_lang, model, tokenizer, ip):\n",
        "    translations = []\n",
        "    for i in range(0, len(input_sentences), BATCH_SIZE):\n",
        "        batch = input_sentences[i : i + BATCH_SIZE]\n",
        "\n",
        "        # Preprocess the batch and extract entity mappings\n",
        "        batch = ip.preprocess_batch(batch, src_lang=src_lang, tgt_lang=tgt_lang)\n",
        "\n",
        "        # Tokenize the batch and generate input encodings\n",
        "        inputs = tokenizer(\n",
        "            batch,\n",
        "            src=True,\n",
        "            truncation=True,\n",
        "            padding=\"longest\",\n",
        "            return_tensors=\"pt\",\n",
        "            return_attention_mask=True,\n",
        "        ).to(DEVICE)\n",
        "\n",
        "        # Generate translations using the model\n",
        "        with torch.inference_mode():\n",
        "            generated_tokens = model.generate(\n",
        "                **inputs,\n",
        "                use_cache=True,\n",
        "                min_length=0,\n",
        "                max_length=256,\n",
        "                num_beams=5,\n",
        "                num_return_sequences=1,\n",
        "            )\n",
        "\n",
        "        # Decode the generated tokens into text\n",
        "        generated_tokens = tokenizer.batch_decode(generated_tokens.detach().cpu().tolist(), src=False)\n",
        "\n",
        "        # Postprocess the translations, including entity replacement\n",
        "        translations += ip.postprocess_batch(generated_tokens, lang=tgt_lang)\n",
        "\n",
        "        del inputs\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    return translations\n",
        "\n",
        "\n",
        "ip = IndicProcessor(inference=True)\n",
        "\n",
        "en_indic_tokenizer, en_indic_model = initialize_model_and_tokenizer(en_indic_ckpt_dir, \"en-indic\", quantization)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
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      "source": [
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "#                              English to Hindi\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "en_sents = [\n",
        "    \"Ajay to Kritika: Hello! How can I help you?\",\n",
        "    \"Kritika to Ajay: Hello! How can I help you?\",\n",
        "    \"Ajay to Kritika: Did you mean 'Do you like pizza?' I don't actually eat it. Can I get a photo of it instead?\",\n",
        "    \"Kritika to Ajay: Did you mean 'Do you like pizza?'' I don't actually eat it. Can I get a photo of it instead?\"\n",
        "]\n",
        "\n",
        "src_lang, tgt_lang = \"eng_Latn\", \"hin_Deva\"\n",
        "hi_translations = batch_translate(en_sents, src_lang, tgt_lang, en_indic_model, en_indic_tokenizer, ip)\n",
        "indictrans2_MTs = indictrans2_api(en_sents)\n",
        "\n",
        "\n",
        "for input_sentence,lora_mt,it2_mt in zip(en_sents, hi_translations,indictrans2_MTs):\n",
        "    print(f\"input: {input_sentence}\")\n",
        "    print(f\"hi_IndicTrans2: {it2_mt}\")\n",
        "    print(f\"hi_LoRA: {lora_mt}\")\n",
        "    print('-'*20)\n"
      ]
    }
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