Finetune_opt_bnb_peft.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "## Fine-tune opt-6.7b with QLoRA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
      },
      "outputs": [],
      "source": [
        "# !pip install -q bitsandbytes datasets accelerate loralib transformers peft"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "cg3fiQOvmI3Q",
        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model_id = \"facebook/opt-6.7b\"\n",
        "\n",
        "try:\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_id,\n",
        "                                                 return_dict=True,\n",
        "                                                 load_in_8bit=True,\n",
        "                                                 device_map='auto')\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "    # Clear any cached memory to optimize GPU usage\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"Error loading model: {e}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fTSZntA1iUG"
      },
      "source": [
        "## Preparing the model\n",
        "\n",
        "- Freeze all our layers\n",
        "\n",
        "- cast the layer-norm in `float32` for stability. \n",
        "\n",
        "- Also cast the output of the last layer in `float32` for the same reasons."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def prepare_model_for_finetuning(model):\n",
        "    for param in model.parameters():\n",
        "        param.requires_grad = False\n",
        "        if param.ndim == 1:\n",
        "            param.data = param.data.to(torch.float32)\n",
        "\n",
        "    model.gradient_checkpointing_enable()\n",
        "    model.enable_input_require_grads()\n",
        "\n",
        "    class CastOutputToFloat(nn.Sequential):\n",
        "        def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "\n",
        "    model.lm_head = CastOutputToFloat(model.lm_head)\n",
        "\n",
        "# In the main script, assuming 'model' is the model instance\n",
        "prepare_model_for_finetuning(model)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwOTr7B3NlM3"
      },
      "source": [
        "## LoRA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4W1j6lxaNnxC"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "    f\"trainable params: {trainable_params} || \"\n",
        "    f\"all params: {all_param} || \"\n",
        "    f\"trainable%: {100 * trainable_params / all_param}\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iwHGzKBN6wk",
        "outputId": "22188e13-df92-4646-fac6-00900686fb89"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\", \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdjWif4CVXR6"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQ_HCYruWIHU"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from datasets import load_dataset\n",
        "data = load_dataset(\"Abirate/english_quotes\")\n",
        "\n",
        "data = data.map(lambda samples: tokenizer(samples['quote']), batched=True)\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data['train'],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=100,\n",
        "        max_steps=200,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir='outputs'\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "DpYr24pR8T_0",
        "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
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      "outputs": [],
      "source": [
        "model.push_to_hub(\"username/opt-6.7b-lora\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "collapsed": true,
        "id": "hsD1VKqeA62Z",
        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
      },
      "outputs": [],
      "source": [
        "peft_model_id = \"username/opt-6.7b-lora\"\n",
        "\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHYljmTjj5wX"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def generate_text(input_text, model, tokenizer, max_new_tokens=50):\n",
        "    batch = tokenizer(input_text, return_tensors='pt')\n",
        "\n",
        "    with torch.cuda.amp.autocast():\n",
        "        output_tokens = model.generate(**batch, max_new_tokens=max_new_tokens)\n",
        "\n",
        "    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
        "\n",
        "# Example usage\n",
        "input_text = \"The AI is coming for the  \"\n",
        "output_text = generate_text(input_text, model, tokenizer)\n",
        "print(output_text)\n"
      ]
    }
  ],
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