gpt-neo-bnb_4bit_training_with_inference.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIyP_0r6zuVc"
      },
      "source": [
        "# `Bitsandbytes` Large Language Models (LLMs) through 4bit quantization\n",
        "\n",
        "Welcome to this notebook that goes through the recent `bitsandbytes` integration that introduces no performance degradation 4bit quantization techniques, for democratizing LLMs inference and training.\n",
        "\n",
        "In this notebook, we will learn together how to load a large model in 4bit (`gpt-neo-x-20b`) and train it using Google Colab and PEFT library from Hugging Face 🤗.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FuXIFTFapAMI",
        "outputId": "69ffc5d7-cd46-424f-f262-d6ad41bc3f60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m105.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m79.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m219.1/219.1 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m69.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJ-5idQwzvg-"
      },
      "source": [
        "First let's load the model we are going to use - GPT-neo-x-20B! Note that the model itself is around 40GB in half precision\n"
      ]
    },
    {
      "cell_type": "code",
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so.11.0\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-1efnc3smi84id --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "data": {
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              "model_id": "9196d4b349e6476499c120b11f1607fa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/46 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "model_id = \"EleutherAI/gpt-neox-20b\"\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mp2gMi1ZzGET"
      },
      "source": [
        "Then we have to apply some preprocessing to the model to prepare it for training. For that use the `prepare_model_for_kbit_training` method from PEFT.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a9EUEDAl0ss3"
      },
      "outputs": [],
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gkIcwsSU01EB"
      },
      "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} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ybeyl20n3dYH",
        "outputId": "43219a92-11c7-4269-f651-fd1618f2e3cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 8650752 || all params: 10597552128 || trainable%: 0.08162971878329976\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"query_key_value\"],\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": "FCc64bfnmd3j"
      },
      "source": [
        "Let's load a common dataset, english quotes, to fine tune our model on famous quotes.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "s6f4z8EYmcJ6",
        "outputId": "08b5e6ee-96ec-4383-ccec-b7b66f1ce126"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66d4e1db7ffb4ddfa44751146941499d",
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset json/Abirate--english_quotes to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n"
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              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.\n"
          ]
        },
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              "model_id": "60acefedfd7a46719945db3ad75c3dad",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
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          "output_type": "display_data"
        },
        {
          "data": {
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            },
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"Abirate/english_quotes\")\n",
        "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0MOtwf3zdZp"
      },
      "source": [
        "Run the cell below to run the training! For the sake of the demo, we just ran it for few steps just to showcase how to use this integration with existing tools on the HF ecosystem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "jq0nX33BmfaC",
        "outputId": "93fccb62-5c55-4c23-91f8-8470e0f32176"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a GPTNeoXTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/gpt_neox/modeling_gpt_neox.py:229: UserWarning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (Triggered internally at ../aten/src/ATen/native/TensorCompare.cpp:493.)\n",
            "  attn_scores = torch.where(causal_mask, attn_scores, mask_value)\n"
          ]
        },
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10/10 02:30, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.382300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.283100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.303600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.823700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.639000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.217600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.300100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.480500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.447800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.466000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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            ]
          },
          "metadata": {},
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        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10, training_loss=2.434360909461975, metrics={'train_runtime': 169.887, 'train_samples_per_second': 0.235, 'train_steps_per_second': 0.059, 'total_flos': 99255709532160.0, 'train_loss': 2.434360909461975, 'epoch': 0.02})"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import transformers\n",
        "\n",
        "# needed for gpt-neo-x tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data[\"train\"],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=2,\n",
        "        max_steps=10,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\"\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": {
        "id": "p66mZk1RAlOR"
      },
      "outputs": [],
      "source": [
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n",
        "model_to_save.save_pretrained(\"outputs\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L2Hllu-bCuN6"
      },
      "outputs": [],
      "source": [
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
        "model = get_peft_model(model, lora_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1TiIH6vAlr_",
        "outputId": "0aa79907-70a3-44dd-d298-c2a375f5a4a0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Elon Musk \n",
            "Elon Musk is a South African-born Canadian-American business magnate, investor, engineer\n"
          ]
        }
      ],
      "source": [
        "text = \"Elon Musk \"\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=20)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ]
    }
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