AutoTrain_LLM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/a-ml/onzo/blob/master/AutoTrain_LLM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "id": "JvMRbVLEJlZT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0aea94ec-b38b-4933-ad34-cdcbc8098c1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104cuda9SetDeviceEi'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
            "  warn(\n",
            "> \u001b[1mINFO    Installing latest xformers\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest xformers\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "#@title 🤗 AutoTrain LLM\n",
        "#@markdown In order to use this colab\n",
        "#@markdown - upload train.csv to a folder named `data/`\n",
        "#@markdown - train.csv must contain a `text` column\n",
        "#@markdown - choose a project name if you wish\n",
        "#@markdown - change model if you wish, you can use most of the text-generation models from Hugging Face Hub\n",
        "#@markdown - add huggingface information (token and repo_id) if you wish to push trained model to huggingface hub\n",
        "#@markdown - update hyperparameters if you wish\n",
        "#@markdown - click `Runtime > Run all` or run each cell individually\n",
        "#@markdown - report issues / feature requests here: https://github.com/huggingface/autotrain-advanced/issues\n",
        "\n",
        "import os\n",
        "!pip install -U autotrain-advanced > install_logs.txt\n",
        "!autotrain setup --colab > setup_logs.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "A2-_lkBS1WKA",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@markdown ---\n",
        "#@markdown #### Project Config\n",
        "#@markdown Note: if you are using a restricted/private model, you need to enter your Hugging Face token in the next step.\n",
        "project_name = 'tiny_llama_ao' # @param {type:\"string\"}\n",
        "model_name = 'abhishek/llama-2-7b-hf-small-shards' # @param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Push to Hub?\n",
        "#@markdown Use these only if you want to push your trained model to a private repo in your Hugging Face Account\n",
        "#@markdown If you dont use these, the model will be saved in Google Colab and you are required to download it manually.\n",
        "#@markdown Please enter your Hugging Face write token. The trained model will be saved to your Hugging Face account.\n",
        "#@markdown You can find your token here: https://huggingface.co/settings/tokens\n",
        "push_to_hub = False # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "hf_token = \"hf_XXX\" #@param {type:\"string\"}\n",
        "repo_id = \"kapenge/tiny_llama_ao\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Hyperparameters\n",
        "learning_rate = 2e-4 # @param {type:\"number\"}\n",
        "num_epochs = 10 #@param {type:\"number\"}\n",
        "batch_size = 4 # @param {type:\"slider\", min:1, max:32, step:1}\n",
        "block_size = 1024 # @param {type:\"number\"}\n",
        "trainer = \"sft\" # @param [\"default\", \"sft\"] {type:\"raw\"}\n",
        "warmup_ratio = 0.1 # @param {type:\"number\"}\n",
        "weight_decay = 0.01 # @param {type:\"number\"}\n",
        "gradient_accumulation = 4 # @param {type:\"number\"}\n",
        "mixed_precision = \"fp16\" # @param [\"fp16\", \"bf16\", \"none\"] {type:\"raw\"}\n",
        "peft = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "quantization = \"int4\" # @param [\"int4\", \"int8\", \"none\"] {type:\"raw\"}\n",
        "lora_r = 16 #@param {type:\"number\"}\n",
        "lora_alpha = 32 #@param {type:\"number\"}\n",
        "lora_dropout = 0.05 #@param {type:\"number\"}\n",
        "\n",
        "os.environ[\"PROJECT_NAME\"] = project_name\n",
        "os.environ[\"MODEL_NAME\"] = model_name\n",
        "os.environ[\"PUSH_TO_HUB\"] = str(push_to_hub)\n",
        "os.environ[\"HF_TOKEN\"] = hf_token\n",
        "os.environ[\"REPO_ID\"] = repo_id\n",
        "os.environ[\"LEARNING_RATE\"] = str(learning_rate)\n",
        "os.environ[\"NUM_EPOCHS\"] = str(num_epochs)\n",
        "os.environ[\"BATCH_SIZE\"] = str(batch_size)\n",
        "os.environ[\"BLOCK_SIZE\"] = str(block_size)\n",
        "os.environ[\"WARMUP_RATIO\"] = str(warmup_ratio)\n",
        "os.environ[\"WEIGHT_DECAY\"] = str(weight_decay)\n",
        "os.environ[\"GRADIENT_ACCUMULATION\"] = str(gradient_accumulation)\n",
        "os.environ[\"MIXED_PRECISION\"] = str(mixed_precision)\n",
        "os.environ[\"PEFT\"] = str(peft)\n",
        "os.environ[\"QUANTIZATION\"] = str(quantization)\n",
        "os.environ[\"LORA_R\"] = str(lora_r)\n",
        "os.environ[\"LORA_ALPHA\"] = str(lora_alpha)\n",
        "os.environ[\"LORA_DROPOUT\"] = str(lora_dropout)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "id": "g3cd_ED_yXXt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acb1737b-e54d-454b-ac82-0ccbb77576c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104cuda9SetDeviceEi'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
            "  warn(\n",
            "> \u001b[1mINFO    Running LLM\u001b[0m\n",
            "> \u001b[1mINFO    Params: Namespace(version=False, text_column='text', rejected_text_column='rejected', prompt_text_column='prompt', model_ref=None, warmup_ratio=0.1, optimizer='adamw_torch', scheduler='linear', weight_decay=0.01, max_grad_norm=1.0, add_eos_token=False, block_size=1024, peft=True, lora_r=16, lora_alpha=32, lora_dropout=0.05, logging_steps=-1, evaluation_strategy='epoch', save_total_limit=1, save_strategy='epoch', auto_find_batch_size=False, mixed_precision='fp16', quantization='int4', model_max_length=1024, trainer='default', target_modules=None, merge_adapter=False, use_flash_attention_2=False, dpo_beta=0.1, apply_chat_template=False, padding=None, train=True, deploy=False, inference=False, username=None, backend='local-cli', token=None, repo_id=None, push_to_hub=False, model='abhishek/llama-2-7b-hf-small-shards', project_name='tiny_llama_ao', seed=42, epochs=10, gradient_accumulation=4, disable_gradient_checkpointing=False, lr=0.0002, log='none', data_path='data/', train_split='train', valid_split=None, batch_size=4, func=<function run_llm_command_factory at 0x7ea4d21e9a20>)\u001b[0m\n",
            "> \u001b[1mINFO    Dataset: tiny_llama_ao (lm_training)\n",
            "Train data: ['data//train.csv']\n",
            "Valid data: []\n",
            "Column mapping: {'text': 'text', 'rejected_text': 'rejected', 'prompt': 'prompt'}\n",
            "\u001b[0m\n",
            "\rSaving the dataset (0/1 shards):   0% 0/89 [00:00<?, ? examples/s]\rSaving the dataset (1/1 shards): 100% 89/89 [00:00<00:00, 7959.85 examples/s]\rSaving the dataset (1/1 shards): 100% 89/89 [00:00<00:00, 7852.85 examples/s]\n",
            "\rSaving the dataset (0/1 shards):   0% 0/89 [00:00<?, ? examples/s]\rSaving the dataset (1/1 shards): 100% 89/89 [00:00<00:00, 41680.78 examples/s]\rSaving the dataset (1/1 shards): 100% 89/89 [00:00<00:00, 39451.81 examples/s]\n",
            "> \u001b[1mINFO    Starting local training...\u001b[0m\n",
            "> \u001b[1mINFO    {\"model\":\"abhishek/llama-2-7b-hf-small-shards\",\"project_name\":\"tiny_llama_ao\",\"data_path\":\"tiny_llama_ao/autotrain-data\",\"train_split\":\"train\",\"valid_split\":null,\"add_eos_token\":false,\"block_size\":1024,\"model_max_length\":1024,\"padding\":null,\"trainer\":\"default\",\"use_flash_attention_2\":false,\"log\":\"none\",\"disable_gradient_checkpointing\":false,\"logging_steps\":-1,\"evaluation_strategy\":\"epoch\",\"save_total_limit\":1,\"save_strategy\":\"epoch\",\"auto_find_batch_size\":false,\"mixed_precision\":\"fp16\",\"lr\":0.0002,\"epochs\":10,\"batch_size\":4,\"warmup_ratio\":0.1,\"gradient_accumulation\":4,\"optimizer\":\"adamw_torch\",\"scheduler\":\"linear\",\"weight_decay\":0.01,\"max_grad_norm\":1.0,\"seed\":42,\"apply_chat_template\":false,\"quantization\":\"int4\",\"target_modules\":null,\"merge_adapter\":false,\"peft\":true,\"lora_r\":16,\"lora_alpha\":32,\"lora_dropout\":0.05,\"model_ref\":null,\"dpo_beta\":0.1,\"prompt_text_column\":\"autotrain_prompt\",\"text_column\":\"autotrain_text\",\"rejected_text_column\":\"autotrain_rejected_text\",\"push_to_hub\":false,\"repo_id\":null,\"username\":null,\"token\":null}\u001b[0m\n",
            "> \u001b[1mINFO    ['accelerate', 'launch', '--num_machines', '1', '--num_processes', '1', '--mixed_precision', 'fp16', '-m', 'autotrain.trainers.clm', '--training_config', 'tiny_llama_ao/training_params.json']\u001b[0m\n",
            "The following values were not passed to `accelerate launch` and had defaults used instead:\n",
            "\t`--dynamo_backend` was set to a value of `'no'`\n",
            "To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
            "  warnings.warn(\n",
            "\u001b[1m🚀 INFO  \u001b[0m | \u001b[32m2024-01-19 20:49:43\u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mprocess_input_data\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mloading dataset from disk\u001b[0m\n",
            "\u001b[1m🚀 INFO  \u001b[0m | \u001b[32m2024-01-19 20:49:43\u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mprocess_input_data\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mTrain data: Dataset({\n",
            "    features: ['Concept', 'Description', 'autotrain_text'],\n",
            "    num_rows: 89\n",
            "})\u001b[0m\n",
            "\u001b[1m🚀 INFO  \u001b[0m | \u001b[32m2024-01-19 20:49:43\u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mprocess_input_data\u001b[0m:\u001b[36m83\u001b[0m - \u001b[1mValid data: None\u001b[0m\n",
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            "Downloading shards: 100% 10/10 [03:44<00:00, 22.45s/it]\n",
            "Loading checkpoint shards: 100% 10/10 [02:40<00:00, 16.01s/it]\n",
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            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:389: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:394: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "\u001b[1m🚀 INFO  \u001b[0m | \u001b[32m2024-01-19 20:56:12\u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m277\u001b[0m - \u001b[1mUsing block size 1024\u001b[0m\n",
            "Running tokenizer on train dataset: 100% 89/89 [00:00<00:00, 703.94 examples/s]\n",
            "Grouping texts in chunks of 1024 (num_proc=4): 100% 89/89 [00:00<00:00, 271.51 examples/s]\n",
            "\u001b[1m🚀 INFO  \u001b[0m | \u001b[32m2024-01-19 20:56:13\u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m339\u001b[0m - \u001b[1mcreating trainer\u001b[0m\n",
            "{'loss': 0.6283, 'learning_rate': 0.0002, 'epoch': 1.0}\n",
            "{'loss': 0.6283, 'learning_rate': 0.00017777777777777779, 'epoch': 2.0}\n",
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            "{'loss': 0.4963, 'learning_rate': 0.0, 'epoch': 10.0}\n",
            "{'train_runtime': 116.3005, 'train_samples_per_second': 0.258, 'train_steps_per_second': 0.086, 'train_loss': 0.5527833789587021, 'epoch': 10.0}\n",
            "100% 10/10 [01:56<00:00, 11.63s/it]\n",
            "\u001b[1m🚀 INFO  \u001b[0m | \u001b[32m2024-01-19 20:58:09\u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m477\u001b[0m - \u001b[1mFinished training, saving model...\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!autotrain llm \\\n",
        "--train \\\n",
        "--model ${MODEL_NAME} \\\n",
        "--project-name ${PROJECT_NAME} \\\n",
        "--data-path data/ \\\n",
        "--text-column text \\\n",
        "--lr ${LEARNING_RATE} \\\n",
        "--batch-size ${BATCH_SIZE} \\\n",
        "--epochs ${NUM_EPOCHS} \\\n",
        "--block-size ${BLOCK_SIZE} \\\n",
        "--warmup-ratio ${WARMUP_RATIO} \\\n",
        "--lora-r ${LORA_R} \\\n",
        "--lora-alpha ${LORA_ALPHA} \\\n",
        "--lora-dropout ${LORA_DROPOUT} \\\n",
        "--weight-decay ${WEIGHT_DECAY} \\\n",
        "--gradient-accumulation ${GRADIENT_ACCUMULATION} \\\n",
        "--quantization ${QUANTIZATION} \\\n",
        "--mixed-precision ${MIXED_PRECISION} \\\n",
        "$( [[ \"$PEFT\" == \"True\" ]] && echo \"--peft\" ) \\\n",
        "$( [[ \"$PUSH_TO_HUB\" == \"True\" ]] && echo \"--push-to-hub --token ${HF_TOKEN} --repo-id ${REPO_ID}\" )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from torch.nn import DataParallel\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"/content/tiny_llama_ao\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"/content/tiny_llama_ao\")\n",
        "\n",
        "#model.cuda(\"cuda\")\n",
        "#model = DataParallel(model)\n",
        "\n",
        "input_context = '''\n",
        "###Human:\n",
        "Explain Sonangol revenue.\n",
        "\n",
        "###Assistant:\n",
        "'''\n",
        "\n",
        "input_ids = tokenizer.encode(input_context, return_tensors=\"pt\")\n",
        "output = model.generate(input_ids, max_length=85, temperature=0.3, num_return_sequences=1)\n",
        "\n",
        "generated_text = tokenizer.decode(output[0], skip_special_tokenz=True)\n",
        "\n",
        "print(generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151,
          "referenced_widgets": [
            "78cb6cc40e8b46248e94aa217b66c2b0",
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            "fb8f5e663883437c8bbc4588a16be016",
            "69118cfba4ef48f5af706702bdebb55c",
            "4b75eeb33d044de085d29ca5d61c4dba",
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            "6f0bf62989004235b89c850205b1c471"
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        "id": "d7pkrk2dhac4",
        "outputId": "680b5cbb-f057-430e-9d45-c9823fcfd2a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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  ],
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    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "include_colab_link": true
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    "kernelspec": {
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