Falcon7bFineTuning.ipynb

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        "!pip install -q -U git+https://github.com/PhilDakin/trl.git \\\n",
        "                   git+https://github.com/huggingface/transformers.git \\\n",
        "                   git+https://github.com/huggingface/peft.git\n",
        "\n",
        "!pip install -q datasets \\\n",
        "                bitsandbytes \\\n",
        "                einops \\\n",
        "                wandb\n",
        "\n",
        "# https://github.com/huggingface/transformers/issues/23935#issuecomment-1593591082\n",
        "!pip install git+https://github.com/PhilDakin/accelerate.git"
      ]
    },
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        },
        "id": "y1mg40rSz7Hz",
        "outputId": "f9c90868-882d-403d-cde9-c6daf6cd0f13"
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      "outputs": [],
      "source": [
        "# Direct HF cache to Drive location s.t. models persist across instances.\n",
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "drive.mount('/drive')\n",
        "os.environ[\"HF_HOME\"] = '/drive/MyDrive/HFCache'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0X3kHnskSWU4"
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      "source": [
        "from datasets import load_from_disk, Dataset, concatenate_datasets\n",
        "\n",
        "TRAINING_TASKS = [\"extract\", \"rank\", \"rewrite\"]\n",
        "\n",
        "def get_dataset(task):\n",
        "  data = load_from_disk(f'/drive/MyDrive/Colab Datasets/summarization/split_datasets/{task}')\n",
        "  def add_task(r):\n",
        "    r['task'] = task\n",
        "    return r\n",
        "  return data.map(add_task)\n",
        "\n",
        "def add_text_if_required(record):\n",
        "  if record['text'] is None:\n",
        "    record['text'] = record['prompt'] + record['result']\n",
        "  return record\n",
        "\n",
        "dataset = concatenate_datasets([full_dataset['train'] for full_dataset in all_datasets.values()]).map(add_text_if_required)\n",
        "eval_dataset = concatenate_datasets([full_dataset['test'].select(range(100)) for full_dataset in all_datasets.values()]).map(add_text_if_required)"
      ]
    },
    {
      "cell_type": "code",
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        "id": "6gcaasPoJc2E"
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      "source": [
        "# Hack! Falcon tokenizer will not add EOS token automatically, so add it manually here.\n",
        "def add_eos_token(record):\n",
        "  record[\"text\"] += ' <|endoftext|>'\n",
        "  return record\n",
        "\n",
        "dataset = dataset.map(add_eos_token)\n",
        "eval_dataset = eval_dataset.map(add_eos_token)"
      ]
    },
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      "source": [
        "TRAINING_MAX_SEQUENCE_LENGTH = 1800\n",
        "TRAINING_MAX_SEQUENCE_LENGTH_CHARS = int(TRAINING_MAX_SEQUENCE_LENGTH * 3.75) # Get a tight upper bound.\n",
        "\n",
        "# Validate train and test dataset for sequence length constraints.\n",
        "\n",
        "def get_invalid_proportion(dataset: Dataset) -> float:\n",
        "  enriched = dataset.map(add_length)\n",
        "  invalid = enriched.filter(lambda record : record[\"length\"] > TRAINING_MAX_SEQUENCE_LENGTH_CHARS)\n",
        "  return len(invalid) / len(dataset)\n",
        "\n",
        "def add_length(record):\n",
        "  record[\"length\"] = len(record[\"text\"])\n",
        "  return record\n",
        "\n",
        "def filter_to_valid(dataset: Dataset) -> Dataset:\n",
        "  enriched = dataset.map(add_length)\n",
        "  valid = enriched.filter(lambda record : record[\"length\"] < TRAINING_MAX_SEQUENCE_LENGTH_CHARS)\n",
        "  valid = valid.remove_columns(\"length\")\n",
        "  return valid\n",
        "\n",
        "get_invalid_proportion(dataset), get_invalid_proportion(eval_dataset)"
      ]
    },
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      "outputs": [],
      "source": [
        "dataset = filter_to_valid(dataset)\n",
        "eval_dataset = filter_to_valid(eval_dataset)\n",
        "\n",
        "get_invalid_proportion(dataset), get_invalid_proportion(eval_dataset)"
      ]
    },
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      "source": [
        "import pandas as pd\n",
        "\n",
        "# Reduce training data bias towards rewrite a little bit.\n",
        "dataset = dataset.select(range(4800))\n",
        "pd.Series(dataset['task']).value_counts()"
      ]
    },
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      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"ybelkada/falcon-7b-sharded-bf16\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XDS2yYmlUAD6"
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "\n",
        "# Hack - see https://github.com/huggingface/transformers/issues/22794#issuecomment-1598977285.\n",
        "# Use a model-defined special token that is unlikely to appear in our training data,\n",
        "# in order to force the model to learn the EOS token.\n",
        "tokenizer.pad_token = '>>TITLE<<'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQdvjTYTT1vQ"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\n",
        "        \"query_key_value\",\n",
        "        \"dense\",\n",
        "        \"dense_h_to_4h\",\n",
        "        \"dense_4h_to_h\",\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OCFTvGW6aspE"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 4\n",
        "gradient_accumulation_steps = 4\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 50\n",
        "logging_steps = 10\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "max_steps = 1250\n",
        "\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    fp16=True,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNeOBgZeTl2H",
        "outputId": "f039b558-de1f-4d73-d274-a709a8b9228d"
      },
      "outputs": [],
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = TRAINING_MAX_SEQUENCE_LENGTH\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDsPsv2C17A_",
        "outputId": "cedb1345-dafd-4b41-d199-71908ff39c35"
      },
      "outputs": [],
      "source": [
        "trainer.eval_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiaK6-n5rm--",
        "outputId": "3f678d9d-c083-4677-d41b-b2d17b46115d"
      },
      "outputs": [],
      "source": [
        "trainer.train_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7OyIvEx7b1GT"
      },
      "outputs": [],
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "0BxBiYsDUOhJ",
        "outputId": "9b8399c8-b217-402d-b951-4528b988bd09"
      },
      "outputs": [],
      "source": [
        "pre_training_evaluation = trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_kbS7nRxcMt7",
        "outputId": "af9c36c7-4cc6-4a46-ea63-ab570c733b2d"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "f710bcHxUQ81",
        "outputId": "e5744c8c-c161-44ac-98b1-0b5c2d4fdaa6"
      },
      "outputs": [],
      "source": [
        "post_training_evaluation = trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7yopSOhp4WJn"
      },
      "outputs": [],
      "source": [
        "# Save model.\n",
        "\n",
        "import time\n",
        "trainer.save_model(f'/drive/MyDrive/Colab Datasets/{int(time.time())}_falcon_fine_tuned')"
      ]
    },
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      "cell_type": "code",
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        },
        "id": "49qYhithUptP",
        "outputId": "0a278484-6bc3-4262-f7e6-5512c796be7b"
      },
      "outputs": [],
      "source": [
        "pre_training_evaluation"
      ]
    },
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        "colab": {
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        "outputId": "f9c87957-8acb-4e79-fdc7-9ee9f6c156aa"
      },
      "outputs": [],
      "source": [
        "post_training_evaluation"
      ]
    }
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