01_Efficiently_train_Large_Language_Models_with_LoRA_and_HF.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/llms/blob/main/1_Efficiently_train_Large_Language_Models_with_LoRA_and_Hugging_Face.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "### 1. Setup Development Environment\n",
        "In our example, we use the [PyTorch Deep Learning AMI](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-pytorch.html) with already set up CUDA drivers and PyTorch installed. We still have to install the Hugging Face Libraries, including transformers and datasets. Running the following cell will install all the required packages."
      ]
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      "source": [
        "# install Hugging Face Libraries\n",
        "!pip install \"peft==0.2.0\"\n",
        "!pip install \"transformers==4.27.2\" \"datasets==2.9.0\" \"accelerate==0.17.1\" \"evaluate==0.4.0\" \"bitsandbytes==0.37.1\" loralib --upgrade --quiet\n",
        "# install additional dependencies needed for training\n",
        "!pip install rouge-score tensorboard py7zr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_s4srVwa7oI"
      },
      "source": [
        "### 2. Load and prepare the dataset\n",
        "we will use the [samsum](https://huggingface.co/datasets/samsum) dataset, a collection of about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English."
      ]
    },
    {
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      "source": [
        "Dataset :\n",
        "```json\n",
        "{\n",
        "  \"id\": \"13818513\",\n",
        "  \"summary\": \"Amanda baked cookies and will bring Jerry some tomorrow.\",\n",
        "  \"dialogue\": \"Amanda: I baked cookies. Do you want some?\\r\\nJerry: Sure!\\r\\nAmanda: I'll bring you tomorrow :-)\"\n",
        "}\n",
        "```\n",
        "\n",
        "To load the samsum dataset, we use the load_dataset() method from the 🤗 Datasets library."
      ]
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            "Downloading and preparing dataset samsum/samsum to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e...\n"
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          "data": {
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          "text": [
            "Dataset samsum downloaded and prepared to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e. Subsequent calls will reuse this data.\n"
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          "text": [
            "Train dataset size: 14732\n",
            "Test dataset size: 819\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"samsum\")\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 14732\n",
        "# Test dataset size: 819"
      ]
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      "source": [
        "To train our model, we need to convert our inputs (text) to token IDs. This is done by a 🤗 Transformers Tokenizer. If you are not sure what this means, check out [chapter 6](https://huggingface.co/course/chapter6/1?fw=tf) of the Hugging Face Course."
      ]
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      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id=\"google/flan-t5-xxl\"\n",
        "\n",
        "# Load tokenizer of FLAN-t5-XL\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ]
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      "source": [
        "Before we can start training, we need to preprocess our data. Abstractive Summarization is a text-generation task. Our model will take a text as input and generate a summary as output. We want to understand how long our input and output will take to batch our data efficiently."
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 50\n"
          ]
        }
      ],
      "source": [
        "from datasets import concatenate_datasets\n",
        "import numpy as np\n",
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"dialogue\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n",
        "input_lenghts = [len(x) for x in tokenized_inputs[\"input_ids\"]]\n",
        "# take 85 percentile of max length for better utilization\n",
        "max_source_length = int(np.percentile(input_lenghts, 85))\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"summary\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n",
        "target_lenghts = [len(x) for x in tokenized_targets[\"input_ids\"]]\n",
        "# take 90 percentile of max length for better utilization\n",
        "max_target_length = int(np.percentile(target_lenghts, 90))\n",
        "print(f\"Max target length: {max_target_length}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0llAPTDXb8mv"
      },
      "source": [
        "We preprocess our dataset before training and save it to disk. You could run this step on your local machine or a CPU and upload it to the [HuggingFaceHub](https://huggingface.co/docs/hub/datasets-overview)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/14732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Saving the dataset (0/1 shards):   0%|          | 0/819 [00:00<?, ? examples/s]"
            ],
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              "model_id": "84735a39e02d4413b2c33112b426f78d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"summarize: \" + item for item in sample[\"dialogue\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"summary\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=[\"dialogue\", \"summary\", \"id\"])\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")\n",
        "\n",
        "# save datasets to disk for later easy loading\n",
        "tokenized_dataset[\"train\"].save_to_disk(\"data/train\")\n",
        "tokenized_dataset[\"test\"].save_to_disk(\"data/eval\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnX--IxIcD5a"
      },
      "source": [
        "### 3. Fine-Tune T5 with LoRA and bnb int-8\n",
        "\n",
        "In addition to the LoRA technique, we will use [bitsanbytes.LLM.int8()](https://huggingface.co/blog/hf-bitsandbytes-integration)  to quantize out frozen LLM to int8. This allows us to reduce the needed memory for FLAN-T5 XXL ~4x.\n",
        "\n",
        "The first step of our training is to load the model. We are going to use [philschmid/flan-t5-xxl-sharded-fp16](https://huggingface.co/philschmid/flan-t5-xxl-sharded-fp16), which is a sharded version of [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl). The sharding will help us to not run off of memory when loading the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ABb1yRDQcBkv"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "# huggingface hub model id\n",
        "model_id = \"philschmid/flan-t5-xxl-sharded-fp16\"\n",
        "\n",
        "# load model from the hub\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map=\"auto\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCe0U1yUcml5"
      },
      "source": [
        "Now, we can prepare our model for the LoRA int-8 training using `peft`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YOz5D6jfcglr"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType\n",
        "\n",
        "# Define LoRA Config\n",
        "lora_config = LoraConfig(\n",
        " r=16,\n",
        " lora_alpha=32,\n",
        " target_modules=[\"q\", \"v\"],\n",
        " lora_dropout=0.05,\n",
        " bias=\"none\",\n",
        " task_type=TaskType.SEQ_2_SEQ_LM\n",
        ")\n",
        "# prepare int-8 model for training\n",
        "model = prepare_model_for_int8_training(model)\n",
        "\n",
        "# add LoRA adaptor\n",
        "model = get_peft_model(model, lora_config)\n",
        "model.print_trainable_parameters()\n",
        "\n",
        "# trainable params: 18874368 || all params: 11154206720 || trainable%: 0.16921300163961817"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_5a798actse"
      },
      "source": [
        "As you can see, here we are only training 0.16% of the parameters of the model! This huge memory gain will enable us to fine-tune the model without memory issues.\n",
        "\n",
        "Next is to create a `DataCollator` that will take care of padding our inputs and labels. We will use the `DataCollatorForSeq2Seq` from the 🤗 Transformers library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fjtBoOaDcpbq"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "# we want to ignore tokenizer pad token in the loss\n",
        "label_pad_token_id = -100\n",
        "# Data collator\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer,\n",
        "    model=model,\n",
        "    label_pad_token_id=label_pad_token_id,\n",
        "    pad_to_multiple_of=8\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWDjJ3hKc3_m"
      },
      "source": [
        "The last step is to define the hyperparameters `(TrainingArguments)` we want to use for our training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dGhpERc8c2mp"
      },
      "outputs": [],
      "source": [
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "\n",
        "output_dir=\"lora-flan-t5-xxl\"\n",
        "\n",
        "# Define training args\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "\tauto_find_batch_size=True,\n",
        "    learning_rate=1e-3, # higher learning rate\n",
        "    num_train_epochs=5,\n",
        "    logging_dir=f\"{output_dir}/logs\",\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_steps=500,\n",
        "    save_strategy=\"no\",\n",
        "    report_to=\"tensorboard\",\n",
        ")\n",
        "\n",
        "# Create Trainer instance\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-93Xyb4dV-r"
      },
      "source": [
        "Let's now train our model and run the cells below. Note that for T5, some layers are kept in `float32` for stability purposes.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8iYaXCRkdbox"
      },
      "outputs": [],
      "source": [
        "# train model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RERkk6cCelb1"
      },
      "source": [
        "For comparison a full [fine-tuning on FLAN-T5-XXL](https://www.philschmid.de/fine-tune-flan-t5-deepspeed#3-results--experiments) with the same duration 10h requires 8x A100 40GBs and costs ~322$.\n",
        "\n",
        "We can save our model to use it for inference and evaluate it. We will save it to disk for now, but you could also upload it to the [Hugging Face Hub](https://huggingface.co/docs/hub/main) using the model.push_to_hub method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZrKOWJ40d6RQ"
      },
      "outputs": [],
      "source": [
        "# Save our LoRA model & tokenizer results\n",
        "peft_model_id=\"results\"\n",
        "trainer.model.save_pretrained(peft_model_id)\n",
        "tokenizer.save_pretrained(peft_model_id)\n",
        "# if you want to save the base model to call\n",
        "# trainer.model.base_model.save_pretrained(peft_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlBfJNZrfbrH"
      },
      "source": [
        "Our LoRA checkpoint is only 84MB small and includes all of the learnt knowleddge for samsum."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9bmu_s4fej3"
      },
      "source": [
        "### 4. Evaluate & run Inference with LoRA FLAN-T5  \n",
        "We are going to use `evaluate` library to evaluate the `rogue` score. We can run inference using `PEFT` and `transformers`. For our FLAN-T5 XXL model, we need at least 18GB of GPU memory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gw8GrAa4fcAQ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "# Load peft config for pre-trained checkpoint etc.\n",
        "peft_model_id = \"results\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "# load base LLM model and tokenizer\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path,  load_in_8bit=True,  device_map={\"\":0})\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, device_map={\"\":0})\n",
        "model.eval()\n",
        "\n",
        "print(\"Peft model loaded\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XGJ5QG2frXZ"
      },
      "source": [
        "Let’s load the dataset again with a random sample to try the summarization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T3eOOkk8frqa"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "from random import randrange\n",
        "\n",
        "\n",
        "# Load dataset from the hub and get a sample\n",
        "dataset = load_dataset(\"samsum\")\n",
        "sample = dataset['test'][randrange(len(dataset[\"test\"]))]\n",
        "\n",
        "input_ids = tokenizer(sample[\"dialogue\"], return_tensors=\"pt\", truncation=True).input_ids.cuda()\n",
        "# with torch.inference_mode():\n",
        "outputs = model.generate(input_ids=input_ids, max_new_tokens=10, do_sample=True, top_p=0.9)\n",
        "print(f\"input sentence: {sample['dialogue']}\\n{'---'* 20}\")\n",
        "\n",
        "print(f\"summary:\\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxHH7xIefwHO"
      },
      "source": [
        "Nice! our model works! Now, lets take a closer look and evaluate it against the test set of processed dataset from samsum. Therefore we need to use and create some utilities to generate the summaries and group them together. The most commonly used metrics to evaluate summarization task is [rogue_score](https://en.wikipedia.org/wiki/ROUGE_(metric) short for Recall-Oriented Understudy for Gisting Evaluation). This metric does not behave like the standard accuracy: it will compare a generated summary against a set of reference summaries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tuxt83Ydfs9B"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "import numpy as np\n",
        "from datasets import load_from_disk\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Metric\n",
        "metric = evaluate.load(\"rouge\")\n",
        "\n",
        "def evaluate_peft_model(sample,max_target_length=50):\n",
        "    # generate summary\n",
        "    outputs = model.generate(input_ids=sample[\"input_ids\"].unsqueeze(0).cuda(), do_sample=True, top_p=0.9, max_new_tokens=max_target_length)\n",
        "    prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)\n",
        "    # decode eval sample\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(sample['labels'] != -100, sample['labels'], tokenizer.pad_token_id)\n",
        "    labels = tokenizer.decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # Some simple post-processing\n",
        "    return prediction, labels\n",
        "\n",
        "# load test dataset from distk\n",
        "test_dataset = load_from_disk(\"data/eval/\").with_format(\"torch\")\n",
        "\n",
        "# run predictions\n",
        "# this can take ~45 minutes\n",
        "predictions, references = [] , []\n",
        "for sample in tqdm(test_dataset):\n",
        "    p,l = evaluate_peft_model(sample)\n",
        "    predictions.append(p)\n",
        "    references.append(l)\n",
        "\n",
        "# compute metric\n",
        "rogue = metric.compute(predictions=predictions, references=references, use_stemmer=True)\n",
        "\n",
        "# print results\n",
        "print(f\"Rogue1: {rogue['rouge1']* 100:2f}%\")\n",
        "print(f\"rouge2: {rogue['rouge2']* 100:2f}%\")\n",
        "print(f\"rougeL: {rogue['rougeL']* 100:2f}%\")\n",
        "print(f\"rougeLsum: {rogue['rougeLsum']* 100:2f}%\")\n",
        "\n",
        "# Rogue1: 50.386161%\n",
        "# rouge2: 24.842412%\n",
        "# rougeL: 41.370130%\n",
        "# rougeLsum: 41.394230%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOcHVuDvf6H4"
      },
      "source": [
        "Our PEFT fine-tuned FLAN-T5-XXL achieved a rogue1 score of 50.38% on the test dataset. For comparison a [full fine-tuning of flan-t5-base achieved a rouge1 score of 47.23](https://www.philschmid.de/fine-tune-flan-t5). That is a 3% improvements.\n",
        "\n",
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