notebooks/finetune/Flan-T5-3B/general-tasks.ipynb

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   "metadata": {
    "id": "lw1cWgq-DI5k",
    "tags": []
   },
   "source": [
    "# Low rank finetune FLAN-T5 on general tasks: sentiment analysis and summarization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kBFPA3-aDT7H",
   "metadata": {
    "id": "kBFPA3-aDT7H"
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   "source": [
    "Fine tuning models where all its parameters are changed is expensive with very large models. For perspective, a T4 16GB GPU will barely be able to fine tune a 1B parameter model. Large models like GPT-3 have 175B and more parameters. Therefore, in this notebook we try efficient ways of adapting a model. \n",
    "\n",
    "In this notebook we will see how to use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large`. The `peft` package allows us to use low rank adaption method of training less than 1% of the original model parameters. The [paper](https://arxiv.org/abs/2106.09685) illustrates how it is comparable to a full training. \n",
    "\n",
    "More information about the implementation of `peft` can be found [here](https://github.com/huggingface/peft). This approach allows us to fine tune a 3B parameter model on a single T4 GPU.\n",
    "\n",
    "First, we will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`.\n",
    "\n",
    "Second, we will finetune the model on a specific summarization task [`samsumsam`](https://huggingface.co/datasets/samsum/viewer/samsum/train?row=0)\n",
    "\n",
    "Inspired from Sources [1](https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb) and [2](https://www.philschmid.de/fine-tune-flan-t5-peft)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ShAuuHCDDkvk",
   "metadata": {
    "id": "ShAuuHCDDkvk",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Install requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8797605e-9bc2-41d0-88eb-7f6d0a9e98ed",
   "metadata": {
    "tags": []
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     "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 /opt/app-root/lib64/python3.9/site-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\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
      "CUDA SETUP: Detected CUDA version 118\n",
      "CUDA SETUP: Loading binary /opt/app-root/lib64/python3.9/site-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib64'), PosixPath('/usr/local/nvidia/lib')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/notebook/opendatahub/jupyter-nb-shanand-40redhat-2ecom')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('image-registry.openshift-image-registry.svc'), PosixPath('py3.9-v2'), PosixPath('5000/opendatahub/jupyter-minimal-gpu-notebook')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('443'), PosixPath('tcp'), PosixPath('//172.30.0.1')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('. /opt/app-root/bin/activate')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('image')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('\"/notebookController/shanand-40redhat-2ecom\"}'), PosixPath('\"https'), PosixPath('--ServerApp.port=8888\\n                  --ServerApp.token=\\'\\'\\n                  --ServerApp.password=\\'\\'\\n                  --ServerApp.base_url=/notebook/opendatahub/jupyter-nb-shanand-40redhat-2ecom\\n                  --ServerApp.quit_button=False\\n                  --ServerApp.tornado_settings={\"user\"'), PosixPath('//odh-dashboard-opendatahub.apps.et-cluster.6mwp.p1.openshiftapps.com\",\"hub_prefix\"'), PosixPath('\"shanand-40redhat-2ecom\",\"hub_host\"')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/opt/app-root/src/foundation-models-for-documentation/notebooks/fine-tune-flan-3B.ipynb')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//matplotlib_inline.backend_inline'), PosixPath('module')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-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'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. 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"
     ]
    }
   ],
   "source": [
    "## Imports\n",
    "import os\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
    "from peft import LoraConfig, get_peft_model, TaskType\n",
    "from peft import prepare_model_for_int8_training\n",
    "from transformers import TrainingArguments, Trainer\n",
    "from transformers import DataCollatorForSeq2Seq\n",
    "from datasets import concatenate_datasets\n",
    "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Select CUDA device index\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "# Utility functions\n",
    "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": "markdown",
   "id": "2ea50db7-be68-490f-a8bb-b10b58413b94",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. Sentiment analysis task"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QBdCIrizDxFw",
   "metadata": {
    "id": "QBdCIrizDxFw"
   },
   "source": [
    "## Import model and tokenizer"
   ]
  },
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    "tags": []
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading (…)lve/main/config.json: 100%|██████████| 1.53k/1.53k [00:00<00:00, 892kB/s]\n",
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n",
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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 /opt/app-root/lib64/python3.9/site-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 /opt/app-root/lib64/python3.9/site-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib64'), PosixPath('/usr/local/nvidia/lib')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/notebook/opendatahub/jupyter-nb-shanand-40redhat-2ecom')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('image-registry.openshift-image-registry.svc'), PosixPath('py3.9-v2'), PosixPath('5000/opendatahub/jupyter-minimal-gpu-notebook')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('tcp'), PosixPath('//172.30.0.1'), PosixPath('443')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('. /opt/app-root/bin/activate')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('image')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//odh-dashboard-opendatahub.apps.et-cluster.6mwp.p1.openshiftapps.com\",\"hub_prefix\"'), PosixPath('\"/notebookController/shanand-40redhat-2ecom\"}'), PosixPath('--ServerApp.port=8888\\n                  --ServerApp.token=\\'\\'\\n                  --ServerApp.password=\\'\\'\\n                  --ServerApp.base_url=/notebook/opendatahub/jupyter-nb-shanand-40redhat-2ecom\\n                  --ServerApp.quit_button=False\\n                  --ServerApp.tornado_settings={\"user\"'), PosixPath('\"shanand-40redhat-2ecom\",\"hub_host\"'), PosixPath('\"https')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//matplotlib_inline.backend_inline')}\n",
      "  warn(msg)\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/cuda_setup/main.py:145: 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",
      "Loading checkpoint shards: 100%|██████████| 3/3 [00:07<00:00,  2.54s/it]\n",
      "Downloading (…)neration_config.json: 100%|██████████| 147/147 [00:00<00:00, 80.9kB/s]\n",
      "Downloading (…)okenizer_config.json: 100%|██████████| 2.50k/2.50k [00:00<00:00, 1.35MB/s]\n",
      "Downloading spiece.model: 100%|██████████| 792k/792k [00:00<00:00, 11.5MB/s]\n",
      "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.42M/2.42M [00:00<00:00, 112MB/s]\n",
      "Downloading (…)cial_tokens_map.json: 100%|██████████| 2.20k/2.20k [00:00<00:00, 1.67MB/s]\n"
     ]
    }
   ],
   "source": [
    "model_name = \"ybelkada/flan-t5-xl-sharded-bf16\"\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, load_in_8bit=True, device_map=\"auto\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VwcHieQzD_dl",
   "metadata": {
    "id": "VwcHieQzD_dl",
    "tags": []
   },
   "source": [
    "## Prepare model for training\n",
    "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_int8_training` that will: \n",
    "- Casts all the non `int8` modules to full precision (`fp32`) for stability\n",
    "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
    "- Enable gradient checkpointing for more memory-efficient training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17566ae3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "17566ae3",
    "outputId": "66cbd0f3-815d-4d68-c0a3-6b2e5f46b021"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 9437184 || all params: 2859194368 || trainable%: 0.33006444422319176\n"
     ]
    }
   ],
   "source": [
    "## Prepare model for training\n",
    "model = prepare_model_for_int8_training(model)\n",
    "\n",
    "lora_config = LoraConfig(\n",
    "    r=16, lora_alpha=32,\n",
    "    target_modules=[\"q\", \"v\"],\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=\"SEQ_2_SEQ_LM\"\n",
    ")\n",
    "\n",
    "# add LoRA adaptor\n",
    "model = get_peft_model(model, lora_config)\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mGkwIgNXyS7U",
   "metadata": {
    "id": "mGkwIgNXyS7U"
   },
   "source": [
    "As you can see, here we are only training 0.6% of the parameters of the model! This is a huge memory gain that will enable us to fine-tune the model without any memory issue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HsG0x6Z7FwjZ",
   "metadata": {
    "id": "HsG0x6Z7FwjZ",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Load and process data\n",
    "\n",
    "Here we will use [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset to fine-tune our model on sentiment classification on financial sentences. We will load the split `sentences_allagree`, which corresponds according to the model card to the split where there is a 100% annotator agreement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "242cdfae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297,
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     ]
    },
    "id": "242cdfae",
    "outputId": "3122a25f-4299-4c3e-cd93-3a507f9f0d91"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 6.04k/6.04k [00:00<00:00, 5.23MB/s]\n",
      "Downloading metadata: 100%|██████████| 13.7k/13.7k [00:00<00:00, 10.1MB/s]\n",
      "Downloading readme: 100%|██████████| 8.86k/8.86k [00:00<00:00, 6.64MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset financial_phrasebank/sentences_allagree to /opt/app-root/src/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data: 100%|██████████| 682k/682k [00:00<00:00, 76.7MB/s]\n",
      "                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset financial_phrasebank downloaded and prepared to /opt/app-root/src/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 301.62it/s]\n",
      "                                                    \r"
     ]
    }
   ],
   "source": [
    "# loading dataset\n",
    "dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
    "dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
    "dataset[\"validation\"] = dataset[\"test\"]\n",
    "del dataset[\"test\"]\n",
    "\n",
    "classes = dataset[\"train\"].features[\"label\"].names\n",
    "dataset = dataset.map(\n",
    "    lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
    "    batched=True,\n",
    "    num_proc=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qzwyi-Z9yzRF",
   "metadata": {
    "id": "qzwyi-Z9yzRF"
   },
   "source": [
    "Let's also apply some pre-processing of the input data, the labels needs to be pre-processed, the tokens corresponding to `pad_token_id` needs to be set to `-100` so that the `CrossEntropy` loss associated with the model will correctly ignore these tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b7ea44c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
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    },
    "id": "6b7ea44c",
    "outputId": "a27a9252-eb13-48ca-cd9e-4953c8bcb75d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                          \r"
     ]
    }
   ],
   "source": [
    "# data preprocessing\n",
    "text_column = \"sentence\"\n",
    "label_column = \"text_label\"\n",
    "max_length = 128\n",
    "\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    inputs = examples[text_column]\n",
    "    targets = examples[label_column]\n",
    "    model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
    "    labels = tokenizer(targets, max_length=3, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
    "    labels = labels[\"input_ids\"]\n",
    "    labels[labels == tokenizer.pad_token_id] = -100\n",
    "    model_inputs[\"labels\"] = labels\n",
    "    return model_inputs\n",
    "\n",
    "\n",
    "processed_datasets = dataset.map(\n",
    "    preprocess_function,\n",
    "    batched=True,\n",
    "    num_proc=1,\n",
    "    remove_columns=dataset[\"train\"].column_names,\n",
    "    load_from_cache_file=False,\n",
    "    desc=\"Running tokenizer on dataset\",\n",
    ")\n",
    "\n",
    "train_dataset = processed_datasets[\"train\"]\n",
    "eval_dataset = processed_datasets[\"validation\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcNTdVypGEPb",
   "metadata": {
    "id": "bcNTdVypGEPb"
   },
   "source": [
    "## Train our model! \n",
    "\n",
    "Let's now train our model, run the cells below.\n",
    "Note that for T5 since some layers are kept in `float32` for stability purposes there is no need to call autocast on the trainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69c756ac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "69c756ac",
    "outputId": "f0d605b1-3b5d-4e22-e108-819edc7b0d52"
   },
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    \"temp\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=1e-3,\n",
    "    gradient_accumulation_steps=1,\n",
    "    auto_find_batch_size=True,\n",
    "    num_train_epochs=1,\n",
    "    save_steps=100,\n",
    "    save_total_limit=8,\n",
    ")\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=eval_dataset,\n",
    ")\n",
    "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab52b651",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 659
    },
    "id": "ab52b651",
    "outputId": "2da171de-e59a-4945-93cb-5704681e84c1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/app-root/lib64/python3.9/site-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/autograd/_functions.py:318: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='255' max='255' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [255/255 09:58, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.016685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/autograd/_functions.py:318: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "/opt/app-root/lib64/python3.9/site-packages/bitsandbytes/autograd/_functions.py:318: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=255, training_loss=0.29072899911917893, metrics={'train_runtime': 603.365, 'train_samples_per_second': 3.376, 'train_steps_per_second': 0.423, 'total_flos': 4370030543241216.0, 'train_loss': 0.29072899911917893, 'epoch': 1.0})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "r98VtofiGXtO",
   "metadata": {
    "id": "r98VtofiGXtO"
   },
   "source": [
    "## Qualitatively test our model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NIm7z3UNzGPP",
   "metadata": {
    "id": "NIm7z3UNzGPP"
   },
   "source": [
    "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c95d6173",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c95d6173",
    "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input sentence:  In January-September 2009 , the Group 's net interest income decreased to EUR 12.4 mn from EUR 74.3 mn in January-September 2008 .\n",
      " output prediction:  ['negative']\n",
      "input sentence:  In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
      " output prediction:  ['positive']\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "input_text_1 = \"In January-September 2009 , the Group 's net interest income decreased to EUR 12.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
    "input_text_2 = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
    "inputs_1 = tokenizer(input_text_1, return_tensors=\"pt\")\n",
    "inputs_2 = tokenizer(input_text_2, return_tensors=\"pt\")\n",
    "\n",
    "outputs_1 = model.generate(input_ids=inputs_1[\"input_ids\"], max_new_tokens=10)\n",
    "outputs_2 = model.generate(input_ids=inputs_2[\"input_ids\"], max_new_tokens=10)\n",
    "\n",
    "print(\"input sentence: \", input_text_1)\n",
    "print(\" output prediction: \", tokenizer.batch_decode(outputs_1.detach().cpu().numpy(), skip_special_tokens=True))\n",
    "\n",
    "print(\"input sentence: \", input_text_2)\n",
    "print(\" output prediction: \", tokenizer.batch_decode(outputs_2.detach().cpu().numpy(), skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4351b43a-f408-41bb-8f41-a4f8063d3786",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 2. Train on summary dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51738890-0c9d-49e1-a794-8573c009c59b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b247b3807a0424a8adfe1b2ad2eb614",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = \"ybelkada/flan-t5-xl-sharded-bf16\"\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, load_in_8bit=True, device_map=\"auto\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2f7b094-ddf3-4d6f-b43d-1cca12472416",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset samsum (/opt/app-root/src/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "010e1291e5af42a2932d41fd0882d584",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 14732\n",
      "Test dataset size: 819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 14732\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 819\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 818\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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",
    "dataset"
   ]
  },
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       "Map:   0%|          | 0/15551 [00:00<?, ? examples/s]"
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       "Map:   0%|          | 0/15551 [00:00<?, ? examples/s]"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max target length: 50\n"
     ]
    }
   ],
   "source": [
    "# The maximum total input sequence length after tokenization.\n",
    "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
    "conc_datasets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]])\n",
    "\n",
    "tokenized_inputs = conc_datasets.map(lambda x: tokenizer(x[\"dialogue\"],\n",
    "                                                         truncation=True),\n",
    "                                     batched=True,\n",
    "                                     remove_columns=[\"dialogue\", \"summary\"])\n",
    "\n",
    "input_lengths = [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_lengths, 85))\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 = conc_datasets.map(lambda x: tokenizer(x[\"summary\"],\n",
    "                                                          truncation=True),\n",
    "                                      batched=True,\n",
    "                                      remove_columns=[\"dialogue\", \"summary\"])\n",
    "target_lengths = [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_lengths, 90))\n",
    "print(f\"Max target length: {max_target_length}\")"
   ]
  },
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   "execution_count": 6,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /opt/app-root/src/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e/cache-bd742c963bc41ca9.arrow\n"
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      "text/plain": [
       "Map:   0%|          | 0/819 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /opt/app-root/src/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e/cache-66ae263e56bdc634.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
     ]
    },
    {
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       "model_id": "6837794df39e4c139e52d91250ef244e",
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       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/14732 [00:00<?, ? examples/s]"
      ]
     },
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    },
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       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/819 [00:00<?, ? examples/s]"
      ]
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   ],
   "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\")"
   ]
  },
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   "execution_count": 7,
   "id": "33ee2ac2-41db-4551-8a2b-4985be9d194a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 9437184 || all params: 2859194368 || trainable%: 0.33006444422319176\n"
     ]
    }
   ],
   "source": [
    "# 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "360eb433-3927-4a8c-8760-11dc1e6bb131",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    ")\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": "code",
   "execution_count": null,
   "id": "90f3e50a-4dd6-4d24-96b8-1fba823c7a2e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a07f98-1e72-4826-946d-9749e7b33433",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.save_model('Summarizer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f223083a-7a41-4087-a3ed-88147ef687ed",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "The notebook shows how we can use PEFT to fine tune the flan T5 model with downstream tasks such as sentiment analysis and summarization. The code can be adapted to other datasets such as documentation search. "
   ]
  }
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