{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "lw1cWgq-DI5k",
   "metadata": {
    "id": "lw1cWgq-DI5k"
   },
   "source": [
    "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "kBFPA3-aDT7H",
   "metadata": {
    "id": "kBFPA3-aDT7H"
   },
   "source": [
    "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n",
    "\n",
    "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",
    "Note that you could use the same notebook to fine-tune `flan-t5-xl` as well, but you would need to shard the models first to avoid CPU RAM issues on Google Colab, check [these weights](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ShAuuHCDDkvk",
   "metadata": {
    "id": "ShAuuHCDDkvk"
   },
   "source": [
    "## Install requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "DRQ4ZrJTDkSy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DRQ4ZrJTDkSy",
    "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m65.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "!pip install -q bitsandbytes datasets accelerate\n",
    "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QBdCIrizDxFw",
   "metadata": {
    "id": "QBdCIrizDxFw"
   },
   "source": [
    "## Import model and tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd3c5acc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350,
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      "608e9f7a14054573b9bd07f0f74b6345"
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    },
    "id": "dd3c5acc",
    "outputId": "fab398b4-dace-4f44-89e5-687ec74b18eb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59782bac41834abbbd6cbd6614c2e57b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbdb787728184aa1a6906f96c5e6f929",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa2c51ad05c14a02a13e5c047779fc05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b839f5b472e44148c9e02a12550fade",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65aacfc888014bdea55253428b2569c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)\"spiece.model\";:   0%|          | 0.00/792k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7d9e2e2090d4226ad89e5ba9cec33df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0363c4d4dc1449148b09061763ea119a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select CUDA device index\n",
    "import os\n",
    "import torch\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig\n",
    "\n",
    "model_name = \"google/flan-t5-large\"\n",
    "\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, quantization_config=BitsAndBytesConfig(load_in_8bit=True))\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VwcHieQzD_dl",
   "metadata": {
    "id": "VwcHieQzD_dl"
   },
   "source": [
    "## Prepare model for training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4o3ePxrjEDzv",
   "metadata": {
    "id": "4o3ePxrjEDzv"
   },
   "source": [
    "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_kbit_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": null,
   "id": "1629ebcb",
   "metadata": {
    "id": "1629ebcb"
   },
   "outputs": [],
   "source": [
    "from peft import prepare_model_for_kbit_training\n",
    "\n",
    "model = prepare_model_for_kbit_training(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iCpAgawAEieu",
   "metadata": {
    "id": "iCpAgawAEieu"
   },
   "source": [
    "## Load your `PeftModel` \n",
    "\n",
    "Here we will use LoRA (Low-Rank Adaptators) to train our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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: 4718592 || all params: 787868672 || trainable%: 0.5989059049678777\n"
     ]
    }
   ],
   "source": [
    "from peft import LoraConfig, get_peft_model, TaskType\n",
    "\n",
    "\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",
    "    )\n",
    "\n",
    "\n",
    "lora_config = LoraConfig(\n",
    "    r=16, lora_alpha=32, target_modules=[\"q\", \"v\"], lora_dropout=0.05, bias=\"none\", task_type=\"SEQ_2_SEQ_LM\"\n",
    ")\n",
    "\n",
    "\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"
   },
   "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": null,
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      "1fa2a7e3ff3c4c99ab95e96a28624846"
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    },
    "id": "242cdfae",
    "outputId": "3122a25f-4299-4c3e-cd93-3a507f9f0d91"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce0213e9d6aa45c5a9ac9954fbe15f62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.04k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2dc5e8a31c348358aca916274899e8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/13.7k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94437f56e5a44fa3bb08c9d798b2eaeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/8.86k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset financial_phrasebank/sentences_allagree to /root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cce112d791dd4b748908756e785ab555",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/682k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41d8dbe04d0c4ec4a9d19662f9e11920",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/2264 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset financial_phrasebank downloaded and prepared to /root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26e1bfb23d3a4797a8dbb9c4ed2aeb22",
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "283bdd8a4daf4f5ca1ec6e9ed19c46ec",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d35a041dbfb4747aea427e76890551a",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": null,
   "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": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b718fba0f1514025a0ca22e7f780a2fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running tokenizer on dataset:   0%|          | 0/3 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f48454eadbfb4953b719bdf44555c90e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running tokenizer on dataset:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": null,
   "id": "69c756ac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "69c756ac",
    "outputId": "f0d605b1-3b5d-4e22-e108-819edc7b0d52"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check  the examples in https://github.com/huggingface/peft for more details.\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "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": null,
   "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": [
      "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: 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",
      "***** Running training *****\n",
      "  Num examples = 2037\n",
      "  Num Epochs = 1\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 255\n",
      "  Number of trainable parameters = 4718592\n",
      "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: 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 06:13, 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>0.017228</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": [
      "Saving model checkpoint to temp/checkpoint-100\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
      "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: 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",
      "Saving model checkpoint to temp/checkpoint-200\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
      "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: 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",
      "***** Running Evaluation *****\n",
      "  Num examples = 227\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=255, training_loss=0.2569344015682445, metrics={'train_runtime': 377.3565, 'train_samples_per_second': 5.398, 'train_steps_per_second': 0.676, 'total_flos': 1181084919791616.0, 'train_loss': 0.2569344015682445, 'epoch': 1.0})"
      ]
     },
     "execution_count": 8,
     "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": null,
   "id": "c95d6173",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c95d6173",
    "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generate config GenerationConfig {\n",
      "  \"_from_model_config\": true,\n",
      "  \"decoder_start_token_id\": 0,\n",
      "  \"eos_token_id\": 1,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"transformers_version\": \"4.27.0.dev0\",\n",
      "  \"use_cache\": false\n",
      "}\n",
      "\n",
      "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: 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",
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 = \"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 = tokenizer(input_text, return_tensors=\"pt\")\n",
    "\n",
    "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
    "\n",
    "print(\"input sentence: \", input_text)\n",
    "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9QqBlwzoGZ3f",
   "metadata": {
    "id": "9QqBlwzoGZ3f"
   },
   "source": [
    "## Share your adapters on 🤗 Hub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NT-C8SjcKqUx",
   "metadata": {
    "id": "NT-C8SjcKqUx"
   },
   "source": [
    "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token is valid.\n",
      "Your token has been saved in your configured git credential helpers (store).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
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     "text": [
      "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n"
     ]
    },
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      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
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       "adapter_model.bin:   0%|          | 0.00/19.0M [00:00<?, ?B/s]"
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       "CommitInfo(commit_url='https://huggingface.co/ybelkada/flan-t5-large-lora/commit/585e4e8cac66188692d506ac107e2d2884c0a4a0', commit_message='Upload model', commit_description='', oid='585e4e8cac66188692d506ac107e2d2884c0a4a0', pr_url=None, pr_revision=None, pr_num=None)"
      ]
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     "execution_count": 13,
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   ],
   "source": [
    "model.push_to_hub(\"ybelkada/flan-t5-large-financial-phrasebank-lora\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xHuDmbCYJ89f",
   "metadata": {
    "id": "xHuDmbCYJ89f"
   },
   "source": [
    "## Load your adapter from the Hub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ANFo6DdfKlU3",
   "metadata": {
    "id": "ANFo6DdfKlU3"
   },
   "source": [
    "You can load the model together with the adapter with few lines of code! Check the snippet below to load the adapter from the Hub and run the example evaluation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "j097aaPWJ-9u",
   "metadata": {
    "id": "j097aaPWJ-9u"
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   "outputs": [],
   "source": [
    "import torch\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
    "\n",
    "peft_model_id = \"ybelkada/flan-t5-large-financial-phrasebank-lora\"\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, torch_dtype=\"auto\", device_map=\"auto\")\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "jmjwWYt0KI_I",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jmjwWYt0KI_I",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generate config GenerationConfig {\n",
      "  \"_from_model_config\": true,\n",
      "  \"decoder_start_token_id\": 0,\n",
      "  \"eos_token_id\": 1,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"transformers_version\": \"4.27.0.dev0\"\n",
      "}\n",
      "\n",
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 = \"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 = tokenizer(input_text, return_tensors=\"pt\")\n",
    "\n",
    "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
    "\n",
    "print(\"input sentence: \", input_text)\n",
    "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
   ]
  }
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