{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n2TWEdtLQbbt"
   },
   "source": [
    "Reference\n",
    "- https://huggingface.co/dfurman/falcon-7b-chat-oasst1/blob/main/finetune_falcon7b_oasst1_with_bnb_peft.ipynb by Daniel Furman"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WE5GJ6s7y0Xo"
   },
   "source": [
    "## Fine-tune large models using 🤗 [`peft`](https://github.com/huggingface/peft) adapters, [`transformers`](https://github.com/huggingface/transformers) & [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes)\n",
    "\n",
    "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in **8-bit**.\n",
    "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model.\n",
    "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TfBzP8gWzkpv"
   },
   "source": [
    "### Install requirements\n",
    "\n",
    "First, run the cells below to install the requirements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "otj46qRbtpnd",
    "outputId": "b47ddf7a-09c4-480f-e8c8-d6555206972f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -q bitsandbytes datasets accelerate loralib einops\n",
    "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
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      "c29ed9fc08594914aa0700437a291270"
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    },
    "id": "rTdXkcWecz3s",
    "outputId": "6ff08928-727a-410b-9bf2-f2d8011c6880",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I2-xmZCnVDg_",
    "outputId": "04ba15ec-9ebd-4bd1-913e-34b80225a659",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "koRYhzEOSyb0",
    "outputId": "d03987fd-49ed-4893-a492-33e7ec13394c",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import bitsandbytes as bnb\n",
    "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p8vHtolPVZJj",
    "outputId": "6feec666-34b3-412f-bab1-871c4f15ada7",
    "tags": []
   },
   "outputs": [],
   "source": [
    "free_in_GB = int(torch.cuda.mem_get_info()[0] / 1024**3)\n",
    "max_memory = f\"{free_in_GB-2}GB\"\n",
    "\n",
    "n_gpus = torch.cuda.device_count()\n",
    "max_memory = {i: max_memory for i in range(n_gpus)}\n",
    "max_memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOtwYRI3zzXI"
   },
   "source": [
    "### Model loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313,
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    },
    "id": "cg3fiQOvmI3Q",
    "outputId": "c2690c1d-079a-45e3-d998-e7f11b87eebc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"tiiuae/falcon-7b\",\n",
    "    load_in_8bit=True,\n",
    "    # device_map={\"\":0},\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    \"tiiuae/falcon-7b\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fTSZntA1iUG"
   },
   "source": [
    "### Prepare model for training\n",
    "\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_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,
   "metadata": {
    "id": "T-gy-LxM0yAi",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import prepare_model_for_kbit_training\n",
    "\n",
    "model.gradient_checkpointing_enable()\n",
    "model = prepare_model_for_kbit_training(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KwOTr7B3NlM3"
   },
   "source": [
    "### Apply LoRA\n",
    "\n",
    "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4W1j6lxaNnxC",
    "tags": []
   },
   "outputs": [],
   "source": [
    "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": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4iwHGzKBN6wk",
    "outputId": "2404f68f-1a52-4f02-c1ed-3d5688eabfa2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    target_modules=[\"query_key_value\"],\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\"\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, config)\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zdnQamMZTEXq",
    "outputId": "e6fd8130-4f20-4218-ceab-9d8466b7457e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Verifying the datatypes.\n",
    "dtypes = {}\n",
    "for _, p in model.named_parameters():\n",
    "    dtype = p.dtype\n",
    "    if dtype not in dtypes:\n",
    "        dtypes[dtype] = 0\n",
    "    dtypes[dtype] += p.numel()\n",
    "total = 0\n",
    "for k, v in dtypes.items():\n",
    "    total += v\n",
    "for k, v in dtypes.items():\n",
    "    print(k, v, v / total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QdjWif4CVXR6"
   },
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AwYPCVsRTO-Y",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import transformers\n",
    "from datasets import load_dataset, Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# dataset_name = \"timdettmers/openassistant-guanaco\"\n",
    "# dataset = load_dataset(dataset_name, split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset_name = \"HiTZ/alpaca_mt\"\n",
    "lang = \"ca\"\n",
    "dataset = load_dataset(dataset_name, lang, split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = dataset.map(lambda x: {\"text\": f\"### Sistema:\\n{x['prompt']}{x['output']}\"})\n",
    "dataset = dataset.remove_columns([\"instruction\", \"input\", \"output\", \"prompt\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "dataset = dataset.map(lambda samples: tokenizer(samples[\"text\"], padding=True, truncation=True,), batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(dataset[6]['text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "InpuNBeCx3hP"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "AQ_HCYruWIHU",
    "outputId": "f0e86f6a-bc2e-43e4-88a2-89f9372cc763",
    "tags": []
   },
   "outputs": [],
   "source": [
    "training_args = transformers.TrainingArguments(\n",
    "    auto_find_batch_size=True,\n",
    "    gradient_accumulation_steps=4,\n",
    "    num_train_epochs=1,\n",
    "    learning_rate=2e-4,\n",
    "    fp16=True,\n",
    "    save_total_limit=4,\n",
    "    logging_steps=25,\n",
    "    output_dir=\"./outputs\",\n",
    "    save_strategy='epoch',\n",
    "    optim=\"paged_adamw_8bit\",\n",
    "    lr_scheduler_type = 'cosine',\n",
    "    warmup_ratio = 0.05,\n",
    ")\n",
    "\n",
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset,\n",
    "    args=training_args,\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    ")\n",
    "\n",
    "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Duak7T_B3VpJ"
   },
   "source": [
    "## Share adapters on the 🤗 Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hf_model_name = \"machinelearnear/falcon-7b-alpaca-lora-ca\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VxB6UV5XAvvP",
    "outputId": "c25bfe26-3e97-4983-f8a5-4544abd13f68",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.push_to_hub(hf_model_name, use_auth_token=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S65GcxNGA9kz"
   },
   "source": [
    "## Load adapters from the Hub\n",
    "\n",
    "You can also directly load adapters from the Hub using the commands below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "hsD1VKqeA62Z",
    "outputId": "ac11b44d-74e6-4281-980f-652a0d323a6a",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "peft_model_id = hf_model_name\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    config.base_model_name_or_path,\n",
    "    return_dict=True,\n",
    "    load_in_8bit=True,\n",
    "    device_map={\"\":0},\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "# Load the Lora model\n",
    "model = PeftModel.from_pretrained(model, peft_model_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MHYljmTjj5wX"
   },
   "source": [
    "## Inference\n",
    "\n",
    "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "jlpAYdyVGoyo",
    "outputId": "4f9acc11-e3f0-4ba6-80b4-ddf74333d764",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# prompt = \"\"\"<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB.\n",
    "# <bot>:\"\"\"\n",
    "# prompt = \"\"\"<human>: Em dic Daniel. Escriu un correu curt als meus amics més íntims convidant-los a venir a casa meva divendres per a un sopar. Jo faré el menjar, però digueu-los que portin la seva beguda.\n",
    "# <bot>:\"\"\"\n",
    "prompt = \"### Instrucció:\\nEm dic Daniel. Escriu un correu curt als meus amics més íntims convidant-los a venir a casa meva divendres per a un sopar. Jo faré el menjar, però digueu-los que portin la seva beguda.\\n\\n### Resposta:\"\n",
    "prompt"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5UpKIshHJQmm",
    "outputId": "e645a521-cc39-419d-fc91-8b8578ba6be4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "batch = tokenizer(\n",
    "    prompt,\n",
    "    padding=True,\n",
    "    truncation=True,\n",
    "    return_tensors='pt'\n",
    ")\n",
    "batch = batch.to('cuda:0')\n",
    "batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MDqJWba-tpnv",
    "outputId": "c79f934f-d7dd-4258-abfd-c3ee85570530",
    "tags": []
   },
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   "source": [
    "with torch.cuda.amp.autocast():\n",
    "    output_tokens = model.generate(\n",
    "        input_ids = batch.input_ids,\n",
    "        max_new_tokens=300,\n",
    "        temperature=0.7,\n",
    "        top_p=0.7,\n",
    "        num_return_sequences=1,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "    )"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "RCe7ly02JVis",
    "outputId": "7864d3b2-c875-41f9-c1cf-3cc0123c3c10",
    "tags": []
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   "outputs": [],
   "source": [
    "generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
    "# Inspect message response in the outputs\n",
    "# print(generated_text.split(\"<human>: \")[1].split(\"<bot>: \")[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(generated_text)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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