Merge_LoRA_Adapters_with_LLMs_the_right_way.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashispapu/LLMs/blob/main/Merge_LoRA_Adapters_with_LLMs_the_right_way.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook benchmarks different ways of loading/merging LoRA adapters fine-tuned with QLoRA. TL;DR For merging, we must quantize and then dequantize the model before merging.\n",
        "More details in this article: [LoRA Adapters: When a Naive Merge Leads to Poor Performance](https://kaitchup.substack.com/p/lora-adapters-when-a-naive-merge)\n"
      ],
      "metadata": {
        "id": "_DP0cTOieefL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JSSlaLyTDbQG"
      },
      "outputs": [],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U transformers\n",
        "!pip install -q -U peft\n",
        "!pip install -q -U accelerate\n",
        "!pip install -q -U datasets\n",
        "!pip install -q -U trl\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You will need your Hugging Face access token to get Llama 2 from the hub."
      ],
      "metadata": {
        "id": "R98R4Q6uexjd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        },
        "id": "TcM2Pz_GHSsG",
        "outputId": "a3c69331-9a99-4b7b-da39-7f415c9845f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "becfca13104241a8b0d9e3b3a3834059"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run the next cell only if you want to fine-tune Llama 2 LoRA adapter on openassistant-guanaco, using Platypus hyperparameters. You can also get my fine-tuned adapter directly from the HF Hub (see next next cell)."
      ],
      "metadata": {
        "id": "G7aoVY9rXtTL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XTalEKN0Gr4N"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig, PeftModel\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    BitsAndBytesConfig,\n",
        "    AutoTokenizer,\n",
        "    TrainingArguments\n",
        ")\n",
        "\n",
        "from trl import SFTTrainer\n",
        "\n",
        "model_name = \"meta-llama/Llama-2-7b-hf\"\n",
        "#Tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, add_eos_token=True, use_fast=True)\n",
        "#Create a new token and add it to the tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = 'right'\n",
        "\n",
        "dataset = load_dataset(\"timdettmers/openassistant-guanaco\")\n",
        "\n",
        "compute_dtype = getattr(torch, \"float16\")\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=compute_dtype,\n",
        "        bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "          model_name, quantization_config=bnb_config, device_map={\"\": 0}\n",
        ")\n",
        "\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "        lora_alpha=16,\n",
        "        lora_dropout=0.05,\n",
        "        r=16,\n",
        "        bias=\"none\",\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "        target_modules= [\"gate_proj\", \"down_proj\", \"up_proj\"]\n",
        ")\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "        output_dir=\"./results\",\n",
        "        evaluation_strategy=\"steps\",\n",
        "        do_eval=True,\n",
        "        per_device_train_batch_size=2,\n",
        "        gradient_accumulation_steps=8,\n",
        "        per_device_eval_batch_size=4,\n",
        "        log_level=\"debug\",\n",
        "        save_steps=100,\n",
        "        logging_steps=100,\n",
        "        learning_rate=4e-4,\n",
        "        eval_steps=100,\n",
        "        fp16=True,\n",
        "        num_train_epochs=1,\n",
        "        warmup_steps=100,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        ")\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "        model=model,\n",
        "        train_dataset=dataset['train'],\n",
        "        eval_dataset=dataset['test'],\n",
        "        peft_config=peft_config,\n",
        "        dataset_text_field=\"text\",\n",
        "        max_seq_length=512,\n",
        "        tokenizer=tokenizer,\n",
        "        args=training_arguments,\n",
        ")\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "adapter = \"./results/checkpoint-615/\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run this cell only if you wish to use my adapter:"
      ],
      "metadata": {
        "id": "Se3NdaAAe6G2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adapter = \"kaitchup/Llama-2-7B-oasstguanaco-adapter\""
      ],
      "metadata": {
        "id": "oLxyXhKjscAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This cell loads the tokenizer and defines a function to compute the perplexity of a model.\n",
        "Remove \".to(\"cuda\")\" if you don't have enough VRAM, but it may take one hour."
      ],
      "metadata": {
        "id": "8CO91vbmBLkt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig, PeftModel\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    BitsAndBytesConfig,\n",
        "    AutoTokenizer,\n",
        "    TrainingArguments\n",
        ")\n",
        "from trl import SFTTrainer\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "\n",
        "model_name = \"meta-llama/Llama-2-7b-hf\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, add_eos_token=True, use_fast=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = 'right'\n",
        "\n",
        "dataset = load_dataset(\"timdettmers/openassistant-guanaco\")['test']\n",
        "\n",
        "\n",
        "#return the perplexity of the model on the dataset\n",
        "#The perplexity is computed on each example, individually, with a sliding window for examples longer than 512 tokens.\n",
        "def ppl_model(model, tokenizer, dataset):\n",
        "  nlls= []\n",
        "  max_length = 2048\n",
        "  stride = 512\n",
        "  for s in tqdm(range(len(dataset['text']))):\n",
        "      encodings = tokenizer(dataset['text'][s], return_tensors=\"pt\")\n",
        "      seq_len = encodings.input_ids.size(1)\n",
        "      prev_end_loc = 0\n",
        "      for begin_loc in range(0, seq_len, stride):\n",
        "          end_loc = min(begin_loc + max_length, seq_len)\n",
        "          trg_len = end_loc - prev_end_loc\n",
        "          input_ids = encodings.input_ids[:, begin_loc:end_loc].to(\"cuda\")\n",
        "          target_ids = input_ids.clone()\n",
        "          target_ids[:, :-trg_len] = -100\n",
        "          with torch.no_grad():\n",
        "              outputs = model(input_ids, labels=target_ids)\n",
        "              neg_log_likelihood = outputs.loss\n",
        "          nlls.append(neg_log_likelihood)\n",
        "          prev_end_loc = end_loc\n",
        "          if end_loc == seq_len:\n",
        "              break\n",
        "  ppl = torch.exp(torch.stack(nlls).mean())\n",
        "  return ppl"
      ],
      "metadata": {
        "id": "mrbqUEp0t27S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Naive merge:\n",
        "- Load the base model\n",
        "- Load and activate the adapter\n",
        "- Merge the adapter\n",
        "- Serialize\n"
      ],
      "metadata": {
        "id": "9QvBEjHpybuQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "model = PeftModel.from_pretrained(model, adapter)\n",
        "model = model.merge_and_unload()\n",
        "model.save_pretrained(\"./naive_merge/\", safe_serialization=True)"
      ],
      "metadata": {
        "id": "P1HajgkBRtgm",
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed6dd479b6d04ecfa1a21e1cd838ca8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/92.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a246dc431d504bc3ad71defa0fb34c8d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate merged model perplexity:"
      ],
      "metadata": {
        "id": "JpY4WjFAGlGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\"./naive_merge/\", device_map={\"\": 0})\n",
        "ppl = ppl_model(model, tokenizer, dataset)\n",
        "print(ppl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112,
          "referenced_widgets": [
            "770d8964089548699f8589d2d434369f",
            "9913c32d8c8b4cb5b2b3c108398f87eb",
            "7671012779454982887c4a66bf903e30",
            "60387bb85af448cfb1f19ef7205fdd12",
            "5e7ab8b8512a49bfa7a3c6bd27aa6ff5",
            "95ab66984f284714ac9d6f126361f333",
            "233eb20c58a842129753e30127b6492b",
            "86959cec66254825b2ddbf1f566cd110",
            "a86ef8d23cff4acd973bf9cabd1e333c",
            "a24a33f3d4fc4f4e85a92514b8efd1bc",
            "1f41095b51a24f42bf2d62fc839b5781"
          ]
        },
        "id": "Kq5lzZSey6w-",
        "outputId": "ada92dd5-1127-49e4-e7bd-ccbd2fddae72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "770d8964089548699f8589d2d434369f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 518/518 [03:30<00:00,  2.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(3.8537, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I run this to clean the memory between each attempt: del model, torch.cuda.empty_cache(), gc.collect()\n",
        "\n",
        "Load and quantize the merge model."
      ],
      "metadata": {
        "id": "I55CSxBt6ern"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "del model\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "compute_dtype = getattr(torch, \"float16\")\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=compute_dtype,\n",
        "        bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"./naive_merge/\", quantization_config=bnb_config, device_map={\"\": 0})\n",
        "ppl = ppl_model(model, tokenizer, dataset)\n",
        "print(ppl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112,
          "referenced_widgets": [
            "7cc3ea374f52432d8ae4d0ad77ab616c",
            "8cb7c767e1c14842b07bc633d1fe42d8",
            "09884c2e318e46e7ac342b451e5320d4",
            "82d9fd2998fb455bad8468f3c556e506",
            "acbdc361b4d14604bc064903a32ca122",
            "8f0a51a18ec2480ab79d7bcd8c7bd873",
            "f25f45a59fcb4bd7aa09cf57b25f3d0a",
            "8c168f866fc8450aaf62a867b74d9c59",
            "c222a91ed2494857b586b0a23618dd30",
            "b86497f828e740558aa4d29200a34db3",
            "8a2adf2e9b8a454b8cee8d9597da5fc4"
          ]
        },
        "id": "pYq3zj5CofC_",
        "outputId": "125104fc-3ff2-40c4-fd4b-c50017043db2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cc3ea374f52432d8ae4d0ad77ab616c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 518/518 [00:56<00:00,  9.19it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(3.7520, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the adapter on top of the base model without quantization."
      ],
      "metadata": {
        "id": "3yy4TNGU6tth"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "del model\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, device_map={\"\": 0})\n",
        "model = PeftModel.from_pretrained(model, adapter)\n",
        "ppl = ppl_model(model, tokenizer, dataset)\n",
        "print(ppl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112,
          "referenced_widgets": [
            "2fb9f457c7684cd98b6b9af0a32c9ef3",
            "3bd2e0a1590c456cb866128c537d8e31",
            "5acd844874e6461fa897f6a1f7dc67f7",
            "fff7a228e9f34ad783fe339ec191d822",
            "6b6de9eb2ba94b1d87fa11051b315c5c",
            "70c38a9fb35842c3be008410b2c71fc6",
            "f833abca5564475399cae671f298841b",
            "0cbfcc49c7e6482a9c2aac2a64b999f9",
            "ec8fe15bf7cb4f998ed1442f89a18936",
            "64f7e784b9e14fbf896892ab15afb997",
            "6057da199e47416bb9fba0296af83efc"
          ]
        },
        "id": "mnhuhrKcHKr7",
        "outputId": "e6df1906-5f7e-4765-dc5a-ec0bc0a5827a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fb9f457c7684cd98b6b9af0a32c9ef3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 518/518 [03:35<00:00,  2.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(3.8537, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the adapter on top of the base model with quantization."
      ],
      "metadata": {
        "id": "8JKVafuS64ah"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "del model\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "compute_dtype = getattr(torch, \"float16\")\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=compute_dtype,\n",
        "        bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "          model_name, quantization_config=bnb_config, device_map={\"\": 0}\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, adapter)\n",
        "ppl = ppl_model(model, tokenizer, dataset)\n",
        "print(ppl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112,
          "referenced_widgets": [
            "0239d212faa54b57bc292fa4f0f0744a",
            "7a49a3966c784a7995c6ae1c25d804af",
            "1e81358f1cb34d998028f8c210b3590f",
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            "be7e7cf92a5b4910b06fba084701bc46",
            "d02c9e9fceb74131b70dab283c060131",
            "daec86e11e2641898efbc35ea810be0d",
            "1fc06041fcaa443489220cd249345f14"
          ]
        },
        "id": "KL-vzC1qHvXB",
        "outputId": "c4e7cb31-b689-4842-cd2e-518f346c37c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0239d212faa54b57bc292fa4f0f0744a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 518/518 [01:07<00:00,  7.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(3.7410, device='cuda:0')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "The following code is the one you have to use to properly merge the adapter.\n",
        "---\n"
      ],
      "metadata": {
        "id": "sOecYDNkf7no"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import peft\n",
        "import json\n",
        "import shutil\n",
        "from peft.utils import _get_submodules\n",
        "import os\n",
        "import bitsandbytes as bnb\n",
        "from bitsandbytes.functional import dequantize_4bit\n",
        "from peft import PeftModel\n",
        "from transformers import AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, BitsAndBytesConfig\n",
        "import gc\n",
        "import copy\n",
        "\n",
        "def dequantize_model(model, to='./dequantized_model', dtype=torch.float16, device=\"cuda\"):\n",
        "    \"\"\"\n",
        "    'model': the peftmodel you loaded with qlora.\n",
        "    'tokenizer': the model's corresponding hf's tokenizer.\n",
        "    'to': directory to save the dequantized model\n",
        "    'dtype': dtype that the model was trained using\n",
        "    'device': device to load the model to\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "    os.makedirs(to, exist_ok=True)\n",
        "\n",
        "    cls = bnb.nn.Linear4bit\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for name, module in model.named_modules():\n",
        "            if isinstance(module, cls):\n",
        "                print(f\"Dequantizing `{name}`...\")\n",
        "                quant_state = copy.deepcopy(module.weight.quant_state)\n",
        "                quant_state[2] = dtype\n",
        "\n",
        "                weights = dequantize_4bit(module.weight.data, quant_state=quant_state, quant_type=\"nf4\").to(dtype)\n",
        "\n",
        "                new_module = torch.nn.Linear(module.in_features, module.out_features, bias=None, dtype=dtype)\n",
        "                new_module.weight = torch.nn.Parameter(weights)\n",
        "                new_module.to(device=device, dtype=dtype)\n",
        "\n",
        "                parent, target, target_name = _get_submodules(model, name)\n",
        "                setattr(parent, target_name, new_module)\n",
        "\n",
        "        # a hack, setting this to avoid hf's saving error because hf\n",
        "        # itself does not support saving a model that is registered to be loaded in 4bit.\n",
        "        model.is_loaded_in_4bit = False\n",
        "\n",
        "        print(\"Saving dequantized model...\")\n",
        "        model.save_pretrained(to)\n",
        "        #tokenizer.save_pretrained(to)\n",
        "        config_data = json.loads(open(os.path.join(to, 'config.json'), 'r').read())\n",
        "        config_data.pop(\"quantization_config\", None)\n",
        "        config_data.pop(\"pretraining_tp\", None)\n",
        "        with open(os.path.join(to, 'config.json'), 'w') as config:\n",
        "            config.write(json.dumps(config_data, indent=2))\n",
        "\n",
        "        return model\n",
        "\n",
        "#To which precision do you want to dequantize?\n",
        "dtype = torch.float16\n",
        "\n",
        "quantization_config=BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_compute_dtype=dtype,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        ")\n",
        "\n",
        "try:\n",
        "    print(f\"Starting to load the model {model_name} into memory\")\n",
        "\n",
        "    model = LlamaForCausalLM.from_pretrained(\n",
        "        model_name,\n",
        "        quantization_config=quantization_config,\n",
        "        device_map={\"\": 0}\n",
        "    )\n",
        "    print(model)\n",
        "    model = dequantize_model(model, to='./dqz_model/',dtype=dtype)\n",
        "    print(model)\n",
        "    model = PeftModel.from_pretrained(model, adapter)\n",
        "    print(model)\n",
        "    model = model.merge_and_unload()\n",
        "    print(model)\n",
        "\n",
        "    print(f\"Successfully loaded the model {model_name} into memory\")\n",
        "    model.save_pretrained(\"./drive/MyDrive//dqz_merge/\", safe_serialization=True)\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {e}\")\n",
        "\n",
        "    # Delete the model object if it exists\n",
        "    if 'model' in locals():\n",
        "        del model\n",
        "\n",
        "    # Clear the GPU cache\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "    # Run the garbage collection\n",
        "    gc.collect()\n",
        "\n",
        "    print(\"Model, GPU cache, and garbage have been cleared.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a4dfd6448e1047889ba45d6b790e33eb",
            "a9ad9f41f03f4a59a92531875189fe0f",
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            "c0c0ee39063143768a887b788e9f23d7",
            "6a06de07709b4708b8d62c0c7cee3889",
            "037c4fe51cb24845b88b42f71f3d50db",
            "a65962e23a8c43ec98406cd1bf27c03b",
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            "9889eef1d1694718bb6816d8edc2ec97",
            "00a8e1f0642c46dfb59d123ea1b2fa09",
            "54acfffa44074b31b0d7da2fca1e9499",
            "bff2a3eac4044908a11457bc29fc8fe7",
            "0ea2fe1a44984f6fa451a71a4a812993",
            "3f1584dee0b64b1986e60a4b8cc57335"
          ]
        },
        "id": "c9yLWqKRKKyd",
        "outputId": "009d3b27-472e-421d-bb9f-aec1fb45fb07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting to load the model meta-llama/Llama-2-7b-hf into memory\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4dfd6448e1047889ba45d6b790e33eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 4096)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            ")\n",
            "Dequantizing `model.layers.0.self_attn.q_proj`...\n",
            "Dequantizing `model.layers.0.self_attn.k_proj`...\n",
            "Dequantizing `model.layers.0.self_attn.v_proj`...\n",
            "Dequantizing `model.layers.0.self_attn.o_proj`...\n",
            "Dequantizing `model.layers.0.mlp.gate_proj`...\n",
            "Dequantizing `model.layers.0.mlp.up_proj`...\n",
            "Dequantizing `model.layers.0.mlp.down_proj`...\n",
            "Dequantizing `model.layers.1.self_attn.q_proj`...\n",
            "Dequantizing `model.layers.1.self_attn.k_proj`...\n",
            "Dequantizing `model.layers.1.self_attn.v_proj`...\n",
            "Dequantizing `model.layers.1.self_attn.o_proj`...\n",
            "Dequantizing `model.layers.1.mlp.gate_proj`...\n",
            "Dequantizing `model.layers.1.mlp.up_proj`...\n",
            "Dequantizing `model.layers.1.mlp.down_proj`...\n",
            "Dequantizing `model.layers.2.self_attn.q_proj`...\n",
            "Dequantizing `model.layers.2.self_attn.k_proj`...\n",
            "Dequantizing `model.layers.2.self_attn.v_proj`...\n",
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            "Saving dequantized model...\n",
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 4096)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
            "          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            ")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acd3088be352421ba9e41d28d4f6427c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c586a1cee624eeda1f4007fd0f6b78a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): LlamaForCausalLM(\n",
            "      (model): LlamaModel(\n",
            "        (embed_tokens): Embedding(32000, 4096)\n",
            "        (layers): ModuleList(\n",
            "          (0-31): 32 x LlamaDecoderLayer(\n",
            "            (self_attn): LlamaAttention(\n",
            "              (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "              (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "              (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "              (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "              (rotary_emb): LlamaRotaryEmbedding()\n",
            "            )\n",
            "            (mlp): LlamaMLP(\n",
            "              (gate_proj): Linear(\n",
            "                in_features=4096, out_features=11008, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=16, out_features=11008, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (up_proj): Linear(\n",
            "                in_features=4096, out_features=11008, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=16, out_features=11008, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (down_proj): Linear(\n",
            "                in_features=11008, out_features=4096, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=11008, out_features=16, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (act_fn): SiLUActivation()\n",
            "            )\n",
            "            (input_layernorm): LlamaRMSNorm()\n",
            "            (post_attention_layernorm): LlamaRMSNorm()\n",
            "          )\n",
            "        )\n",
            "        (norm): LlamaRMSNorm()\n",
            "      )\n",
            "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            "    )\n",
            "  )\n",
            ")\n",
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 4096)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
            "          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            ")\n",
            "Successfully loaded the model meta-llama/Llama-2-7b-hf into memory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the merged model and evaluate it."
      ],
      "metadata": {
        "id": "y7qgbwSO7G_R"
      }
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      "source": [
        "del model\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "model = AutoModelForCausalLM.from_pretrained(\"./dqz_merge/\", device_map={\"\": 0})\n",
        "ppl = ppl_model(model, tokenizer, dataset)\n",
        "print(ppl)"
      ],
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            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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          "text": [
            "100%|██████████| 518/518 [03:30<00:00,  2.46it/s]"
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            "tensor(3.7411, device='cuda:0')\n"
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        "Load the merged model with quantization. Don't do this."
      ],
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        "del model\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "compute_dtype = getattr(torch, \"float16\")\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=compute_dtype,\n",
        "        bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"./drive/MyDrive/dqz_merge/\",  quantization_config=bnb_config, device_map={\"\": 0})\n",
        "ppl = ppl_model(model, tokenizer, dataset)\n",
        "print(ppl)"
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              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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            "100%|██████████| 518/518 [00:56<00:00,  9.21it/s]"
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