16_Merge_PEFT_Adapter_and_Quantize.ipynb

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   "cell_type": "code",
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   "id": "32211756-ad06-4e24-a6f4-35ddd202b16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass, field\n",
    "from typing import Optional\n",
    "\n",
    "import peft\n",
    "import torch\n",
    "from peft import PeftConfig, PeftModel\n",
    "from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser\n",
    "\n",
    "\n",
    "DEFAULT_PAD_TOKEN = \"[PAD]\"\n",
    "DEFAULT_EOS_TOKEN = \"</s>\"\n",
    "DEFAULT_BOS_TOKEN = \"</s>\"\n",
    "DEFAULT_UNK_TOKEN = \"</s>\"\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class ScriptArguments:\n",
    "    \"\"\"\n",
    "    The name of the Casual LM model we wish to fine with PPO\n",
    "    \"\"\"\n",
    "\n",
    "    adapter_model_name: Optional[str] = field(default=None, metadata={\"help\": \"the model name\"})\n",
    "    base_model_name: Optional[str] = field(default=None, metadata={\"help\": \"the model name\"})\n",
    "    output_name: Optional[str] = field(default=None, metadata={\"help\": \"the model name\"})\n",
    "\n",
    "\n",
    "parser = HfArgumentParser(ScriptArguments)\n",
    "script_args = parser.parse_args_into_dataclasses(return_remaining_strings=True)[0]\n",
    "assert script_args.adapter_model_name is not None, \"please provide the name of the Adapter you would like to merge\"\n",
    "assert script_args.base_model_name is not None, \"please provide the name of the Base model\"\n",
    "assert script_args.base_model_name is not None, \"please provide the output name of the merged model\"\n",
    "\n",
    "peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16)\n",
    "tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)\n",
    "config = AutoConfig.from_pretrained(script_args.base_model_name)\n",
    "architecture = config.architectures[0]\n",
    "if \"Llama\" in architecture:\n",
    "    print(\"Setting EOS, BOS, and UNK tokens for LLama tokenizer\")\n",
    "    tokenizer.add_special_tokens(\n",
    "        {\n",
    "            \"eos_token\": DEFAULT_EOS_TOKEN,\n",
    "            \"bos_token\": DEFAULT_BOS_TOKEN,\n",
    "            \"unk_token\": DEFAULT_UNK_TOKEN,\n",
    "            \"pad_token\": DEFAULT_PAD_TOKEN,\n",
    "        }\n",
    "    )\n",
    "\n",
    "# Load the Lora model\n",
    "model = PeftModel.from_pretrained(model, script_args.adapter_model_name)\n",
    "model.eval()\n",
    "\n",
    "key_list = [key for key, _ in model.base_model.model.named_modules() if \"lora\" not in key]\n",
    "for key in key_list:\n",
    "    parent, target, target_name = model.base_model._get_submodules(key)\n",
    "    if isinstance(target, peft.tuners.lora.Linear):\n",
    "        bias = target.bias is not None\n",
    "        new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)\n",
    "        model.base_model._replace_module(parent, target_name, new_module, target)\n",
    "\n",
    "model = model.base_model.model\n",
    "\n",
    "model.save_pretrained(f\"{script_args.output_name}\")\n",
    "model.push_to_hub(f\"{script_args.output_name}\", use_temp_dir=False)\n"
   ]
  }
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