fingpt/FinGPT_Sentiment_Analysis_v3/training_8bit/train_Llama2_13B.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only for WSL\n",
    "import os \n",
    "os.environ[\"PATH\"] = f\"{os.environ['PATH']}:/usr/local/cuda-12.0/bin\"\n",
    "# os.environ['LD_LIBRARY_PATH'] = \"/usr/lib/wsl/lib:/usr/local/cuda/lib64\"\n",
    "os.environ['LD_LIBRARY_PATH'] = \"/usr/local/cuda-12.0/lib64\"\n",
    "os.environ[\"BNB_CUDA_VERSION\"]=\"120\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/cuda_setup/main.py:106: UserWarning: \n",
      "\n",
      "================================================================================\n",
      "WARNING: Manual override via BNB_CUDA_VERSION env variable detected!\n",
      "BNB_CUDA_VERSION=XXX can be used to load a bitsandbytes version that is different from the PyTorch CUDA version.\n",
      "If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=\n",
      "If you use the manual override make sure the right libcudart.so is in your LD_LIBRARY_PATH\n",
      "For example by adding the following to your .bashrc: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<path_to_cuda_dir/lib64\n",
      "Loading CUDA version: BNB_CUDA_VERSION=120\n",
      "================================================================================\n",
      "\n",
      "\n",
      "  warn((f'\\n\\n{\"=\"*80}\\n'\n"
     ]
    }
   ],
   "source": [
    "from typing import List, Dict, Optional\n",
    "\n",
    "import datasets\n",
    "import torch\n",
    "from loguru import logger\n",
    "from datasets import load_dataset\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    TrainingArguments,\n",
    "    Trainer,\n",
    "    BitsAndBytesConfig\n",
    ")\n",
    "from peft import (\n",
    "    TaskType,\n",
    "    LoraConfig,\n",
    "    get_peft_model,\n",
    "    set_peft_model_state_dict,\n",
    "    prepare_model_for_kbit_training,\n",
    "    prepare_model_for_int8_training,\n",
    ")\n",
    "from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "        output_dir='./finetuned_model',    # saved model path\n",
    "        logging_steps = 500,\n",
    "        # max_steps=10000,\n",
    "        num_train_epochs = 2,\n",
    "        per_device_train_batch_size=4,\n",
    "        gradient_accumulation_steps=8,\n",
    "        learning_rate=1e-4,\n",
    "        weight_decay=0.01,\n",
    "        warmup_steps=1000,\n",
    "        save_steps=500,\n",
    "        fp16=True,\n",
    "        # bf16=True,\n",
    "        torch_compile = False,\n",
    "        load_best_model_at_end = True,\n",
    "        evaluation_strategy=\"steps\",\n",
    "        remove_unused_columns=False,\n",
    "\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  # Quantization\n",
    "# q_config = BitsAndBytesConfig(load_in_4bit=True,\n",
    "#                                 bnb_4bit_quant_type='nf4',\n",
    "#                                 bnb_4bit_use_double_quant=True,\n",
    "#                                 bnb_4bit_compute_dtype=torch.float16\n",
    "#                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7af9696743dd4ce0b9cb7dcc61a37ef1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/peft/utils/other.py:122: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Load tokenizer & model\n",
    "model_name = \"NousResearch/Llama-2-13b-hf\"  \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "model =  AutoModelForCausalLM.from_pretrained(\n",
    "        model_name, \n",
    "        # quantization_config=q_config,\n",
    "        load_in_8bit = True,\n",
    "        trust_remote_code=True, \n",
    "        device_map='auto',\n",
    "    )\n",
    "model = prepare_model_for_int8_training(model, use_gradient_checkpointing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 6553600 || all params: 13022417920 || trainable%: 0.05032552357220002\n"
     ]
    }
   ],
   "source": [
    "# LoRA\n",
    "target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING['llama']\n",
    "lora_config = LoraConfig(\n",
    "    task_type=TaskType.CAUSAL_LM,\n",
    "    inference_mode=False,\n",
    "    r=8,\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.1,\n",
    "    target_modules=target_modules,\n",
    "    bias='none',\n",
    ")\n",
    "model = get_peft_model(model, lora_config)\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resume_from_checkpoint = None\n",
    "if resume_from_checkpoint is not None:\n",
    "    checkpoint_name = os.path.join(resume_from_checkpoint, 'pytorch_model.bin')\n",
    "    if not os.path.exists(checkpoint_name):\n",
    "        checkpoint_name = os.path.join(\n",
    "            resume_from_checkpoint, 'adapter_model.bin'\n",
    "        )\n",
    "        resume_from_checkpoint = False\n",
    "    if os.path.exists(checkpoint_name):\n",
    "        logger.info(f'Restarting from {checkpoint_name}')\n",
    "        adapters_weights = torch.load(checkpoint_name)\n",
    "        set_peft_model_state_dict(model, adapters_weights)\n",
    "    else:\n",
    "        logger.info(f'Checkpoint {checkpoint_name} not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 6,553,600 || all params: 13,022,417,920 || trainable%: 0.05032552357220002\n"
     ]
    }
   ],
   "source": [
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "dataset = datasets.load_from_disk(\"./dataset_new\")\n",
    "dataset = dataset.train_test_split(0.2, shuffle=True, seed = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModifiedTrainer(Trainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        return model(\n",
    "            input_ids=inputs[\"input_ids\"],\n",
    "            labels=inputs[\"labels\"],\n",
    "        ).loss\n",
    "\n",
    "    def prediction_step(self, model: torch.nn.Module, inputs, prediction_loss_only: bool, ignore_keys = None):\n",
    "        with torch.no_grad():\n",
    "            res = model(\n",
    "                input_ids=inputs[\"input_ids\"].to(model.device),\n",
    "                labels=inputs[\"labels\"].to(model.device),\n",
    "            ).loss\n",
    "        return (res, None, None)\n",
    "\n",
    "    def save_model(self, output_dir=None, _internal_call=False):\n",
    "        from transformers.trainer import TRAINING_ARGS_NAME\n",
    "\n",
    "        os.makedirs(output_dir, exist_ok=True)\n",
    "        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n",
    "        saved_params = {\n",
    "            k: v.to(\"cpu\") for k, v in self.model.named_parameters() if v.requires_grad\n",
    "        }\n",
    "        torch.save(saved_params, os.path.join(output_dir, \"adapter_model.bin\"))\n",
    "\n",
    "def data_collator(features: list) -> dict:\n",
    "    len_ids = [len(feature[\"input_ids\"]) for feature in features]\n",
    "    longest = max(len_ids)\n",
    "    input_ids = []\n",
    "    labels_list = []\n",
    "    for ids_l, feature in sorted(zip(len_ids, features), key=lambda x: -x[0]):\n",
    "        ids = feature[\"input_ids\"]\n",
    "        seq_len = feature[\"seq_len\"]\n",
    "        labels = (\n",
    "            [tokenizer.pad_token_id] * (seq_len - 1) + ids[(seq_len - 1) :] + [tokenizer.pad_token_id] * (longest - ids_l)\n",
    "        )\n",
    "        ids = ids + [tokenizer.pad_token_id] * (longest - ids_l)\n",
    "        _ids = torch.LongTensor(ids)\n",
    "        labels_list.append(torch.LongTensor(labels))\n",
    "        input_ids.append(_ids)\n",
    "    input_ids = torch.stack(input_ids)\n",
    "    labels = torch.stack(labels_list)\n",
    "    return {\n",
    "        \"input_ids\": input_ids,\n",
    "        \"labels\": labels,\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from transformers.integrations import TensorBoardCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are adding a <class 'transformers.integrations.integration_utils.TensorBoardCallback'> to the callbacks of this Trainer, but there is already one. The currentlist of callbacks is\n",
      ":DefaultFlowCallback\n",
      "TensorBoardCallback\n",
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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='3838' max='3838' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3838/3838 17:14:17, Epoch 1/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>4.337800</td>\n",
       "      <td>0.005839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.005600</td>\n",
       "      <td>0.004147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>0.003341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.003795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.002809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.002647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.002470</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": [
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/autograd/_functions.py:322: 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"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "writer = SummaryWriter()\n",
    "trainer = ModifiedTrainer(\n",
    "    model=model, \n",
    "    args=training_args,             # Trainer args\n",
    "    train_dataset=dataset[\"train\"], # Training set\n",
    "    eval_dataset=dataset[\"test\"],   # Testing set\n",
    "    data_collator=data_collator,    # Data Collator\n",
    "    callbacks=[TensorBoardCallback(writer)],\n",
    ")\n",
    "trainer.train()\n",
    "writer.close()\n",
    "# save model\n",
    "model.save_pretrained(training_args.output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPU Trining MEM: 46.5%\n",
    "## GPU Evaling MEM: 96.9%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Push to hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/bitsandbytes-0.41.1-py3.10.egg/bitsandbytes/cuda_setup/main.py:106: UserWarning: \n",
      "\n",
      "================================================================================\n",
      "WARNING: Manual override via BNB_CUDA_VERSION env variable detected!\n",
      "BNB_CUDA_VERSION=XXX can be used to load a bitsandbytes version that is different from the PyTorch CUDA version.\n",
      "If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=\n",
      "If you use the manual override make sure the right libcudart.so is in your LD_LIBRARY_PATH\n",
      "For example by adding the following to your .bashrc: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<path_to_cuda_dir/lib64\n",
      "Loading CUDA version: BNB_CUDA_VERSION=120\n",
      "================================================================================\n",
      "\n",
      "\n",
      "  warn((f'\\n\\n{\"=\"*80}\\n'\n",
      "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcca437b56d84a1297b588632d4bab07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/home/oliverwang15/miniconda3/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast   # 4.30.2\n",
    "from peft import PeftModel  # 0.5.0\n",
    "base_model = \"NousResearch/Llama-2-13b-hf\" \n",
    "peft_model = \"finetuned_model\"\n",
    "tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = \"cuda:0\", load_in_8bit = True,)\n",
    "model = PeftModel.from_pretrained(model, peft_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32727599a10246c484c1b9a2d9d6f01b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.bin:   0%|          | 0.00/26.3M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/oliverwang15/FinGPT_v33_Llama2_13B_Sentiment_Instruction_LoRA_FT_8bit/commit/9be5d932a4e9138ba7ac18c250b7aec522f2e17f', commit_message='Upload model', commit_description='', oid='9be5d932a4e9138ba7ac18c250b7aec522f2e17f', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.push_to_hub(\"FinGPT_v33_Llama2_13B_Sentiment_Instruction_LoRA_FT_8bit\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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