notebooks/Llama2_4bit_finetune.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "htVrPtG7YAXH"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/llm_train/lib/python3.11/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML\n",
      "  warnings.warn(\"Can't initialize NVML\")\n",
      "/opt/conda/envs/llm_train/lib/python3.11/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n",
      "  warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/llm_train/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32\n"
     ]
    }
   ],
   "source": [
    "# Import necessary packages for the fine-tuning process\n",
    "import os                          # Operating system functionalities\n",
    "import torch                       # PyTorch library for deep learning\n",
    "from datasets import load_dataset  # Loading datasets for training\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,          # AutoModel for language modeling tasks\n",
    "    AutoTokenizer,                # AutoTokenizer for tokenization\n",
    "    BitsAndBytesConfig,           # Configuration for BitsAndBytes\n",
    "    HfArgumentParser,             # Argument parser for Hugging Face models\n",
    "    TrainingArguments,            # Training arguments for model training\n",
    "    pipeline,                     # Creating pipelines for model inference\n",
    "    logging,\n",
    "AutoConfig\n",
    ")\n",
    "from peft import LoraConfig, PeftModel  # Packages for parameter-efficient fine-tuning (PEFT)\n",
    "from trl import SFTTrainer         # SFTTrainer for supervised fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_NAQWYv9eRnM"
   },
   "outputs": [],
   "source": [
    "# The model that you want to train from the Hugging Face hub\n",
    "model_name = \"mistralai/Mistral-7B-v0.1\" ############## CHNAGE HERE FOR BASE MODEL\n",
    "\n",
    "# The instruction dataset to use\n",
    "dataset_name = \"opennyaiorg/aalap_instruction_dataset\"\n",
    "dataset_text_field = \"combined_input_prompt\"\n",
    "# Fine-tuned model name\n",
    "new_model = \"Aalap-Mistral-7B-v0.1-bf16\"       ############## CHNAGE HERE FOR BASE MODEL\n",
    "# Output directory where the model predictions and checkpoints will be stored\n",
    "root_output = \"/home/amantiwari/legal_llm_training/models\"\n",
    "output_dir = os.path.join(root_output,\"adapter_checkpoints\",new_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "BGufVOIOe23a"
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# QLoRA parameters\n",
    "################################################################################\n",
    "\n",
    "# LoRA attention dimension\n",
    "lora_r = 8\n",
    "\n",
    "# Alpha parameter for LoRA scaling\n",
    "lora_alpha = 16\n",
    "\n",
    "# Dropout probability for LoRA layers\n",
    "lora_dropout = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "YrAujYOee6SV"
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# bitsandbytes parameters\n",
    "################################################################################\n",
    "\n",
    "# Activate 4-bit precision base model loading\n",
    "use_4bit = True\n",
    "\n",
    "# Compute dtype for 4-bit base models\n",
    "bnb_4bit_compute_dtype = \"bfloat16\"\n",
    "\n",
    "# Quantization type (fp4 or nf4)\n",
    "bnb_4bit_quant_type = \"nf4\"\n",
    "\n",
    "# Activate nested quantization for 4-bit base models (double quantization)\n",
    "use_nested_quant = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "tDE0lx-Pe7ki"
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# TrainingArguments parameters\n",
    "################################################################################\n",
    "\n",
    "# Number of training epochs\n",
    "num_train_epochs = 6\n",
    "\n",
    "# Evalution\n",
    "eval_steps = 100\n",
    "do_eval = True\n",
    "\n",
    "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
    "fp16 = False\n",
    "bf16 = True\n",
    "\n",
    "# Batch size per GPU for training\n",
    "per_device_train_batch_size = 2\n",
    "\n",
    "# Batch size per GPU for evaluation\n",
    "per_device_eval_batch_size = 1\n",
    "\n",
    "# Number of update steps to accumulate the gradients for\n",
    "gradient_accumulation_steps = 8\n",
    "\n",
    "# Enable gradient checkpointing\n",
    "gradient_checkpointing = False\n",
    "\n",
    "# Maximum gradient normal (gradient clipping)\n",
    "max_grad_norm = 0.3\n",
    "\n",
    "# Initial learning rate (AdamW optimizer)\n",
    "learning_rate = 2e-5\n",
    "\n",
    "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
    "weight_decay = 0.001\n",
    "\n",
    "# Optimizer to use\n",
    "optim = \"paged_adamw_32bit\"\n",
    "\n",
    "# Learning rate schedule (constant a bit better than cosine)\n",
    "lr_scheduler_type = \"constant\"\n",
    "\n",
    "# Number of training steps (overrides num_train_epochs)\n",
    "max_steps = -1\n",
    "\n",
    "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
    "warmup_ratio = 0.03\n",
    "\n",
    "# Group sequences into batches with same length\n",
    "# Saves memory and speeds up training considerably\n",
    "group_by_length = False\n",
    "\n",
    "# Save checkpoint every X updates steps\n",
    "save_steps = 10\n",
    "\n",
    "# Log every X updates steps\n",
    "logging_steps = 10\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "BBXapfcZe-U8"
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# SFT parameters\n",
    "################################################################################\n",
    "\n",
    "# Maximum sequence length to use\n",
    "max_seq_length = 16384\n",
    "\n",
    "# Pack multiple short examples in the same input sequence to increase efficiency\n",
    "packing = False\n",
    "\n",
    "# Load the entire model on the GPU 0\n",
    "device_map = \"auto\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ajqpAQQoe_vk"
   },
   "outputs": [],
   "source": [
    "# Step 1 : Load dataset (you can process it here)\n",
    "dataset_split = load_dataset(dataset_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "_meFORX1fMgu"
   },
   "outputs": [],
   "source": [
    "# Step 2 :Load tokenizer and model with QLoRA configuration\n",
    "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=use_4bit,\n",
    "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
    "    bnb_4bit_compute_dtype=compute_dtype,\n",
    "    bnb_4bit_use_double_quant=use_nested_quant,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "oeuDUNAlfOyP"
   },
   "outputs": [],
   "source": [
    "# Step 3 :Check GPU compatibility with bfloat16\n",
    "if compute_dtype == torch.float16 and use_4bit:\n",
    "    major, _ = torch.cuda.get_device_capability()\n",
    "    if major >= 8:\n",
    "        print(\"=\" * 80)\n",
    "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
    "        print(\"=\" * 80)"
   ]
  },
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    "outputId": "74863fc6-8805-411f-8da3-02c6b51493db"
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   "outputs": [
    {
     "data": {
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       "model_id": "28b01a9e84b14b08bc9476157cd1f7cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 4 :Load base model\n",
    "model_config =  AutoConfig.from_pretrained(model_name,trust_remote_code=True)\n",
    "if max_seq_length > model_config.max_position_embeddings:\n",
    "    model_config.max_position_embeddings = max_seq_length\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,config=model_config,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=device_map,use_flash_attention_2=True\n",
    "    \n",
    ")\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1"
   ]
  },
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    "id": "p4LEH6J2fRcv",
    "outputId": "e9d15b63-3513-4999-fcbc-00f666bcad1e"
   },
   "outputs": [],
   "source": [
    "# Step 5 :Load LLaMA tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
    "tokenizer.padding_side = \"right\"\n",
    "tokenizer.model_max_length = max_seq_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "9JFXcBfNitQl"
   },
   "outputs": [],
   "source": [
    "# Step 6 :Load LoRA configuration\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    r=lora_r,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",target_modules=\"q_proj,k_proj,v_proj,o_proj,down_proj,up_proj,gate_proj,embed_tokens,lm_head\".split(\",\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "w9sTAqRUiukA"
   },
   "outputs": [],
   "source": [
    "# Step 7 :Set training parameters\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir=output_dir,\n",
    "    num_train_epochs=num_train_epochs,\n",
    "    do_eval=do_eval,\n",
    "    eval_steps=eval_steps,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    per_device_train_batch_size=per_device_train_batch_size,\n",
    "    per_device_eval_batch_size=per_device_eval_batch_size,\n",
    "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
    "    optim=optim,\n",
    "    save_steps=save_steps,\n",
    "    logging_steps=logging_steps,\n",
    "    learning_rate=learning_rate,\n",
    "    weight_decay=weight_decay,\n",
    "    fp16=fp16,\n",
    "    bf16=bf16,\n",
    "    max_grad_norm=max_grad_norm,\n",
    "    max_steps=max_steps,\n",
    "    warmup_ratio=warmup_ratio,\n",
    "    group_by_length=group_by_length,\n",
    "    lr_scheduler_type=lr_scheduler_type,\n",
    "    report_to=\"all\"\n",
    ")"
   ]
  },
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      "/opt/conda/envs/llm_train/lib/python3.11/site-packages/peft/utils/other.py:107: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
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    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset_split['train'],\n",
    "    eval_dataset=dataset_split['test'],\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=dataset_text_field,\n",
    "    max_seq_length=max_seq_length,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing=packing,\n",
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    "outputId": "516a8c41-832a-4d93-f653-f3a576d1f7e5"
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