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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import os\n",
"import torch\n",
"import torch.multiprocessing as mp\n",
"from torch.nn.parallel import DistributedDataParallel as DDP\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, TrainingArguments\n",
"from datasets import load_dataset\n",
"from trl import SFTTrainer\n",
"from peft import LoraConfig\n",
"\n",
"def init_distributed(rank, world_size):\n",
" os.environ[\"MASTER_ADDR\"] = \"localhost\"\n",
" os.environ[\"MASTER_PORT\"] = \"12345\"\n",
" if rank == 0:\n",
" print(\"Initializing distributed process group...\")\n",
" torch.distributed.init_process_group(backend='nccl', world_size=world_size, rank=rank)\n",
"\n",
"def cleanup_distributed():\n",
" torch.distributed.destroy_process_group()\n",
"\n",
"def main_worker(rank, world_size):\n",
" init_distributed(rank, world_size)\n",
"\n",
" # Move the finetune() function here\n",
" # Load the dataset\n",
" dataset_name = \"ruslanmv/ai-medical-dataset\"\n",
" dataset = load_dataset(dataset_name, split=\"train\")\n",
" # Select the first 1M rows of the dataset\n",
" dataset = dataset.select(range(100))\n",
" # Load the model + tokenizer\n",
" model_name = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
" bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.float16,\n",
" )\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" quantization_config=bnb_config,\n",
" trust_remote_code=True,\n",
" use_cache=False,\n",
" )\n",
" # Replace the DDP wrapping part with the following lines\n",
" model = model.to(rank)\n",
" model = DDP(model, device_ids=[rank], output_device=rank)\n",
"\n",
" # PEFT config\n",
" lora_alpha = 16\n",
" lora_dropout = 0.1\n",
" lora_r = 32 # 64\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\",\n",
" target_modules=[\"k_proj\", \"q_proj\", \"v_proj\", \"up_proj\", \"down_proj\", \"gate_proj\"],\n",
" modules_to_save=[\"embed_tokens\", \"input_layernorm\", \"post_attention_layernorm\", \"norm\"],\n",
" )\n",
" # Args\n",
" max_seq_length = 512\n",
" output_dir = \"./results\"\n",
" per_device_train_batch_size = 2 # reduced batch size to avoid OOM\n",
" gradient_accumulation_steps = 2\n",
" optim = \"adamw_torch\"\n",
" save_steps = 10\n",
" logging_steps = 1\n",
" learning_rate = 2e-4\n",
" max_grad_norm = 0.3\n",
" max_steps = 1 # 300 Approx the size of guanaco at bs 8, ga 2, 2 GPUs.\n",
" warmup_ratio = 0.1\n",
" lr_scheduler_type = \"cosine\"\n",
"\n",
" training_arguments = TrainingArguments(\n",
" output_dir=output_dir,\n",
" per_device_train_batch_size=per_device_train_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",
" fp16=True,\n",
" max_grad_norm=max_grad_norm,\n",
" max_steps=max_steps,\n",
" warmup_ratio=warmup_ratio,\n",
" group_by_length=True,\n",
" lr_scheduler_type=lr_scheduler_type,\n",
" gradient_checkpointing=True, # gradient checkpointing\n",
" report_to=\"wandb\",\n",
" )\n",
" # Trainer\n",
" trainer = SFTTrainer(\n",
" model=model,\n",
" train_dataset=dataset,\n",
" peft_config=peft_config,\n",
" dataset_text_field=\"context\",\n",
" max_seq_length=max_seq_length,\n",
" tokenizer=tokenizer,\n",
" args=training_arguments,\n",
" )\n",
" # Train :)\n",
" trainer.train()\n",
" cleanup_distributed()\n",
"\n",
"if __name__ == \"__main__\":\n",
" world_size = torch.cuda.device_count()\n",
"\n",
" processes = []\n",
" for rank in range(world_size):\n",
" p = mp.Process(target=main_worker, args=(rank, world_size))\n",
" p.start()\n",
" processes.append(p)\n",
"\n",
" for p in processes:\n",
" p.join()\n"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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