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Maxime Labonne committed 2023-07-23 16:56:24 +01:00
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Fine_tune_Llama_2_in_Google_Colab.ipynb
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@@ -6,7 +6,7 @@
"provenance": [],
"machine_shape": "hm",
"gpuType": "V100",
"authorship_tag": "ABX9TyPHtqq96zm8/DDNC9+543fd",
"authorship_tag": "ABX9TyPNl/WKBYXOzuJCP/puYm6d",
"include_colab_link": true
},
"kernelspec": {
@@ -35,7 +35,9 @@
"# Fine-tune Llama 2 in Google Colab\n",
"> 🗣️ Large Language Model Course\n",
"\n",
"❤️ Created by [@maximelabonne](), based on Younes Belkada's [GitHub Gist](https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da).\n"
"❤️ Created by [@maximelabonne](), based on Younes Belkada's [GitHub Gist](https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da).\n",
"\n",
"This notebook runs on a T4 GPU with high RAM. (Last update: 23 Jul 2023)\n"
],
"metadata": {
"id": "OSHlAbqzDFDq"
@@ -79,78 +81,110 @@
{
"cell_type": "code",
"source": [
"# Used for multi-gpu\n",
"local_rank = -1\n",
"per_device_train_batch_size = 4\n",
"per_device_eval_batch_size = 1\n",
"gradient_accumulation_steps = 4\n",
"learning_rate = 2e-4\n",
"max_grad_norm = 0.3\n",
"weight_decay = 0.001\n",
"lora_alpha = 16\n",
"lora_dropout = 0.1\n",
"lora_r = 64\n",
"max_seq_length = 512\n",
"\n",
"# The model that you want to train from the Hugging Face hub\n",
"model_name = \"daryl149/llama-2-7b-chat-hf\"\n",
"\n",
"# The instruction dataset to use\n",
"dataset_name = \"mlabonne/guanaco-llama2-1k\"\n",
"\n",
"# Fine-tuned model name\n",
"new_model = \"llama-2-7b-guanaco\"\n",
"\n",
"# The instruction dataset to use\n",
"dataset_name = \"timdettmers/openassistant-guanaco\"\n",
"################################################################################\n",
"# QLoRA parameters\n",
"################################################################################\n",
"\n",
"# Lora attention dimension\n",
"lora_r = 64\n",
"\n",
"# Alpha parameter for Lora scaling\n",
"lora_alpha = 16\n",
"\n",
"# Dropout probability for Lora layers\n",
"lora_dropout = 0.1\n",
"\n",
"################################################################################\n",
"# bitsandbytes parameters\n",
"################################################################################\n",
"\n",
"# Activate 4-bit precision base model loading\n",
"use_4bit = True\n",
"\n",
"# Activate nested quantization for 4-bit base models\n",
"use_nested_quant = False\n",
"\n",
"# Compute dtype for 4-bit base models\n",
"bnb_4bit_compute_dtype = \"float16\"\n",
"\n",
"# Quantization type (fp4 or nf4=\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\n",
"\n",
"################################################################################\n",
"# TrainingArguments parameters\n",
"################################################################################\n",
"\n",
"# Output directory where the model predictions and checkpoints will be stored\n",
"output_dir = \"./results\"\n",
"\n",
"# Number of training epochs\n",
"num_train_epochs = 1\n",
"\n",
"# Enable fp16 training\n",
"# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
"fp16 = False\n",
"\n",
"# Enable bf16 training\n",
"bf16 = False\n",
"\n",
"# Use packing dataset creating\n",
"packing = False\n",
"# Batch size per GPU for training\n",
"per_device_train_batch_size = 4\n",
"\n",
"# Batch size per GPU for evaluation\n",
"per_device_eval_batch_size = 4\n",
"\n",
"# Number of update steps to accumulate the gradients for\n",
"gradient_accumulation_steps = 1\n",
"\n",
"# Enable gradient checkpointing\n",
"gradient_checkpointing = True\n",
"\n",
"# Maximum gradient normal (gradient clipping)\n",
"max_grad_norm = 0.3\n",
"\n",
"# Initial learning rate (AdamW optimizer)\n",
"learning_rate = 2e-4\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, and has advantage for analysis)\n",
"# Learning rate schedule (constant a bit better than cosine)\n",
"lr_scheduler_type = \"constant\"\n",
"\n",
"# Number of optimizer update steps\n",
"max_steps = 10000\n",
"# Number of training steps (overrides num_train_epochs)\n",
"max_steps = -1\n",
"\n",
"# Fraction of steps to do a warmup for\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 (saves memory and speeds up training considerably)\n",
"# Group sequences into batches with same length\n",
"# Saves memory and speeds up training considerably\n",
"group_by_length = True\n",
"\n",
"# Save checkpoint every X updates steps\n",
"save_steps = 10\n",
"\n",
"# Log every X updates steps\n",
"logging_steps = 10\n",
"logging_steps = 1\n",
"\n",
"# The output directory where the model predictions and checkpoints will be written\n",
"output_dir = \"./results\"\n",
"################################################################################\n",
"# SFT parameters\n",
"################################################################################\n",
"\n",
"# Maximum sequence length to use\n",
"max_seq_length = None\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 = {\"\": 0}"
@@ -164,6 +198,7 @@
{
"cell_type": "code",
"source": [
"# Load dataset (you can process it here)\n",
"dataset = load_dataset(dataset_name, split=\"train\")\n",
"\n",
"# Load tokenizer and model with QLoRA configuration\n",
@@ -176,13 +211,15 @@
" bnb_4bit_use_double_quant=use_nested_quant,\n",
")\n",
"\n",
"# 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, you can accelerate training with the argument --bf16\")\n",
" print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
" print(\"=\" * 80)\n",
"\n",
"# Load base model\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" quantization_config=bnb_config,\n",
@@ -191,6 +228,7 @@
"model.config.use_cache = False\n",
"model.config.pretraining_tp = 1\n",
"\n",
"# Load LoRA configuration\n",
"peft_config = LoraConfig(\n",
" lora_alpha=lora_alpha,\n",
" lora_dropout=lora_dropout,\n",
@@ -199,19 +237,22 @@
" task_type=\"CAUSAL_LM\",\n",
")\n",
"\n",
"# Load LLaMA tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"# Fix weird overflow issue with fp16 training\n",
"tokenizer.padding_side = \"right\"\n",
"tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
"\n",
"# Set training parameters\n",
"training_arguments = TrainingArguments(\n",
" output_dir=output_dir,\n",
" num_train_epochs=num_train_epochs,\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",
" weight_decay=weight_decay,\n",
" fp16=fp16,\n",
" bf16=bf16,\n",
" max_grad_norm=max_grad_norm,\n",
@@ -219,8 +260,10 @@
" warmup_ratio=warmup_ratio,\n",
" group_by_length=group_by_length,\n",
" lr_scheduler_type=lr_scheduler_type,\n",
" report_to=\"tensorboard\"\n",
")\n",
"\n",
"# Set supervised fine-tuning parameters\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" train_dataset=dataset,\n",
@@ -232,7 +275,10 @@
" packing=packing,\n",
")\n",
"\n",
"# Train model\n",
"trainer.train()\n",
"\n",
"# Save trained model\n",
"trainer.model.save_pretrained(output_dir)"
],
"metadata": {
@@ -267,29 +313,21 @@
{
"cell_type": "code",
"source": [
"from numba import cuda\n",
"# Reload model in FP16 and merge it with LoRA weights\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" low_cpu_mem_usage=True,\n",
" return_dict=True,\n",
" torch_dtype=torch.float16,\n",
" device_map=device_map,\n",
")\n",
"model = PeftModel.from_pretrained(base_model, output_dir)\n",
"model = model.merge_and_unload()\n",
"\n",
"if use_4bit:\n",
" del model\n",
" torch.cuda.empty_cache()\n",
" cuda.select_device(0)\n",
" cuda.close()\n",
"\n",
" base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" low_cpu_mem_usage=True,\n",
" return_dict=True,\n",
" torch_dtype=torch.float16,\n",
" device_map=device_map,\n",
" )\n",
" model = PeftModel.from_pretrained(base_model, output_dir, offload_folder=\"/content/sample_data\")\n",
" model = model.merge_and_unload()\n",
"\n",
"# Save merged weights and tokenizer\n",
"model.save_pretrained(new_model, use_safetensors=True)\n",
"# Reload tokenizer to save it\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"tokenizer.save_pretrained(new_model)"
"tokenizer.padding_side = \"right\""
],
"metadata": {
"id": "QQn30cRtAZ-P"