train/train_codellama_qlora_linux_bugfix.py

# QLoRA fine-tuning for CodeLLaMA-7B-Instruct on 1x H200
# Requirements: transformers, peft, accelerate, bitsandbytes, datasets
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TrainingArguments,
    Trainer,
    BitsAndBytesConfig,
    DataCollatorForSeq2Seq
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from datasets import load_dataset
import torch
import os
import wandb

os.environ["WANDB_PROJECT"] = "codellama-7b-instruct-qlora-linux-bugfix"
os.environ["WANDB_NAME"] = "run-v1"
# Paths and model
BASE_MODEL = "codellama/CodeLLaMA-7b-Instruct-hf"
DATA_PATH = "../dataset/training_data_100k.jsonl"
OUTPUT_DIR = "./output/qlora-codellama-bugfix"

# Load dataset (prompt-completion format)
dataset = load_dataset("json", data_files=DATA_PATH, split="train")

# BitsandBytes config for QLoRA
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16  # optimized for H100/H200
)

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    device_map="auto"
)
model = prepare_model_for_kbit_training(model)
model.gradient_checkpointing_enable()
torch.backends.cuda.matmul.allow_tf32 = True

# Apply QLoRA (LoRA config)
lora_config = LoraConfig(
    r=64,
    lora_alpha=16,
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)
model.config.use_cache = False
model.config.return_dict = True
model.config.pad_token_id = tokenizer.pad_token_id
model.print_trainable_parameters()

# Format and tokenize the dataset
model_max_len = tokenizer.model_max_length

def format(example):
    prompt_ids = tokenizer(example["prompt"], truncation=True, max_length=1024)["input_ids"]
    completion_ids = tokenizer(example["completion"], truncation=True, max_length=512)["input_ids"]

    input_ids = prompt_ids + completion_ids
    labels = [-100] * len(prompt_ids) + completion_ids

    # pad both input_ids and labels to the same length
    max_len = min(len(input_ids), tokenizer.model_max_length)
    input_ids = input_ids[:max_len]
    labels = labels[:max_len]

    return {
        "input_ids": input_ids,
        "labels": labels,
    }


# Sanity check
print("__ Sanity checking one example...")
sample = format(dataset[0])
test_input = torch.tensor(sample["input_ids"]).unsqueeze(0).to(model.device)
test_labels = torch.tensor(sample["labels"]).unsqueeze(0).to(model.device)
model.train()
out = model(input_ids=test_input, labels=test_labels)
assert out.loss.requires_grad, "Sanity check failed: Loss does not require grad."
print("__ Sanity check passed. Proceeding to map()...")

# Apply formatting to entire dataset
dataset = dataset.map(format, remove_columns=["prompt", "completion"])
collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors="pt", pad_to_multiple_of=8)

# Training arguments
training_args = TrainingArguments(
    report_to="wandb",
    run_name="codellama-7b-instruct-qlora-linux-bugfix",
    logging_dir=f"{OUTPUT_DIR}/logs",
    
    output_dir=OUTPUT_DIR,
    num_train_epochs=3,
    per_device_train_batch_size=64,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    lr_scheduler_type="cosine",
    warmup_ratio=0.03,
    gradient_checkpointing=True,
    bf16=True,                     # Important for H200
    fp16=False,
    max_grad_norm=1.0,
    save_strategy="steps",
    save_steps=500,
    save_total_limit=2,
    logging_steps=50,
    push_to_hub=False,
    label_names=["labels"],
    remove_unused_columns=False,  # Critical to prevent data loss
)

# Trainer setup
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    data_collator=collator
)


# Begin training
model.train()
print(f"Track this run in Weights & Biases: https://wandb.ai/{os.environ['WANDB_PROJECT']}/{os.environ['WANDB_NAME']}")
trainer.train(resume_from_checkpoint=True)


# Save final model
model.save_pretrained(OUTPUT_DIR, safe_serialization=True)
tokenizer.save_pretrained(OUTPUT_DIR)
print(f"[DONE] Model saved to {OUTPUT_DIR}")