train_llama.py

from transformers import LlamaForCausalLM, LlamaTokenizer, Trainer, TrainingArguments
from transformers import BitsAndBytesConfig
import datasets
import torch
from torch.nn.utils.rnn import pad_sequence
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from accelerate import Accelerator

# Version and CUDA check
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA version: {torch.version.cuda}")
print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

# Load Llama model and tokenizer
MODEL_ID = "meta-llama/Llama-2-7b-hf"
tokenizer = LlamaTokenizer.from_pretrained(MODEL_ID)

if tokenizer.pad_token is None:
    tokenizer.add_special_tokens({'pad_token': '[PAD]'})

# Quantization config
quantization_config = BitsAndBytesConfig(load_in_8bit=True)

# Load model without FlashAttention
model = LlamaForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config
)

# Prepare for LoRA
model = prepare_model_for_kbit_training(model)
peft_config = LoraConfig(
    r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM",
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

# Load dataset
dataset = datasets.load_dataset("json", data_files="final_combined_fraud_data.json", field="training_pairs")
print("First example from dataset:", dataset["train"][0])

# Tokenization with fixed length
def tokenize_data(example):
    formatted_text = f"{example['input']} {example['output']}"
    inputs = tokenizer(formatted_text, truncation=True, max_length=512, padding="max_length", return_tensors="pt")
    input_ids = inputs["input_ids"].squeeze(0).tolist()
    attention_mask = inputs["attention_mask"].squeeze(0).tolist()
    labels = input_ids.copy()
    input_len = len(tokenizer(example['input'])["input_ids"])
    labels[:input_len] = [-100] * input_len
    print(f"Debug: input_ids[:5] = {input_ids[:5]}, labels[:5] = {labels[:5]}, attention_mask[:5] = {attention_mask[:5]}")
    return {
        "input_ids": input_ids,
        "labels": labels,
        "attention_mask": attention_mask
    }

tokenized_dataset = dataset["train"].map(tokenize_data, batched=False, remove_columns=dataset["train"].column_names)
first_example = tokenized_dataset[0]
print("First tokenized example:", {k: (type(v), len(v)) for k, v in first_example.items()})

# Data collator with tensor stacking
def custom_data_collator(features):
    input_ids = [torch.tensor(f["input_ids"]) for f in features]
    attention_mask = [torch.tensor(f["attention_mask"]) for f in features]
    labels = [torch.tensor(f["labels"]) for f in features]
    return {
        "input_ids": torch.stack(input_ids),
        "attention_mask": torch.stack(attention_mask),
        "labels": torch.stack(labels)
    }

# Accelerator and training
accelerator = Accelerator()
training_args = TrainingArguments(
    output_dir="./fine_tuned_llama2", per_device_train_batch_size=4, gradient_accumulation_steps=4,
    eval_strategy="steps", eval_steps=50, save_strategy="steps", save_steps=100, save_total_limit=3,
    num_train_epochs=3, learning_rate=2e-5, weight_decay=0.01, logging_dir="./logs", logging_steps=10,
    bf16=True, gradient_checkpointing=True, optim="adamw_torch", warmup_steps=100
)
trainer = Trainer(
    model=model, args=training_args,
    train_dataset=tokenized_dataset.select(range(90)),
    eval_dataset=tokenized_dataset.select(range(90, 112)),
    data_collator=custom_data_collator
)
trainer.train()
model.save_pretrained("./fine_tuned_llama2")
tokenizer.save_pretrained("./fine_tuned_llama2")
print("Training complete. Model and tokenizer saved to ./fine_tuned_llama2")