generate.py

#!/usr/bin/env python3
import os
import torch
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

# Set constants
USERNAME = "jc4121"  # Your username
BASE_PATH = f"/vol/bitbucket/{USERNAME}"
OUTPUT_PATH = f"{BASE_PATH}/gptneo-2.7Bloratunning/output"

def parse_args():
    parser = argparse.ArgumentParser(description="Generate lyrics using fine-tuned GPT-Neo 2.7B")
    parser.add_argument("--model_path", type=str, default=f"{OUTPUT_PATH}/final_model", help="Path to the fine-tuned model")
    parser.add_argument("--artist", type=str, default="Taylor Swift", help="Artist name for conditioning")
    parser.add_argument("--max_length", type=int, default=512, help="Maximum length of generated text")
    parser.add_argument("--temperature", type=float, default=0.7, help="Sampling temperature")
    parser.add_argument("--top_p", type=float, default=0.9, help="Top-p sampling parameter")
    parser.add_argument("--top_k", type=int, default=50, help="Top-k sampling parameter")
    parser.add_argument("--num_return_sequences", type=int, default=1, help="Number of sequences to generate")
    parser.add_argument("--seed", type=int, default=42, help="Random seed")
    return parser.parse_args()

def main():
    args = parse_args()
    torch.manual_seed(args.seed)
    
    print(f"Loading model from {args.model_path}")
    
    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(args.model_path)
    
    # Load model
    model = AutoModelForCausalLM.from_pretrained(
        args.model_path,
        load_in_8bit=True,
        device_map="auto",
        torch_dtype=torch.float16,
    )
    
    # Set model to evaluation mode
    model.eval()
    
    # Prepare prompt
    prompt = f"Artist: {args.artist}\nLyrics:"
    print(f"Generating lyrics for artist: {args.artist}")
    
    # Tokenize prompt
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate text
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=args.max_length,
            temperature=args.temperature,
            top_p=args.top_p,
            top_k=args.top_k,
            num_return_sequences=args.num_return_sequences,
            pad_token_id=tokenizer.eos_token_id,
            do_sample=True,
        )
    
    # Decode and print generated text
    for i, output in enumerate(outputs):
        generated_text = tokenizer.decode(output, skip_special_tokens=True)
        print(f"\n--- Generated Lyrics {i+1} ---\n")
        print(generated_text)
        print("\n" + "-" * 50)

if __name__ == "__main__":
    main()