DiffuCoder-7B-cpGRPO Evaluation

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Human written README

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This repository was created for the prupose of evaluating the efficacy and speed of Apple's recent DiffuCoder-7B-cpGRPO code generation model.

Result: With a single A100 (SXM4) machine on lambda labs, the empiracle evidence shows a speed of upwards of 25 tok / second in performance.

The code snippets generated appear reasonably created, you can run python diffucoder_example.py to have it generate example outputs for your input prompt, along with a few sample prompts.

The exact machine used was the following configuration on lambdalabs: 1x A100 (40 GB SXM4) 30 vCPUs, 200 GiB RAM, 0.5 TiB SSD

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AI Generated README

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This repository contains example code for using Apple's DiffuCoder-7B-cpGRPO model, a diffusion-based large language model specifically designed for code generation.

About DiffuCoder

DiffuCoder is a novel approach to code generation that uses diffusion models instead of traditional autoregressive generation. Key features:

• Diffusion-based generation: Uses a diffusion process for better code quality
• Reduced AR bias: Less reliance on autoregressive bias during decoding
• Code-specific training: Specialized for code generation tasks
• 7B parameters: Efficient yet powerful model size

Model Details

• Model: apple/DiffuCoder-7B-cpGRPO
• Paper: DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
• Base Model: Qwen2.5-7B-Coder
• Training: Reinforcement learning via Coupled-GRPO

Requirements

Hardware

• GPU: A100 or similar high-end GPU recommended
• VRAM: At least 16GB (for bfloat16 precision)
• RAM: 32GB+ recommended

Software

• Python 3.8+
• CUDA-compatible PyTorch
• Internet connection (for model download)

Quick Start

1. Clone and setup:

   # Install dependencies
   pip install -r requirements.txt
   
   # Or use the setup script
   python setup_and_run.py
   

2. Run the example:

   python diffucoder_example.py
   

3. Monitor performance:

   # Quick tokens/second check
   python tokens_per_second_monitor.py
   
   # Full benchmark suite
   python benchmark_diffucoder.py
   
   # Test with very large prompts
   python large_prompt_benchmark.py
   

Files Overview

diffucoder_example.py

The main example script that demonstrates DiffuCoder usage:

• DiffuCoderDemo class: Handles model loading and generation
• Multiple examples: Various code generation tasks
• Interactive mode: Real-time code generation
• GPU optimization: Efficient memory usage for A100
• Detailed tokens/second tracking: Accurate performance metrics

tokens_per_second_monitor.py

Dedicated tokens/second performance monitoring:

• Quick tests: Fast performance checks
• Parameter sweeps: Find optimal settings for speed
• Continuous monitoring: Real-time performance tracking
• Performance history: Track performance over time
• Interactive menu: Easy-to-use interface

benchmark_diffucoder.py

Comprehensive benchmarking suite:

• Parameter combinations: Test different settings
• Query types: Benchmark various code generation tasks
• Detailed metrics: Tokens/second, memory usage, efficiency
• Performance comparison: Find optimal configurations
• JSON export: Save results for analysis

large_prompt_benchmark.py

Large prompt performance testing:

• Prompt size scaling: Test small to extremely large prompts
• Context limit testing: Find maximum context length
• Performance correlation: Analyze speed vs context size
• Memory monitoring: Track GPU usage with large contexts
• Scaling analysis: Understand how performance changes with prompt size

requirements.txt

Python dependencies needed to run the example:

• torch>=2.0.0
• transformers>=4.36.0
• accelerate>=0.20.0
• safetensors>=0.3.0
• sentencepiece>=0.1.99
• protobuf>=3.20.0

setup_and_run.py

Automated setup script that:

• Checks GPU availability
• Installs dependencies
• Runs the example

Usage Examples

Basic Code Generation

from diffucoder_example import DiffuCoderDemo

demo = DiffuCoderDemo()
result = demo.generate_code("Write a function to reverse a string")
print(result)

Custom Parameters

result = demo.generate_code(
    "Create a sorting algorithm",
    max_new_tokens=300,
    temperature=0.6,
    top_p=0.9
)

Batch Processing

queries = [
    "Write a binary search function",
    "Create a linked list class",
    "Implement a hash table"
]

for query in queries:
    result = demo.generate_code(query)
    print(f"Query: {query}")
    print(f"Result: {result}")
    print("-" * 50)

Key Features

Diffusion Generation

Unlike traditional autoregressive models, DiffuCoder uses a diffusion process:

• Steps: Number of diffusion steps (default: max_tokens // token_per_step)
• Algorithm: Diffusion algorithm ('entropy' by default)
• Token per step: Tokens generated per diffusion step

Optimizations for A100

• bfloat16 precision: Reduces memory usage
• Device mapping: Automatic GPU placement
• Memory cleanup: Efficient VRAM management
• Batch processing: Optimized for multiple generations

Chat Template

DiffuCoder uses a specific chat template format:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{user_query}<|im_end|>
<|im_start|>assistant

Performance Tips

Tokens/Second Optimization

• Monitor actual vs requested tokens: Use return_stats=True to get accurate metrics
• Optimal parameters: Use the parameter sweep tool to find best settings
• Temperature tuning: Lower temperatures (0.2-0.4) often yield better speeds
• Step optimization: Balance between quality and speed with the steps parameter
• Token efficiency: Track the percentage of requested tokens actually generated

Monitoring Tools

• Quick performance check: python tokens_per_second_monitor.py
• Real-time monitoring: Continuous tracking with customizable intervals
• Performance history: Track improvements over time
• Parameter sweeps: Automatically find optimal settings

Large Prompt Optimization

• Test context limits: Use python large_prompt_benchmark.py to find maximum context
• Performance scaling: Understand how speed changes with prompt size
• Memory management: Monitor GPU usage with large contexts
• Context efficiency: Balance prompt length with generation quality
• Progressive testing: Start small and scale up to find optimal size

For A100 GPU

• Use torch_dtype=torch.bfloat16 for efficiency
• Set device_map="auto" for optimal placement
• Use batch processing for multiple queries
• Enable gradient checkpointing if needed

Memory Management

• Call torch.cuda.empty_cache() between generations
• Use smaller max_new_tokens for memory-constrained scenarios
• Consider using torch.cuda.memory_summary() for monitoring

Troubleshooting

Common Issues

1. CUDA Out of Memory:

   • Reduce max_new_tokens
   • Use torch.cuda.empty_cache()
   • Ensure no other processes are using GPU

2. Model Loading Errors:

   • Check internet connection
   • Verify HuggingFace token if needed
   • Ensure sufficient disk space

3. Slow Performance:

   • Verify GPU usage with nvidia-smi
   • Check if using bfloat16 precision
   • Ensure CUDA version compatibility

GPU Monitoring

# Check GPU status
nvidia-smi

# Monitor in real-time
watch -n 1 nvidia-smi

Advanced Usage

Custom System Messages

result = demo.generate_code(
    "Create a web scraper",
    system_message="You are an expert Python developer focused on web scraping."
)

Parameter Tuning

• Temperature: Controls randomness (0.1-1.0)
• Top-p: Nucleus sampling (0.1-1.0)
• Steps: More steps = potentially better quality
• Token per step: Balance between quality and speed

Contributing

Feel free to submit issues and enhancement requests!

License

This example code is provided as-is for educational purposes. Please check the model's license on HuggingFace.

References

• DiffuCoder Paper
• HuggingFace Model Page
• Apple ML Research