test_zero_gpu.py

#!/usr/bin/env python3
"""
Test script for Zero GPU functionality
"""

import os
import sys
import torch
import gradio as gr

def test_environment():
    """Test environment setup for Zero GPU"""
    print("=" * 60)
    print("Testing Zero GPU Environment")
    print("=" * 60)

    # Check if CUDA is available
    cuda_available = torch.cuda.is_available()
    print(f"✓ CUDA Available: {cuda_available}")

    if cuda_available:
        print(f"  - Device Name: {torch.cuda.get_device_name()}")
        print(f"  - Device Capability: {torch.cuda.get_device_capability()}")
        print(f"  - Memory Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
        print(f"  - Memory Cached: {torch.cuda.memory_reserved() / 1024**2:.2f} MB")

    # Check environment variables
    print("\n✓ Environment Variables:")
    zero_gpu_vars = {
        "ZERO_GPU_MODE": os.environ.get("ZERO_GPU_MODE", "not set"),
        "SPACES_ZERO_GPU": os.environ.get("SPACES_ZERO_GPU", "not set"),
        "MAX_IMAGES": os.environ.get("MAX_IMAGES", "not set"),
        "MAX_RESOLUTION": os.environ.get("MAX_RESOLUTION", "not set"),
        "BATCH_SIZE": os.environ.get("BATCH_SIZE", "not set"),
        "USE_FLOAT16": os.environ.get("USE_FLOAT16", "not set"),
    }

    for key, value in zero_gpu_vars.items():
        print(f"  - {key}: {value}")

    # Check if spaces module is available
    try:
        import spaces
        print("\n✓ Spaces module: Available")
        print(f"  - Has GPU decorator: {hasattr(spaces, 'GPU')}")
    except ImportError:
        print("\n✗ Spaces module: Not available (install with 'pip install spaces')")

    print("\n" + "=" * 60)

def test_model_loading():
    """Test model loading with Zero GPU optimizations"""
    print("\nTesting Model Loading...")
    print("-" * 60)

    # Set Zero GPU mode for testing
    os.environ["ZERO_GPU_MODE"] = "1"
    os.environ["MAX_IMAGES"] = "10"
    os.environ["BATCH_SIZE"] = "2"
    os.environ["USE_FLOAT16"] = "1"

    # Import mvp module to test ModelManager
    try:
        import mvp
        print("✓ MVP module imported successfully")

        # Check if ModelManager exists
        if hasattr(mvp, 'model_manager'):
            print("✓ ModelManager instance available")

            # Check manager configuration
            manager = mvp.model_manager
            print(f"  - Device: {manager.device}")
            print(f"  - USE_FLOAT16: {mvp.USE_FLOAT16}")
            print(f"  - MAX_IMAGES: {mvp.MAX_IMAGES}")
            print(f"  - BATCH_SIZE: {mvp.BATCH_SIZE}")
        else:
            print("✗ ModelManager not found in mvp module")

    except ImportError as e:
        print(f"✗ Failed to import mvp module: {e}")
    except Exception as e:
        print(f"✗ Error during model loading test: {e}")

    print("-" * 60)

def test_cpu_mode():
    """Test CPU fallback mode"""
    print("\nTesting CPU Fallback Mode...")
    print("-" * 60)

    # Temporarily disable CUDA
    original_cuda = os.environ.get("CUDA_VISIBLE_DEVICES")
    os.environ["CUDA_VISIBLE_DEVICES"] = ""

    try:
        cuda_available = torch.cuda.is_available()
        print(f"✓ CUDA disabled: {not cuda_available}")

        # Test CPU mode flags
        os.environ["ZOO3D_ALLOW_CPU"] = "1"
        os.environ["ZOO3D_CPU_DUMMY"] = "1"

        import mvp
        print("✓ MVP module works in CPU mode")
        print(f"  - ZOO3D_ALLOW_CPU: {mvp.ZOO3D_ALLOW_CPU}")
        print(f"  - ZOO3D_CPU_DUMMY: {mvp.ZOO3D_CPU_DUMMY}")

    except Exception as e:
        print(f"✗ CPU mode test failed: {e}")
    finally:
        # Restore original CUDA setting
        if original_cuda:
            os.environ["CUDA_VISIBLE_DEVICES"] = original_cuda
        else:
            os.environ.pop("CUDA_VISIBLE_DEVICES", None)

    print("-" * 60)

def test_memory_optimization():
    """Test memory optimization features"""
    print("\nTesting Memory Optimization...")
    print("-" * 60)

    if not torch.cuda.is_available():
        print("⚠ Skipping GPU memory tests (CUDA not available)")
        return

    # Test float16 usage
    print("✓ Testing float16 precision:")
    test_tensor = torch.randn(100, 100, device='cuda')
    fp16_tensor = test_tensor.half()

    fp32_memory = test_tensor.element_size() * test_tensor.nelement()
    fp16_memory = fp16_tensor.element_size() * fp16_tensor.nelement()

    print(f"  - FP32 memory: {fp32_memory / 1024:.2f} KB")
    print(f"  - FP16 memory: {fp16_memory / 1024:.2f} KB")
    print(f"  - Memory saved: {(1 - fp16_memory/fp32_memory) * 100:.1f}%")

    # Test batch processing
    print("\n✓ Testing batch processing:")
    batch_size = int(os.environ.get("BATCH_SIZE", "2"))
    total_images = 10

    num_batches = (total_images + batch_size - 1) // batch_size
    print(f"  - Total images: {total_images}")
    print(f"  - Batch size: {batch_size}")
    print(f"  - Number of batches: {num_batches}")

    # Clean up
    del test_tensor, fp16_tensor
    torch.cuda.empty_cache()

    print("-" * 60)

def main():
    """Run all tests"""
    print("\n🚀 Zoo3D Zero GPU Compatibility Test Suite\n")

    # Run tests
    test_environment()
    test_model_loading()
    test_cpu_mode()
    test_memory_optimization()

    print("\n✅ All tests completed!")
    print("\nTo test in different modes, try:")
    print("  - Zero GPU mode: ZERO_GPU_MODE=1 python test_zero_gpu.py")
    print("  - CPU mode: CUDA_VISIBLE_DEVICES='' python test_zero_gpu.py")
    print("  - Normal GPU: python test_zero_gpu.py")

if __name__ == "__main__":
    main()