tests/verify_implementation.py

#!/usr/bin/env python3
"""

Verification script to demonstrate all implemented functionality.

Run this to see layers.py and packing.py in action!

"""

import torch
import torch.nn as nn
from bitlinear import BitLinear, MultiTernaryLinear, convert_linear_to_bitlinear
from bitlinear.packing import (
    pack_ternary_base3,
    unpack_ternary_base3,
    estimate_memory_savings,
)


def demo_bitlinear():
    """Demonstrate BitLinear layer."""
    print("=" * 70)
    print("1. BitLinear Layer Demo")
    print("=" * 70)
    
    # Create layer
    layer = BitLinear(512, 256, bias=True)
    print(f"✓ Created BitLinear(512 → 256)")
    print(f"  - W_ternary shape: {layer.W_ternary.shape}")
    print(f"  - Gamma shape: {layer.gamma.shape}")
    print(f"  - Unique weight values: {sorted(layer.W_ternary.unique().tolist())}")
    
    # Forward pass
    x = torch.randn(16, 512)
    y = layer(x)
    print(f"\n✓ Forward pass: {x.shape} → {y.shape}")
    
    # Convert from Linear
    linear = nn.Linear(512, 256)
    bitlinear = BitLinear.from_linear(linear)
    print(f"✓ Converted nn.Linear to BitLinear")
    print()


def demo_multi_ternary():
    """Demonstrate MultiTernaryLinear layer."""
    print("=" * 70)
    print("2. MultiTernaryLinear Layer Demo")
    print("=" * 70)
    
    # Test different k values
    for k in [1, 2, 4]:
        layer = MultiTernaryLinear(256, 128, k=k, bias=True)
        print(f"✓ MultiTernaryLinear(256 → 128, k={k})")
        print(f"  - W_ternary shape: {layer.W_ternary.shape}")
        print(f"  - Gammas shape: {layer.gammas.shape}")
    
    # Compare approximation quality
    print("\n✓ Approximation quality test:")
    linear = nn.Linear(128, 128)
    x = torch.randn(8, 128)
    dense_out = linear(x)
    
    errors = []
    for k in [1, 2, 4]:
        multi = MultiTernaryLinear.from_linear(linear, k=k)
        ternary_out = multi(x)
        error = torch.norm(dense_out - ternary_out).item()
        errors.append(error)
        print(f"  - k={k}: reconstruction error = {error:.4f}")
    
    print(f"  - Error decreases with k: {errors[0] > errors[1] > errors[2]}")
    print()


def demo_model_conversion():
    """Demonstrate model conversion utility."""
    print("=" * 70)
    print("3. Model Conversion Utility Demo")
    print("=" * 70)
    
    # Create a simple model
    class SimpleModel(nn.Module):
        def __init__(self):
            super().__init__()
            self.fc1 = nn.Linear(128, 256)
            self.relu = nn.ReLU()
            self.fc2 = nn.Linear(256, 128)
            self.fc3 = nn.Linear(128, 10)
        
        def forward(self, x):
            x = self.relu(self.fc1(x))
            x = self.relu(self.fc2(x))
            return self.fc3(x)
    
    model = SimpleModel()
    
    # Count Linear layers
    linear_count = sum(1 for m in model.modules() if isinstance(m, nn.Linear))
    print(f"✓ Original model: {linear_count} Linear layers")
    
    # Convert to BitLinear
    model_converted = convert_linear_to_bitlinear(model, inplace=False)
    bitlinear_count = sum(1 for m in model_converted.modules() if isinstance(m, BitLinear))
    print(f"✓ Converted model: {bitlinear_count} BitLinear layers")
    
    # Test forward pass
    x = torch.randn(4, 128)
    y = model_converted(x)
    print(f"✓ Forward pass works: {x.shape} → {y.shape}")
    print()


def demo_packing():
    """Demonstrate base-3 packing."""
    print("=" * 70)
    print("4. Base-3 Packing Demo")
    print("=" * 70)
    
    # Create ternary weights
    W = torch.tensor([
        [-1, 0, 1, -1, 0],
        [1, 1, -1, 0, 1],
        [0, -1, 1, 1, -1],
    ], dtype=torch.float32)
    
    print(f"✓ Original ternary weights shape: {W.shape}")
    print(f"  - Float32 memory: {W.numel() * 4} bytes")
    
    # Pack
    packed, original_shape = pack_ternary_base3(W)
    print(f"\n✓ Packed into uint8 tensor")
    print(f"  - Packed shape: {packed.shape}")
    print(f"  - Packed memory: {packed.numel()} bytes")
    print(f"  - Compression: {W.numel() * 4 / packed.numel():.2f}x")
    
    # Unpack
    W_unpacked = unpack_ternary_base3(packed, original_shape)
    print(f"\n✓ Unpacked back to ternary")
    print(f"  - Unpacked shape: {W_unpacked.shape}")
    print(f"  - Perfect round-trip: {torch.allclose(W, W_unpacked)}")
    print()


def demo_memory_estimation():
    """Demonstrate memory savings estimation."""
    print("=" * 70)
    print("5. Memory Savings Estimation")
    print("=" * 70)
    
    configs = [
        (768, 3072, 1, "Single Transformer FFN layer"),
        (768, 3072, 12, "BERT-base (12 layers)"),
        (1024, 4096, 24, "BERT-large (24 layers)"),
    ]
    
    for in_dim, out_dim, num_layers, description in configs:
        stats = estimate_memory_savings(in_dim, out_dim, num_layers)
        print(f"\n✓ {description}")
        print(f"  Configuration: {in_dim} → {out_dim} × {num_layers} layers")
        print(f"  Float32 memory: {stats['float32_bytes'] / 1e6:.2f} MB")
        print(f"  Packed memory:  {stats['packed_bytes'] / 1e6:.2f} MB")
        print(f"  Savings:        {stats['savings_bytes'] / 1e6:.2f} MB")
        print(f"  Compression:    {stats['compression_ratio']:.2f}x")
    print()


def main():
    """Run all demos."""
    print("\n" + "=" * 70)
    print(" BitLinear Implementation Verification")
    print(" All functionality implemented and working!")
    print("=" * 70)
    print()
    
    demo_bitlinear()
    demo_multi_ternary()
    demo_model_conversion()
    demo_packing()
    demo_memory_estimation()
    
    print("=" * 70)
    print(" ✓ All implementations verified!")
    print(" ✓ Ready for C++/CUDA optimization")
    print("=" * 70)
    print()


if __name__ == "__main__":
    main()