implement_kernel_fusion.py

#!/usr/bin/env python3
"""
Kernel Fusion Implementation for ProteinMPNN

Strategy: Fuse message passing operations into single Metal kernel
Goal: Reduce memory bandwidth bottleneck via MLX custom operations

This implementation demonstrates kernel fusion for the message passing step.
"""

import sys
from pathlib import Path

print("=" * 70)
print("KERNEL FUSION IMPLEMENTATION")
print("=" * 70)

# Check dependencies
print("\n1. CHECKING DEPENDENCIES")
print("-" * 70)

deps_available = True

try:
    import torch
    import torch.nn as nn
    print(f"✅ PyTorch {torch.__version__}")
    TORCH_AVAILABLE = True
except ImportError:
    print("❌ PyTorch not available")
    deps_available = False
    TORCH_AVAILABLE = False

try:
    import mlx
    import mlx.core as mx
    import mlx.nn as mlxnn
    print(f"✅ MLX {mlx.__version__}")
    MLX_AVAILABLE = True
except ImportError:
    print("❌ MLX not available - install with: pip install mlx")
    deps_available = False
    MLX_AVAILABLE = False

if not deps_available:
    print("\n⚠️  Missing dependencies. Please install:")
    print("   pip install mlx torch")
    print("\nProceeding with implementation design...")

print("\n2. MESSAGE PASSING OPERATION ANALYSIS")
print("-" * 70)

print("""
Current ProteinMPNN message passing (non-fused):

Step 1: Gather neighbors
  h_neighbors = h[E_idx]  # Shape: (B, L, k, D)
  → Memory reads: B*L*k*D values

Step 2: Compute edge features
  edge_features = EdgeFeatures(X, E_idx)  # Distances, angles
  → Memory reads: B*L*k*3 values
  → Compute: Distance calculations
  → Memory writes: B*L*k*edge_dim values

Step 3: Message MLP
  messages = MessageMLP(concat(h_i, h_neighbors, edge_features))
  → Memory reads: B*L*k*(2*D + edge_dim) values
  → Compute: Linear + GELU + Linear
  → Memory writes: B*L*k*D values

Step 4: Aggregate
  m_agg = messages.mean(dim=2)  # Average over k neighbors
  → Memory reads: B*L*k*D values
  → Memory writes: B*L*D values

Step 5: Update MLP
  h_update = UpdateMLP(m_agg)
  → Memory reads: B*L*D values
  → Compute: Linear + GELU + Linear
  → Memory writes: B*L*D values

Step 6: Residual + Norm
  h_new = LayerNorm(h + h_update)
  → Memory reads: 2 * B*L*D values
  → Memory writes: B*L*D values

Total memory operations (unfused):
  - Reads: ~8-10 passes over data
  - Writes: ~4-5 passes over data
  → If 50% memory-bound: Fusion can provide 2-3x speedup
""")

print("\n3. FUSED KERNEL DESIGN")
print("-" * 70)

print("""
Fused Message Passing Kernel:

Input:
  - h: Node features (B, L, D)
  - X: Node coordinates (B, L, 3)
  - E_idx: Neighbor indices (B, L, k)
  - Weights: MLP weights (W1, b1, W2, b2, W3, b3, W4, b4)
  - Mask: Node mask (B, L)

Output:
  - h_new: Updated node features (B, L, D)

Pseudo-code for fused kernel:
```metal
kernel void fused_message_passing(
    device const float* h [[buffer(0)]],
    device const float* X [[buffer(1)]],
    device const int* E_idx [[buffer(2)]],
    device const float* weights [[buffer(3)]],
    device float* h_out [[buffer(4)]],
    uint2 gid [[thread_position_in_grid]])
{
    // Each thread processes one node
    int b = gid.y;  // Batch
    int i = gid.x;  // Node index

    // Load node feature into threadgroup memory (tile memory)
    threadgroup float h_local[D];
    load_vector(h_local, &h[b*L*D + i*D], D);

    // Message passing loop
    float messages[D] = {0};
    for (int k_idx = 0; k_idx < k; k_idx++) {
        // 1. Gather neighbor
        int j = E_idx[b*L*k + i*k + k_idx];
        threadgroup float h_j[D];
        load_vector(h_j, &h[b*L*D + j*D], D);

        // 2. Compute edge features (in registers)
        float3 pos_i = load_float3(&X[b*L*3 + i*3]);
        float3 pos_j = load_float3(&X[b*L*3 + j*3]);
        float dist = distance(pos_i, pos_j);
        float3 direction = normalize(pos_j - pos_i);

        // 3. Message MLP (all in registers/tile memory)
        float concat[2*D + 4];  // [h_i, h_j, dist, direction]
        copy(concat, h_local, D);
        copy(concat + D, h_j, D);
        concat[2*D] = dist;
        concat[2*D + 1] = direction.x;
        concat[2*D + 2] = direction.y;
        concat[2*D + 3] = direction.z;

        // MLP: W1(concat) + b1
        float hidden[2*D];
        matmul(hidden, weights.W1, concat, 2*D, 2*D+4);
        add_bias(hidden, weights.b1, 2*D);
        gelu(hidden, 2*D);

        // MLP: W2(hidden) + b2
        float message[D];
        matmul(message, weights.W2, hidden, D, 2*D);
        add_bias(message, weights.b2, D);

        // 4. Accumulate message (in registers)
        add_vectors(messages, message, D);
    }

    // 5. Average messages
    scale_vector(messages, 1.0 / k, D);

    // 6. Update MLP
    float hidden2[2*D];
    matmul(hidden2, weights.W3, messages, 2*D, D);
    add_bias(hidden2, weights.b3, 2*D);
    gelu(hidden2, 2*D);

    float h_update[D];
    matmul(h_update, weights.W4, hidden2, D, 2*D);
    add_bias(h_update, weights.b4, D);

    // 7. Residual + LayerNorm
    float h_new[D];
    add_vectors(h_new, h_local, h_update, D);
    layer_norm(h_new, D);  // In-place

    // 8. Write output (single write)
    store_vector(&h_out[b*L*D + i*D], h_new, D);
}
```

Memory operations (fused):
  - Reads: 2 passes (h, X once; E_idx for indices)
  - Writes: 1 pass (h_out)
  → 3 total passes vs 12-15 unfused

Expected speedup:
  - If 80% memory-bound: 4-5x reduction in memory traffic → 3-4x speedup
  - If 50% memory-bound: 4-5x reduction → 2-2.5x speedup
  - Realistic: 1.5-2.5x on message passing step
  - If message passing is 60% of time: 1.3-1.5x overall
""")

print("\n4. MLX IMPLEMENTATION")
print("-" * 70)

if MLX_AVAILABLE and TORCH_AVAILABLE:
    print("Implementing fused message passing in MLX...\n")

    # MLX Implementation
    class FusedMessagePassing(mlxnn.Module):
        """
        Fused message passing operation using MLX.

        This combines gather, edge features, message MLP, aggregation,
        update MLP, and layer norm into a single operation.
        """

        def __init__(self, hidden_dim, k_neighbors):
            super().__init__()
            self.hidden_dim = hidden_dim
            self.k_neighbors = k_neighbors

            # Message MLP
            self.message_mlp = mlxnn.Sequential(
                mlxnn.Linear(2 * hidden_dim + 4, 2 * hidden_dim),
                mlxnn.GELU(),
                mlxnn.Linear(2 * hidden_dim, hidden_dim)
            )

            # Update MLP
            self.update_mlp = mlxnn.Sequential(
                mlxnn.Linear(hidden_dim, 2 * hidden_dim),
                mlxnn.GELU(),
                mlxnn.Linear(2 * hidden_dim, hidden_dim)
            )

            # Layer norm
            self.norm = mlxnn.LayerNorm(hidden_dim)

        def __call__(self, h, X, E_idx, mask):
            """
            Fused message passing.

            Args:
                h: Node features (B, L, D)
                X: Node coordinates (B, L, 3)
                E_idx: Neighbor indices (B, L, k)
                mask: Node mask (B, L)

            Returns:
                h_new: Updated features (B, L, D)
            """
            B, L, D = h.shape
            k = self.k_neighbors

            # This would be implemented as a custom Metal kernel
            # For now, showing the logical flow:

            # 1. Gather neighbors
            # h_neighbors = h[E_idx]  # (B, L, k, D)
            h_flat = h.reshape(B * L, D)
            E_idx_flat = E_idx.reshape(B * L * k)
            h_neighbors = h_flat[E_idx_flat].reshape(B, L, k, D)

            # 2. Compute edge features
            X_i = mx.expand_dims(X, 2)  # (B, L, 1, 3)
            X_j_indices = E_idx.reshape(B, L * k)
            X_flat = X.reshape(B * L, 3)
            X_j = mx.take(X_flat, X_j_indices, axis=0).reshape(B, L, k, 3)

            # Edge features: distance and direction
            delta = X_j - X_i  # (B, L, k, 3)
            dist = mx.sqrt(mx.sum(delta ** 2, axis=-1, keepdims=True))  # (B, L, k, 1)
            direction = delta / (dist + 1e-8)  # (B, L, k, 3)
            edge_features = mx.concatenate([dist, direction], axis=-1)  # (B, L, k, 4)

            # 3. Message MLP
            h_i = mx.expand_dims(h, 2)  # (B, L, 1, D)
            h_i = mx.broadcast_to(h_i, (B, L, k, D))
            message_input = mx.concatenate([h_i, h_neighbors, edge_features], axis=-1)
            messages = self.message_mlp(message_input)  # (B, L, k, D)

            # 4. Aggregate
            m_agg = mx.mean(messages, axis=2)  # (B, L, D)

            # 5. Update MLP
            h_update = self.update_mlp(m_agg)  # (B, L, D)

            # 6. Residual + Norm
            h_new = self.norm(h + h_update)

            # Apply mask
            mask_expanded = mx.expand_dims(mask, -1)
            h_new = h_new * mask_expanded

            return h_new

    print("✅ MLX FusedMessagePassing class defined")
    print("   This is the logical implementation.")
    print("   For true fusion, would need custom Metal kernel via @mx.custom_vjp")

else:
    print("⚠️  Cannot implement - MLX or PyTorch not available")
    print("   Showing design only")

print("\n5. PYTORCH BASELINE FOR COMPARISON")
print("-" * 70)

if TORCH_AVAILABLE:
    print("Implementing PyTorch baseline (unfused)...\n")

    class UnfusedMessagePassing(nn.Module):
        """Standard PyTorch message passing (unfused)."""

        def __init__(self, hidden_dim, k_neighbors):
            super().__init__()
            self.hidden_dim = hidden_dim
            self.k_neighbors = k_neighbors

            # Message MLP
            self.message_mlp = nn.Sequential(
                nn.Linear(2 * hidden_dim + 4, 2 * hidden_dim),
                nn.GELU(),
                nn.Linear(2 * hidden_dim, hidden_dim)
            )

            # Update MLP
            self.update_mlp = nn.Sequential(
                nn.Linear(hidden_dim, 2 * hidden_dim),
                nn.GELU(),
                nn.Linear(2 * hidden_dim, hidden_dim)
            )

            # Layer norm
            self.norm = nn.LayerNorm(hidden_dim)

        def forward(self, h, X, E_idx, mask):
            """Unfused message passing."""
            B, L, D = h.shape
            k = self.k_neighbors

            # 1. Gather neighbors (memory read)
            h_flat = h.reshape(B * L, D)
            E_idx_flat = E_idx.reshape(B * L * k)
            h_neighbors = torch.index_select(h_flat, 0, E_idx_flat).reshape(B, L, k, D)

            # 2. Edge features (memory read + write)
            X_i = X.unsqueeze(2)
            X_j = torch.index_select(
                X.reshape(B * L, 3), 0, E_idx.reshape(B, L * k)
            ).reshape(B, L, k, 3)

            delta = X_j - X_i
            dist = torch.sqrt(torch.sum(delta ** 2, dim=-1, keepdim=True))
            direction = delta / (dist + 1e-8)
            edge_features = torch.cat([dist, direction], dim=-1)

            # 3. Message MLP (memory read + write)
            h_i = h.unsqueeze(2).expand(-1, -1, k, -1)
            message_input = torch.cat([h_i, h_neighbors, edge_features], dim=-1)
            messages = self.message_mlp(message_input)

            # 4. Aggregate (memory read + write)
            m_agg = messages.mean(dim=2)

            # 5. Update MLP (memory read + write)
            h_update = self.update_mlp(m_agg)

            # 6. Residual + Norm (memory read + write)
            h_new = self.norm(h + h_update)

            # Apply mask
            h_new = h_new * mask.unsqueeze(-1)

            return h_new

    print("✅ PyTorch UnfusedMessagePassing class defined")

else:
    print("⚠️  Cannot implement - PyTorch not available")

print("\n6. BENCHMARKING PLAN")
print("-" * 70)

print("""
Benchmark setup:
1. Create test inputs: h, X, E_idx, mask
2. Initialize both models with same weights
3. Warmup: 10 runs each
4. Timing: 100 runs each with proper synchronization
5. Compare: PyTorch MPS vs MLX fused

Metrics:
- Time per forward pass (ms)
- Speedup ratio (PyTorch / MLX)
- Memory bandwidth utilization
- GPU kernel execution time

Expected results:
- If memory-bound (likely): 1.5-2.5x speedup
- If compute-bound: 1.1-1.3x speedup
- If overhead-bound: 0.9-1.1x (no benefit)

Success criteria:
- >1.2x speedup on forward pass
- Numerically equivalent outputs (< 1e-5 difference)
- Stable timing (low variance)
""")

print("\n7. INTEGRATION WITH PROTEINMPNN")
print("-" * 70)

print("""
If kernel fusion is successful:

1. Replace message passing in encoder layers
   - Current: 3 encoder layers × message passing
   - Fused: 3 × FusedMessagePassing

2. Replace message passing in decoder layers
   - Current: 3 decoder layers × message passing
   - Fused: 3 × FusedMessagePassing

3. Full model speedup estimate:
   - If message passing is 60% of time
   - If fusion gives 2x on message passing
   - Overall: 1 / (0.4 + 0.6/2) = 1 / 0.7 = 1.43x speedup

4. Combined with EXTREME-v2 (8.18x):
   - New total: 8.18 × 1.43 = 11.7x speedup

5. Throughput:
   - Current: 55,613 res/sec
   - With fusion: 79,526 res/sec
""")

print("\n8. IMPLEMENTATION STATUS")
print("-" * 70)

if MLX_AVAILABLE and TORCH_AVAILABLE:
    print("✅ Dependencies available")
    print("✅ Design complete")
    print("✅ Logical implementation complete")
    print("⚠️  Custom Metal kernel required for true fusion")
    print("⚠️  Benchmarking requires test data")
    print("\nNext steps:")
    print("1. Implement custom Metal kernel via MLX")
    print("2. Create benchmark script")
    print("3. Test on real ProteinMPNN data")
    print("4. Measure actual speedup")
else:
    print("❌ Dependencies not available")
    print("✅ Design documented")
    print("✅ Implementation plan complete")
    print("\nTo proceed:")
    print("1. Install MLX: pip install mlx")
    print("2. Ensure PyTorch with MPS")
    print("3. Run this script again")

print("\n" + "=" * 70)
print("KERNEL FUSION IMPLEMENTATION DESIGN COMPLETE")
print("=" * 70)

# Save design summary
output_dir = Path('output')
output_dir.mkdir(exist_ok=True)

design_summary = {
    'optimization': 'Kernel Fusion',
    'strategy': 'Fuse message passing into single Metal kernel',
    'operations_fused': [
        'Gather neighbors',
        'Compute edge features',
        'Message MLP',
        'Aggregate messages',
        'Update MLP',
        'Residual + LayerNorm'
    ],
    'memory_reduction': '12-15 passes → 3 passes',
    'expected_speedup': {
        'message_passing': '1.5-2.5x',
        'full_model': '1.3-1.5x',
        'combined_with_extreme_v2': '11.7x (from 8.18x)'
    },
    'implementation_status': 'Design complete, requires custom Metal kernel',
    'dependencies': {
        'mlx': MLX_AVAILABLE if 'MLX_AVAILABLE' in locals() else False,
        'pytorch': TORCH_AVAILABLE if 'TORCH_AVAILABLE' in locals() else False
    }
}

import json
with open(output_dir / 'kernel_fusion_design.json', 'w') as f:
    json.dump(design_summary, f, indent=2)

print(f"\n✅ Design summary saved to: {output_dir / 'kernel_fusion_design.json'}")