DeepSeek-V3/experimental/src/backends/metal/root.zig

// Metal Backend for DeepSeek V3 on Apple Silicon
// Leverages Metal Performance Shaders and unified memory architecture

const std = @import("std");
const deepseek_core = @import("deepseek_core");
const Allocator = std.mem.Allocator;
const metal_device = @import("device.zig");
const MetalDeviceInfo = metal_device.MetalDeviceInfo;

/// Metal backend implementation for Apple Silicon
pub const MetalBackend = struct {
    allocator: Allocator,
    device_available: bool,
    unified_memory_size: u64,
    device_info: ?MetalDeviceInfo,
    optimal_work_group_size: u32,
    memory_strategy: metal_device.getMemoryStrategy(),
    tensor_block_size: u32,
    
    const Self = @This();
    
    pub fn init(allocator: Allocator) !Self {
        // Check if Metal is available (compile-time check for macOS)
        const metal_available = @import("builtin").os.tag == .macos;
        
        var device_info: ?MetalDeviceInfo = null;
        var unified_memory_size: u64 = 0;
        var optimal_work_group_size: u32 = 64; // Default
        var tensor_block_size: u32 = 128; // Default
        
        if (metal_available) {
            // Detect Apple Silicon and M-series capabilities
            device_info = try metal_device.detectAppleSilicon(allocator);
            unified_memory_size = device_info.?.unified_memory_size;
            optimal_work_group_size = metal_device.getOptimalWorkGroupSize();
            tensor_block_size = metal_device.getOptimalTensorBlockSize();
            
            std.log.info("Metal Backend initialized on {s}", .{device_info.?.device_name});
            // Log detailed device information
            if (device_info.?.is_apple_silicon) {
                if (device_info.?.is_m_series) {
                    std.log.info("Detected M{d} {s} with {d}GB unified memory", 
                        .{
                            device_info.?.series_generation, 
                            device_info.?.variant,
                            unified_memory_size / (1024 * 1024 * 1024),
                        }
                    );
                } else {
                    std.log.info("Detected Apple Silicon (non-M series) with {d}GB unified memory", 
                        .{unified_memory_size / (1024 * 1024 * 1024)}
                    );
                }
            } else {
                std.log.warn("Metal is available but not running on Apple Silicon");
            }
        } else {
            std.log.warn("Metal Backend not available on this platform");
        }
        
        return Self{
            .allocator = allocator,
            .device_available = metal_available,
            .unified_memory_size = unified_memory_size,
            .device_info = device_info,
            .optimal_work_group_size = optimal_work_group_size,
            .memory_strategy = metal_device.getMemoryStrategy(),
            .tensor_block_size = tensor_block_size,
        };
    }
    
    pub fn deinit(self: *Self) void {
        // TODO: Release Metal resources
        _ = self;
    }
    
    /// Matrix multiplication using Metal Performance Shaders
    pub fn matmul(
        self: *Self,
        a: *deepseek_core.Tensor,
        b: *const deepseek_core.Tensor,
        c: *deepseek_core.Tensor,
    ) !void {
        if (!self.device_available) {
            return error.MetalNotAvailable;
        }
        
        std.log.debug("Metal matmul: {}x{} * {}x{} -> {}x{}", .{
            a.shape.dims[0], a.shape.dims[1],
            b.shape.dims[0], b.shape.dims[1], 
            c.shape.dims[0], c.shape.dims[1]
        });
        
        // Check if we're on Apple Silicon M series for optimized path
        if (self.device_info != null and self.device_info.?.is_m_series) {
            std.log.debug("Using optimized M{d} {s} matrix multiplication", 
                .{
                    self.device_info.?.series_generation, 
                    self.device_info.?.variant
                }
            );
            
            // Select appropriate implementation based on M series generation
            switch (self.device_info.?.series_generation) {
                3 => return try self.matmulM3(a, b, c), // M3 optimized path
                2 => return try self.matmulM2(a, b, c), // M2 optimized path
                1 => return try self.matmulM1(a, b, c), // M1 optimized path
                else => {} // Fall through to generic implementation
            }
        }
        
        // TODO: Implement actual Metal compute shader
        // This would involve:
        // 1. Create MTLBuffer from tensor data
        // 2. Set up compute pipeline with matmul shader
        // 3. Dispatch compute commands with optimized workgroup size based on device
        // 4. Copy results back to tensor
        
        // For now, fallback to CPU implementation
        std.log.warn("Falling back to CPU implementation, Metal not implemented");
        return error.NotImplemented;
    }
    
    /// M1-optimized matrix multiplication
    fn matmulM1(
        self: *Self,
        a: *deepseek_core.Tensor,
        b: *const deepseek_core.Tensor,
        c: *deepseek_core.Tensor,
    ) !void {
        _ = self;
        _ = a;
        _ = b;
        _ = c;
        
        // TODO: M1-specific optimizations
        // - Use MPSMatrixMultiplication with M1-specific parameters
        // - Optimize for 7/8 GPU cores typically found in M1
        // - Account for unified memory bandwidth on M1
        
        return error.NotImplemented;
    }
    
    /// M2-optimized matrix multiplication
    fn matmulM2(
        self: *Self,
        a: *deepseek_core.Tensor,
        b: *const deepseek_core.Tensor,
        c: *deepseek_core.Tensor,
    ) !void {
        _ = self;
        _ = a;
        _ = b;
        _ = c;
        
        // TODO: M2-specific optimizations
        // - Use MPSMatrixMultiplication with M2-specific parameters
        // - Optimize for 8/10 GPU cores typically found in M2
        // - Account for increased memory bandwidth on M2
        
        return error.NotImplemented;
    }
    
    /// M3-optimized matrix multiplication
    fn matmulM3(
        self: *Self,
        a: *deepseek_core.Tensor,
        b: *const deepseek_core.Tensor,
        c: *deepseek_core.Tensor,
    ) !void {
        _ = self;
        _ = a;
        _ = b;
        _ = c;
        
        // TODO: M3-specific optimizations
        // - Use MPSMatrixMultiplication with M3-specific parameters
        // - Optimize for 10/16 GPU cores typically found in M3
        // - Account for dynamic core switching on M3
        
        return error.NotImplemented;
    }
    
    /// RMS normalization using Metal compute shader
    pub fn rmsNorm(
        self: *Self,
        input: []const f32,
        weight: []const f32,
        output: []f32,
        eps: f32,
    ) !void {
        if (!self.device_available) {
            return error.MetalNotAvailable;
        }
        
        std.log.debug("Metal RMS normalization with {} elements", .{input.len});
        
        // Check if we're on Apple Silicon M series for optimized path
        if (self.device_info != null and self.device_info.?.is_m_series) {
            std.log.debug("Using optimized M{d} {s} RMS normalization", 
                .{
                    self.device_info.?.series_generation, 
                    self.device_info.?.variant
                }
            );
            
            // Select optimal workgroup size based on M series generation
            const workgroup_size = switch (self.device_info.?.series_generation) {
                3 => 256, // M3 has more GPU cores
                2 => 192, // M2 optimization
                else => 128, // M1 and others
            };
            
            // Determine if we should use unified memory approach
            const use_unified_memory = self.memory_strategy == .UnifiedMemory;
            
            // Calculate optimal thread count based on input size and GPU cores
            const thread_count = std.math.min(
                std.math.alignForward(usize, input.len, workgroup_size),
                workgroup_size * 1024 // Maximum reasonable thread count
            );
            
            std.log.debug("RMS Norm using workgroup size: {}, threads: {}", 
                .{workgroup_size, thread_count});
            
            // TODO: Implement Metal compute shader for RMS norm with M-series optimizations
            // 1. Create buffers (potentially using managed storage mode for unified memory)
            // 2. Set up compute pipeline with RMS norm shader
            // 3. Dispatch compute with optimal work group size
            // 4. Handle results with zero-copy when possible on unified memory
            
            if (!use_unified_memory) {
                // Would handle non-unified memory path differently
                std.log.debug("Using discrete memory path");
            }
            
            // thread_count is used in the log message above, don't discard it
        }
        
        // TODO: Complete implementation of Metal compute shader for RMS norm
        // Metal excels at parallel operations like normalization
        
        // Don't discard input since it's used above for thread_count calculation
        // Only discard these if not used above
        _ = weight;
        _ = output;
        _ = eps;
        
        return error.NotImplemented;
    }
    
    /// SwiGLU activation using Metal
    pub fn swiglu(
        self: *Self,
        input: []const f32,
        gate: []const f32,
        output: []f32,
    ) !void {
        if (!self.device_available) {
            return error.MetalNotAvailable;
        }
        
        _ = input;
        _ = gate;
        _ = output;
        
        std.log.debug("Metal SwiGLU activation");
        
        // TODO: Implement Metal compute shader for SwiGLU
        // GPU is perfect for element-wise operations like activations
        
        return error.NotImplemented;
    }
    
    /// Attention mechanism optimized for Apple Silicon
    pub fn attention(
        self: *Self,
        query: *deepseek_core.Tensor,
        key: *const deepseek_core.Tensor,
        value: *const deepseek_core.Tensor,
        output: *deepseek_core.Tensor,
    ) !void {
        if (!self.device_available) {
            return error.MetalNotAvailable;
        }
        
        _ = query;
        _ = key;
        _ = value;
        _ = output;
        
        std.log.debug("Metal attention mechanism");
        
        // TODO: Implement optimized attention for Apple Silicon
        // This would leverage:
        // - Unified memory for zero-copy operations
        // - Metal Performance Shaders for optimized GEMM
        // - Custom shaders for attention-specific operations
        
        return error.NotImplemented;
    }
    
    /// Check GPU memory usage
    pub fn getMemoryInfo(self: *Self) struct { used: u64, total: u64 } {
        if (!self.device_available) {
            return .{ .used = 0, .total = 0 };
        }
        
        // TODO: Query actual Metal device memory usage
        return .{ 
            .used = 0, // TODO: Get current usage
            .total = self.unified_memory_size,
        };
    }
};

/// Create the Metal backend interface
pub fn init(allocator: Allocator) !deepseek_core.Backend {
    // For now, return a simple backend struct
    // In a full implementation, this would create a MetalBackend and wrap it
    return deepseek_core.Backend.init(allocator, .metal, 0);
}

/// Metal compute shader templates (would be loaded from .metal files)
const metal_shaders = struct {
    // Matrix multiplication shader (simplified)
    const matmul_shader = 
        \\#include <metal_stdlib>
        \\using namespace metal;
        \\
        \\kernel void matmul_kernel(
        \\    device const float* a [[buffer(0)]],
        \\    device const float* b [[buffer(1)]],
        \\    device float* c [[buffer(2)]],
        \\    constant uint& M [[buffer(3)]],
        \\    constant uint& N [[buffer(4)]],
        \\    constant uint& K [[buffer(5)]],
        \\    uint2 gid [[thread_position_in_grid]]
        \\) {
        \\    if (gid.x >= N || gid.y >= M) return;
        \\    
        \\    float sum = 0.0;
        \\    for (uint k = 0; k < K; k++) {
        \\        sum += a[gid.y * K + k] * b[k * N + gid.x];
        \\    }
        \\    c[gid.y * N + gid.x] = sum;
        \\}
    ;
    
    // RMS normalization shader
    const rms_norm_shader = 
        \\#include <metal_stdlib>
        \\using namespace metal;
        \\
        \\kernel void rms_norm_kernel(
        \\    device const float* input [[buffer(0)]],
        \\    device const float* weight [[buffer(1)]],
        \\    device float* output [[buffer(2)]],
        \\    constant uint& size [[buffer(3)]],
        \\    constant float& eps [[buffer(4)]],
        \\    uint gid [[thread_position_in_grid]]
        \\) {
        \\    // Simplified RMS norm - would need proper reduction
        \\    if (gid >= size) return;
        \\    
        \\    // TODO: Implement proper parallel reduction for mean square
        \\    float mean_square = 0.0;
        \\    for (uint i = 0; i < size; i++) {
        \\        mean_square += input[i] * input[i];
        \\    }
        \\    mean_square /= size;
        \\    
        \\    float rms = sqrt(mean_square + eps);
        \\    output[gid] = (input[gid] / rms) * weight[gid];
        \\}
    ;
};

/// Capabilities for Apple Silicon
fn getAppleSiliconCapabilities() deepseek_core.Backend.Capabilities {
    return .{
        .supports_fp16 = true,  // Native fp16 support
        .supports_bf16 = true,  // M3+ supports bf16
        .supports_int8 = true,  // Efficient int8 operations
        .max_memory_gb = 128,   // Up to 128GB unified memory on Mac Studio
        .compute_capability = null,
        .simd_width = 32,       // Metal SIMD-group size
    };
}