use crate::context::GpuContext;
use crate::error::{GpuError, GpuResult};
use std::collections::HashMap;
use tracing::{debug, info};
#[derive(Debug, Clone)]
pub struct MetalOptimizationConfig {
pub enable_mps: bool,
pub enable_unified_memory: bool,
pub enable_threadgroup_memory: bool,
pub enable_argument_buffers: bool,
pub threadgroup_size: (u32, u32, u32),
}
impl Default for MetalOptimizationConfig {
fn default() -> Self {
Self {
enable_mps: true,
enable_unified_memory: true,
enable_threadgroup_memory: true,
enable_argument_buffers: true,
threadgroup_size: (256, 1, 1),
}
}
}
pub struct MetalFeatureDetector {
features: MetalFeatures,
}
#[derive(Debug, Clone)]
pub struct MetalFeatures {
pub family: MetalFamily,
pub unified_memory: bool,
pub mps_support: bool,
pub max_threadgroup_memory: u64,
pub simd_width: u32,
pub argument_buffers: bool,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MetalFamily {
Apple7,
Apple8,
Apple9,
Mac2,
Mac3,
Mac4,
Unknown,
}
impl Default for MetalFeatures {
fn default() -> Self {
Self {
family: MetalFamily::Mac2,
unified_memory: true,
mps_support: true,
max_threadgroup_memory: 32 * 1024,
simd_width: 32,
argument_buffers: true,
}
}
}
impl MetalFeatureDetector {
pub fn new(context: &GpuContext) -> Self {
let features = Self::detect_features(context);
info!(
"Metal features: family={:?}, unified_memory={}, mps={}, simd_width={}",
features.family, features.unified_memory, features.mps_support, features.simd_width
);
Self { features }
}
pub fn features(&self) -> &MetalFeatures {
&self.features
}
fn detect_features(context: &GpuContext) -> MetalFeatures {
let adapter_info = context.adapter_info();
let name = adapter_info.name.to_lowercase();
let family = if name.contains("m1")
|| name.contains("m2")
|| name.contains("m3")
|| name.contains("m4")
{
if name.contains("m4") {
MetalFamily::Mac4
} else if name.contains("m3") {
MetalFamily::Mac3
} else {
MetalFamily::Mac2
}
} else if name.contains("apple") {
MetalFamily::Apple7
} else {
MetalFamily::Unknown
};
let unified_memory = matches!(
family,
MetalFamily::Mac2 | MetalFamily::Mac3 | MetalFamily::Mac4
);
MetalFeatures {
family,
unified_memory,
mps_support: true,
max_threadgroup_memory: 32 * 1024,
simd_width: 32,
argument_buffers: true,
}
}
}
pub struct MetalShaderOptimizer {
features: MetalFeatures,
config: MetalOptimizationConfig,
}
impl MetalShaderOptimizer {
pub fn new(features: MetalFeatures, config: MetalOptimizationConfig) -> Self {
Self { features, config }
}
pub fn optimize_shader(&self, shader_code: &str) -> String {
let mut optimized = shader_code.to_string();
if !optimized.contains("SIMD_WIDTH") {
let simd_decl = format!("\nconst SIMD_WIDTH: u32 = {}u;\n", self.features.simd_width);
optimized.insert_str(0, &simd_decl);
}
if self.config.enable_threadgroup_memory {
optimized.push_str(Self::threadgroup_memory_helpers());
}
if self.config.enable_unified_memory && self.features.unified_memory {
optimized.insert_str(0, "\n// Unified Memory Optimized for Apple Silicon\n");
}
optimized
}
pub fn calculate_threadgroup_size(&self, num_elements: u64) -> (u32, u32, u32) {
let max_threads = 1024;
if num_elements <= max_threads as u64 {
return (num_elements as u32, 1, 1);
}
self.config.threadgroup_size
}
fn threadgroup_memory_helpers() -> &'static str {
r#"
// Metal threadgroup memory helpers
// In WGSL, this uses workgroup memory
// Threadgroup barrier
fn threadgroup_barrier() {
workgroupBarrier();
}
// Threadgroup memory fence
fn threadgroup_memory_fence() {
storageBarrier();
}
"#
}
}
pub struct MetalPerformanceShaders {
available_kernels: HashMap<String, MPSKernel>,
}
#[derive(Debug, Clone)]
struct MPSKernel {
name: String,
kernel_type: MPSKernelType,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MPSKernelType {
MatrixMultiplication,
Convolution,
ImageFilter,
Reduction,
NeuralNetwork,
}
impl MetalPerformanceShaders {
pub fn new() -> Self {
let mut available_kernels = HashMap::new();
available_kernels.insert(
"matmul".to_string(),
MPSKernel {
name: "matmul".to_string(),
kernel_type: MPSKernelType::MatrixMultiplication,
},
);
available_kernels.insert(
"conv2d".to_string(),
MPSKernel {
name: "conv2d".to_string(),
kernel_type: MPSKernelType::Convolution,
},
);
available_kernels.insert(
"reduce_sum".to_string(),
MPSKernel {
name: "reduce_sum".to_string(),
kernel_type: MPSKernelType::Reduction,
},
);
Self { available_kernels }
}
pub fn is_available(&self, name: &str) -> bool {
self.available_kernels.contains_key(name)
}
pub fn get_kernel(&self, name: &str) -> Option<&MPSKernel> {
self.available_kernels.get(name)
}
pub fn list_kernels(&self) -> Vec<String> {
self.available_kernels.keys().cloned().collect()
}
pub fn generate_mps_shader(&self, kernel_type: MPSKernelType) -> String {
match kernel_type {
MPSKernelType::MatrixMultiplication => self.generate_matmul_shader(),
MPSKernelType::Convolution => self.generate_conv_shader(),
MPSKernelType::ImageFilter => self.generate_filter_shader(),
MPSKernelType::Reduction => self.generate_reduction_shader(),
MPSKernelType::NeuralNetwork => self.generate_nn_shader(),
}
}
fn generate_matmul_shader(&self) -> String {
r#"
// Metal-optimized matrix multiplication
@group(0) @binding(0) var<storage, read> matrix_a: array<f32>;
@group(0) @binding(1) var<storage, read> matrix_b: array<f32>;
@group(0) @binding(2) var<storage, read_write> matrix_c: array<f32>;
struct MatmulParams {
m: u32,
n: u32,
k: u32,
}
@group(1) @binding(0) var<uniform> params: MatmulParams;
@compute @workgroup_size(16, 16)
fn matmul(@builtin(global_invocation_id) global_id: vec3<u32>) {
let row = global_id.x;
let col = global_id.y;
if (row >= params.m || col >= params.n) {
return;
}
var sum = 0.0;
for (var i = 0u; i < params.k; i++) {
let a_val = matrix_a[row * params.k + i];
let b_val = matrix_b[i * params.n + col];
sum += a_val * b_val;
}
matrix_c[row * params.n + col] = sum;
}
"#
.to_string()
}
fn generate_conv_shader(&self) -> String {
r#"
// Metal-optimized 2D convolution
@group(0) @binding(0) var<storage, read> input: array<f32>;
@group(0) @binding(1) var<storage, read> kernel: array<f32>;
@group(0) @binding(2) var<storage, read_write> output: array<f32>;
struct ConvParams {
width: u32,
height: u32,
kernel_size: u32,
}
@group(1) @binding(0) var<uniform> params: ConvParams;
@compute @workgroup_size(16, 16)
fn conv2d(@builtin(global_invocation_id) global_id: vec3<u32>) {
let x = global_id.x;
let y = global_id.y;
if (x >= params.width || y >= params.height) {
return;
}
var sum = 0.0;
let half_k = params.kernel_size / 2u;
for (var ky = 0u; ky < params.kernel_size; ky++) {
for (var kx = 0u; kx < params.kernel_size; kx++) {
let ix = i32(x) + i32(kx) - i32(half_k);
let iy = i32(y) + i32(ky) - i32(half_k);
if (ix >= 0 && ix < i32(params.width) && iy >= 0 && iy < i32(params.height)) {
let input_idx = u32(iy) * params.width + u32(ix);
let kernel_idx = ky * params.kernel_size + kx;
sum += input[input_idx] * kernel[kernel_idx];
}
}
}
output[y * params.width + x] = sum;
}
"#
.to_string()
}
fn generate_filter_shader(&self) -> String {
"// MPS image filter shader placeholder\n".to_string()
}
fn generate_reduction_shader(&self) -> String {
"// MPS reduction shader placeholder\n".to_string()
}
fn generate_nn_shader(&self) -> String {
"// MPS neural network shader placeholder\n".to_string()
}
}
impl Default for MetalPerformanceShaders {
fn default() -> Self {
Self::new()
}
}
pub struct UnifiedMemoryOptimizer {
enabled: bool,
}
impl UnifiedMemoryOptimizer {
pub fn new(enabled: bool) -> Self {
Self { enabled }
}
pub fn is_enabled(&self) -> bool {
self.enabled
}
pub fn optimize_allocation(&self, size: u64) -> AllocationHint {
if !self.enabled {
return AllocationHint::Standard;
}
if size < 1024 * 1024 {
AllocationHint::Shared
} else {
AllocationHint::Private
}
}
pub fn optimize_access_pattern(&self, width: u32, height: u32) -> AccessPattern {
AccessPattern {
width,
height,
prefer_linear: true,
prefer_tiled: false,
}
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AllocationHint {
Standard,
Shared,
Private,
Managed,
}
#[derive(Debug, Clone)]
pub struct AccessPattern {
pub width: u32,
pub height: u32,
pub prefer_linear: bool,
pub prefer_tiled: bool,
}
pub struct ArgumentBufferManager {
buffers: HashMap<u32, ArgumentBuffer>,
next_id: u32,
}
#[derive(Debug, Clone)]
struct ArgumentBuffer {
id: u32,
name: String,
arguments: Vec<ArgumentDescriptor>,
}
#[derive(Debug, Clone)]
struct ArgumentDescriptor {
name: String,
binding: u32,
arg_type: ArgumentType,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ArgumentType {
Buffer,
Texture,
Sampler,
}
impl ArgumentBufferManager {
pub fn new() -> Self {
Self {
buffers: HashMap::new(),
next_id: 0,
}
}
pub fn create(&mut self, name: String) -> u32 {
let id = self.next_id;
self.next_id += 1;
self.buffers.insert(
id,
ArgumentBuffer {
id,
name: name.clone(),
arguments: Vec::new(),
},
);
debug!("Created argument buffer '{}' (ID: {})", name, id);
id
}
pub fn add_argument(
&mut self,
buffer_id: u32,
name: String,
binding: u32,
arg_type: ArgumentType,
) -> GpuResult<()> {
let buffer = self
.buffers
.get_mut(&buffer_id)
.ok_or_else(|| GpuError::invalid_buffer("Argument buffer not found"))?;
buffer.arguments.push(ArgumentDescriptor {
name: name.clone(),
binding,
arg_type,
});
debug!(
"Added argument '{}' to buffer {} at binding {}",
name, buffer_id, binding
);
Ok(())
}
pub fn get(&self, buffer_id: u32) -> Option<&ArgumentBuffer> {
self.buffers.get(&buffer_id)
}
pub fn destroy(&mut self, buffer_id: u32) {
if let Some(buffer) = self.buffers.remove(&buffer_id) {
debug!("Destroyed argument buffer '{}'", buffer.name);
}
}
}
impl Default for ArgumentBufferManager {
fn default() -> Self {
Self::new()
}
}
pub struct ThreadgroupMemoryAllocator {
max_memory: u64,
allocated: u64,
allocations: HashMap<u32, ThreadgroupAllocation>,
next_id: u32,
}
#[derive(Debug, Clone)]
struct ThreadgroupAllocation {
id: u32,
size: u64,
offset: u64,
}
impl ThreadgroupMemoryAllocator {
pub fn new(max_memory: u64) -> Self {
Self {
max_memory,
allocated: 0,
allocations: HashMap::new(),
next_id: 0,
}
}
pub fn allocate(&mut self, size: u64) -> GpuResult<u32> {
if self.allocated + size > self.max_memory {
return Err(GpuError::out_of_memory(
size,
self.max_memory - self.allocated,
));
}
let id = self.next_id;
self.next_id += 1;
let offset = self.allocated;
self.allocated += size;
self.allocations
.insert(id, ThreadgroupAllocation { id, size, offset });
debug!(
"Allocated {} bytes of threadgroup memory at offset {}",
size, offset
);
Ok(id)
}
pub fn free(&mut self, id: u32) -> GpuResult<()> {
let alloc = self
.allocations
.remove(&id)
.ok_or_else(|| GpuError::invalid_buffer("Allocation not found"))?;
self.allocated = self.allocated.saturating_sub(alloc.size);
debug!("Freed {} bytes of threadgroup memory", alloc.size);
Ok(())
}
pub fn usage(&self) -> (u64, u64) {
(self.allocated, self.max_memory)
}
pub fn reset(&mut self) {
self.allocations.clear();
self.allocated = 0;
}
}
pub struct SimdGroupOperations;
impl SimdGroupOperations {
pub fn simd_shuffle_shader() -> &'static str {
r#"
// SIMD group shuffle operations (Metal-style)
fn simd_shuffle(value: f32, lane: u32) -> f32 {
// Placeholder for Metal's simd_shuffle
return value;
}
fn simd_shuffle_down(value: f32, delta: u32) -> f32 {
// Placeholder for Metal's simd_shuffle_down
return value;
}
fn simd_shuffle_up(value: f32, delta: u32) -> f32 {
// Placeholder for Metal's simd_shuffle_up
return value;
}
fn simd_shuffle_xor(value: f32, mask: u32) -> f32 {
// Placeholder for Metal's simd_shuffle_xor
return value;
}
"#
}
pub fn simd_reduce_shader() -> &'static str {
r#"
// SIMD group reduction operations
fn simd_sum(value: f32) -> f32 {
// Placeholder for Metal's simd_sum
return value;
}
fn simd_max(value: f32) -> f32 {
// Placeholder for Metal's simd_max
return value;
}
fn simd_min(value: f32) -> f32 {
// Placeholder for Metal's simd_min
return value;
}
fn simd_product(value: f32) -> f32 {
// Placeholder for Metal's simd_product
return value;
}
"#
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_metal_features() {
let features = MetalFeatures::default();
assert_eq!(features.simd_width, 32);
assert!(features.unified_memory);
assert!(features.mps_support);
}
#[test]
fn test_metal_performance_shaders() {
let mps = MetalPerformanceShaders::new();
assert!(mps.is_available("matmul"));
assert!(mps.is_available("conv2d"));
let kernels = mps.list_kernels();
assert!(!kernels.is_empty());
}
#[test]
fn test_unified_memory_optimizer() {
let optimizer = UnifiedMemoryOptimizer::new(true);
assert!(optimizer.is_enabled());
let hint = optimizer.optimize_allocation(512 * 1024);
assert_eq!(hint, AllocationHint::Shared);
let hint = optimizer.optimize_allocation(2 * 1024 * 1024);
assert_eq!(hint, AllocationHint::Private);
}
#[test]
fn test_argument_buffer_manager() {
let mut manager = ArgumentBufferManager::new();
let buffer_id = manager.create("test_args".to_string());
manager
.add_argument(buffer_id, "input".to_string(), 0, ArgumentType::Buffer)
.expect("Failed to add argument");
let buffer = manager.get(buffer_id).expect("Buffer not found");
assert_eq!(buffer.arguments.len(), 1);
}
#[test]
fn test_threadgroup_memory_allocator() {
let mut allocator = ThreadgroupMemoryAllocator::new(32 * 1024);
let id1 = allocator.allocate(1024).expect("Failed to allocate");
let _id2 = allocator.allocate(2048).expect("Failed to allocate");
let (used, total) = allocator.usage();
assert_eq!(used, 3072);
assert_eq!(total, 32 * 1024);
allocator.free(id1).expect("Failed to free");
let (used, _) = allocator.usage();
assert_eq!(used, 2048);
}
}