//! CoreML Backend Implementation (Placeholder)
//! CoreMLバックエンド実装（プレースホルダー）
//!
//! This module provides placeholder CoreML backend implementation
//! that will be completed when objc2-core-ml bindings are properly configured.
use crate::error::{RusTorchError, RusTorchResult};
use crate::tensor::Tensor;
use ndarray::ScalarOperand;
use num_traits::{Float, FromPrimitive};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
/// CoreML Model Cache for efficient model reuse
/// 効率的なモデル再利用のためのCoreMLモデルキャッシュ
#[cfg(all(feature = "coreml", target_os = "macos"))]
type ModelCache = HashMap<String, Arc<Mutex<String>>>;
#[cfg(not(all(feature = "coreml", target_os = "macos")))]
type ModelCache = HashMap<String, Arc<Mutex<String>>>;
/// CoreML Device Manager
/// CoreMLデバイスマネージャー
#[derive(Debug, Clone)]
pub struct CoreMLDevice {
device_id: usize,
is_available: bool,
neural_engine_available: bool,
}
impl CoreMLDevice {
/// Create a new CoreML device
/// 新しいCoreMLデバイスを作成
pub fn new(device_id: usize) -> RusTorchResult<Self> {
Ok(Self {
device_id,
is_available: Self::check_availability(),
neural_engine_available: Self::check_neural_engine(),
})
}
/// Check if CoreML is available on this system
/// このシステムでCoreMLが利用可能かチェック
pub fn check_availability() -> bool {
#[cfg(all(feature = "coreml", target_os = "macos"))]
{
// Check if we're on macOS 10.13+ (minimum for CoreML)
cfg!(target_os = "macos")
}
#[cfg(not(all(feature = "coreml", target_os = "macos")))]
{
false
}
}
/// Check if Neural Engine is available
/// Neural Engineが利用可能かチェック
pub fn check_neural_engine() -> bool {
#[cfg(all(feature = "coreml", target_os = "macos"))]
{
// Check for Apple Silicon (Neural Engine availability)
cfg!(target_arch = "aarch64")
}
#[cfg(not(all(feature = "coreml", target_os = "macos")))]
{
false
}
}
/// Get device capabilities
/// デバイス能力を取得
pub fn capabilities(&self) -> CoreMLCapabilities {
CoreMLCapabilities {
supports_f16: true,
supports_f32: true,
supports_f64: false, // CoreML limitation
supports_int8: true,
supports_int16: true,
supports_int32: true,
neural_engine: self.neural_engine_available,
max_tensor_size: if self.neural_engine_available {
1024 * 1024 * 1024 // 1GB for Neural Engine
} else {
512 * 1024 * 1024 // 512MB for GPU fallback
},
}
}
}
/// CoreML device capabilities
/// CoreMLデバイス能力
#[derive(Debug, Clone)]
pub struct CoreMLCapabilities {
pub supports_f16: bool,
pub supports_f32: bool,
pub supports_f64: bool,
pub supports_int8: bool,
pub supports_int16: bool,
pub supports_int32: bool,
pub neural_engine: bool,
pub max_tensor_size: usize,
}
/// CoreML Model Builder for dynamic model creation
/// 動的モデル作成用CoreMLモデルビルダー
pub struct CoreMLModelBuilder {
input_shapes: Vec<Vec<usize>>,
output_shapes: Vec<Vec<usize>>,
operation_type: CoreMLOperationType,
}
/// Supported CoreML operation types
/// サポートされるCoreML演算タイプ
#[derive(Debug, Clone, PartialEq)]
pub enum CoreMLOperationType {
MatrixMultiplication,
Convolution2D,
Activation(ActivationType),
Transpose,
Softmax,
}
/// Activation types supported by CoreML
/// CoreMLでサポートされる活性化タイプ
#[derive(Debug, Clone, PartialEq)]
pub enum ActivationType {
ReLU,
Sigmoid,
Tanh,
Softmax,
GELU,
LeakyReLU(f64),
ELU(f64),
Swish,
}
impl CoreMLModelBuilder {
/// Create a new model builder
/// 新しいモデルビルダーを作成
pub fn new(operation_type: CoreMLOperationType) -> Self {
Self {
input_shapes: Vec::new(),
output_shapes: Vec::new(),
operation_type,
}
}
/// Add input shape
/// 入力形状を追加
pub fn add_input_shape(mut self, shape: Vec<usize>) -> Self {
self.input_shapes.push(shape);
self
}
/// Add output shape
/// 出力形状を追加
pub fn add_output_shape(mut self, shape: Vec<usize>) -> Self {
self.output_shapes.push(shape);
self
}
/// Build the CoreML model (placeholder implementation)
/// CoreMLモデルをビルド（プレースホルダー実装）
pub fn build(&self) -> RusTorchResult<Arc<Mutex<String>>> {
// Validate inputs
if self.input_shapes.is_empty() || self.output_shapes.is_empty() {
return Err(RusTorchError::TensorOp {
message: "Model requires at least one input and output shape".to_string(),
source: None,
});
}
// Create placeholder model description
let model_desc = format!(
"CoreML Model: {:?} with {} inputs and {} outputs",
self.operation_type,
self.input_shapes.len(),
self.output_shapes.len()
);
Ok(Arc::new(Mutex::new(model_desc)))
}
}
/// CoreML Executor for running operations
/// 演算実行用CoreMLエグゼキューター
pub struct CoreMLExecutor {
device: CoreMLDevice,
model_cache: Arc<Mutex<ModelCache>>,
}
impl CoreMLExecutor {
/// Create a new CoreML executor
/// 新しいCoreMLエグゼキューターを作成
pub fn new(device_id: usize) -> RusTorchResult<Self> {
let device = CoreMLDevice::new(device_id)?;
if !device.is_available {
return Err(RusTorchError::TensorOp {
message: format!("CoreML device {} not available", device_id),
source: None,
});
}
Ok(Self {
device,
model_cache: Arc::new(Mutex::new(HashMap::new())),
})
}
/// Execute matrix multiplication using CoreML (placeholder)
/// CoreMLを使用した行列乗算の実行（プレースホルダー）
pub fn matmul<T>(&self, a: &Tensor<T>, b: &Tensor<T>) -> RusTorchResult<Tensor<T>>
where
T: Float + FromPrimitive + ScalarOperand + Send + Sync + 'static,
{
// Validate tensor compatibility
self.validate_tensors(a, b)?;
// For now, delegate to CPU implementation
// In actual implementation, this would use CoreML
a.matmul(b)
}
/// Validate tensors for CoreML operations
/// CoreML演算用のテンソル検証
fn validate_tensors<T>(&self, a: &Tensor<T>, b: &Tensor<T>) -> RusTorchResult<()>
where
T: Float + FromPrimitive + ScalarOperand + Send + Sync + 'static,
{
let capabilities = self.device.capabilities();
// Check tensor size limits
let total_elements_a = a.numel();
let total_elements_b = b.numel();
if total_elements_a * std::mem::size_of::<T>() > capabilities.max_tensor_size
|| total_elements_b * std::mem::size_of::<T>() > capabilities.max_tensor_size
{
return Err(RusTorchError::TensorOp {
message: format!(
"Tensor too large for CoreML: max size is {} bytes",
capabilities.max_tensor_size
),
source: None,
});
}
// Check data type support
let type_name = std::any::type_name::<T>();
match type_name {
"f32" if !capabilities.supports_f32 => {
return Err(RusTorchError::TensorOp {
message: "f32 data type not supported by CoreML".to_string(),
source: None,
});
}
"f64" if !capabilities.supports_f64 => {
return Err(RusTorchError::TensorOp {
message: "f64 data type not supported by CoreML".to_string(),
source: None,
});
}
_ => {}
}
Ok(())
}
/// Get device capabilities
/// デバイス能力を取得
pub fn get_capabilities(&self) -> CoreMLCapabilities {
self.device.capabilities()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_coreml_device_availability() {
let availability = CoreMLDevice::check_availability();
#[cfg(target_os = "macos")]
{
// On macOS, CoreML should be available on recent versions
println!("CoreML availability on macOS: {}", availability);
}
#[cfg(not(target_os = "macos"))]
{
// On non-macOS platforms, CoreML should not be available
assert!(!availability);
}
}
#[test]
fn test_neural_engine_detection() {
let neural_engine = CoreMLDevice::check_neural_engine();
#[cfg(all(target_os = "macos", target_arch = "aarch64"))]
{
// On Apple Silicon Macs, Neural Engine should be available
assert!(neural_engine);
}
#[cfg(not(all(target_os = "macos", target_arch = "aarch64")))]
{
// On other platforms, Neural Engine should not be available
assert!(!neural_engine);
}
}
#[test]
fn test_coreml_capabilities() {
if let Ok(device) = CoreMLDevice::new(0) {
let caps = device.capabilities();
// CoreML should support common data types
assert!(caps.supports_f32);
assert!(caps.supports_int32);
// CoreML has known limitations
assert!(!caps.supports_f64);
println!("CoreML capabilities: {:?}", caps);
}
}
#[test]
fn test_model_builder() {
let builder = CoreMLModelBuilder::new(CoreMLOperationType::MatrixMultiplication)
.add_input_shape(vec![2, 3])
.add_input_shape(vec![3, 4])
.add_output_shape(vec![2, 4]);
let model_result = builder.build();
assert!(model_result.is_ok());
if let Ok(model) = model_result {
let model_desc = model.lock().unwrap();
assert!(model_desc.contains("MatrixMultiplication"));
}
}
#[test]
fn test_coreml_executor_creation() {
let executor_result = CoreMLExecutor::new(0);
#[cfg(all(feature = "coreml", target_os = "macos"))]
{
// On macOS with CoreML feature, executor should be created
assert!(executor_result.is_ok());
}
#[cfg(not(all(feature = "coreml", target_os = "macos")))]
{
// On other platforms, executor creation may fail
// This is acceptable for placeholder implementation
}
}
}