backends - Module

`trait ComputeBackend: Send + Sync`

Trait for compute backends

Source: backends.joule:25

`fn name(&self) -> &str;`

Backend name

Source: backends.joule:27

`fn engine(&self) -> Engine;`

Associated engine type

Source: backends.joule:30

`fn is_available(&self) -> bool;`

Check if backend is available on this system

Source: backends.joule:33

`fn supported_dtypes(&self) -> Vec<ComputeDType>;`

Supported data types

Source: backends.joule:36

`fn gemm_f32(`

Execute GEMM: C = alpha * A @ B + beta * C

Source: backends.joule:39

`fn gemm_f16(`

Execute GEMM with half precision

Source: backends.joule:57

`fn sync(&self);`

Synchronize (wait for completion)

Source: backends.joule:75

`struct AccelerateBackend`

Apple Accelerate framework backend for CPU Uses vecLib/BLAS for linear algebra, vDSP for signal processing, BNNS (Basic Neural Network Subroutines) for neural network ops

Source: backends.joule:85

`fn new() -> Self`

Create new Accelerate backend

Source: backends.joule:91

`fn instance() -> &'static Self`

Get singleton instance

Source: backends.joule:103

`fn name(&self) -> &str`

Source: backends.joule:110

`fn engine(&self) -> Engine`

Source: backends.joule:114

`fn is_available(&self) -> bool`

Source: backends.joule:118

`fn supported_dtypes(&self) -> Vec<ComputeDType>`

Source: backends.joule:122

`fn gemm_f32(`

Source: backends.joule:134

`fn gemm_f32(`

Source: backends.joule:212

`fn gemm_f16(`

Source: backends.joule:231

`fn sync(&self)`

Source: backends.joule:286

`fn default() -> Self`

Source: backends.joule:292

`struct MetalBackend`

Metal Performance Shaders backend for GPU Uses MPS for optimized neural network and linear algebra operations

Source: backends.joule:303

`fn new() -> Self`

Create new Metal backend

Source: backends.joule:311

`fn instance() -> &'static Self`

Get singleton instance

Source: backends.joule:327

`fn device_name(&self) -> String`

Source: backends.joule:334

`fn device_name(&self) -> String`

Source: backends.joule:340

`fn name(&self) -> &str`

Source: backends.joule:346

`fn engine(&self) -> Engine`

Source: backends.joule:350

`fn is_available(&self) -> bool`

Source: backends.joule:354

`fn supported_dtypes(&self) -> Vec<ComputeDType>`

Source: backends.joule:358

`fn gemm_f32(`

Source: backends.joule:370

`fn gemm_f32(`

Source: backends.joule:412

`fn gemm_f16(`

Source: backends.joule:432

`fn gemm_f16(`

Source: backends.joule:501

`fn sync(&self)`

Source: backends.joule:520

`fn default() -> Self`

Source: backends.joule:526

`struct CoreMLBackend`

Core ML backend for Neural Engine Uses Core ML for neural network inference on the ANE

Source: backends.joule:537

`fn new() -> Self`

Create new Core ML backend

Source: backends.joule:543

`fn instance() -> &'static Self`

Get singleton instance

Source: backends.joule:558

`fn compile_mlprogram(&self, _spec: &MLProgramSpec) -> Result<CompiledModel, CoreMLError>`

Source: backends.joule:565

`fn compile_mlprogram(&self, _spec: &MLProgramSpec) -> Result<CompiledModel, CoreMLError>`

Source: backends.joule:578

`fn predict(&self, _model: &CompiledModel, _inputs: &[const u8]) -> Result<Vec<mut u8>, CoreMLError>`

Source: backends.joule:584

`fn predict(&self, _model: &CompiledModel, _inputs: &[const u8]) -> Result<Vec<mut u8>, CoreMLError>`

Source: backends.joule:594

`fn name(&self) -> &str`

Source: backends.joule:600

`fn engine(&self) -> Engine`

Source: backends.joule:604

`fn is_available(&self) -> bool`

Source: backends.joule:608

`fn supported_dtypes(&self) -> Vec<ComputeDType>`

Source: backends.joule:612

`fn gemm_f32(`

Source: backends.joule:621

`fn gemm_f16(`

Source: backends.joule:642

`fn sync(&self)`

Source: backends.joule:662

`fn default() -> Self`

Source: backends.joule:668

`struct MLProgramSpec`

Source: backends.joule:679

`struct TensorSpec`

Source: backends.joule:692

`enum MLOp`

Source: backends.joule:700

`enum ComputeUnits`

Source: backends.joule:777

`struct CompiledModel`

Source: backends.joule:790

`enum CoreMLError`

Source: backends.joule:797

`struct BackendRegistry`

Global backend registry

Source: backends.joule:809

`fn new() -> Self`

Create new registry with all backends

Source: backends.joule:817

`fn instance() -> &'static Self`

Get singleton instance

Source: backends.joule:826

`fn get(&self, engine: Engine) -> &dyn ComputeBackend`

Get backend for engine type

Source: backends.joule:832

`fn accelerate(&self) -> &AccelerateBackend`

Get Accelerate backend

Source: backends.joule:842

`fn metal(&self) -> &MetalBackend`

Get Metal backend

Source: backends.joule:847

`fn coreml(&self) -> &CoreMLBackend`

Get Core ML backend

Source: backends.joule:852

`fn available_backends(&self) -> Vec<Engine>`

List available backends

Source: backends.joule:857

`fn print_info(&self)`

Print backend information

Source: backends.joule:872

`fn default() -> Self`

Source: backends.joule:895

`fn gemm_auto(`

Execute GEMM on best available backend

Source: backends.joule:905

`fn build_transformer_layer_mlprogram(`

Build MLProgram for transformer layer

Source: backends.joule:950

`fn f16_to_f32(h: u16) -> f32`

Convert an IEEE 754 half-precision float (stored as u16) to f32.

Source: backends.joule:1104

`fn f32_to_f16(f: f32) -> u16`

Convert an f32 to IEEE 754 half-precision float (stored as u16).

Source: backends.joule:1137

`fn test_backend_availability()`

Source: backends.joule:1182

`fn test_mlprogram_builder()`

Source: backends.joule:1194

trait ComputeBackend: Send + Sync

fn name(&self) -> &str;

fn engine(&self) -> Engine;

fn is_available(&self) -> bool;

fn supported_dtypes(&self) -> Vec<ComputeDType>;

fn gemm_f32(

fn gemm_f16(

fn sync(&self);

struct AccelerateBackend

fn new() -> Self

fn instance() -> &'static Self

fn name(&self) -> &str

fn engine(&self) -> Engine

fn is_available(&self) -> bool

fn supported_dtypes(&self) -> Vec<ComputeDType>

fn gemm_f32(

fn gemm_f32(

fn gemm_f16(

fn sync(&self)

fn default() -> Self

struct MetalBackend

fn new() -> Self

fn instance() -> &'static Self

fn device_name(&self) -> String

fn device_name(&self) -> String

fn name(&self) -> &str

fn engine(&self) -> Engine

fn is_available(&self) -> bool

fn supported_dtypes(&self) -> Vec<ComputeDType>

fn gemm_f32(

fn gemm_f32(

fn gemm_f16(

fn gemm_f16(

fn sync(&self)

fn default() -> Self

struct CoreMLBackend

fn new() -> Self

fn instance() -> &'static Self

fn compile_mlprogram(&self, _spec: &MLProgramSpec) -> Result<CompiledModel, CoreMLError>

fn compile_mlprogram(&self, _spec: &MLProgramSpec) -> Result<CompiledModel, CoreMLError>

fn predict(&self, _model: &CompiledModel, _inputs: &[*const u8]) -> Result<Vec<*mut u8>, CoreMLError>

fn predict(&self, _model: &CompiledModel, _inputs: &[*const u8]) -> Result<Vec<*mut u8>, CoreMLError>

fn name(&self) -> &str

fn engine(&self) -> Engine

fn is_available(&self) -> bool

fn supported_dtypes(&self) -> Vec<ComputeDType>

fn gemm_f32(

fn gemm_f16(

fn sync(&self)

fn default() -> Self

struct MLProgramSpec

struct TensorSpec

enum MLOp

enum ComputeUnits

struct CompiledModel

enum CoreMLError

struct BackendRegistry

fn new() -> Self

fn instance() -> &'static Self

fn get(&self, engine: Engine) -> &dyn ComputeBackend

fn accelerate(&self) -> &AccelerateBackend

fn metal(&self) -> &MetalBackend

fn coreml(&self) -> &CoreMLBackend

fn available_backends(&self) -> Vec<Engine>

fn print_info(&self)

fn default() -> Self

fn gemm_auto(

fn build_transformer_layer_mlprogram(

fn f16_to_f32(h: u16) -> f32

fn f32_to_f16(f: f32) -> u16

fn test_backend_availability()

fn test_mlprogram_builder()

`trait ComputeBackend: Send + Sync`

`fn name(&self) -> &str;`

`fn engine(&self) -> Engine;`

`fn is_available(&self) -> bool;`

`fn supported_dtypes(&self) -> Vec<ComputeDType>;`

`fn gemm_f32(`

`fn gemm_f16(`

`fn sync(&self);`

`struct AccelerateBackend`

`fn new() -> Self`

`fn instance() -> &'static Self`

`fn name(&self) -> &str`

`fn engine(&self) -> Engine`

`fn is_available(&self) -> bool`

`fn supported_dtypes(&self) -> Vec<ComputeDType>`

`fn gemm_f32(`

`fn gemm_f32(`

`fn gemm_f16(`

`fn sync(&self)`

`fn default() -> Self`

`struct MetalBackend`

`fn new() -> Self`

`fn instance() -> &'static Self`

`fn device_name(&self) -> String`

`fn device_name(&self) -> String`

`fn name(&self) -> &str`

`fn engine(&self) -> Engine`

`fn is_available(&self) -> bool`

`fn supported_dtypes(&self) -> Vec<ComputeDType>`

`fn gemm_f32(`

`fn gemm_f32(`

`fn gemm_f16(`

`fn gemm_f16(`

`fn sync(&self)`

`fn default() -> Self`

`struct CoreMLBackend`

`fn new() -> Self`

`fn instance() -> &'static Self`

`fn compile_mlprogram(&self, _spec: &MLProgramSpec) -> Result<CompiledModel, CoreMLError>`

`fn compile_mlprogram(&self, _spec: &MLProgramSpec) -> Result<CompiledModel, CoreMLError>`

`fn predict(&self, _model: &CompiledModel, _inputs: &[const u8]) -> Result<Vec<mut u8>, CoreMLError>`

`fn predict(&self, _model: &CompiledModel, _inputs: &[const u8]) -> Result<Vec<mut u8>, CoreMLError>`

`fn name(&self) -> &str`

`fn engine(&self) -> Engine`

`fn is_available(&self) -> bool`

`fn supported_dtypes(&self) -> Vec<ComputeDType>`

`fn gemm_f32(`

`fn gemm_f16(`

`fn sync(&self)`

`fn default() -> Self`

`struct MLProgramSpec`

`struct TensorSpec`

`enum MLOp`

`enum ComputeUnits`

`struct CompiledModel`

`enum CoreMLError`

`struct BackendRegistry`

`fn new() -> Self`

`fn instance() -> &'static Self`

`fn get(&self, engine: Engine) -> &dyn ComputeBackend`

`fn accelerate(&self) -> &AccelerateBackend`

`fn metal(&self) -> &MetalBackend`

`fn coreml(&self) -> &CoreMLBackend`

`fn available_backends(&self) -> Vec<Engine>`

`fn print_info(&self)`

`fn default() -> Self`

`fn gemm_auto(`

`fn build_transformer_layer_mlprogram(`

`fn f16_to_f32(h: u16) -> f32`

`fn f32_to_f16(f: f32) -> u16`

`fn test_backend_availability()`

`fn test_mlprogram_builder()`