Struct NDArrayMatrixMultiplication 

pub struct NDArrayMatrixMultiplication { /* private fields */ }

Implementations

impl NDArrayMatrixMultiplication

pub const fn as_ptr(&self) -> *mut c_void

impl NDArrayMatrixMultiplication

pub fn new(device: &MetalDevice, source_count: usize) -> Option<Self>

Examples found in repository

examples/06_ndarray_matrix_multiplication.rs (line 51)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let queue = device.new_command_queue().expect("command queue");

    let descriptor = NDArrayDescriptor::with_dimension_sizes(data_type::FLOAT32, &[2, 2, 1, 1]).expect("descriptor");
    let template = NDArray::new(&device, &descriptor).expect("template ndarray");
    let byte_len = template.resource_size();
    let rows = descriptor.length_of_dimension(1);
    let row_stride_floats = byte_len / core::mem::size_of::<f32>() / rows;
    let left_buffer = buffer_with_f32_values_padded(
        &device,
        &[1.0, 2.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0.0],
        byte_len,
    );
    let right_buffer = buffer_with_f32_values_padded(
        &device,
        &[5.0, 6.0, 0.0, 0.0, 7.0, 8.0, 0.0, 0.0],
        byte_len,
    );
    let destination_buffer = buffer_with_f32_values_padded(&device, &[0.0; 8], byte_len);

    let left = NDArray::new_with_buffer(&left_buffer, 0, &descriptor).expect("left ndarray");
    let right = NDArray::new_with_buffer(&right_buffer, 0, &descriptor).expect("right ndarray");
    let destination = NDArray::new_with_buffer(&destination_buffer, 0, &descriptor).expect("destination ndarray");

    let kernel = NDArrayMatrixMultiplication::new(&device, 2).expect("ndarray matmul");
    kernel.set_alpha(1.0);
    kernel.set_beta(0.0);

    let command_buffer = queue.new_command_buffer().expect("command buffer");
    kernel.encode_to_destination(&command_buffer, &[&left, &right], &destination);
    command_buffer.commit();
    command_buffer.wait_until_completed();

    let padded_output = read_f32_values(&destination_buffer, row_stride_floats * rows);
    let output = [
        padded_output[0],
        padded_output[1],
        padded_output[row_stride_floats],
        padded_output[row_stride_floats + 1],
    ];
    println!("{output:?}");
}

pub fn alpha(&self) -> f64

pub fn set_alpha(&self, alpha: f64)

Examples found in repository

examples/06_ndarray_matrix_multiplication.rs (line 52)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let queue = device.new_command_queue().expect("command queue");

    let descriptor = NDArrayDescriptor::with_dimension_sizes(data_type::FLOAT32, &[2, 2, 1, 1]).expect("descriptor");
    let template = NDArray::new(&device, &descriptor).expect("template ndarray");
    let byte_len = template.resource_size();
    let rows = descriptor.length_of_dimension(1);
    let row_stride_floats = byte_len / core::mem::size_of::<f32>() / rows;
    let left_buffer = buffer_with_f32_values_padded(
        &device,
        &[1.0, 2.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0.0],
        byte_len,
    );
    let right_buffer = buffer_with_f32_values_padded(
        &device,
        &[5.0, 6.0, 0.0, 0.0, 7.0, 8.0, 0.0, 0.0],
        byte_len,
    );
    let destination_buffer = buffer_with_f32_values_padded(&device, &[0.0; 8], byte_len);

    let left = NDArray::new_with_buffer(&left_buffer, 0, &descriptor).expect("left ndarray");
    let right = NDArray::new_with_buffer(&right_buffer, 0, &descriptor).expect("right ndarray");
    let destination = NDArray::new_with_buffer(&destination_buffer, 0, &descriptor).expect("destination ndarray");

    let kernel = NDArrayMatrixMultiplication::new(&device, 2).expect("ndarray matmul");
    kernel.set_alpha(1.0);
    kernel.set_beta(0.0);

    let command_buffer = queue.new_command_buffer().expect("command buffer");
    kernel.encode_to_destination(&command_buffer, &[&left, &right], &destination);
    command_buffer.commit();
    command_buffer.wait_until_completed();

    let padded_output = read_f32_values(&destination_buffer, row_stride_floats * rows);
    let output = [
        padded_output[0],
        padded_output[1],
        padded_output[row_stride_floats],
        padded_output[row_stride_floats + 1],
    ];
    println!("{output:?}");
}

pub fn beta(&self) -> f64

pub fn set_beta(&self, beta: f64)

Examples found in repository

examples/06_ndarray_matrix_multiplication.rs (line 53)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let queue = device.new_command_queue().expect("command queue");

    let descriptor = NDArrayDescriptor::with_dimension_sizes(data_type::FLOAT32, &[2, 2, 1, 1]).expect("descriptor");
    let template = NDArray::new(&device, &descriptor).expect("template ndarray");
    let byte_len = template.resource_size();
    let rows = descriptor.length_of_dimension(1);
    let row_stride_floats = byte_len / core::mem::size_of::<f32>() / rows;
    let left_buffer = buffer_with_f32_values_padded(
        &device,
        &[1.0, 2.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0.0],
        byte_len,
    );
    let right_buffer = buffer_with_f32_values_padded(
        &device,
        &[5.0, 6.0, 0.0, 0.0, 7.0, 8.0, 0.0, 0.0],
        byte_len,
    );
    let destination_buffer = buffer_with_f32_values_padded(&device, &[0.0; 8], byte_len);

    let left = NDArray::new_with_buffer(&left_buffer, 0, &descriptor).expect("left ndarray");
    let right = NDArray::new_with_buffer(&right_buffer, 0, &descriptor).expect("right ndarray");
    let destination = NDArray::new_with_buffer(&destination_buffer, 0, &descriptor).expect("destination ndarray");

    let kernel = NDArrayMatrixMultiplication::new(&device, 2).expect("ndarray matmul");
    kernel.set_alpha(1.0);
    kernel.set_beta(0.0);

    let command_buffer = queue.new_command_buffer().expect("command buffer");
    kernel.encode_to_destination(&command_buffer, &[&left, &right], &destination);
    command_buffer.commit();
    command_buffer.wait_until_completed();

    let padded_output = read_f32_values(&destination_buffer, row_stride_floats * rows);
    let output = [
        padded_output[0],
        padded_output[1],
        padded_output[row_stride_floats],
        padded_output[row_stride_floats + 1],
    ];
    println!("{output:?}");
}

pub fn encode( &self, command_buffer: &MetalCommandBuffer, source_arrays: &[&NDArray], ) -> Option<NDArray>

pub fn encode_to_destination( &self, command_buffer: &MetalCommandBuffer, source_arrays: &[&NDArray], destination: &NDArray, )

Examples found in repository

examples/06_ndarray_matrix_multiplication.rs (line 56)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let queue = device.new_command_queue().expect("command queue");

    let descriptor = NDArrayDescriptor::with_dimension_sizes(data_type::FLOAT32, &[2, 2, 1, 1]).expect("descriptor");
    let template = NDArray::new(&device, &descriptor).expect("template ndarray");
    let byte_len = template.resource_size();
    let rows = descriptor.length_of_dimension(1);
    let row_stride_floats = byte_len / core::mem::size_of::<f32>() / rows;
    let left_buffer = buffer_with_f32_values_padded(
        &device,
        &[1.0, 2.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0.0],
        byte_len,
    );
    let right_buffer = buffer_with_f32_values_padded(
        &device,
        &[5.0, 6.0, 0.0, 0.0, 7.0, 8.0, 0.0, 0.0],
        byte_len,
    );
    let destination_buffer = buffer_with_f32_values_padded(&device, &[0.0; 8], byte_len);

    let left = NDArray::new_with_buffer(&left_buffer, 0, &descriptor).expect("left ndarray");
    let right = NDArray::new_with_buffer(&right_buffer, 0, &descriptor).expect("right ndarray");
    let destination = NDArray::new_with_buffer(&destination_buffer, 0, &descriptor).expect("destination ndarray");

    let kernel = NDArrayMatrixMultiplication::new(&device, 2).expect("ndarray matmul");
    kernel.set_alpha(1.0);
    kernel.set_beta(0.0);

    let command_buffer = queue.new_command_buffer().expect("command buffer");
    kernel.encode_to_destination(&command_buffer, &[&left, &right], &destination);
    command_buffer.commit();
    command_buffer.wait_until_completed();

    let padded_output = read_f32_values(&destination_buffer, row_stride_floats * rows);
    let output = [
        padded_output[0],
        padded_output[1],
        padded_output[row_stride_floats],
        padded_output[row_stride_floats + 1],
    ];
    println!("{output:?}");
}

Trait Implementations

impl Drop for NDArrayMatrixMultiplication

fn drop(&mut self)

Executes the destructor for this type.

fn pin_drop(self: Pin<&mut Self>)

🔬This is a nightly-only experimental API. (pin_ergonomics)

Execute the destructor for this type, but different to Drop::drop, it requires self to be pinned.

impl Send for NDArrayMatrixMultiplication

impl Sync for NDArrayMatrixMultiplication