Struct TensorData 

pub struct TensorData { /* private fields */ }

Safe owner for an Objective-C MPSGraphTensorData.

Implementations

impl TensorData

pub fn from_bytes( device: &MetalDevice, bytes: &[u8], shape: &[usize], data_type: u32, ) -> Option<Self>

Build tensor data by copying CPU bytes onto the given Metal device.

pub fn from_f32_slice( device: &MetalDevice, values: &[f32], shape: &[usize], ) -> Option<Self>

Build tensor data from a contiguous f32 slice.

Examples found in repository

examples/05_concat_split.rs (line 16)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let graph = Graph::new().expect("graph");
    let input = graph
        .placeholder(Some(&[2, 2]), data_type::FLOAT32, Some("input"))
        .expect("placeholder");
    let concat = graph
        .concat_pair(&input, &input, 1, Some("concat"))
        .expect("concat");
    let split = graph.split_num(&concat, 2, 1, Some("split"));
    let stacked = graph.stack(&[&split[0], &split[1]], 0, Some("stack")).expect("stack");

    let input_data = TensorData::from_f32_slice(&device, &[1.0, 2.0, 3.0, 4.0], &[2, 2])
        .expect("tensor data");
    let results = graph
        .run(&[Feed::new(&input, &input_data)], &[&stacked])
        .expect("run");

    println!("stacked tensor bytes: {}", results[0].byte_len().expect("byte len"));
}

examples/03_arithmetic_topk.rs (line 20)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let graph = Graph::new().expect("graph");
    let input = graph
        .placeholder(Some(&[2, 3]), data_type::FLOAT32, Some("input"))
        .expect("placeholder");
    let squared = graph
        .unary_arithmetic(UnaryArithmeticOp::Square, &input, Some("square"))
        .expect("square");
    let row_sum = graph
        .reduce_axes(ReductionAxesOp::Sum, &squared, &[1], Some("row_sum"))
        .expect("reduce");
    let topk = graph.top_k(&input, 2, Some("topk")).expect("topk");

    let input_data = TensorData::from_f32_slice(&device, &[1.0, 3.0, 2.0, 4.0, 6.0, 5.0], &[2, 3])
        .expect("tensor data");
    let results = graph
        .run(&[Feed::new(&input, &input_data)], &[&row_sum, &topk.0])
        .expect("run");

    println!("row sums: {:?}", results[0].read_f32().expect("row sums"));
    println!("top-k values: {:?}", results[1].read_f32().expect("topk values"));
}

examples/01_add_relu.rs (line 21)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let graph = Graph::new().expect("failed to create MPSGraph");

    let input = graph
        .placeholder(Some(&[2, 2]), data_type::FLOAT32, Some("input"))
        .expect("failed to create placeholder");
    let bias = graph
        .constant_scalar(1.0, data_type::FLOAT32)
        .expect("failed to create scalar constant");
    let added = graph
        .addition(&input, &bias, Some("add"))
        .expect("failed to create addition op");
    let output = graph
        .relu(&added, Some("relu"))
        .expect("failed to create relu op");

    let input_data = TensorData::from_f32_slice(&device, &[1.0, -2.0, 3.0, -4.0], &[2, 2])
        .expect("failed to create tensor data");
    let results = graph
        .run(&[Feed::new(&input, &input_data)], &[&output])
        .expect("failed to execute graph");
    let values = results[0].read_f32().expect("failed to read tensor output");

    assert_eq!(values, vec![2.0, 0.0, 4.0, 0.0]);
    println!("add+relu smoke passed: {values:?}");
}

examples/02_compile_matmul.rs (line 32)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let queue = device
        .new_command_queue()
        .expect("failed to create command queue");
    let graph = Graph::new().expect("failed to create MPSGraph");

    let left = graph
        .placeholder(Some(&[2, 3]), data_type::FLOAT32, Some("left"))
        .expect("failed to create left placeholder");
    let right = graph
        .placeholder(Some(&[3, 2]), data_type::FLOAT32, Some("right"))
        .expect("failed to create right placeholder");
    let output = graph
        .matrix_multiplication(&left, &right, Some("matmul"))
        .expect("failed to create matrix multiplication op");

    let executable = graph
        .compile(
            &device,
            &[
                FeedDescription::new(&left, &[2, 3], data_type::FLOAT32),
                FeedDescription::new(&right, &[3, 2], data_type::FLOAT32),
            ],
            &[&output],
        )
        .expect("failed to compile executable");

    let left_data = TensorData::from_f32_slice(&device, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], &[2, 3])
        .expect("failed to create left tensor data");
    let right_data =
        TensorData::from_f32_slice(&device, &[7.0, 8.0, 9.0, 10.0, 11.0, 12.0], &[3, 2])
            .expect("failed to create right tensor data");

    let results = executable
        .run(&queue, &[&left_data, &right_data])
        .expect("failed to run executable");
    let values = results[0].read_f32().expect("failed to read tensor output");
    let expected = [58.0_f32, 64.0, 139.0, 154.0];
    for (actual, expected_value) in values.iter().zip(expected) {
        assert!(
            (actual - expected_value).abs() < 1.0e-4,
            "unexpected matrix multiply result: {values:?}"
        );
    }

    println!("compile+matmul smoke passed: {values:?}");
}

pub fn from_buffer( buffer: &MetalBuffer, shape: &[usize], data_type: u32, ) -> Option<Self>

Alias an existing MTLBuffer as tensor data.

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

pub fn data_type(&self) -> u32

pub fn shape(&self) -> Vec<usize>

pub fn element_count(&self) -> usize

pub fn byte_len(&self) -> Result<usize>

Examples found in repository

examples/05_concat_split.rs (line 22)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let graph = Graph::new().expect("graph");
    let input = graph
        .placeholder(Some(&[2, 2]), data_type::FLOAT32, Some("input"))
        .expect("placeholder");
    let concat = graph
        .concat_pair(&input, &input, 1, Some("concat"))
        .expect("concat");
    let split = graph.split_num(&concat, 2, 1, Some("split"));
    let stacked = graph.stack(&[&split[0], &split[1]], 0, Some("stack")).expect("stack");

    let input_data = TensorData::from_f32_slice(&device, &[1.0, 2.0, 3.0, 4.0], &[2, 2])
        .expect("tensor data");
    let results = graph
        .run(&[Feed::new(&input, &input_data)], &[&stacked])
        .expect("run");

    println!("stacked tensor bytes: {}", results[0].byte_len().expect("byte len"));
}

pub fn read_bytes(&self) -> Result<Vec<u8>>

pub fn read_f32(&self) -> Result<Vec<f32>>

Examples found in repository

examples/03_arithmetic_topk.rs (line 26)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let graph = Graph::new().expect("graph");
    let input = graph
        .placeholder(Some(&[2, 3]), data_type::FLOAT32, Some("input"))
        .expect("placeholder");
    let squared = graph
        .unary_arithmetic(UnaryArithmeticOp::Square, &input, Some("square"))
        .expect("square");
    let row_sum = graph
        .reduce_axes(ReductionAxesOp::Sum, &squared, &[1], Some("row_sum"))
        .expect("reduce");
    let topk = graph.top_k(&input, 2, Some("topk")).expect("topk");

    let input_data = TensorData::from_f32_slice(&device, &[1.0, 3.0, 2.0, 4.0, 6.0, 5.0], &[2, 3])
        .expect("tensor data");
    let results = graph
        .run(&[Feed::new(&input, &input_data)], &[&row_sum, &topk.0])
        .expect("run");

    println!("row sums: {:?}", results[0].read_f32().expect("row sums"));
    println!("top-k values: {:?}", results[1].read_f32().expect("topk values"));
}

examples/01_add_relu.rs (line 26)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let graph = Graph::new().expect("failed to create MPSGraph");

    let input = graph
        .placeholder(Some(&[2, 2]), data_type::FLOAT32, Some("input"))
        .expect("failed to create placeholder");
    let bias = graph
        .constant_scalar(1.0, data_type::FLOAT32)
        .expect("failed to create scalar constant");
    let added = graph
        .addition(&input, &bias, Some("add"))
        .expect("failed to create addition op");
    let output = graph
        .relu(&added, Some("relu"))
        .expect("failed to create relu op");

    let input_data = TensorData::from_f32_slice(&device, &[1.0, -2.0, 3.0, -4.0], &[2, 2])
        .expect("failed to create tensor data");
    let results = graph
        .run(&[Feed::new(&input, &input_data)], &[&output])
        .expect("failed to execute graph");
    let values = results[0].read_f32().expect("failed to read tensor output");

    assert_eq!(values, vec![2.0, 0.0, 4.0, 0.0]);
    println!("add+relu smoke passed: {values:?}");
}

examples/02_compile_matmul.rs (line 41)

fn main() {
    let device = MetalDevice::system_default().expect("no Metal device available");
    let queue = device
        .new_command_queue()
        .expect("failed to create command queue");
    let graph = Graph::new().expect("failed to create MPSGraph");

    let left = graph
        .placeholder(Some(&[2, 3]), data_type::FLOAT32, Some("left"))
        .expect("failed to create left placeholder");
    let right = graph
        .placeholder(Some(&[3, 2]), data_type::FLOAT32, Some("right"))
        .expect("failed to create right placeholder");
    let output = graph
        .matrix_multiplication(&left, &right, Some("matmul"))
        .expect("failed to create matrix multiplication op");

    let executable = graph
        .compile(
            &device,
            &[
                FeedDescription::new(&left, &[2, 3], data_type::FLOAT32),
                FeedDescription::new(&right, &[3, 2], data_type::FLOAT32),
            ],
            &[&output],
        )
        .expect("failed to compile executable");

    let left_data = TensorData::from_f32_slice(&device, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], &[2, 3])
        .expect("failed to create left tensor data");
    let right_data =
        TensorData::from_f32_slice(&device, &[7.0, 8.0, 9.0, 10.0, 11.0, 12.0], &[3, 2])
            .expect("failed to create right tensor data");

    let results = executable
        .run(&queue, &[&left_data, &right_data])
        .expect("failed to run executable");
    let values = results[0].read_f32().expect("failed to read tensor output");
    let expected = [58.0_f32, 64.0, 139.0, 154.0];
    for (actual, expected_value) in values.iter().zip(expected) {
        assert!(
            (actual - expected_value).abs() < 1.0e-4,
            "unexpected matrix multiply result: {values:?}"
        );
    }

    println!("compile+matmul smoke passed: {values:?}");
}

impl TensorData

pub fn graph_device_type(&self) -> Option<u32>

Return the graph-device type that backs this tensor data.

Trait Implementations

impl Drop for TensorData

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 TensorData

impl Sync for TensorData