juice 0.3.0

Machine Learning Framework for Hackers
Documentation 
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//! Provides common utility functions

use crate::co::frameworks::native::flatbox::FlatBox;
use crate::co::prelude::*;
use crate::coblas::plugin::*;
use crate::conn;
use num::traits::{cast, NumCast};
use std::sync::{Arc, RwLock};

/// Shared Lock used for our tensors
pub type ArcLock<T> = Arc<RwLock<T>>;

/// Create a simple native backend.
///
/// This is handy when you need to sync data to host memory to read/write it.
pub fn native_backend() -> Backend<Native> {
    let framework = Native::new();
    let hardwares = &framework.hardwares().to_vec();
    let backend_config = BackendConfig::new(framework, hardwares);
    Backend::new(backend_config).unwrap()
}

/// Write into a native Coaster Memory.
pub fn write_to_memory<T: NumCast + ::std::marker::Copy>(mem: &mut FlatBox, data: &[T]) {
    write_to_memory_offset(mem, data, 0);
}

/// Write into a native Coaster Memory with a offset.
pub fn write_to_memory_offset<T: NumCast + ::std::marker::Copy>(mem: &mut FlatBox, data: &[T], offset: usize) {
    let mem_buffer = mem.as_mut_slice::<f32>();
    for (index, datum) in data.iter().enumerate() {
        // mem_buffer[index + offset] = *datum;
        mem_buffer[index + offset] = cast(*datum).unwrap();
    }
}

/// Write the `i`th sample of a batch into a SharedTensor.
///
/// The size of a single sample is inferred through
/// the first dimension of the SharedTensor, which
/// is assumed to be the batchsize.
///
/// Allocates memory on a Native Backend if neccessary.
pub fn write_batch_sample<T: NumCast + ::std::marker::Copy>(tensor: &mut SharedTensor<f32>, data: &[T], i: usize) {
    let native_backend = native_backend();
    let tensor_desc = tensor.desc();
    let batch_size = tensor_desc[0];
    let batch_sample_size = tensor_desc.size();
    let sample_size = batch_sample_size / batch_size;

    write_to_memory_offset(
        tensor.write_only(native_backend.device()).unwrap(),
        &data,
        i * sample_size,
    );
}

/// Create a Coaster SharedTensor for a scalar value.
pub fn native_scalar<T: NumCast + ::std::marker::Copy>(scalar: T) -> SharedTensor<T> {
    let native = native_backend();
    let mut shared_scalar = SharedTensor::<T>::new(&[1]);
    write_to_memory(shared_scalar.write_only(native.device()).unwrap(), &[scalar]);
    shared_scalar
}

/// Casts a Vec<usize> to as Vec<i32>
pub fn cast_vec_usize_to_i32(input: Vec<usize>) -> Vec<i32> {
    let mut out = Vec::new();
    for i in input.iter() {
        out.push(*i as i32);
    }
    out
}

/// Extends IBlas with Axpby
pub trait Axpby<F>: Axpy<F> + Scal<F> {
    /// Performs the operation y := a*x + b*y .
    ///
    /// Consists of a scal(b, y) followed by a axpby(a,x,y).
    fn axpby(
        &self,
        a: &SharedTensor<F>,
        x: &SharedTensor<F>,
        b: &SharedTensor<F>,
        y: &mut SharedTensor<F>,
    ) -> Result<(), crate::co::error::Error> {
        self.scal(b, y)?;
        self.axpy(a, x, y)?;
        Ok(())
    }
}

impl<T: Axpy<f32> + Scal<f32>> Axpby<f32> for T {}

/// Encapsulates all traits required by Solvers.
// pub trait SolverOps<F> : Axpby<F> + Dot<F> + Copy<F> {}
//
// impl<T: Axpby<f32> + Dot<f32> + Copy<f32>> SolverOps<f32> for T {}
pub trait SolverOps<F>: LayerOps<F> + Axpby<F> + Dot<F> + Copy<F> {}

impl<T: LayerOps<f32> + Axpby<f32> + Dot<f32> + Copy<f32>> SolverOps<f32> for T {}

/// Encapsulates all traits used in Layers.
pub trait LayerOps<F>:
    conn::Convolution<F>
    + conn::Rnn<F>
    + conn::Pooling<F>
    + conn::Relu<F>
    + conn::ReluPointwise<F>
    + conn::Sigmoid<F>
    + conn::SigmoidPointwise<F>
    + conn::Tanh<F>
    + conn::TanhPointwise<F>
    + conn::Softmax<F>
    + conn::LogSoftmax<F>
    + conn::Dropout<F>
    + Gemm<F>
    + Axpby<F>
    + Copy<F>
{
}

impl<
        T: conn::Convolution<f32>
            + conn::Rnn<f32>
            + conn::Pooling<f32>
            + conn::Relu<f32>
            + conn::ReluPointwise<f32>
            + conn::Sigmoid<f32>
            + conn::SigmoidPointwise<f32>
            + conn::Tanh<f32>
            + conn::TanhPointwise<f32>
            + conn::Softmax<f32>
            + conn::LogSoftmax<f32>
            + conn::Dropout<f32>
            + Gemm<f32>
            + Axpby<f32>
            + Copy<f32>,
    > LayerOps<f32> for T
{
}