redstone-ml 0.0.0

High-performance Machine Learning, Auto-Differentiation and Tensor Algebra crate for Rust
Documentation 
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use crate::autograd::util::reduce_gradient;
use crate::gradient_function::{GradientFuncTrait, GradientFunction};
use crate::{call_next_backward, FloatDataType, NdArray, StridedMemory, Tensor};
use std::cell::RefCell;
use std::rc::Rc;


pub(crate) struct MulBackwards<T: FloatDataType> {
    next_functions: [GradientFunction<T>; 2],

    lhs: Rc<NdArray<'static, T>>,
    rhs: Rc<NdArray<'static, T>>,
}

pub(crate) struct MulScalarBackwards<T: FloatDataType> {
    next_function: GradientFunction<T>,

    shape: Vec<usize>,
    scalar: T,
}


impl<T: FloatDataType> GradientFuncTrait<T> for MulBackwards<T> {
    fn backward(&mut self, grad: &NdArray<T>) {
        call_next_backward!(self.rhs.as_ref() * grad,
                            self.lhs.shape(), self.next_functions[0]);

        call_next_backward!(self.lhs.as_ref() *
                            grad, self.rhs.shape(), self.next_functions[1]);
    }
}

impl<T: FloatDataType> GradientFuncTrait<T> for MulScalarBackwards<T> {
    fn backward(&mut self, grad: &NdArray<T>) {
        call_next_backward!(grad * self.scalar,
                            &self.shape, self.next_function);
    }
}


impl<T: FloatDataType> MulBackwards<T> {
    pub(crate) fn new(lhs: &Tensor<T>, rhs: &Tensor<T>) -> GradientFunction<T> {
        Rc::new(RefCell::new(Self {
            next_functions: [lhs.grad_fn(), rhs.grad_fn()],
            lhs: lhs.get_ndarray(),
            rhs: rhs.get_ndarray(),
        }))
    }
}

impl<T: FloatDataType> MulScalarBackwards<T> {
    pub(crate) fn new(lhs: &Tensor<T>, rhs: T) -> GradientFunction<T> {
        Rc::new(RefCell::new(Self {
            next_function: lhs.grad_fn(),
            shape: lhs.shape().to_vec(),
            scalar: rhs
        }))
    }
}