ferrite-ml 0.1.0

Basic deep learning library written in pure Rust
Documentation 
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use crate::tensor_storage::*;
use super::base::*;
use super::function::*;
use std::ops::{Add, AddAssign, Mul, MulAssign, Sub, SubAssign, Div, DivAssign};
use std::rc::Rc;
use std::cell::RefCell;

impl TensorOps for Tensor {
  fn add_tensor(&self, other: &Self) -> Self {
    // Compute the actual tensor addition
    let tensor = self.tensor().add_tensor(other.tensor());
    
    // Create result tensor
    let requires_grad = *self.requires_grad() || *other.requires_grad();
    let mut result = Tensor::new(tensor, requires_grad);
    
    // Set up gradient function if needed
    if requires_grad {
      result.set_grad_fn(Some(Rc::new(AddGrad::new(
        self, 
        other,
        &result
      ))));
    }
    
    result
  }

  fn mul_tensor(&self, other: &Self) -> Self {
    let tensor = self.tensor().mul_tensor(other.tensor());
    
    let requires_grad = *self.requires_grad() || *other.requires_grad();
    let mut result = Tensor::new(tensor, requires_grad);
    
    if requires_grad {
      result.set_grad_fn(Some(Rc::new(MulGrad::new(
        self,
        other,
        &result
      ))));
    }
    
    result
  }
  
  fn sum(&self) -> Self {
    let tensor = self.tensor().sum();
    let requires_grad = *self.requires_grad();
    let mut result = Tensor::new(tensor, requires_grad);
    
    if requires_grad {
      result.set_grad_fn(Some(Rc::new(SumGrad::new(self, &result))));
    }
    
    result
  }

  fn mean(&self) -> Self {
    let tensor = self.tensor().mean();
    let requires_grad = *self.requires_grad();
    let mut result = Tensor::new(tensor, requires_grad);
    
    if requires_grad {
      result.set_grad_fn(Some(Rc::new(MeanGrad::new(self, &result))));
    }
    
    result
  }

  fn product(&self) -> Self {
    let tensor = self.tensor().product();
    let requires_grad = *self.requires_grad();
    let mut result = Tensor::new(tensor, requires_grad);
    
    if requires_grad {
      result.set_grad_fn(Some(Rc::new(ProductGrad::new(self, &result))));
    }
    
    result
  }

  // Additional operations that don't have gradient implementations yet
  fn sub_tensor(&self, other: &Self) -> Self {
    let tensor = self.tensor().sub_tensor(other.tensor());
    Tensor::new(tensor, false)
  }

  fn div_tensor(&self, other: &Self) -> Self {
    let tensor = self.tensor().div_tensor(other.tensor());
    Tensor::new(tensor, false)
  }

  fn add_f32(&self, other: f32) -> Self {
    let tensor = self.tensor().add_f32(other);
    Tensor::new(tensor, false)
  }

  fn sub_f32(&self, other: f32) -> Self {
    let tensor = self.tensor().sub_f32(other);
    Tensor::new(tensor, false)
  }

  fn mul_f32(&self, other: f32) -> Self {
    let tensor = self.tensor().mul_f32(other);
    Tensor::new(tensor, false)
  }

  fn div_f32(&self, other: f32) -> Self {
    let tensor = self.tensor().div_f32(other);
    Tensor::new(tensor, false)
  }

  // Assignment operations 
  fn add_tensor_assign(&mut self, other: &Self) {
    self.tensor_mut().add_tensor_assign(other.tensor());
  }

  fn sub_tensor_assign(&mut self, other: &Self) {
    self.tensor_mut().sub_tensor_assign(other.tensor());
  }

  fn mul_tensor_assign(&mut self, other: &Self) {
    self.tensor_mut().mul_tensor_assign(other.tensor());
  }

  fn div_tensor_assign(&mut self, other: &Self) {
    self.tensor_mut().div_tensor_assign(other.tensor());
  }

  fn add_f32_assign(&mut self, other: f32) {
    self.tensor_mut().add_f32_assign(other);
  }

  fn sub_f32_assign(&mut self, other: f32) {
    self.tensor_mut().sub_f32_assign(other);
  }

  fn mul_f32_assign(&mut self, other: f32) {
    self.tensor_mut().mul_f32_assign(other);
  }

  fn div_f32_assign(&mut self, other: f32) {
    self.tensor_mut().div_f32_assign(other);
  }

  fn matmul(&self, other: &Self) -> Self {
    let tensor = self.tensor().matmul(other.tensor());
    Tensor::new(tensor, false)
  }
}

impl Add<&Tensor> for &Tensor {
  type Output = Tensor;
  fn add(self, rhs: &Tensor) -> Self::Output {
    self.add_tensor(rhs)
  }
}

impl Mul<&Tensor> for &Tensor {
  type Output = Tensor;
  fn mul(self, rhs: &Tensor) -> Self::Output {
    self.mul_tensor(rhs)
  }
}