scirs2-autograd 0.3.2

Automatic differentiation module for SciRS2 (scirs2-autograd)
Documentation 
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// Debug operators for testing gradient computation
use crate::ndarray;
use crate::op::{ComputeContext, GradientContext, Op, OpError};
use crate::tensor::Tensor;
use crate::Float;

/// A simple operator that passes through the input unchanged but has a very simple gradient
pub struct DebugIdentityWithGradient;

impl<F: Float> Op<F> for DebugIdentityWithGradient {
    fn name(&self) -> &'static str {
        "DebugIdentityWithGradient"
    }

    fn compute(&self, ctx: &mut ComputeContext<F>) -> Result<(), OpError> {
        // Just pass the input through directly
        let input = ctx.input(0);
        ctx.append_output(input.to_owned());
        Ok(())
    }

    fn grad(&self, ctx: &mut GradientContext<F>) {
        println!("DEBUG: DebugIdentityWithGradient::grad is called");

        // Get the output gradient
        let grad_output = ctx.output_grad();
        println!("DEBUG: Output gradient tensor id: {}", grad_output.id);

        // Pass it straight through as the input gradient
        ctx.append_input_grad(0, Some(*grad_output));
        println!("DEBUG: Input gradient appended");
    }
}

/// A simple operator that returns a scalar filled with 1.0 and has a simple gradient of all 1s
pub struct DebugScalarOne;

impl<F: Float> Op<F> for DebugScalarOne {
    fn name(&self) -> &'static str {
        "DebugScalarOne"
    }

    fn compute(&self, ctx: &mut ComputeContext<F>) -> Result<(), OpError> {
        // Return a scalar with value 1.0
        ctx.append_output(scirs2_core::ndarray::arr0(F::one()).into_dyn());
        Ok(())
    }

    fn grad(&self, ctx: &mut GradientContext<F>) {
        println!("DEBUG: DebugScalarOne::grad is called");

        // Get input shape
        let input = ctx.input(0);
        let g = ctx.graph();

        // Input shape may not be available, so we create a simple array of the same shape filled with 1s
        if let Ok(input_array) = input.eval(g) {
            let gradient = scirs2_core::ndarray::Array::ones(input_array.raw_dim());
            let grad_tensor = crate::tensor_ops::convert_to_tensor(gradient, g);
            ctx.append_input_grad(0, Some(grad_tensor));
            println!("DEBUG: DebugScalarOne full gradient appended");
        } else {
            // Fallback (shouldn't happen)
            println!("DEBUG: DebugScalarOne fallback path (input eval failed)");
            ctx.append_input_grad(0, None);
        }
    }
}

// Public API function
#[allow(dead_code)]
pub fn debug_identity_with_gradient<'g, F: Float>(tensor: &Tensor<'g, F>) -> Tensor<'g, F> {
    let g = tensor.graph();
    Tensor::builder(g)
        .append_input(tensor, false)
        .build(DebugIdentityWithGradient)
}

// Public API function
#[allow(dead_code)]
pub fn debug_scalar_one<'g, F: Float>(tensor: &Tensor<'g, F>) -> Tensor<'g, F> {
    let g = tensor.graph();
    Tensor::builder(g)
        .append_input(tensor, false)
        .build(DebugScalarOne)
}