torsh_ffi/python/

module.rs

//! Python neural network module wrappers

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::error::FfiError;
use crate::python::tensor::PyTensor;
use pyo3::prelude::*;

/// Base class for neural network modules
#[pyclass(name = "Module", subclass)]
#[derive(Clone)]
pub struct PyModule {
    // In a full implementation, this would wrap torsh_nn::Module
    name: String,
}

#[pymethods]
impl PyModule {
    /// Forward pass (to be overridden)
    fn forward(&self, _input: &PyTensor) -> PyResult<PyTensor> {
        Err(FfiError::UnsupportedOperation {
            operation: "forward not implemented for base Module".to_string(),
        }
        .into())
    }

    /// Set training mode
    fn train(&mut self, mode: Option<bool>) {
        let _training = mode.unwrap_or(true);
        // Set training mode
    }

    /// Set evaluation mode
    fn eval(&mut self) {
        self.train(Some(false));
    }

    /// Get module parameters (placeholder)
    fn parameters(&self) -> Vec<PyTensor> {
        Vec::new()
    }

    fn __repr__(&self) -> String {
        format!("{}()", self.name)
    }
}

/// Linear (fully connected) layer  
#[pyclass(name = "Linear")]
pub struct PyLinear {
    in_features: usize,
    out_features: usize,
    bias: bool,
    weight: PyTensor,
    bias_tensor: Option<PyTensor>,
}

#[pymethods]
impl PyLinear {
    #[new]
    fn new(in_features: usize, out_features: usize, bias: Option<bool>) -> PyResult<Self> {
        let use_bias = bias.unwrap_or(true);

        // Initialize weight with random values (simplified)
        let weight_data: Vec<f32> = (0..out_features * in_features)
            .map(|i| (i as f32) * 0.01 - 0.005) // Simple initialization
            .collect();

        let weight = Python::attach(|py| {
            let data = pyo3::types::PyList::new(py, &weight_data)?;
            PyTensor::new(
                data.as_ref(),
                Some(vec![out_features, in_features]),
                Some("f32"),
                true,
            )
        })?;

        let bias_tensor = if use_bias {
            let bias_data: Vec<f32> = (0..out_features).map(|_| 0.0).collect();
            let bias_tensor = Python::attach(|py| {
                let data = pyo3::types::PyList::new(py, &bias_data)?;
                PyTensor::new(data.as_ref(), Some(vec![out_features]), Some("f32"), true)
            })?;
            Some(bias_tensor)
        } else {
            None
        };

        Ok(PyLinear {
            in_features,
            out_features,
            bias: use_bias,
            weight,
            bias_tensor,
        })
    }

    /// Forward pass through linear layer
    fn forward(&self, input: &PyTensor) -> PyResult<PyTensor> {
        // Check input shape
        if input.shape().len() < 2 {
            return Err(FfiError::ShapeMismatch {
                expected: vec![0, self.in_features], // Batch x features
                actual: input.shape(),
            }
            .into());
        }

        let input_features = input.shape()[input.shape().len() - 1];
        if input_features != self.in_features {
            return Err(FfiError::ShapeMismatch {
                expected: vec![self.in_features],
                actual: vec![input_features],
            }
            .into());
        }

        // Simplified matrix multiplication: input @ weight.T
        // For now, assuming 2D input [batch, features]
        if input.shape().len() != 2 {
            return Err(FfiError::UnsupportedOperation {
                operation: "Only 2D input currently supported".to_string(),
            }
            .into());
        }

        let weight_t = self
            .weight
            .t_internal()
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(format!("{:?}", e)))?;
        let output = input
            .matmul_internal(&weight_t)
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(format!("{:?}", e)))?;

        // Add bias if present
        if let Some(ref _bias) = self.bias_tensor {
            // Broadcast bias across batch dimension
            // This is a simplified implementation
            for batch_idx in 0..input.shape()[0] {
                for _feature_idx in 0..self.out_features {
                    let _output_idx = batch_idx * self.out_features + _feature_idx;
                    // Note: This would need proper implementation in a real scenario
                }
            }
        }

        Ok(output)
    }

    #[getter]
    fn weight(&self) -> PyTensor {
        self.weight.clone()
    }

    #[getter]
    fn bias(&self) -> Option<PyTensor> {
        self.bias_tensor.clone()
    }

    #[getter]
    fn in_features(&self) -> usize {
        self.in_features
    }

    #[getter]
    fn out_features(&self) -> usize {
        self.out_features
    }

    fn __repr__(&self) -> String {
        format!(
            "Linear(in_features={}, out_features={}, bias={})",
            self.in_features, self.out_features, self.bias
        )
    }
}

/// Convolutional 2D layer (placeholder)
#[pyclass(name = "Conv2d")]
pub struct PyConv2d {
    in_channels: usize,
    out_channels: usize,
    kernel_size: (usize, usize),
    stride: (usize, usize),
    padding: (usize, usize),
}

#[pymethods]
impl PyConv2d {
    #[new]
    fn new(
        in_channels: usize,
        out_channels: usize,
        kernel_size: (usize, usize),
        stride: Option<(usize, usize)>,
        padding: Option<(usize, usize)>,
    ) -> Self {
        PyConv2d {
            in_channels,
            out_channels,
            kernel_size,
            stride: stride.unwrap_or((1, 1)),
            padding: padding.unwrap_or((0, 0)),
        }
    }

    fn forward(&self, _input: &PyTensor) -> PyResult<PyTensor> {
        Err(FfiError::UnsupportedOperation {
            operation: "Conv2d forward not yet implemented".to_string(),
        }
        .into())
    }

    fn __repr__(&self) -> String {
        format!(
            "Conv2d({}, {}, kernel_size={:?}, stride={:?}, padding={:?})",
            self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding
        )
    }
}

/// ReLU activation layer
#[pyclass(name = "ReLU")]
pub struct PyReLU {
    inplace: bool,
}

#[pymethods]
impl PyReLU {
    #[new]
    fn new(inplace: Option<bool>) -> Self {
        PyReLU {
            inplace: inplace.unwrap_or(false),
        }
    }

    fn forward(&self, input: &PyTensor) -> PyResult<PyTensor> {
        // Apply ReLU: max(0, x)
        let result_data: Vec<f32> = input.data.iter().map(|&x| x.max(0.0)).collect();

        Python::attach(|py| {
            let data = pyo3::types::PyList::new(py, &result_data)?;
            PyTensor::new(
                data.as_ref(),
                Some(input.shape()),
                Some("f32"),
                input.requires_grad,
            )
        })
    }

    fn __repr__(&self) -> String {
        if self.inplace {
            "ReLU(inplace=True)".to_string()
        } else {
            "ReLU()".to_string()
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use pyo3::types::PyList;
    use pyo3::Python;

    #[test]
    fn test_linear_creation() {
        Python::initialize();
        let linear = PyLinear::new(10, 5, None).unwrap();
        assert_eq!(linear.in_features(), 10);
        assert_eq!(linear.out_features(), 5);
    }

    #[test]
    fn test_relu_forward() {
        Python::initialize();
        Python::attach(|py| {
            let data = PyList::new(py, vec![-1.0, 0.0, 1.0, 2.0]).unwrap();
            let input = PyTensor::new(data.as_ref(), None, None, false).unwrap();

            let relu = PyReLU::new(None);
            let output = relu.forward(&input).unwrap();

            // Should be [0.0, 0.0, 1.0, 2.0]
            assert!(output.data[0] == 0.0);
            assert!(output.data[1] == 0.0);
            assert!(output.data[2] == 1.0);
            assert!(output.data[3] == 2.0);
        });
    }
}