torsh_ffi/

java.rs

//! Java JNI bindings for ToRSh
//!
//! This module provides Java Native Interface (JNI) bindings for ToRSh,
//! enabling integration with Java applications.

#![allow(dead_code)]
// JNI type names are conventional and follow the JNI specification naming
#![allow(non_camel_case_types)]

use crate::c_api::*;
use std::ptr;

// JNI types (simplified for demonstration)
#[repr(C)]
pub struct _jobject {
    _private: [u8; 0],
}
pub type jobject = *mut _jobject;

#[repr(C)]
pub struct _jclass {
    _private: [u8; 0],
}
pub type jclass = *mut _jclass;

#[repr(C)]
pub struct _JNIEnv {
    _private: [u8; 0],
}
pub type JNIEnv = *mut _JNIEnv;

pub type jlong = i64;
pub type jint = i32;
pub type jfloat = f32;
pub type jdouble = f64;
pub type jboolean = u8;
pub type jsize = jint;

// JNI array types
#[repr(C)]
pub struct _jfloatArray {
    _private: [u8; 0],
}
pub type jfloatArray = *mut _jfloatArray;

#[repr(C)]
pub struct _jintArray {
    _private: [u8; 0],
}
pub type jintArray = *mut _jintArray;

/// JNI function to create a new tensor
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeCreateTensor(
    _env: JNIEnv,
    _class: jclass,
    _data: jfloatArray,
    _shape: jintArray,
    _dtype: jint,
) -> jlong {
    // Note: In a real implementation, you would use JNI functions to access the arrays
    // For now, we'll use a simplified approach

    // This is a placeholder implementation
    // In practice, you would:
    // 1. Use (*env).GetFloatArrayElements() to access the data
    // 2. Use (*env).GetIntArrayElements() to access the shape
    // 3. Call the underlying C API

    let tensor_ptr = torsh_tensor_new(
        ptr::null(), // Would be actual data pointer
        ptr::null(), // Would be actual shape pointer
        0,           // Would be actual ndim
        TorshDType::F32,
    );

    tensor_ptr as jlong
}

/// JNI function to add two tensors
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeAdd(
    _env: JNIEnv,
    _class: jclass,
    a_handle: jlong,
    b_handle: jlong,
) -> jlong {
    let a = a_handle as *mut TorshTensor;
    let b = b_handle as *mut TorshTensor;

    // Perform in-place addition (result stored in 'a')
    let error = torsh_tensor_add(a, b, a);
    if error != TorshError::Success {
        return 0; // Return null handle on error
    }
    a as jlong
}

/// JNI function to multiply two tensors
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeMultiply(
    _env: JNIEnv,
    _class: jclass,
    a_handle: jlong,
    b_handle: jlong,
) -> jlong {
    let a = a_handle as *mut TorshTensor;
    let b = b_handle as *mut TorshTensor;

    // Perform in-place multiplication (result stored in 'a')
    let error = torsh_tensor_mul(a, b, a);
    if error != TorshError::Success {
        return 0; // Return null handle on error
    }
    a as jlong
}

/// JNI function to perform matrix multiplication
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeMatmul(
    _env: JNIEnv,
    _class: jclass,
    a_handle: jlong,
    b_handle: jlong,
) -> jlong {
    let a = a_handle as *mut TorshTensor;
    let b = b_handle as *mut TorshTensor;

    // Perform in-place matrix multiplication (result stored in 'a')
    let error = torsh_tensor_matmul(a, b, a);
    if error != TorshError::Success {
        return 0; // Return null handle on error
    }
    a as jlong
}

/// JNI function to apply ReLU activation
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeRelu(
    _env: JNIEnv,
    _class: jclass,
    tensor_handle: jlong,
) -> jlong {
    let tensor = tensor_handle as *mut TorshTensor;

    // Perform in-place ReLU activation
    let error = torsh_tensor_relu(tensor, tensor);
    if error != TorshError::Success {
        return 0; // Return null handle on error
    }
    tensor as jlong
}

/// JNI function to get tensor shape
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeGetShape(
    _env: JNIEnv,
    _class: jclass,
    tensor_handle: jlong,
) -> jintArray {
    let _tensor = tensor_handle as *mut TorshTensor;

    // Note: In a real implementation, you would:
    // 1. Get the shape from the tensor using torsh_tensor_shape
    // 2. Create a new jintArray using (*env).NewIntArray()
    // 3. Fill the array with the shape data

    // Placeholder implementation
    ptr::null_mut()
}

/// JNI function to get tensor data
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeGetData(
    _env: JNIEnv,
    _class: jclass,
    tensor_handle: jlong,
) -> jfloatArray {
    let _tensor = tensor_handle as *mut TorshTensor;

    // Note: In a real implementation, you would:
    // 1. Get the data from the tensor using torsh_tensor_data
    // 2. Create a new jfloatArray using (*env).NewFloatArray()
    // 3. Fill the array with the tensor data

    // Placeholder implementation
    ptr::null_mut()
}

/// JNI function to free a tensor
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_Tensor_nativeFreeTensor(
    _env: JNIEnv,
    _class: jclass,
    tensor_handle: jlong,
) {
    let tensor = tensor_handle as *mut TorshTensor;
    torsh_tensor_free(tensor);
}

/// JNI function to create a linear layer
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_nn_Linear_nativeCreateLinear(
    _env: JNIEnv,
    _class: jclass,
    in_features: jint,
    out_features: jint,
    bias: jboolean,
) -> jlong {
    let module = torsh_linear_new(in_features as usize, out_features as usize, bias != 0);
    module as jlong
}

/// JNI function to perform linear layer forward pass
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_nn_Linear_nativeForward(
    _env: JNIEnv,
    _class: jclass,
    module_handle: jlong,
    input_handle: jlong,
) -> jlong {
    let module = module_handle as *mut TorshModule;
    let input = input_handle as *mut TorshTensor;

    // Perform in-place linear forward pass
    let error = torsh_linear_forward(module, input, input);
    if error != TorshError::Success {
        return 0; // Return null handle on error
    }
    input as jlong
}

/// JNI function to free a module
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_nn_Linear_nativeFreeModule(
    _env: JNIEnv,
    _class: jclass,
    module_handle: jlong,
) {
    let module = module_handle as *mut TorshModule;
    torsh_module_free(module);
}

/// JNI function to create SGD optimizer
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_optim_SGD_nativeCreateSGD(
    _env: JNIEnv,
    _class: jclass,
    learning_rate: jfloat,
    momentum: jfloat,
) -> jlong {
    let optimizer = torsh_sgd_new(learning_rate, momentum);
    optimizer as jlong
}

/// JNI function to create Adam optimizer
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_optim_Adam_nativeCreateAdam(
    _env: JNIEnv,
    _class: jclass,
    learning_rate: jfloat,
    beta1: jfloat,
    beta2: jfloat,
    epsilon: jfloat,
) -> jlong {
    let optimizer = torsh_adam_new(learning_rate, beta1, beta2, epsilon);
    optimizer as jlong
}

/// JNI function to perform optimizer step
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_optim_Optimizer_nativeStep(
    _env: JNIEnv,
    _class: jclass,
    optimizer_handle: jlong,
    _parameters: jlong, // Array of parameter handles
    _gradients: jlong,  // Array of gradient handles
    _param_count: jint,
) -> jboolean {
    let optimizer = optimizer_handle as *mut TorshOptimizer;

    // Note: In a real implementation, you would convert the jlong arrays
    // to proper *mut *mut TorshTensor arrays

    let result = torsh_optimizer_step(optimizer);

    (result == TorshError::Success) as jboolean
}

/// JNI function to free an optimizer
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_optim_Optimizer_nativeFreeOptimizer(
    _env: JNIEnv,
    _class: jclass,
    optimizer_handle: jlong,
) {
    let optimizer = optimizer_handle as *mut TorshOptimizer;
    torsh_optimizer_free(optimizer);
}

/// JNI function to check CUDA availability
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_cuda_CUDA_nativeIsAvailable(
    _env: JNIEnv,
    _class: jclass,
) -> jboolean {
    (torsh_cuda_is_available() != 0) as jboolean
}

/// JNI function to get CUDA device count
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_cuda_CUDA_nativeDeviceCount(
    _env: JNIEnv,
    _class: jclass,
) -> jint {
    torsh_cuda_device_count()
}

/// JNI function to get library version
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_TorshNative_nativeGetVersion(
    _env: JNIEnv,
    _class: jclass,
) -> jobject {
    // Note: In a real implementation, you would:
    // 1. Get the version string from torsh_version()
    // 2. Create a Java String object using (*env).NewStringUTF()

    // Placeholder implementation
    ptr::null_mut()
}

/// JNI function to get last error
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_TorshNative_nativeGetLastError(
    _env: JNIEnv,
    _class: jclass,
) -> jobject {
    // Note: In a real implementation, you would:
    // 1. Get the error string from torsh_get_last_error()
    // 2. Create a Java String object using (*env).NewStringUTF()

    // Placeholder implementation
    ptr::null_mut()
}

/// JNI function to clear last error
#[no_mangle]
pub unsafe extern "C" fn Java_com_torsh_TorshNative_nativeClearLastError(
    _env: JNIEnv,
    _class: jclass,
) {
    torsh_clear_last_error();
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_java_jni_function_names() {
        // Test that the function names follow JNI conventions
        // This is mainly a compile-time test to ensure the functions are properly exported
        assert!(true);
    }

    #[test]
    fn test_java_tensor_handle_conversions() {
        // Test that handle conversions work correctly
        let test_ptr = 0x123456789abcdef0u64 as *mut TorshTensor;
        let handle = test_ptr as jlong;
        let converted_back = handle as *mut TorshTensor;
        assert_eq!(test_ptr, converted_back);
    }

    #[test]
    fn test_java_module_handle_conversions() {
        // Test that module handle conversions work correctly
        let test_ptr = 0x123456789abcdef0u64 as *mut TorshModule;
        let handle = test_ptr as jlong;
        let converted_back = handle as *mut TorshModule;
        assert_eq!(test_ptr, converted_back);
    }

    #[test]
    fn test_java_optimizer_handle_conversions() {
        // Test that optimizer handle conversions work correctly
        let test_ptr = 0x123456789abcdef0u64 as *mut TorshOptimizer;
        let handle = test_ptr as jlong;
        let converted_back = handle as *mut TorshOptimizer;
        assert_eq!(test_ptr, converted_back);
    }
}