cuda-rust-wasm 0.1.7

//! GPU Neural Operations for CUDA-WASM Integration
//!
//! This module implements high-performance neural network operations
//! using GPU acceleration through WebGPU/CUDA backends.

use super::{
    BufferHandle, GpuBackendTrait, MemoryManagerTrait, NeuralIntegrationError, 
    NeuralOperation, NeuralResult, ActivationFunction,
};
use std::sync::Arc;

/// Execute a neural operation on GPU with automatic optimization
pub fn execute_operation<T>(
    operation: NeuralOperation<T>,
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    match operation {
        NeuralOperation::MatrixMultiply { a_rows, a_cols, b_cols, _phantom } => {
            execute_matrix_multiply(a_rows, a_cols, b_cols, inputs, backend, memory_manager)
        }
        
        NeuralOperation::VectorAdd { size, _phantom } => {
            execute_vector_add(size, inputs, backend, memory_manager)
        }
        
        NeuralOperation::ActivationFunction { function, size, _phantom } => {
            execute_activation_function(function, size, inputs, backend, memory_manager)
        }
        
        NeuralOperation::Convolution { channels, kernel_size, stride, _phantom } => {
            execute_convolution(channels, kernel_size, stride, inputs, backend, memory_manager)
        }
        
        NeuralOperation::ForwardPropagation { layer_sizes, _phantom } => {
            execute_forward_propagation(&layer_sizes, inputs, backend, memory_manager)
        }
        
        NeuralOperation::BackwardPropagation { layer_sizes, _phantom } => {
            execute_backward_propagation(&layer_sizes, inputs, backend, memory_manager)
        }
        
        NeuralOperation::Custom { kernel_source, name, _phantom } => {
            execute_custom_kernel(&kernel_source, &name, inputs, backend, memory_manager)
        }
    }
}

/// Process multiple operations in batch for efficiency
pub fn process_batch<T>(
    operations: Vec<NeuralOperation<T>>,
    inputs: Vec<Vec<T>>,
    backend: &Option<Arc<dyn GpuBackendTrait>>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
    batch_size: usize,
) -> NeuralResult<Vec<Vec<T>>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod + num_traits::Float,
{
    if operations.len() != inputs.len() {
        return Err(NeuralIntegrationError::OperationError(
            "Operations and inputs count mismatch".to_string()
        ));
    }
    
    let mut results = Vec::with_capacity(operations.len());
    
    if let Some(backend) = backend {
        // GPU batch processing
        for chunk in operations.chunks(batch_size).zip(inputs.chunks(batch_size)) {
            let (ops, ins) = chunk;
            let batch_results = execute_batch_gpu(ops, ins, backend, memory_manager)?;
            results.extend(batch_results);
        }
    } else {
        // CPU batch processing
        for (operation, input) in operations.into_iter().zip(inputs.into_iter()) {
            let result = super::bridge::execute_cpu_fallback(operation, &input)?;
            results.push(result);
        }
    }
    
    Ok(results)
}

/// Execute matrix multiplication on GPU
fn execute_matrix_multiply<T>(
    a_rows: usize,
    a_cols: usize,
    b_cols: usize,
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    // Validate input dimensions
    let expected_size = a_rows * a_cols + a_cols * b_cols;
    if inputs.len() < expected_size {
        return Err(NeuralIntegrationError::OperationError(
            format!("Expected {} elements, got {}", expected_size, inputs.len())
        ));
    }
    
    // Convert input data to bytes for GPU transfer
    let input_bytes: &[u8] = bytemuck::cast_slice(inputs);
    
    // Create GPU buffers
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Generate optimized matrix multiplication kernel
    let kernel_source = generate_matrix_multiply_kernel(a_rows, a_cols, b_cols);
    let kernel = super::bridge::extract_wgsl_from_rust(&kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back to CPU
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute vector addition on GPU
fn execute_vector_add<T>(
    size: usize,
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    if inputs.len() < size * 2 {
        return Err(NeuralIntegrationError::OperationError(
            format!("Expected {} elements, got {}", size * 2, inputs.len())
        ));
    }
    
    // Transfer data to GPU
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Generate vector addition kernel
    let kernel_source = generate_vector_add_kernel(size);
    let kernel = super::bridge::extract_wgsl_from_rust(&kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .take(size)
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute activation function on GPU
fn execute_activation_function<T>(
    function: ActivationFunction,
    size: usize,
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    if inputs.len() < size {
        return Err(NeuralIntegrationError::OperationError(
            format!("Expected {} elements, got {}", size, inputs.len())
        ));
    }
    
    // Transfer data to GPU
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().take(size).map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Generate activation kernel based on function type
    let kernel_source = generate_activation_kernel(function, size);
    let kernel = super::bridge::extract_wgsl_from_rust(&kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .take(size)
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute convolution operation on GPU
fn execute_convolution<T>(
    channels: usize,
    kernel_size: usize,
    stride: usize,
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    // Transfer data to GPU
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Generate convolution kernel
    let kernel_source = generate_convolution_kernel(channels, kernel_size, stride);
    let kernel = super::bridge::extract_wgsl_from_rust(&kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute forward propagation through neural network layers
fn execute_forward_propagation<T>(
    layer_sizes: &[usize],
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    // Transfer data to GPU
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Generate forward propagation kernel
    let kernel_source = generate_forward_propagation_kernel(layer_sizes);
    let kernel = super::bridge::extract_wgsl_from_rust(&kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute backward propagation for training
fn execute_backward_propagation<T>(
    layer_sizes: &[usize],
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    // Transfer data to GPU
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Generate backward propagation kernel
    let kernel_source = generate_backward_propagation_kernel(layer_sizes);
    let kernel = super::bridge::extract_wgsl_from_rust(&kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute custom CUDA kernel
fn execute_custom_kernel<T>(
    kernel_source: &str,
    name: &str,
    inputs: &[T],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<T>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    // Transfer data to GPU
    let input_buffer = memory_manager.transfer_to_gpu(
        &inputs.iter().map(|&x| unsafe { std::mem::transmute_copy(&x) }).collect::<Vec<f32>>()
    )?;
    
    // Transpile custom CUDA kernel
    let kernel = super::bridge::extract_wgsl_from_rust(kernel_source)?;
    
    // Execute kernel
    let output_buffer = backend.execute_kernel(&kernel, &[input_buffer])?;
    
    // Transfer result back
    let result_f32 = memory_manager.transfer_from_gpu(output_buffer)?;
    let result: Vec<T> = result_f32.iter()
        .map(|&x| unsafe { std::mem::transmute_copy(&x) })
        .collect();
    
    Ok(result)
}

/// Execute batch of operations on GPU
fn execute_batch_gpu<T>(
    operations: &[NeuralOperation<T>],
    inputs: &[Vec<T>],
    backend: &Arc<dyn GpuBackendTrait>,
    memory_manager: &Arc<dyn MemoryManagerTrait>,
) -> NeuralResult<Vec<Vec<T>>>
where
    T: Clone + Send + Sync + 'static + bytemuck::Pod,
{
    let mut results = Vec::with_capacity(operations.len());
    
    for (operation, input) in operations.iter().zip(inputs.iter()) {
        let result = execute_operation(operation.clone(), input, backend, memory_manager)?;
        results.push(result);
    }
    
    Ok(results)
}

/// Generate CUDA/WGSL kernel for matrix multiplication
fn generate_matrix_multiply_kernel(a_rows: usize, a_cols: usize, b_cols: usize) -> String {
    format!(r#"
        __global__ void matrix_multiply(float* a, float* b, float* c) {{
            int row = blockIdx.y * blockDim.y + threadIdx.y;
            int col = blockIdx.x * blockDim.x + threadIdx.x;
            
            if (row < {a_rows} && col < {b_cols}) {{
                float sum = 0.0f;
                for (int k = 0; k < {a_cols}; k++) {{
                    sum += a[row * {a_cols} + k] * b[k * {b_cols} + col];
                }}
                c[row * {b_cols} + col] = sum;
            }}
        }}
    "#)
}

/// Generate CUDA/WGSL kernel for vector addition
fn generate_vector_add_kernel(size: usize) -> String {
    format!(r#"
        __global__ void vector_add(float* a, float* b, float* c) {{
            int i = blockIdx.x * blockDim.x + threadIdx.x;
            if (i < {size}) {{
                c[i] = a[i] + b[i];
            }}
        }}
    "#)
}

/// Generate CUDA/WGSL kernel for activation functions
fn generate_activation_kernel(function: ActivationFunction, size: usize) -> String {
    let activation_code = match function {
        ActivationFunction::Sigmoid => "1.0f / (1.0f + expf(-x))",
        ActivationFunction::ReLU => "fmaxf(0.0f, x)",
        ActivationFunction::Tanh => "tanhf(x)",
        ActivationFunction::LeakyReLU => "x > 0.0f ? x : 0.01f * x",
        ActivationFunction::Swish => "x / (1.0f + expf(-x))",
        ActivationFunction::GELU => "0.5f * x * (1.0f + tanhf(0.7978845608f * (x + 0.044715f * x * x * x)))",
    };
    
    format!(r#"
        __global__ void activation_function(float* input, float* output) {{
            int i = blockIdx.x * blockDim.x + threadIdx.x;
            if (i < {size}) {{
                float x = input[i];
                output[i] = {activation_code};
            }}
        }}
    "#)
}

/// Generate CUDA/WGSL kernel for convolution
fn generate_convolution_kernel(channels: usize, kernel_size: usize, stride: usize) -> String {
    format!(r#"
        __global__ void convolution(float* input, float* kernel, float* output, 
                                    int input_width, int input_height, int output_width, int output_height) {{
            int out_x = blockIdx.x * blockDim.x + threadIdx.x;
            int out_y = blockIdx.y * blockDim.y + threadIdx.y;
            int channel = blockIdx.z;
            
            if (out_x < output_width && out_y < output_height && channel < {channels}) {{
                float sum = 0.0f;
                
                for (int ky = 0; ky < {kernel_size}; ky++) {{
                    for (int kx = 0; kx < {kernel_size}; kx++) {{
                        int in_x = out_x * {stride} + kx;
                        int in_y = out_y * {stride} + ky;
                        
                        if (in_x < input_width && in_y < input_height) {{
                            int input_idx = channel * input_width * input_height + in_y * input_width + in_x;
                            int kernel_idx = channel * {kernel_size} * {kernel_size} + ky * {kernel_size} + kx;
                            sum += input[input_idx] * kernel[kernel_idx];
                        }}
                    }}
                }}
                
                int output_idx = channel * output_width * output_height + out_y * output_width + out_x;
                output[output_idx] = sum;
            }}
        }}
    "#)
}

/// Generate CUDA/WGSL kernel for forward propagation
fn generate_forward_propagation_kernel(layer_sizes: &[usize]) -> String {
    let num_layers = layer_sizes.len();
    let weights_calculation = (0..num_layers-1).map(|i| {
        format!(r#"
            // Layer {} to {}
            if (layer == {}) {{
                for (int j = 0; j < {}; j++) {{
                    float sum = 0.0f;
                    for (int k = 0; k < {}; k++) {{
                        sum += activations[prev_layer_offset + k] * weights[weight_offset + j * {} + k];
                    }}
                    activations[current_layer_offset + j] = 1.0f / (1.0f + expf(-sum)); // Sigmoid
                }}
            }}
        "#, i, i+1, i, layer_sizes[i+1], layer_sizes[i], layer_sizes[i])
    }).collect::<Vec<_>>().join("\n");
    
    format!(r#"
        __global__ void forward_propagation(float* inputs, float* weights, float* biases, float* activations) {{
            int neuron = blockIdx.x * blockDim.x + threadIdx.x;
            int layer = blockIdx.y;
            
            // Copy inputs to first layer
            if (layer == 0 && neuron < {}) {{
                activations[neuron] = inputs[neuron];
                return;
            }}
            
            // Calculate layer offsets
            int prev_layer_offset = 0;
            int current_layer_offset = 0;
            int weight_offset = 0;
            
            for (int i = 0; i < layer; i++) {{
                if (i < layer - 1) prev_layer_offset += layer_sizes[i];
                current_layer_offset += layer_sizes[i];
                if (i < layer - 1) weight_offset += layer_sizes[i] * layer_sizes[i + 1];
            }}
            
            {}
        }}
    "#, layer_sizes[0], weights_calculation)
}

/// Generate CUDA/WGSL kernel for backward propagation
fn generate_backward_propagation_kernel(layer_sizes: &[usize]) -> String {
    format!(r#"
        __global__ void backward_propagation(float* activations, float* weights, 
                                           float* errors, float* gradients, float* targets) {{
            int neuron = blockIdx.x * blockDim.x + threadIdx.x;
            int layer = blockIdx.y;
            
            // Calculate output layer errors
            if (layer == {} - 1) {{
                if (neuron < {}) {{
                    float output = activations[neuron]; // Assuming output layer offset
                    float target = targets[neuron];
                    errors[neuron] = (output - target) * output * (1.0f - output); // Sigmoid derivative
                }}
                return;
            }}
            
            // Backpropagate errors for hidden layers
            // Implementation depends on network architecture
            // This is a simplified version
            if (layer > 0 && neuron < layer_sizes[layer]) {{
                float error_sum = 0.0f;
                for (int next_neuron = 0; next_neuron < layer_sizes[layer + 1]; next_neuron++) {{
                    error_sum += errors[next_neuron] * weights[neuron * layer_sizes[layer + 1] + next_neuron];
                }}
                float activation = activations[neuron];
                errors[neuron] = error_sum * activation * (1.0f - activation);
            }}
        }}
    "#, layer_sizes.len(), layer_sizes.last().unwrap_or(&0))
}

#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_kernel_generation() {
        let kernel = generate_matrix_multiply_kernel(4, 4, 4);
        assert!(kernel.contains("matrix_multiply"));
        assert!(kernel.contains("blockIdx"));
        assert!(kernel.contains("threadIdx"));
    }
    
    #[test]
    fn test_activation_kernel_generation() {
        let kernel = generate_activation_kernel(ActivationFunction::ReLU, 128);
        assert!(kernel.contains("fmaxf"));
        assert!(kernel.contains("activation_function"));
    }
    
    #[test]
    fn test_vector_add_kernel() {
        let kernel = generate_vector_add_kernel(256);
        assert!(kernel.contains("vector_add"));
        assert!(kernel.contains("256"));
    }
}