torsh-nn 0.1.2

//! Functional interface for neural network operations
//! Enhanced with SciRS2-Neural integration for optimized performance
//!
//! This module provides a comprehensive, functional API for neural network operations
//! with modular architecture, standardized error handling, parameter validation, and performance optimizations.
//!
//! # Modular Architecture
//!
//! The functional API is organized into specialized modules for improved maintainability:
//!
//! - **core**: Core infrastructure, configuration, validation, and utilities
//! - **activation**: Activation functions (ReLU, Sigmoid, Tanh, GELU, etc.)
//! - **conv**: Convolution operations (1D, 2D, 3D) with comprehensive parameter support
//! - **pooling**: Pooling operations (max, average, adaptive, global)
//! - **linear**: Linear transformations, attention mechanisms, and embedding operations
//! - **loss**: Basic loss functions (cross entropy, MSE, L1, KL divergence, etc.)
//! - **loss_advanced**: Advanced loss framework with composable building blocks
//! - **norm**: Normalization operations (batch norm, layer norm, group norm, etc.)
//!
//! All components maintain full backward compatibility through comprehensive re-exports.

// Modular architecture imports
pub mod activation;
pub mod conv;
pub mod core;
pub mod linear;
pub mod loss;
pub mod loss_advanced;
pub mod norm;
pub mod pooling;

// Re-export core functionality for convenience
pub use core::*;

// =============================================================================
// ACTIVATION FUNCTIONS - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Enhanced activation functions with SciRS2 integration
pub use activation::{
    dropout, elu, gelu, leaky_relu, log_softmax, mish, relu, relu_inplace, selu, sigmoid, softmax,
    swish, tanh,
};

// Activation function structs for trait-based usage
pub use activation::{
    LeakyReLU, LogSoftmax, Mish, ReLU, Sigmoid, Softmax, Swish, Tanh, ELU, GELU, SELU,
};

// Configured activation functions with validation
pub use activation::configured::{
    gelu_configured, mish_configured, relu_configured, sigmoid_configured, softmax_configured,
    swish_configured, tanh_configured,
};

// =============================================================================
// CONVOLUTION OPERATIONS - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Standard convolution operations
pub use conv::{conv1d, conv2d, conv3d, conv_transpose1d, conv_transpose2d, conv_transpose3d};

// Utility functions
pub use conv::{conv_output_size, conv_transpose_output_size, validate_conv_params};

// =============================================================================
// POOLING OPERATIONS - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Max pooling operations
pub use pooling::{
    adaptive_max_pool1d, adaptive_max_pool2d, adaptive_max_pool3d, global_max_pool1d,
    global_max_pool2d, global_max_pool3d, max_pool1d, max_pool2d, max_pool3d,
};

// Average pooling operations
pub use pooling::{
    adaptive_avg_pool1d, adaptive_avg_pool2d, adaptive_avg_pool3d, avg_pool1d, avg_pool2d,
    avg_pool3d, global_avg_pool1d, global_avg_pool2d, global_avg_pool3d,
};

// Padding operations
pub use pooling::{
    pad, reflection_pad1d, reflection_pad2d, replication_pad1d, replication_pad2d, zero_pad2d,
};

// Utility functions
pub use pooling::{adaptive_pool_params, pool_output_size};

// =============================================================================
// LINEAR AND ATTENTION OPERATIONS - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Linear transformations
pub use linear::{bilinear, linear};

// Embedding operations
pub use linear::{embedding, embedding_bag, one_hot};

// Attention mechanisms
pub use linear::{
    grouped_query_attention, multi_head_attention, multi_query_attention,
    scaled_dot_product_attention,
};

// Positional encoding
pub use linear::{
    learnable_positional_encoding, rotary_positional_encoding, sinusoidal_positional_encoding,
};

// Normalization in attention
pub use linear::{post_norm_layer_norm, pre_norm_layer_norm, rms_norm};

// Gating mechanisms
pub use linear::{geglu, glu, swiglu};

// Utility functions
pub use linear::{apply_attention_mask, create_causal_mask, create_padding_mask};

// =============================================================================
// LOSS FUNCTIONS - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Classification losses
pub use loss::{
    binary_cross_entropy, binary_cross_entropy_with_logits, cross_entropy, focal_loss,
    multi_margin_loss, multilabel_margin_loss, nll_loss,
};

// Regression losses
pub use loss::{huber_loss, l1_loss, mse_loss, smooth_l1_loss};

// Probabilistic losses
pub use loss::kl_div;

// Ranking and similarity losses
pub use loss::{contrastive_loss, cosine_embedding_loss, triplet_margin_loss};

// Modern loss functions
pub use loss::{center_loss, dice_loss, infonce_loss, tversky_loss, wing_loss};

// =============================================================================
// ADVANCED LOSS FRAMEWORK - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Advanced loss framework
pub use loss_advanced::{CustomLoss, LossBuilder, LossFactory, Reduction};

// Advanced loss implementations
pub use loss_advanced::{
    AdaptiveLoss, CombinedLoss, DiceLoss, IoULoss, SmoothL1Loss, WeightedLoss,
};

// Pre-built loss function structs
pub use loss_advanced::{
    BinaryCrossEntropy, CategoricalCrossEntropy, CosineEmbeddingLoss, FocalLoss, HingeLoss,
    HuberLoss, KLDivLoss, L1Loss, MSELoss, NLLLoss, TripletMarginLoss,
};

// Validation utilities
pub use loss_advanced::validation as loss_validation;

// =============================================================================
// NORMALIZATION OPERATIONS - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

// Batch normalization
pub use norm::{
    batch_norm, batch_norm_1d, batch_norm_2d, batch_norm_2d_with_config, batch_norm_3d,
};

// Layer normalization
pub use norm::{layer_norm, layer_norm_configured, layer_norm_enhanced};

// Other normalization methods
pub use norm::{
    group_norm, instance_norm, local_response_norm, rms_norm as rms_norm_standalone, spectral_norm,
    weight_norm,
};

// Configured normalization functions
pub use norm::configured::batch_norm_configured;

// Utility functions
pub use norm::{create_affine_params, get_norm_features, validate_norm_params};

// =============================================================================
// CONVENIENT API MODULES - COMPLETE BACKWARD COMPATIBILITY
// =============================================================================

/// Convenient activation functions with standardized API
pub mod activations {
    pub use super::activation::configured::*;
    // Re-export base functions for convenience
    pub use super::activation::{gelu, mish, relu, sigmoid, softmax, swish, tanh};
}

/// Convenient loss functions with standardized API
pub mod losses {
    pub use super::loss::*;

    /// MSE loss with configuration
    pub fn mse_loss_configured(
        input: &crate::Tensor,
        target: &crate::Tensor,
        reduction: &str,
        config: &super::FunctionalConfig,
    ) -> super::FuncResult<crate::Tensor> {
        crate::validate_inputs!(
            config,
            super::validation::validate_not_empty(input, "input"),
            super::validation::validate_not_empty(target, "target"),
            super::validation::validate_compatible_shapes(input, target, "MSE loss")
        );
        crate::func_error!(super::mse_loss(input, target, reduction), "MSE loss")
    }

    /// L1 loss with configuration
    pub fn l1_loss_configured(
        input: &crate::Tensor,
        target: &crate::Tensor,
        reduction: &str,
        config: &super::FunctionalConfig,
    ) -> super::FuncResult<crate::Tensor> {
        crate::validate_inputs!(
            config,
            super::validation::validate_not_empty(input, "input"),
            super::validation::validate_not_empty(target, "target"),
            super::validation::validate_compatible_shapes(input, target, "L1 loss")
        );
        crate::func_error!(super::l1_loss(input, target, reduction), "L1 loss")
    }

    /// Cross entropy loss with configuration
    pub fn cross_entropy_configured(
        input: &crate::Tensor,
        target: &crate::Tensor<i64>,
        weight: Option<&crate::Tensor>,
        ignore_index: Option<i64>,
        reduction: &str,
        config: &super::FunctionalConfig,
    ) -> super::FuncResult<crate::Tensor> {
        crate::validate_inputs!(
            config,
            super::validation::validate_not_empty(input, "input"),
            super::validation::validate_not_empty(target, "target"),
            super::validation::validate_min_ndim(input, 2, "input")
        );
        crate::func_error!(
            super::cross_entropy(input, target, weight, reduction, ignore_index),
            "Cross entropy loss"
        )
    }
}

/// Convenient normalization functions with standardized API
pub mod normalization {
    pub use super::norm::configured::*;
    // Re-export base functions for convenience
    pub use super::norm::{batch_norm_2d, layer_norm_enhanced};
}

// =============================================================================
// FUNCTIONAL API PRELUDE
// =============================================================================

/// Prelude module for convenient functional API imports
pub mod prelude {
    pub use super::{
        activations, default_config, losses, normalization, numerics, optimized, performance, safe,
        validation, Activation, ActivationConfig, CustomLoss, FunctionalBuilder, FunctionalConfig,
        LossBuilder, MemoryOptLevel, Reduction,
    };
}

// =============================================================================
// UTILITY RE-EXPORTS FOR BACKWARD COMPATIBILITY
// =============================================================================

// Import types needed for compatibility
use torsh_core::error::Result;
use torsh_tensor::Tensor;

/// Extension trait to add tensor casting for compatibility
#[allow(dead_code)]
trait TensorCast {
    fn cast_i64(&self) -> Result<Tensor<i64>>;
}

#[allow(dead_code)]
impl TensorCast for Tensor {
    fn cast_i64(&self) -> Result<Tensor<i64>> {
        // Simplified casting - in practice would need proper tensor type conversion
        let data = self.to_vec()?;
        let i64_data: Vec<i64> = data.into_iter().map(|x| x as i64).collect();
        Ok(Tensor::from_data(
            i64_data,
            self.shape().dims().to_vec(),
            self.device(),
        )?)
    }
}

/// Sparse Matrix placeholder for compatibility
pub struct SparseMatrix;

impl SparseMatrix {
    pub fn new() -> Self {
        Self
    }
}

impl Default for SparseMatrix {
    fn default() -> Self {
        Self::new()
    }
}

// =============================================================================
// ENHANCED OPERATIONS WITH SCIRS2 INTEGRATION
// =============================================================================

/// Enhanced batch normalization with standardized API and SciRS2 numerical stability
/// Re-exported from norm module for backward compatibility (covered by group import above)

/// Enhanced layer normalization with SciRS2 numerical stability
/// Re-exported from norm module for backward compatibility (covered by group import above)

// =============================================================================
// TESTS AND EXAMPLES
// =============================================================================

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

    #[test]
    fn test_modular_functional_system() {
        // Test that all modules are accessible and working

        // Test activation functions
        let input = torsh_tensor::creation::randn::<f32>(&[2, 4]).unwrap();
        let _relu_result = relu(&input).unwrap();
        let _sigmoid_result = sigmoid(&input).unwrap();
        let _tanh_result = tanh(&input).unwrap();

        // Test configuration-based functions
        let config = FunctionalConfig::default();
        let _configured_relu = activations::relu_configured(&input, &config).unwrap();

        // Test builder patterns
        let _optimized_config = optimized().build();
        let _safe_config = safe().build();
    }

    #[test]
    fn test_backward_compatibility() {
        // Ensure that the modular system maintains full backward compatibility
        // All original function names and APIs should work exactly as before

        let input = torsh_tensor::creation::randn::<f32>(&[4, 3, 32, 32]).unwrap();
        let weight = torsh_tensor::creation::ones(&[3]).unwrap();
        let bias = torsh_tensor::creation::zeros(&[3]).unwrap();

        // Test batch normalization
        let _batch_norm_result = batch_norm_2d(
            &input,
            Some(&weight),
            Some(&bias),
            None,
            None,
            true,
            0.1,
            1e-5,
        )
        .unwrap();

        // Test original activation functions still work
        let activation_input = torsh_tensor::creation::randn::<f32>(&[2, 4]).unwrap();
        let _relu_result = relu(&activation_input).unwrap();
        let _gelu_result = gelu(&activation_input).unwrap();
        let _swish_result = swish(&activation_input).unwrap();
    }

    #[test]
    fn test_modular_structure_integrity() {
        // Test that all modules are properly accessible

        // Test core functionality
        let config = FunctionalConfig::default();
        assert_eq!(config.validate_inputs, true);
        assert_eq!(config.eps, 1e-8);

        // Test that builder pattern works
        let custom_config = FunctionalBuilder::new().eps(1e-6).inplace(true).build();
        assert_eq!(custom_config.eps, 1e-6);
        assert_eq!(custom_config.inplace, true);

        // Test that prelude imports work
        let _default_conf = prelude::default_config();
    }

    #[test]
    fn test_loss_framework() {
        // Test the advanced loss framework
        let predictions = torsh_tensor::creation::randn::<f32>(&[4, 10]).unwrap();
        let targets = torsh_tensor::creation::randn::<f32>(&[4, 10]).unwrap();

        // Test MSE loss
        let mse = MSELoss::new(Reduction::Mean);
        let _loss_result = mse.compute_loss(&predictions, &targets).unwrap();

        // Test builder pattern
        let _smooth_l1 = LossBuilder::new()
            .with_reduction(Reduction::Sum)
            .smooth_l1(1.0);
    }
}

/// Example usage demonstrating the modular functional API
#[cfg(test)]
mod examples {
    use super::*;

    #[test]
    fn example_basic_usage() {
        // Create some sample data
        let input = torsh_tensor::creation::randn::<f32>(&[4, 3, 32, 32]).unwrap();
        let target = torsh_tensor::creation::randn::<f32>(&[4, 10]).unwrap();

        // Use activation functions
        let activated = relu(&input).unwrap();
        let _softmax_result = softmax(&activated, Some(-1)).unwrap();

        // Test batch normalization
        let weight = torsh_tensor::creation::ones(&[3]).unwrap();
        let bias = torsh_tensor::creation::zeros(&[3]).unwrap();
        let _normalized = batch_norm_2d(
            &input,
            Some(&weight),
            Some(&bias),
            None,
            None,
            true,
            0.1,
            1e-5,
        )
        .unwrap();

        // Use loss functions
        let predictions = torsh_tensor::creation::randn::<f32>(&[4, 10]).unwrap();
        let _mse_loss = mse_loss(&predictions, &target, "mean").unwrap();
    }

    #[test]
    fn example_configured_usage() {
        // Create configuration
        let config = FunctionalBuilder::new()
            .validate(true)
            .eps(1e-6)
            .memory_opt(MemoryOptLevel::Maximum)
            .build();

        let input = torsh_tensor::creation::randn::<f32>(&[4, 8]).unwrap();

        // Use configured functions
        let _relu_result = activations::relu_configured(&input, &config).unwrap();
        let _sigmoid_result = activations::sigmoid_configured(&input, &config).unwrap();
    }

    #[test]
    fn example_advanced_loss_usage() {
        let predictions = torsh_tensor::creation::randn::<f32>(&[4, 10]).unwrap();
        let targets = torsh_tensor::creation::randn::<f32>(&[4, 10]).unwrap();

        // Use advanced loss framework
        let dice_loss = LossBuilder::new()
            .with_reduction(Reduction::Mean)
            .dice(1e-6);

        let _loss_result = dice_loss.compute_loss(&predictions, &targets).unwrap();

        // Combine multiple losses
        let mse = Box::new(MSELoss::new(Reduction::None));
        let l1 = Box::new(L1Loss::new(Reduction::None));

        let combined = LossBuilder::new()
            .with_reduction(Reduction::Mean)
            .combined(vec![mse, l1], vec![0.7, 0.3]);

        let _combined_loss = combined.compute_loss(&predictions, &targets).unwrap();
    }
}