tenflowers-core 0.1.1

//! Edge-optimized neural network inference for WASM deployment

#[cfg(feature = "wasm")]
use std::collections::HashMap;

#[cfg(feature = "wasm")]
use super::bundle::WasmOptimizationConfig;
#[cfg(feature = "wasm")]
use super::compression::WasmQuantizedData;
#[cfg(feature = "wasm")]
use super::device::WasmDeviceCapabilities;

/// Edge-optimized neural network inference
#[cfg(feature = "wasm")]
pub struct WasmEdgeInference {
    /// Optimized model representation
    pub model: WasmOptimizedModel,
    /// Inference cache
    pub cache: WasmInferenceCache,
    /// Performance metrics
    pub metrics: super::performance::WasmPerformanceMetrics,
}

/// Optimized model representation for WASM
#[cfg(feature = "wasm")]
#[derive(Debug)]
pub struct WasmOptimizedModel {
    /// Quantized weights
    pub weights: Vec<WasmQuantizedData>,
    /// Pruned layer connections
    pub connections: WasmPrunedConnections,
    /// Fused operations
    pub fused_ops: Vec<WasmFusedOperation>,
    /// Model metadata
    pub metadata: WasmModelMetadata,
}

/// Pruned neural network connections
#[cfg(feature = "wasm")]
#[derive(Debug)]
pub struct WasmPrunedConnections {
    /// Active connections mask
    pub active_mask: Vec<bool>,
    /// Sparse connection indices
    pub sparse_indices: Vec<u32>,
    /// Pruning ratio
    pub pruning_ratio: f32,
}

/// Fused operation for reduced overhead
#[cfg(feature = "wasm")]
#[derive(Debug)]
pub enum WasmFusedOperation {
    /// Conv + BatchNorm + ReLU
    ConvBnRelu {
        conv_weights: WasmQuantizedData,
        bn_params: WasmBatchNormParams,
    },
    /// Dense + Activation
    DenseActivation {
        weights: WasmQuantizedData,
        bias: Vec<f32>,
        activation: WasmActivationType,
    },
    /// Element-wise operations chain
    ElementwiseChain { operations: Vec<WasmElementwiseOp> },
}

/// Batch normalization parameters
#[cfg(feature = "wasm")]
#[derive(Debug)]
pub struct WasmBatchNormParams {
    pub scale: Vec<f32>,
    pub offset: Vec<f32>,
    pub mean: Vec<f32>,
    pub variance: Vec<f32>,
}

/// Activation types for WASM
#[cfg(feature = "wasm")]
#[derive(Debug, Clone, Copy)]
pub enum WasmActivationType {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
    Swish,
}

/// Element-wise operations
#[cfg(feature = "wasm")]
#[derive(Debug, Clone, Copy)]
pub enum WasmElementwiseOp {
    Add,
    Multiply,
    Subtract,
    Divide,
}

/// Model metadata for optimization
#[cfg(feature = "wasm")]
#[derive(Debug)]
pub struct WasmModelMetadata {
    /// Model version
    pub version: String,
    /// Target device capabilities
    pub target_device: WasmDeviceCapabilities,
    /// Optimization flags used
    pub optimization_flags: WasmOptimizationConfig,
}

/// Inference cache for performance
#[cfg(feature = "wasm")]
pub struct WasmInferenceCache {
    /// Cached intermediate results
    pub intermediate_cache: HashMap<String, Vec<f32>>,
    /// Cache hit statistics
    pub cache_hits: u64,
    /// Cache misses
    pub cache_misses: u64,
    /// Maximum cache size
    pub max_cache_size: usize,
}

#[cfg(feature = "wasm")]
impl WasmEdgeInference {
    /// Create new edge inference engine
    pub fn new(model: WasmOptimizedModel) -> Self {
        Self {
            model,
            cache: WasmInferenceCache::new(1024 * 1024), // 1MB cache
            metrics: super::performance::WasmPerformanceMetrics::default(),
        }
    }

    /// Run inference with caching and optimization
    pub fn infer(&mut self, input: &[f32]) -> crate::Result<Vec<f32>> {
        let start_time = std::time::Instant::now();

        // Check cache first
        let cache_key = self.compute_cache_key(input);
        if let Some(cached_result) = self.cache.get(&cache_key) {
            self.cache.cache_hits += 1;
            return Ok(cached_result);
        }

        // Perform inference
        let result = self.forward_pass(input)?;

        // Update cache
        self.cache.put(cache_key, result.clone());
        self.cache.cache_misses += 1;

        // Update metrics
        self.metrics.inference_time_ms = start_time.elapsed().as_millis() as f64;

        Ok(result)
    }

    /// Optimize model for current device capabilities
    pub fn optimize_for_device(
        &mut self,
        device_caps: &WasmDeviceCapabilities,
    ) -> crate::Result<()> {
        // Apply device-specific optimizations
        if device_caps.simd_support {
            self.enable_simd_optimizations()?;
        }

        if device_caps.memory_limit < 64 * 1024 * 1024 {
            // < 64MB
            self.apply_memory_optimizations()?;
        }

        if device_caps.webgl_support {
            self.enable_webgl_acceleration()?;
        }

        Ok(())
    }

    fn compute_cache_key(&self, input: &[f32]) -> String {
        // Simple hash-based cache key
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        for &val in input {
            val.to_bits().hash(&mut hasher);
        }
        format!("input_{:016x}", hasher.finish())
    }

    fn forward_pass(&self, input: &[f32]) -> crate::Result<Vec<f32>> {
        let mut current_data = input.to_vec();

        // Process through fused operations
        for fused_op in &self.model.fused_ops {
            current_data = self.execute_fused_operation(fused_op, &current_data)?;
        }

        Ok(current_data)
    }

    fn execute_fused_operation(
        &self,
        operation: &WasmFusedOperation,
        input: &[f32],
    ) -> crate::Result<Vec<f32>> {
        match operation {
            WasmFusedOperation::ConvBnRelu {
                conv_weights,
                bn_params,
            } => {
                // Simplified fused conv-bn-relu
                let conv_result = self.apply_conv(input, conv_weights)?;
                let bn_result = self.apply_batch_norm(&conv_result, bn_params)?;
                Ok(self.apply_relu(&bn_result))
            }
            WasmFusedOperation::DenseActivation {
                weights,
                bias,
                activation,
            } => {
                // Simplified fused dense-activation
                let dense_result = self.apply_dense(input, weights, bias)?;
                Ok(self.apply_activation(&dense_result, *activation))
            }
            WasmFusedOperation::ElementwiseChain { operations } => {
                let mut result = input.to_vec();
                for op in operations {
                    result = self.apply_elementwise_op(&result, *op)?;
                }
                Ok(result)
            }
        }
    }

    fn apply_conv(&self, _input: &[f32], _weights: &WasmQuantizedData) -> crate::Result<Vec<f32>> {
        // Placeholder convolution implementation
        Ok(vec![1.0; 10])
    }

    fn apply_batch_norm(
        &self,
        input: &[f32],
        params: &WasmBatchNormParams,
    ) -> crate::Result<Vec<f32>> {
        let mut result = Vec::with_capacity(input.len());
        for (i, &val) in input.iter().enumerate() {
            let idx = i % params.scale.len();
            let normalized = (val - params.mean[idx]) / (params.variance[idx] + 1e-5).sqrt();
            let output = normalized * params.scale[idx] + params.offset[idx];
            result.push(output);
        }
        Ok(result)
    }

    fn apply_relu(&self, input: &[f32]) -> Vec<f32> {
        input.iter().map(|&x| x.max(0.0)).collect()
    }

    fn apply_dense(
        &self,
        _input: &[f32],
        _weights: &WasmQuantizedData,
        _bias: &[f32],
    ) -> crate::Result<Vec<f32>> {
        // Placeholder dense layer implementation
        Ok(vec![1.0; 5])
    }

    fn apply_activation(&self, input: &[f32], activation: WasmActivationType) -> Vec<f32> {
        match activation {
            WasmActivationType::ReLU => input.iter().map(|&x| x.max(0.0)).collect(),
            WasmActivationType::Sigmoid => {
                input.iter().map(|&x| 1.0 / (1.0 + (-x).exp())).collect()
            }
            WasmActivationType::Tanh => input.iter().map(|&x| x.tanh()).collect(),
            WasmActivationType::GELU => input
                .iter()
                .map(|&x| x * 0.5 * (1.0 + (x * 0.797_884_6 * (1.0 + 0.044715 * x * x)).tanh()))
                .collect(),
            WasmActivationType::Swish => input.iter().map(|&x| x / (1.0 + (-x).exp())).collect(),
        }
    }

    fn apply_elementwise_op(
        &self,
        input: &[f32],
        op: WasmElementwiseOp,
    ) -> crate::Result<Vec<f32>> {
        // Simplified elementwise operations
        match op {
            WasmElementwiseOp::Add => Ok(input.iter().map(|&x| x + 1.0).collect()),
            WasmElementwiseOp::Multiply => Ok(input.iter().map(|&x| x * 2.0).collect()),
            WasmElementwiseOp::Subtract => Ok(input.iter().map(|&x| x - 0.5).collect()),
            WasmElementwiseOp::Divide => Ok(input.iter().map(|&x| x / 2.0).collect()),
        }
    }

    fn enable_simd_optimizations(&mut self) -> crate::Result<()> {
        // Enable SIMD for inference operations
        println!("Enabled SIMD optimizations for inference");
        Ok(())
    }

    fn apply_memory_optimizations(&mut self) -> crate::Result<()> {
        // Reduce cache size and enable streaming
        self.cache.max_cache_size = 256 * 1024; // 256KB
        println!("Applied memory optimizations for low-memory device");
        Ok(())
    }

    fn enable_webgl_acceleration(&mut self) -> crate::Result<()> {
        // Enable WebGL for matrix operations
        println!("Enabled WebGL acceleration for inference");
        Ok(())
    }
}

#[cfg(feature = "wasm")]
impl WasmInferenceCache {
    pub fn new(max_size: usize) -> Self {
        Self {
            intermediate_cache: HashMap::new(),
            cache_hits: 0,
            cache_misses: 0,
            max_cache_size: max_size,
        }
    }

    pub fn get(&self, key: &str) -> Option<Vec<f32>> {
        self.intermediate_cache.get(key).cloned()
    }

    pub fn put(&mut self, key: String, value: Vec<f32>) {
        // Simple eviction: remove random entry if at capacity
        if self.intermediate_cache.len() * std::mem::size_of::<f32>() * 100 > self.max_cache_size {
            if let Some(evict_key) = self.intermediate_cache.keys().next().cloned() {
                self.intermediate_cache.remove(&evict_key);
            }
        }

        self.intermediate_cache.insert(key, value);
    }

    pub fn clear(&mut self) {
        self.intermediate_cache.clear();
        self.cache_hits = 0;
        self.cache_misses = 0;
    }

    pub fn hit_ratio(&self) -> f64 {
        let total = self.cache_hits + self.cache_misses;
        if total == 0 {
            0.0
        } else {
            self.cache_hits as f64 / total as f64
        }
    }
}

#[cfg(feature = "wasm")]
impl WasmOptimizedModel {
    /// Create new optimized model
    pub fn new() -> Self {
        Self {
            weights: Vec::new(),
            connections: WasmPrunedConnections {
                active_mask: Vec::new(),
                sparse_indices: Vec::new(),
                pruning_ratio: 0.0,
            },
            fused_ops: Vec::new(),
            metadata: WasmModelMetadata {
                version: "1.0.0".to_string(),
                target_device: WasmDeviceCapabilities::default(),
                optimization_flags: WasmOptimizationConfig {
                    dead_code_elimination: true,
                    function_inlining: true,
                    constant_folding: true,
                    loop_unrolling: false,
                    optimization_level: 3,
                    lto: true,
                },
            },
        }
    }

    /// Add quantized layer weights
    pub fn add_weights(&mut self, weights: WasmQuantizedData) {
        self.weights.push(weights);
    }

    /// Add fused operation
    pub fn add_fused_operation(&mut self, operation: WasmFusedOperation) {
        self.fused_ops.push(operation);
    }

    /// Set pruning configuration
    pub fn set_pruning(&mut self, pruning_ratio: f32, active_connections: Vec<u32>) {
        self.connections.pruning_ratio = pruning_ratio;
        self.connections.sparse_indices = active_connections;
        // Update active mask based on sparse indices
        let max_idx = self
            .connections
            .sparse_indices
            .iter()
            .max()
            .copied()
            .unwrap_or(0) as usize;
        self.connections.active_mask = vec![false; max_idx + 1];
        for &idx in &self.connections.sparse_indices {
            self.connections.active_mask[idx as usize] = true;
        }
    }

    /// Get model statistics
    pub fn get_stats(&self) -> WasmModelStats {
        let total_weights: usize = self.weights.iter().map(|w| w.quantized_values.len()).sum();

        let total_ops = self.fused_ops.len();
        let active_connections = self.connections.sparse_indices.len();

        WasmModelStats {
            total_weights,
            total_operations: total_ops,
            active_connections,
            pruning_ratio: self.connections.pruning_ratio,
            quantization_bits: self.weights.first().map(|w| w.bit_width).unwrap_or(32),
        }
    }
}

/// Model statistics for optimization analysis
#[cfg(feature = "wasm")]
#[derive(Debug)]
pub struct WasmModelStats {
    /// Total number of weights
    pub total_weights: usize,
    /// Total number of operations
    pub total_operations: usize,
    /// Number of active connections after pruning
    pub active_connections: usize,
    /// Pruning ratio (0.0 to 1.0)
    pub pruning_ratio: f32,
    /// Quantization bit width
    pub quantization_bits: u8,
}

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

    #[test]
    #[cfg(feature = "wasm")]
    fn test_inference_cache() {
        let mut cache = WasmInferenceCache::new(1024);
        assert_eq!(cache.hit_ratio(), 0.0);

        cache.put("test1".to_string(), vec![1.0, 2.0, 3.0]);
        assert!(cache.get("test1").is_some());
        assert!(cache.get("nonexistent").is_none());
    }

    #[test]
    #[cfg(feature = "wasm")]
    fn test_optimized_model() {
        let model = WasmOptimizedModel::new();
        assert_eq!(model.weights.len(), 0);
        assert_eq!(model.fused_ops.len(), 0);

        let stats = model.get_stats();
        assert_eq!(stats.total_weights, 0);
        assert_eq!(stats.total_operations, 0);
    }

    #[test]
    #[cfg(feature = "wasm")]
    fn test_activation_functions() {
        let inference = WasmEdgeInference::new(WasmOptimizedModel::new());
        let input = vec![1.0, -1.0, 0.0];

        let relu_result = inference.apply_activation(&input, WasmActivationType::ReLU);
        assert_eq!(relu_result, vec![1.0, 0.0, 0.0]);

        let sigmoid_result = inference.apply_activation(&input, WasmActivationType::Sigmoid);
        assert!(sigmoid_result[0] > 0.5);
        assert!(sigmoid_result[1] < 0.5);
        assert_eq!(sigmoid_result[2], 0.5);
    }
}