ronn-providers 0.1.0

//! BitNet execution provider implementation.
//!
//! This provider implements 1-bit and 1.58-bit quantized neural network execution
//! with highly optimized kernels for ultra-fast inference and minimal memory usage.

use std::collections::{HashMap, HashSet};
use std::sync::Arc;

use anyhow::{Result, anyhow};
use ronn_core::{
    CompiledKernel, DataType, ExecutionProvider, GraphNode, MemoryType, OperatorSpec,
    PerformanceProfile, ProviderCapability, ProviderConfig, ProviderId, ResourceRequirements,
    SubGraph, TensorAllocator,
};
use tracing::{debug, info, warn};

use super::allocator::{BitNetMemoryStats, create_bitnet_allocator};
use super::kernels::{
    BitNetKernel, BitNetOperation, create_binary_elementwise_kernel, create_binary_matmul_kernel,
    create_ternary_matmul_kernel,
};
use super::quantization::{BitNetQuantizer, QuantizationMethod};

/// BitNet execution provider for 1-bit quantized models.
pub struct BitNetExecutionProvider {
    /// Provider configuration.
    config: BitNetProviderConfig,
    /// Memory allocator optimized for bit-packed tensors.
    allocator: Arc<dyn TensorAllocator>,
    /// Quantizer for converting tensors.
    quantizer: Arc<BitNetQuantizer>,
    /// Set of supported operations.
    supported_ops: HashSet<String>,
    /// Memory usage statistics.
    memory_stats: BitNetMemoryStats,
    /// Compiled kernel cache.
    kernel_cache: HashMap<String, Box<dyn CompiledKernel>>,
}

/// Configuration for BitNet execution provider.
#[derive(Debug, Clone)]
pub struct BitNetProviderConfig {
    /// Quantization method to use.
    pub quantization_method: QuantizationMethod,
    /// Enable kernel caching for repeated patterns.
    pub enable_kernel_caching: bool,
    /// Maximum cache size in bytes.
    pub max_cache_size: usize,
    /// Enable mixed precision (FP16 activations with 1-bit weights).
    pub enable_mixed_precision: bool,
    /// Memory optimization level.
    pub memory_optimization_level: u8,
    /// Enable SIMD optimizations.
    pub enable_simd: bool,
}

impl Default for BitNetProviderConfig {
    fn default() -> Self {
        Self {
            quantization_method: QuantizationMethod::Binary,
            enable_kernel_caching: true,
            max_cache_size: 64 * 1024 * 1024, // 64MB cache
            enable_mixed_precision: true,
            memory_optimization_level: 2, // Aggressive optimization
            enable_simd: true,
        }
    }
}

impl BitNetExecutionProvider {
    /// Create a new BitNet execution provider with default configuration.
    pub fn new() -> Result<Self> {
        Self::with_config(BitNetProviderConfig::default())
    }

    /// Create a BitNet execution provider with custom configuration.
    pub fn with_config(config: BitNetProviderConfig) -> Result<Self> {
        info!(
            "Creating BitNet execution provider with method: {:?}",
            config.quantization_method
        );

        let allocator = create_bitnet_allocator();
        let quantizer = Arc::new(BitNetQuantizer::new(config.quantization_method));

        // Define supported operations
        let mut supported_ops = HashSet::new();
        supported_ops.insert("MatMul".to_string());
        supported_ops.insert("Gemm".to_string());
        supported_ops.insert("Conv".to_string());
        supported_ops.insert("Add".to_string());
        supported_ops.insert("Sub".to_string());
        supported_ops.insert("Mul".to_string());
        supported_ops.insert("And".to_string());
        supported_ops.insert("Or".to_string());
        supported_ops.insert("Xor".to_string());
        supported_ops.insert("BatchNormalization".to_string());
        supported_ops.insert("ReLU".to_string());
        supported_ops.insert("Sigmoid".to_string());

        info!(
            "BitNet provider supports {} operations",
            supported_ops.len()
        );

        Ok(Self {
            config,
            allocator,
            quantizer,
            supported_ops,
            memory_stats: BitNetMemoryStats::new(),
            kernel_cache: HashMap::new(),
        })
    }

    /// Get the current configuration.
    pub fn get_config(&self) -> &BitNetProviderConfig {
        &self.config
    }

    /// Get memory usage statistics.
    pub fn get_memory_stats(&self) -> &BitNetMemoryStats {
        &self.memory_stats
    }

    /// Check if an operation is supported.
    pub fn supports_operation(&self, op_type: &str) -> bool {
        self.supported_ops.contains(op_type)
    }

    /// Estimate speedup compared to FP32 for an operation.
    pub fn estimate_speedup(&self, op_spec: &OperatorSpec) -> f32 {
        match op_spec.op_type.as_str() {
            "MatMul" | "Gemm" => {
                match self.config.quantization_method {
                    QuantizationMethod::Binary => 64.0, // Massive speedup for matrix ops
                    QuantizationMethod::Ternary => 32.0,
                    QuantizationMethod::AsymmetricBinary { .. } => 48.0,
                }
            }
            "Conv" => match self.config.quantization_method {
                QuantizationMethod::Binary => 32.0,
                QuantizationMethod::Ternary => 24.0,
                QuantizationMethod::AsymmetricBinary { .. } => 28.0,
            },
            "Add" | "Sub" | "Mul" => 16.0, // Element-wise ops benefit from bit operations
            "And" | "Or" | "Xor" => 128.0, // Bitwise ops are extremely fast
            _ => 4.0,                      // Conservative estimate for other operations
        }
    }

    /// Create a kernel for the specified operation.
    fn create_kernel_for_operation(&self, node: &GraphNode) -> Result<Box<dyn CompiledKernel>> {
        match node.op_type.as_str() {
            "MatMul" | "Gemm" => {
                // Extract dimensions from attributes or infer from inputs
                let m = 64; // Default dimensions - would be inferred from actual inputs
                let n = 64;
                let k = 64;

                let kernel: Box<dyn CompiledKernel> = match self.config.quantization_method {
                    QuantizationMethod::Binary | QuantizationMethod::AsymmetricBinary { .. } => {
                        Box::new(create_binary_matmul_kernel(m, n, k))
                    }
                    QuantizationMethod::Ternary => Box::new(create_ternary_matmul_kernel(m, n, k)),
                };

                Ok(kernel)
            }
            "And" | "Or" | "Xor" | "Add" | "Sub" | "Mul" => {
                let element_count = 1024; // Default size - would be inferred
                Ok(Box::new(create_binary_elementwise_kernel(
                    &node.op_type,
                    element_count,
                )))
            }
            "Conv" => {
                // Convolution can be implemented as im2col + MatMul
                let m = 256; // Output channels * spatial dimensions
                let n = 64; // Batch size
                let k = 512; // Input channels * kernel size

                Ok(Box::new(create_binary_matmul_kernel(m, n, k)))
            }
            "ReLU" => {
                // ReLU for quantized values is essentially a sign operation
                let element_count = 1024;
                Ok(Box::new(create_binary_elementwise_kernel(
                    "ReLU",
                    element_count,
                )))
            }
            "BatchNormalization" => {
                // Simplified quantized batch norm
                let channels = 64;
                let spatial_size = 256;
                Ok(Box::new(BitNetKernel::new(
                    BitNetOperation::QuantizedBatchNorm {
                        channels,
                        spatial_size,
                    },
                    self.config.quantization_method,
                )))
            }
            _ => Err(anyhow!(
                "Unsupported operation for BitNet: {}",
                node.op_type
            )),
        }
    }

    /// Generate cache key for kernel caching.
    fn generate_cache_key(&self, subgraph: &SubGraph) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();

        // Hash the subgraph structure
        for node in &subgraph.nodes {
            node.op_type.hash(&mut hasher);
            node.inputs.len().hash(&mut hasher);
            node.outputs.len().hash(&mut hasher);
        }

        // Include quantization method
        format!(
            "bitnet_{}_{:x}",
            match self.config.quantization_method {
                QuantizationMethod::Binary => "bin",
                QuantizationMethod::Ternary => "tern",
                QuantizationMethod::AsymmetricBinary { .. } => "asym",
            },
            hasher.finish()
        )
    }
}

impl Default for BitNetExecutionProvider {
    fn default() -> Self {
        Self::new().expect("Failed to create default BitNet provider")
    }
}

impl ExecutionProvider for BitNetExecutionProvider {
    fn provider_id(&self) -> ProviderId {
        ProviderId::BitNet
    }

    fn get_capability(&self) -> ProviderCapability {
        ProviderCapability {
            supported_ops: self.supported_ops.clone(),
            data_types: vec![
                DataType::Bool, // Binary quantized
                DataType::U8,   // Ternary quantized
                DataType::F32,  // Mixed precision activations
                DataType::F16,  // Mixed precision activations
            ],
            memory_types: vec![MemoryType::SystemRAM],
            performance_profile: PerformanceProfile::MemoryOptimized,
            resource_requirements: ResourceRequirements {
                min_memory_bytes: Some(16 * 1024 * 1024), // 16MB minimum
                cpu_features: vec![
                    "popcnt".to_string(), // Population count for bit operations
                    "bmi1".to_string(),   // Bit manipulation instructions
                    "bmi2".to_string(),   // Advanced bit manipulation
                ],
                gpu_memory_bytes: None,
            },
        }
    }

    fn can_handle(&self, operators: &[OperatorSpec]) -> Vec<bool> {
        operators
            .iter()
            .map(|op| self.supports_operation(&op.op_type))
            .collect()
    }

    fn compile_subgraph(&self, subgraph: SubGraph) -> Result<Box<dyn CompiledKernel>> {
        debug!(
            "Compiling BitNet subgraph with {} nodes",
            subgraph.nodes.len()
        );

        // Check cache first
        let cache_key = self.generate_cache_key(&subgraph);
        if self.config.enable_kernel_caching {
            if let Some(cached_kernel) = self.kernel_cache.get(&cache_key) {
                debug!("Retrieved cached BitNet kernel for key: {}", cache_key);
                // Note: This is a simplified approach - real implementation would need proper cloning
                return Err(anyhow!("Kernel caching not fully implemented"));
            }
        }

        // For now, compile single-node subgraphs
        if subgraph.nodes.len() != 1 {
            return Err(anyhow!(
                "BitNet provider currently supports only single-node subgraphs"
            ));
        }

        let node = &subgraph.nodes[0];
        debug!("Compiling BitNet kernel for operation: {}", node.op_type);

        let kernel = self.create_kernel_for_operation(node)?;

        info!("Successfully compiled BitNet kernel for {}", node.op_type);
        Ok(kernel)
    }

    fn get_allocator(&self) -> Arc<dyn TensorAllocator> {
        self.allocator.clone()
    }

    fn configure(&mut self, config: ProviderConfig) -> Result<()> {
        // Update configuration based on generic provider config
        if let Some(memory_limit) = config.memory_limit {
            self.config.max_cache_size = (memory_limit / 4).min(128 * 1024 * 1024);
        }

        match config.optimization_level {
            ronn_core::OptimizationLevel::None => {
                self.config.memory_optimization_level = 0;
                self.config.enable_simd = false;
            }
            ronn_core::OptimizationLevel::Basic => {
                self.config.memory_optimization_level = 1;
                self.config.enable_simd = true;
            }
            ronn_core::OptimizationLevel::Aggressive => {
                self.config.memory_optimization_level = 2;
                self.config.enable_simd = true;
                self.config.enable_mixed_precision = true;
            }
            ronn_core::OptimizationLevel::Custom => {
                // Custom settings would be parsed from custom_options
                for (key, value) in &config.custom_options {
                    match key.as_str() {
                        "quantization_method" => {
                            self.config.quantization_method = match value.as_str() {
                                "binary" => QuantizationMethod::Binary,
                                "ternary" => QuantizationMethod::Ternary,
                                _ => QuantizationMethod::Binary,
                            };
                        }
                        "enable_mixed_precision" => {
                            self.config.enable_mixed_precision = value.parse().unwrap_or(true);
                        }
                        "enable_simd" => {
                            self.config.enable_simd = value.parse().unwrap_or(true);
                        }
                        _ => warn!("Unknown BitNet config option: {}", key),
                    }
                }
            }
        }

        info!(
            "BitNet provider reconfigured with optimization level: {:?}",
            config.optimization_level
        );
        Ok(())
    }

    fn shutdown(&self) -> Result<()> {
        info!("Shutting down BitNet execution provider");

        // Log final statistics
        let memory_info = self.allocator.get_memory_info();
        let stats = self.get_memory_stats();

        info!("BitNet provider statistics:");
        info!("  Memory allocated: {} bytes", memory_info.allocated_bytes);
        info!("  Peak memory: {} bytes", memory_info.peak_bytes);
        info!("  Binary tensors: {}", stats.binary_tensor_count);
        info!("  Ternary tensors: {}", stats.ternary_tensor_count);
        info!("  Memory saved: {} bytes", stats.memory_saved_bytes);
        info!(
            "  Average compression: {:.1}x",
            stats.average_compression_ratio
        );

        Ok(())
    }
}

/// Create a BitNet execution provider with default configuration.
pub fn create_bitnet_provider() -> Result<Arc<dyn ExecutionProvider>> {
    Ok(Arc::new(BitNetExecutionProvider::new()?))
}

/// Create a BitNet execution provider with custom configuration.
pub fn create_bitnet_provider_with_config(
    config: BitNetProviderConfig,
) -> Result<Arc<dyn ExecutionProvider>> {
    Ok(Arc::new(BitNetExecutionProvider::with_config(config)?))
}

#[cfg(test)]
mod tests {
    use super::*;
    use ronn_core::{AttributeValue, GraphNode};

    #[test]
    fn test_bitnet_provider_creation() -> Result<()> {
        let provider = BitNetExecutionProvider::new()?;

        assert_eq!(provider.provider_id(), ProviderId::BitNet);
        assert!(provider.supports_operation("MatMul"));
        assert!(provider.supports_operation("Conv"));
        assert!(!provider.supports_operation("LSTM")); // Not supported

        Ok(())
    }

    #[test]
    fn test_provider_capabilities() -> Result<()> {
        let provider = BitNetExecutionProvider::new()?;
        let capability = provider.get_capability();

        assert!(capability.supported_ops.contains("MatMul"));
        assert!(capability.supported_ops.contains("And"));
        assert!(capability.data_types.contains(&DataType::Bool));
        assert!(capability.data_types.contains(&DataType::F32));
        assert_eq!(
            capability.performance_profile,
            PerformanceProfile::MemoryOptimized
        );

        Ok(())
    }

    #[test]
    fn test_speedup_estimation() -> Result<()> {
        let provider = BitNetExecutionProvider::with_config(BitNetProviderConfig {
            quantization_method: QuantizationMethod::Binary,
            ..Default::default()
        })?;

        let matmul_spec = OperatorSpec {
            op_type: "MatMul".to_string(),
            input_types: vec![DataType::F32, DataType::F32],
            output_types: vec![DataType::F32],
            attributes: HashMap::new(),
        };

        let speedup = provider.estimate_speedup(&matmul_spec);
        assert_eq!(speedup, 64.0); // Binary MatMul should have 64x speedup

        Ok(())
    }

    #[test]
    fn test_subgraph_compilation() -> Result<()> {
        let provider = BitNetExecutionProvider::new()?;

        let subgraph = SubGraph {
            nodes: vec![GraphNode {
                id: 0,
                op_type: "MatMul".to_string(),
                attributes: HashMap::new(),
                inputs: vec!["A".to_string(), "B".to_string()],
                outputs: vec!["C".to_string()],
                name: Some("test_matmul".to_string()),
            }],
            edges: vec![],
            inputs: vec!["A".to_string(), "B".to_string()],
            outputs: vec!["C".to_string()],
        };

        let kernel = provider.compile_subgraph(subgraph)?;
        assert!(kernel.get_performance_stats().execution_count == 0);

        Ok(())
    }

    #[test]
    fn test_provider_configuration() -> Result<()> {
        let mut provider = BitNetExecutionProvider::new()?;

        let config = ProviderConfig {
            thread_count: None,
            memory_limit: Some(256 * 1024 * 1024), // 256MB
            optimization_level: ronn_core::OptimizationLevel::Aggressive,
            custom_options: {
                let mut opts = HashMap::new();
                opts.insert("quantization_method".to_string(), "ternary".to_string());
                opts.insert("enable_mixed_precision".to_string(), "false".to_string());
                opts
            },
        };

        provider.configure(config)?;

        assert_eq!(
            provider.config.quantization_method,
            QuantizationMethod::Ternary
        );
        assert!(!provider.config.enable_mixed_precision);

        Ok(())
    }
}