trustformers-core 0.1.1

// Copyright (c) 2025-2026 COOLJAPAN OU (Team KitaSan)
// SPDX-License-Identifier: Apache-2.0

//! RISC-V Vector Extensions backend implementation for TrustformeRS
//!
//! This module provides support for RISC-V Vector (RVV) extensions,
//! enabling efficient vectorized tensor operations on RISC-V processors
//! with Vector 1.0 and future extensions.

#![allow(dead_code)] // RISC-V backend implementation with architecture-specific features
#![allow(unused_variables)] // Backend implementation with reserved parameters

use crate::errors::compute_error;
use crate::hardware::{DataType, HardwareCapabilities, HardwareMetrics, HardwareResult};
use crate::tensor::Tensor;
use crate::TrustformersError;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// RISC-V Vector backend for tensor operations
#[derive(Debug)]
pub struct RiscVBackend {
    /// RISC-V processor configuration
    processor: Arc<Mutex<RiscVProcessor>>,
    /// Backend configuration
    config: RiscVConfig,
    /// Vectorized operation cache
    operation_cache: HashMap<String, VectorizedOperation>,
    /// Performance metrics
    metrics: Arc<Mutex<HardwareMetrics>>,
    /// Vector register manager
    register_manager: VectorRegisterManager,
}

/// RISC-V processor representation
#[derive(Debug)]
pub struct RiscVProcessor {
    /// Processor model
    model: String,
    /// Vector extension version
    vector_version: RiscVVectorVersion,
    /// Vector register length (VLEN)
    vlen: usize,
    /// Element length multipliers supported (LMUL)
    supported_lmul: Vec<f32>,
    /// Maximum vector register groups
    max_vector_groups: usize,
    /// Supported element widths
    supported_element_widths: Vec<usize>,
    /// Current processor status
    status: ProcessorStatus,
}

/// RISC-V Vector extension versions
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum RiscVVectorVersion {
    /// Vector 1.0 specification
    V1_0,
    /// Vector 2.0 specification (future)
    V2_0,
    /// Custom implementation
    Custom(u32),
}

/// Processor status information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessorStatus {
    /// Processor is available
    pub available: bool,
    /// Current frequency in Hz
    pub frequency: u64,
    /// Temperature in Celsius
    pub temperature: Option<f64>,
    /// Power consumption in watts
    pub power_consumption: Option<f64>,
    /// Vector unit utilization (0.0 to 1.0)
    pub vector_utilization: f64,
    /// Cache hit ratio
    pub cache_hit_ratio: f64,
}

/// RISC-V Vector configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiscVConfig {
    /// Target vector length (VLEN)
    pub target_vlen: usize,
    /// Preferred LMUL setting
    pub preferred_lmul: f32,
    /// Enable auto-vectorization
    pub enable_auto_vectorization: bool,
    /// Vector register allocation strategy
    pub register_allocation: RegisterAllocation,
    /// Memory alignment requirement
    pub memory_alignment: usize,
    /// Enable vector chaining
    pub enable_vector_chaining: bool,
    /// Optimization level (0-3)
    pub optimization_level: u32,
    /// Custom RISC-V options
    pub custom_options: HashMap<String, String>,
}

/// Vector register allocation strategies
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum RegisterAllocation {
    /// Conservative allocation
    Conservative,
    /// Aggressive allocation for performance
    Aggressive,
    /// Balanced allocation
    Balanced,
    /// Custom allocation strategy
    Custom,
}

/// Vectorized operation representation
#[derive(Debug, Clone)]
pub struct VectorizedOperation {
    /// Operation name
    name: String,
    /// Vector assembly code
    assembly_code: String,
    /// Input specifications
    input_specs: Vec<VectorSpec>,
    /// Output specifications
    output_specs: Vec<VectorSpec>,
    /// Operation metadata
    metadata: VectorOperationMetadata,
}

/// Vector specification for operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorSpec {
    /// Element data type
    pub element_type: DataType,
    /// Vector length multiplier (LMUL)
    pub lmul: f32,
    /// Element width in bits
    pub element_width: usize,
    /// Vector length
    pub vector_length: usize,
    /// Memory layout
    pub layout: VectorLayout,
}

/// Vector memory layout options
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum VectorLayout {
    /// Unit stride (contiguous)
    UnitStride,
    /// Constant stride
    ConstantStride(usize),
    /// Indexed (gather/scatter)
    Indexed,
    /// Segment load/store
    Segmented(usize),
}

/// Vector operation metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorOperationMetadata {
    /// Vector length used
    pub vector_length: usize,
    /// LMUL setting used
    pub lmul_used: f32,
    /// Estimated cycles
    pub estimated_cycles: u64,
    /// Memory bandwidth utilization
    pub memory_bandwidth_utilization: f64,
    /// Vector register pressure
    pub register_pressure: f32,
    /// Optimizations applied
    pub optimizations: Vec<String>,
}

/// Vector register manager
#[derive(Debug)]
pub struct VectorRegisterManager {
    /// Available vector registers
    total_registers: usize,
    /// Currently allocated registers
    allocated_registers: HashMap<String, RegisterAllocationInfo>,
    /// Register usage statistics
    usage_stats: RegisterUsageStats,
}

/// Register allocation information
#[derive(Debug, Clone)]
pub struct RegisterAllocationInfo {
    /// Allocation ID
    pub id: String,
    /// Register indices
    pub register_indices: Vec<usize>,
    /// LMUL setting
    pub lmul: f32,
    /// Element width
    pub element_width: usize,
    /// Allocated timestamp
    pub allocated_at: Instant,
}

/// Register usage statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegisterUsageStats {
    /// Peak register usage
    pub peak_usage: usize,
    /// Average register usage
    pub average_usage: f64,
    /// Register spill count
    pub spill_count: u64,
    /// Allocation conflicts
    pub allocation_conflicts: u64,
}

/// RISC-V vector operation types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RiscVVectorOp {
    /// Vector arithmetic operations
    Arithmetic(ArithmeticOp),
    /// Vector load/store operations
    Memory(MemoryOp),
    /// Vector mask operations
    Mask(MaskOp),
    /// Vector permutation operations
    Permutation(PermutationOp),
    /// Vector reduction operations
    Reduction(ReductionOp),
    /// Vector fixed-point operations
    FixedPoint(FixedPointOp),
    /// Vector floating-point operations
    FloatingPoint(FloatingPointOp),
}

/// Vector arithmetic operation types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ArithmeticOp {
    Add,
    Sub,
    Mul,
    Div,
    Rem,
    Min,
    Max,
    And,
    Or,
    Xor,
    Sll,
    Srl,
    Sra, // Shift operations
}

/// Vector memory operation types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum MemoryOp {
    Load(VectorLayout),
    Store(VectorLayout),
    LoadFault, // Load with fault-only-first
    Prefetch,
}

/// Vector mask operation types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum MaskOp {
    Compare(CompareOp),
    MaskLogical(MaskLogicalOp),
    PopCount,
    FirstSet,
    MaskSet,
}

/// Vector comparison operations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CompareOp {
    Equal,
    NotEqual,
    Less,
    LessEqual,
    Greater,
    GreaterEqual,
}

/// Vector mask logical operations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum MaskLogicalOp {
    And,
    Or,
    Xor,
    Not,
}

/// Vector permutation operations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PermutationOp {
    Slide(i32), // Slide up/down
    Gather,
    Scatter,
    Compress,
    Expand,
    Merge,
}

/// Vector reduction operations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ReductionOp {
    Sum,
    Product,
    Min,
    Max,
    And,
    Or,
    Xor,
}

/// Vector fixed-point operations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum FixedPointOp {
    Saturate,
    Clip,
    Scale,
    Narrow,
    Widen,
}

/// Vector floating-point operations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum FloatingPointOp {
    Add,
    Sub,
    Mul,
    Div,
    Sqrt,
    Min,
    Max,
    FusedMultiplyAdd,
    Compare(CompareOp),
    Classify,
    Convert,
}

impl RiscVBackend {
    /// Create a new RISC-V Vector backend
    pub fn new(config: RiscVConfig) -> HardwareResult<Self> {
        let processor = Arc::new(Mutex::new(Self::initialize_processor(&config)?));

        let metrics = Arc::new(Mutex::new(HardwareMetrics {
            ops_per_second: 0.0,
            memory_bandwidth: Self::get_memory_bandwidth(&config.target_vlen),
            utilization: 0.0,
            power_consumption: 0.0,
            temperature: None,
            error_rate: 0.0,
            latency: 0.0,
            throughput: 0.0,
        }));

        let register_manager = VectorRegisterManager::new(32); // 32 vector registers in RVV

        Ok(Self {
            processor,
            config,
            operation_cache: HashMap::new(),
            metrics,
            register_manager,
        })
    }

    /// Compile a vectorized operation
    pub fn compile_vector_operation(
        &mut self,
        name: &str,
        operation: RiscVVectorOp,
        input_specs: &[VectorSpec],
    ) -> HardwareResult<String> {
        let op_id = format!("{}_{:?}", name, operation);

        if self.operation_cache.contains_key(&op_id) {
            return Ok(op_id);
        }

        let assembly_code = self.generate_vector_assembly(&operation, input_specs)?;
        let output_specs = self.infer_output_specs(&operation, input_specs)?;

        let metadata = VectorOperationMetadata {
            vector_length: input_specs[0].vector_length,
            lmul_used: input_specs[0].lmul,
            estimated_cycles: self.estimate_cycles(&operation, input_specs),
            memory_bandwidth_utilization: self.estimate_memory_bandwidth(&operation),
            register_pressure: self.calculate_register_pressure(input_specs),
            optimizations: self.get_applied_optimizations(&operation),
        };

        let vectorized_op = VectorizedOperation {
            name: name.to_string(),
            assembly_code,
            input_specs: input_specs.to_vec(),
            output_specs,
            metadata,
        };

        self.operation_cache.insert(op_id.clone(), vectorized_op);
        Ok(op_id)
    }

    /// Execute a vectorized operation
    pub fn execute_vector_operation(
        &mut self,
        op_id: &str,
        inputs: &[Tensor],
    ) -> HardwareResult<Vec<Tensor>> {
        let operation = self.operation_cache.get(op_id).ok_or_else(|| {
            compute_error("riscv_operation".to_string(), "Vector operation not found")
        })?;

        let start_time = Instant::now();

        // Allocate vector registers
        let register_allocation = self.register_manager.allocate_registers(
            op_id.to_string(),
            operation.input_specs.len() + operation.output_specs.len(),
            operation.metadata.lmul_used,
            operation.input_specs[0].element_width,
        )?;

        // Execute the vectorized operation (simulation)
        let outputs = self.simulate_vector_execution(operation, inputs)?;

        // Deallocate registers
        self.register_manager.deallocate_registers(&register_allocation.id)?;

        // Update metrics
        let execution_time = start_time.elapsed();
        let metadata = operation.metadata.clone();
        self.update_execution_metrics(execution_time, &metadata);

        Ok(outputs)
    }

    /// Execute vector matrix multiplication
    pub fn execute_vector_matmul(&mut self, a: &Tensor, b: &Tensor) -> HardwareResult<Tensor> {
        let a_shape = a.shape();
        let b_shape = b.shape();

        if a_shape.len() != 2 || b_shape.len() != 2 || a_shape[1] != b_shape[0] {
            return Err(compute_error(
                "riscv_operation".to_string(),
                "Invalid matrix dimensions".to_string(),
            ));
        }

        let m = a_shape[0];
        let n = b_shape[1];
        let k = a_shape[1];

        // Create vector specifications for GEMM
        let vector_spec = VectorSpec {
            element_type: DataType::F32,
            lmul: self.config.preferred_lmul,
            element_width: 32,
            vector_length: self.config.target_vlen / 32, // VLEN / SEW
            layout: VectorLayout::UnitStride,
        };

        // Compile optimized vector GEMM
        let op_id = self.compile_vector_operation(
            "gemm",
            RiscVVectorOp::FloatingPoint(FloatingPointOp::FusedMultiplyAdd),
            &[vector_spec.clone(), vector_spec.clone()],
        )?;

        // Execute the operation
        let inputs = vec![a.clone(), b.clone()];
        let outputs = self.execute_vector_operation(&op_id, &inputs)?;

        outputs.into_iter().next().ok_or_else(|| {
            TrustformersError::hardware_error(
                "Vector operation produced no outputs",
                "RiscvVectorBackend::execute_vector_matmul",
            )
        })
    }

    /// Execute vector convolution
    pub fn execute_vector_conv2d(
        &mut self,
        input: &Tensor,
        kernel: &Tensor,
        stride: &[usize],
        padding: &[usize],
    ) -> HardwareResult<Tensor> {
        let input_shape = input.shape();
        let kernel_shape = kernel.shape();

        if input_shape.len() != 4 || kernel_shape.len() != 4 {
            return Err(compute_error(
                "riscv_operation".to_string(),
                "Invalid tensor dimensions for conv2d".to_string(),
            ));
        }

        // Calculate output dimensions
        let output_height = (input_shape[2] + 2 * padding[0] - kernel_shape[2]) / stride[0] + 1;
        let output_width = (input_shape[3] + 2 * padding[1] - kernel_shape[3]) / stride[1] + 1;
        let output_shape = [input_shape[0], kernel_shape[0], output_height, output_width];

        // Create vector specifications for convolution
        let vector_spec = VectorSpec {
            element_type: DataType::F32,
            lmul: self.config.preferred_lmul,
            element_width: 32,
            vector_length: self.config.target_vlen / 32,
            layout: VectorLayout::UnitStride,
        };

        // Compile optimized vector convolution
        let op_id = self.compile_vector_operation(
            "conv2d",
            RiscVVectorOp::FloatingPoint(FloatingPointOp::FusedMultiplyAdd),
            &[vector_spec.clone(), vector_spec.clone()],
        )?;

        // Execute the operation
        let inputs = vec![input.clone(), kernel.clone()];
        let outputs = self.execute_vector_operation(&op_id, &inputs)?;

        outputs.into_iter().next().ok_or_else(|| {
            TrustformersError::hardware_error(
                "Vector operation produced no outputs",
                "RiscvVectorBackend::execute_vector_matmul",
            )
        })
    }

    /// Get backend capabilities
    pub fn get_capabilities(&self) -> HardwareCapabilities {
        let data_types = vec![
            DataType::F32,
            DataType::F64,
            DataType::I32,
            DataType::I64,
            DataType::I16,
            DataType::I8,
            DataType::U32,
            DataType::U16,
            DataType::U8,
            DataType::Bool,
        ];

        let memory_bandwidth = Self::get_memory_bandwidth(&self.config.target_vlen);
        let compute_units = self.config.target_vlen / 64; // Estimated based on VLEN
        let power_consumption = match self.config.target_vlen {
            128 => 15.0,  // Low-power embedded
            256 => 25.0,  // Mid-range
            512 => 40.0,  // High-performance
            1024 => 60.0, // Server-class
            _ => 30.0,    // Default
        };

        HardwareCapabilities {
            data_types,
            max_dimensions: 8,
            memory_size: Some(8 * 1024 * 1024 * 1024), // 8GB typical
            clock_frequency: Some(2_000_000_000),      // 2 GHz typical
            compute_units: Some(compute_units as u32),
            operations: vec![
                "vector_add".to_string(),
                "vector_mul".to_string(),
                "vector_fma".to_string(),
                "vector_load".to_string(),
                "vector_store".to_string(),
                "vector_reduce".to_string(),
                "vector_permute".to_string(),
                "vector_compare".to_string(),
                "vector_convert".to_string(),
                "matmul".to_string(),
                "conv2d".to_string(),
                "activation".to_string(),
            ],
            power_consumption: Some(power_consumption),
            thermal_design_power: Some(power_consumption * 1.5), // 50% overhead
        }
    }

    /// Get current performance metrics
    pub fn get_metrics(&self) -> HardwareMetrics {
        self.metrics.lock().expect("Lock poisoned").clone()
    }

    /// Optimize vector operations for specific VLEN
    pub fn optimize_for_vlen(&mut self, vlen: usize) -> HardwareResult<()> {
        // Recompile operations with new VLEN
        let mut operations_to_recompile = Vec::new();
        for (op_id, operation) in &self.operation_cache {
            if operation.metadata.vector_length != vlen / operation.input_specs[0].element_width {
                operations_to_recompile.push((op_id.clone(), operation.clone()));
            }
        }

        for (op_id, mut operation) in operations_to_recompile {
            // Update vector specifications
            for spec in &mut operation.input_specs {
                spec.vector_length = vlen / spec.element_width;
            }
            for spec in &mut operation.output_specs {
                spec.vector_length = vlen / spec.element_width;
            }

            // Update metadata
            operation.metadata.vector_length = vlen / operation.input_specs[0].element_width;

            self.operation_cache.insert(op_id, operation);
        }

        Ok(())
    }

    // Private helper methods
    fn initialize_processor(config: &RiscVConfig) -> HardwareResult<RiscVProcessor> {
        Ok(RiscVProcessor {
            model: "RISC-V Vector Processor".to_string(),
            vector_version: RiscVVectorVersion::V1_0,
            vlen: config.target_vlen,
            supported_lmul: vec![0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0],
            max_vector_groups: 8,
            supported_element_widths: vec![8, 16, 32, 64],
            status: ProcessorStatus {
                available: true,
                frequency: 2_000_000_000, // 2 GHz
                temperature: Some(65.0),
                power_consumption: Some(25.0),
                vector_utilization: 0.0,
                cache_hit_ratio: 0.95,
            },
        })
    }

    fn get_memory_bandwidth(vlen: &usize) -> f64 {
        match vlen {
            128 => 25e9,   // 25 GB/s
            256 => 50e9,   // 50 GB/s
            512 => 100e9,  // 100 GB/s
            1024 => 200e9, // 200 GB/s
            _ => 50e9,     // Default 50 GB/s
        }
    }

    fn generate_vector_assembly(
        &self,
        operation: &RiscVVectorOp,
        input_specs: &[VectorSpec],
    ) -> HardwareResult<String> {
        let vtype = self.calculate_vtype(&input_specs[0]);

        let assembly = match operation {
            RiscVVectorOp::Arithmetic(ArithmeticOp::Add) => {
                format!(
                    r#"
    vsetvli t0, a0, {}
    vle32.v v0, (a1)
    vle32.v v1, (a2)
    vadd.vv v2, v0, v1
    vse32.v v2, (a3)
"#,
                    vtype
                )
            },
            RiscVVectorOp::Arithmetic(ArithmeticOp::Mul) => {
                format!(
                    r#"
    vsetvli t0, a0, {}
    vle32.v v0, (a1)
    vle32.v v1, (a2)
    vmul.vv v2, v0, v1
    vse32.v v2, (a3)
"#,
                    vtype
                )
            },
            RiscVVectorOp::FloatingPoint(FloatingPointOp::FusedMultiplyAdd) => {
                format!(
                    r#"
    vsetvli t0, a0, {}
    vle32.v v0, (a1)
    vle32.v v1, (a2)
    vle32.v v2, (a3)
    vfmadd.vv v2, v0, v1
    vse32.v v2, (a4)
"#,
                    vtype
                )
            },
            RiscVVectorOp::Reduction(ReductionOp::Sum) => {
                format!(
                    r#"
    vsetvli t0, a0, {}
    vle32.v v0, (a1)
    vmv.s.x v1, zero
    vredsum.vs v1, v0, v1
    vfmv.f.s f0, v1
    fsw f0, 0(a2)
"#,
                    vtype
                )
            },
            _ => {
                return Err(compute_error(
                    "riscv_operation".to_string(),
                    format!("Vector operation {:?} not implemented", operation),
                ));
            },
        };

        Ok(assembly)
    }

    fn calculate_vtype(&self, spec: &VectorSpec) -> String {
        let sew = match spec.element_width {
            8 => "e8".to_string(),
            16 => "e16".to_string(),
            32 => "e32".to_string(),
            64 => "e64".to_string(),
            _ => "e32".to_string(), // Default
        };

        let lmul = match spec.lmul {
            0.125 => "mf8".to_string(),
            0.25 => "mf4".to_string(),
            0.5 => "mf2".to_string(),
            1.0 => "m1".to_string(),
            2.0 => "m2".to_string(),
            4.0 => "m4".to_string(),
            8.0 => "m8".to_string(),
            _ => "m1".to_string(), // Default
        };

        format!("{},{},ta,ma", sew, lmul)
    }

    fn infer_output_specs(
        &self,
        operation: &RiscVVectorOp,
        input_specs: &[VectorSpec],
    ) -> HardwareResult<Vec<VectorSpec>> {
        let output_spec = match operation {
            RiscVVectorOp::Reduction(_) => VectorSpec {
                element_type: input_specs[0].element_type,
                lmul: 1.0, // Scalar result
                element_width: input_specs[0].element_width,
                vector_length: 1,
                layout: VectorLayout::UnitStride,
            },
            _ => input_specs[0].clone(), // Most operations preserve input spec
        };

        Ok(vec![output_spec])
    }

    fn estimate_cycles(&self, operation: &RiscVVectorOp, input_specs: &[VectorSpec]) -> u64 {
        let base_cycles = match operation {
            RiscVVectorOp::Arithmetic(ArithmeticOp::Add) => 1,
            RiscVVectorOp::Arithmetic(ArithmeticOp::Mul) => 3,
            RiscVVectorOp::FloatingPoint(FloatingPointOp::Add) => 3,
            RiscVVectorOp::FloatingPoint(FloatingPointOp::Mul) => 4,
            RiscVVectorOp::FloatingPoint(FloatingPointOp::FusedMultiplyAdd) => 4,
            RiscVVectorOp::Memory(MemoryOp::Load(_)) => 2,
            RiscVVectorOp::Memory(MemoryOp::Store(_)) => 2,
            RiscVVectorOp::Reduction(_) => input_specs[0].vector_length as u64 / 2,
            _ => 2,
        };

        let vector_length = input_specs[0].vector_length as u64;
        let cycles_per_element = base_cycles;

        // Account for LMUL
        let lmul_cycles =
            if input_specs[0].lmul > 1.0 { (input_specs[0].lmul as u64).max(1) } else { 1 };

        (vector_length * cycles_per_element) / lmul_cycles
    }

    fn estimate_memory_bandwidth(&self, operation: &RiscVVectorOp) -> f64 {
        match operation {
            RiscVVectorOp::Memory(_) => 0.8,        // High memory usage
            RiscVVectorOp::Arithmetic(_) => 0.2,    // Low memory usage
            RiscVVectorOp::FloatingPoint(_) => 0.3, // Medium memory usage
            _ => 0.1,                               // Minimal memory usage
        }
    }

    fn calculate_register_pressure(&self, input_specs: &[VectorSpec]) -> f32 {
        let total_registers_needed: f32 = input_specs.iter().map(|spec| spec.lmul).sum();

        total_registers_needed / 32.0 // 32 vector registers available
    }

    fn get_applied_optimizations(&self, operation: &RiscVVectorOp) -> Vec<String> {
        let mut optimizations = vec![
            "vector_length_agnostic".to_string(),
            "register_allocation".to_string(),
        ];

        match operation {
            RiscVVectorOp::Arithmetic(_) => {
                optimizations.push("arithmetic_optimization".to_string());
            },
            RiscVVectorOp::Memory(_) => {
                optimizations.extend(vec![
                    "memory_coalescing".to_string(),
                    "stride_optimization".to_string(),
                ]);
            },
            RiscVVectorOp::FloatingPoint(_) => {
                optimizations.push("floating_point_optimization".to_string());
            },
            _ => {},
        }

        if self.config.enable_vector_chaining {
            optimizations.push("vector_chaining".to_string());
        }

        optimizations
    }

    fn simulate_vector_execution(
        &self,
        operation: &VectorizedOperation,
        inputs: &[Tensor],
    ) -> HardwareResult<Vec<Tensor>> {
        // Simplified simulation - in practice would execute actual vector instructions
        let output_shape = inputs[0].shape().to_vec();
        let output_data = match operation.name.as_str() {
            "add" => {
                let a = &inputs[0];
                let b = &inputs[1];
                let a_data = a.data()?;
                let b_data = b.data()?;
                a_data.iter().zip(b_data.iter()).map(|(x, y)| x + y).collect()
            },
            "mul" => {
                let a = &inputs[0];
                let b = &inputs[1];
                let a_data = a.data()?;
                let b_data = b.data()?;
                a_data.iter().zip(b_data.iter()).map(|(x, y)| x * y).collect()
            },
            "gemm" => {
                // Simplified GEMM simulation
                let a = &inputs[0];
                let b = &inputs[1];
                let a_shape = a.shape();
                let b_shape = b.shape();
                let mut result = vec![0.0f32; a_shape[0] * b_shape[1]];

                for i in 0..a_shape[0] {
                    for j in 0..b_shape[1] {
                        let mut sum = 0.0;
                        for k in 0..a_shape[1] {
                            let a_data = a.data()?;
                            let b_data = b.data()?;
                            sum += a_data[i * a_shape[1] + k] * b_data[k * b_shape[1] + j];
                        }
                        result[i * b_shape[1] + j] = sum;
                    }
                }
                result
            },
            _ => inputs[0].data()?.clone(), // Pass-through for other operations
        };

        let output_tensor = Tensor::from_vec(output_data, &output_shape)?;
        Ok(vec![output_tensor])
    }

    fn update_execution_metrics(
        &mut self,
        execution_time: Duration,
        metadata: &VectorOperationMetadata,
    ) {
        let mut metrics = self.metrics.lock().expect("Lock poisoned");
        let execution_ms = execution_time.as_millis() as f64;

        metrics.ops_per_second = metadata.estimated_cycles as f64 / (execution_ms / 1000.0);
        metrics.latency = execution_ms;
        metrics.throughput = metrics.ops_per_second;
        metrics.utilization = metadata.register_pressure.min(1.0) as f64;
        metrics.memory_bandwidth = Self::get_memory_bandwidth(&self.config.target_vlen)
            * metadata.memory_bandwidth_utilization;
    }
}

impl VectorRegisterManager {
    fn new(total_registers: usize) -> Self {
        Self {
            total_registers,
            allocated_registers: HashMap::new(),
            usage_stats: RegisterUsageStats {
                peak_usage: 0,
                average_usage: 0.0,
                spill_count: 0,
                allocation_conflicts: 0,
            },
        }
    }

    /// Allocate vector registers
    pub fn allocate_registers(
        &mut self,
        id: String,
        count: usize,
        lmul: f32,
        element_width: usize,
    ) -> HardwareResult<RegisterAllocationInfo> {
        let registers_needed = (count as f32 * lmul).ceil() as usize;

        if self.get_available_registers() < registers_needed {
            self.usage_stats.allocation_conflicts += 1;
            return Err(compute_error(
                "riscv_operation".to_string(),
                "Insufficient vector registers".to_string(),
            ));
        }

        let mut register_indices = Vec::new();
        let mut allocated = 0;

        for i in 0..self.total_registers {
            if allocated >= registers_needed {
                break;
            }

            if !self.is_register_allocated(i) {
                register_indices.push(i);
                allocated += 1;
            }
        }

        let allocation_info = RegisterAllocationInfo {
            id: id.clone(),
            register_indices,
            lmul,
            element_width,
            allocated_at: Instant::now(),
        };

        self.allocated_registers.insert(id, allocation_info.clone());
        self.update_usage_stats();

        Ok(allocation_info)
    }

    /// Deallocate vector registers
    pub fn deallocate_registers(&mut self, id: &str) -> HardwareResult<()> {
        if self.allocated_registers.remove(id).is_some() {
            self.update_usage_stats();
            Ok(())
        } else {
            Err(compute_error(
                "riscv_operation".to_string(),
                "Register allocation not found".to_string(),
            ))
        }
    }

    fn get_available_registers(&self) -> usize {
        let allocated_count: usize = self
            .allocated_registers
            .values()
            .map(|info| (info.register_indices.len() as f32 * info.lmul).ceil() as usize)
            .sum();
        self.total_registers - allocated_count
    }

    fn is_register_allocated(&self, register_index: usize) -> bool {
        self.allocated_registers
            .values()
            .any(|info| info.register_indices.contains(&register_index))
    }

    fn update_usage_stats(&mut self) {
        let current_usage = self.total_registers - self.get_available_registers();
        self.usage_stats.peak_usage = self.usage_stats.peak_usage.max(current_usage);
        // Simplified average calculation
        self.usage_stats.average_usage =
            (self.usage_stats.average_usage + current_usage as f64) / 2.0;
    }
}

impl Default for RiscVConfig {
    fn default() -> Self {
        Self {
            target_vlen: 256, // 256-bit vectors
            preferred_lmul: 1.0,
            enable_auto_vectorization: true,
            register_allocation: RegisterAllocation::Balanced,
            memory_alignment: 16, // 16-byte alignment
            enable_vector_chaining: true,
            optimization_level: 2,
            custom_options: HashMap::new(),
        }
    }
}

/// Utility functions for RISC-V Vector operations
pub mod utils {
    use super::*;

    /// Check if RISC-V Vector extensions are available
    pub fn is_riscv_vector_available() -> bool {
        // Simplified check - in practice would query CPUID/system info
        cfg!(target_arch = "riscv64") || cfg!(target_arch = "riscv32")
    }

    /// Get optimal VLEN for given workload
    pub fn get_optimal_vlen(tensor_size: usize, element_width: usize) -> usize {
        let min_vlen = 128;
        let max_vlen = 1024;

        let elements_per_vector = tensor_size / element_width;
        let optimal_vlen = (elements_per_vector * element_width).next_power_of_two();

        optimal_vlen.clamp(min_vlen, max_vlen)
    }

    /// Calculate optimal LMUL for operation
    pub fn calculate_optimal_lmul(vector_length: usize, vlen: usize) -> f32 {
        let max_elements = vlen / 32; // Assuming 32-bit elements

        if vector_length <= max_elements / 8 {
            0.125
        } else if vector_length <= max_elements / 4 {
            0.25
        } else if vector_length <= max_elements / 2 {
            0.5
        } else if vector_length <= max_elements {
            1.0
        } else if vector_length <= max_elements * 2 {
            2.0
        } else if vector_length <= max_elements * 4 {
            4.0
        } else {
            8.0
        }
    }

    /// Generate optimized vector loop for given operation
    pub fn generate_vector_loop(
        operation: &RiscVVectorOp,
        vector_length: usize,
        element_width: usize,
    ) -> String {
        let elements_per_iteration = 256 / element_width; // Assuming 256-bit VLEN

        format!(
            r#"
vector_loop_{:?}:
    li t0, {}
    vsetvli t1, t0, e{},m1,ta,ma

    // Load vectors
    vle{}.v v0, (a0)
    vle{}.v v1, (a1)

    // Perform operation
    {}

    // Store result
    vse{}.v v2, (a2)

    // Update pointers and length
    slli t2, t1, {} // t2 = vl * element_width_bytes
    add a0, a0, t2
    add a1, a1, t2
    add a2, a2, t2
    sub t0, t0, t1
    bnez t0, vector_loop_{:?}

    ret
"#,
            operation,
            vector_length,
            element_width,
            element_width,
            element_width,
            match operation {
                RiscVVectorOp::Arithmetic(ArithmeticOp::Add) => "vadd.vv v2, v0, v1".to_string(),
                RiscVVectorOp::Arithmetic(ArithmeticOp::Mul) => "vmul.vv v2, v0, v1".to_string(),
                RiscVVectorOp::FloatingPoint(FloatingPointOp::Add) =>
                    "vfadd.vv v2, v0, v1".to_string(),
                RiscVVectorOp::FloatingPoint(FloatingPointOp::Mul) =>
                    "vfmul.vv v2, v0, v1".to_string(),
                _ => "// Operation not implemented".to_string(),
            },
            element_width,
            element_width / 8, // Convert bits to bytes
            operation
        )
    }
}

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

    #[test]
    fn test_riscv_vector_version_serialization() {
        let version = RiscVVectorVersion::V1_0;
        let serialized = serde_json::to_string(&version).expect("JSON serialization failed");
        let deserialized: RiscVVectorVersion =
            serde_json::from_str(&serialized).expect("JSON deserialization failed");
        assert_eq!(version, deserialized);
    }

    #[test]
    fn test_riscv_config_default() {
        let config = RiscVConfig::default();
        assert_eq!(config.target_vlen, 256);
        assert_eq!(config.preferred_lmul, 1.0);
        assert!(config.enable_auto_vectorization);
        assert_eq!(config.register_allocation, RegisterAllocation::Balanced);
    }

    #[test]
    fn test_vector_spec_creation() {
        let spec = VectorSpec {
            element_type: DataType::F32,
            lmul: 2.0,
            element_width: 32,
            vector_length: 8,
            layout: VectorLayout::UnitStride,
        };
        assert_eq!(spec.element_type, DataType::F32);
        assert_eq!(spec.lmul, 2.0);
        assert_eq!(spec.layout, VectorLayout::UnitStride);
    }

    #[test]
    fn test_vector_operations() {
        let ops = [
            RiscVVectorOp::Arithmetic(ArithmeticOp::Add),
            RiscVVectorOp::FloatingPoint(FloatingPointOp::Mul),
            RiscVVectorOp::Reduction(ReductionOp::Sum),
            RiscVVectorOp::Memory(MemoryOp::Load(VectorLayout::UnitStride)),
        ];
        assert_eq!(ops.len(), 4);
    }

    #[test]
    fn test_utils_optimal_vlen() {
        let vlen = utils::get_optimal_vlen(1024, 32);
        assert!((128..=1024).contains(&vlen));
        assert!(vlen.is_power_of_two());
    }

    #[test]
    fn test_utils_optimal_lmul() {
        // With vlen=256 and 32-bit elements: max_elements = 8
        // LMUL=0.125 can hold 1 element, LMUL=0.25 can hold 2 elements
        assert_eq!(utils::calculate_optimal_lmul(1, 256), 0.125);
        assert_eq!(utils::calculate_optimal_lmul(2, 256), 0.25);
        assert_eq!(utils::calculate_optimal_lmul(8, 256), 1.0);
        assert_eq!(utils::calculate_optimal_lmul(32, 256), 4.0);
    }

    #[test]
    fn test_register_manager() {
        let mut manager = VectorRegisterManager::new(32);
        let allocation = manager
            .allocate_registers("test".to_string(), 4, 1.0, 32)
            .expect("operation failed in test");
        assert_eq!(allocation.register_indices.len(), 4);
        assert_eq!(allocation.lmul, 1.0);

        manager.deallocate_registers("test").expect("operation failed in test");
        assert_eq!(manager.allocated_registers.len(), 0);
    }
}