quantrs2-sim 0.1.3

//! CUDA kernel compilation and execution for quantum operations.
//!
//! This module provides CUDA kernel management, compilation,
//! and execution for GPU-accelerated quantum simulations.

use crate::prelude::SimulatorError;
use scirs2_core::ndarray::Array1;
use scirs2_core::Complex64;

#[cfg(feature = "advanced_math")]
use std::collections::HashMap;
#[cfg(feature = "advanced_math")]
use std::sync::{Arc, Mutex};

#[cfg(feature = "advanced_math")]
use super::context::CudaContext;
#[cfg(feature = "advanced_math")]
use super::memory::GpuMemory;
pub use super::memory::GpuMemoryType;
#[cfg(feature = "advanced_math")]
use super::streams::CudaStream;
use crate::error::Result;
use crate::scirs2_integration::SciRS2Backend;

// Placeholder types for actual CUDA handles
#[cfg(feature = "advanced_math")]
pub type CudaFunctionHandle = usize;

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OptimizationLevel {
    Conservative,
    Balanced,
    Aggressive,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GateType {
    Hadamard,
    PauliX,
    PauliY,
    PauliZ,
    CNOT,
    CZ,
    Rotation,
    Custom,
}

/// CUDA kernel configuration
#[derive(Debug, Clone)]
pub struct CudaKernelConfig {
    /// Device ID to use
    pub device_id: i32,
    /// Number of CUDA streams for parallel execution
    pub num_streams: usize,
    /// Block size for CUDA kernels
    pub block_size: usize,
    /// Grid size for CUDA kernels
    pub grid_size: usize,
    /// Enable unified memory
    pub unified_memory: bool,
    /// Memory pool size in bytes
    pub memory_pool_size: usize,
    /// Enable kernel profiling
    pub enable_profiling: bool,
    /// Kernel optimization level
    pub optimization_level: OptimizationLevel,
}

#[derive(Debug, Clone, Default)]
pub struct CudaKernelStats {
    pub total_kernels_launched: usize,
    pub total_execution_time_ms: f64,
    pub average_execution_time_ms: f64,
    pub memory_allocated_bytes: usize,
    pub memory_bandwidth_gb_s: f64,
    pub gpu_utilization_percent: f64,
}

#[cfg(feature = "advanced_math")]
pub struct CudaKernel {
    name: String,
    ptx_code: String,
    function_handle: Option<CudaFunctionHandle>,
    register_count: u32,
    shared_memory_size: usize,
    max_threads_per_block: u32,
}

pub struct CudaQuantumKernels {
    /// Configuration
    config: CudaKernelConfig,
    /// CUDA context
    #[cfg(feature = "advanced_math")]
    context: Option<CudaContext>,
    /// CUDA streams for parallel execution
    #[cfg(feature = "advanced_math")]
    streams: Vec<CudaStream>,
    /// Compiled kernels
    #[cfg(feature = "advanced_math")]
    kernels: HashMap<String, CudaKernel>,
    /// GPU memory pool
    #[cfg(feature = "advanced_math")]
    memory_pool: Arc<Mutex<GpuMemory>>,
    /// SciRS2 backend
    backend: Option<SciRS2Backend>,
    /// Performance statistics
    stats: CudaKernelStats,
}

impl Default for CudaKernelConfig {
    fn default() -> Self {
        Self {
            device_id: 0,
            num_streams: 4,
            block_size: 256,
            grid_size: 0, // Auto-calculate
            unified_memory: false,
            memory_pool_size: 1024 * 1024 * 1024, // 1GB
            enable_profiling: false,
            optimization_level: OptimizationLevel::Balanced,
        }
    }
}

#[cfg(feature = "advanced_math")]
impl CudaKernel {
    pub fn compile(source: &str, name: &str, config: &CudaKernelConfig) -> Result<Self> {
        // Compile CUDA source to PTX
        let ptx_code = Self::compile_cuda_source(source, config)?;

        // Load and create function handle
        let function_handle = Self::load_cuda_function(&ptx_code, name)?;

        // Query kernel properties
        let (register_count, shared_memory_size, max_threads_per_block) =
            Self::query_kernel_properties(function_handle)?;

        Ok(Self {
            name: name.to_string(),
            ptx_code,
            function_handle: Some(function_handle),
            register_count,
            shared_memory_size,
            max_threads_per_block,
        })
    }

    pub fn launch(
        &self,
        grid_size: usize,
        block_size: usize,
        params: &[*const std::ffi::c_void],
        stream: &CudaStream,
    ) -> Result<()> {
        if let Some(function_handle) = self.function_handle {
            // Validate launch parameters
            self.validate_launch_parameters(grid_size, block_size)?;

            // Get stream handle
            let stream_handle = stream.get_handle_value();

            // Launch kernel
            if let Some(handle) = stream_handle {
                Self::cuda_launch_kernel(
                    function_handle,
                    grid_size,
                    block_size,
                    params,
                    Some(handle),
                )?;
            } else {
                return Err(SimulatorError::InvalidState(
                    "CUDA stream not initialized".to_string(),
                ));
            }
        } else {
            return Err(SimulatorError::UnsupportedOperation(format!(
                "Kernel '{}' not compiled",
                self.name
            )));
        }

        Ok(())
    }

    pub fn get_occupancy(&self, block_size: usize) -> Result<f64> {
        if let Some(_function_handle) = self.function_handle {
            // Calculate theoretical occupancy
            let max_blocks_per_sm = self.calculate_max_blocks_per_sm(block_size)?;
            let active_warps = block_size.div_ceil(32); // Round up to warp size
            let max_warps_per_sm = 64; // Typical for modern GPUs

            let occupancy = (max_blocks_per_sm * active_warps) as f64 / max_warps_per_sm as f64;
            Ok(occupancy.min(1.0))
        } else {
            Err(SimulatorError::UnsupportedOperation(
                "Cannot calculate occupancy for uncompiled kernel".to_string(),
            ))
        }
    }

    pub fn get_name(&self) -> &str {
        &self.name
    }

    pub fn get_register_count(&self) -> u32 {
        self.register_count
    }

    pub fn get_shared_memory_size(&self) -> usize {
        self.shared_memory_size
    }

    pub fn get_max_threads_per_block(&self) -> u32 {
        self.max_threads_per_block
    }

    fn compile_cuda_source(source: &str, config: &CudaKernelConfig) -> Result<String> {
        // In real implementation: nvrtcCompileProgram
        let optimization_flags = match config.optimization_level {
            OptimizationLevel::Conservative => "-O1",
            OptimizationLevel::Balanced => "-O2",
            OptimizationLevel::Aggressive => "-O3 --use_fast_math",
        };

        // Simulate compilation process
        let ptx_header = format!(
            ".version 7.0\n.target sm_75\n.address_size 64\n// Compiled with {}\n",
            optimization_flags
        );

        // In real implementation, this would be actual PTX code from nvrtc
        Ok(format!("{}{}", ptx_header, source))
    }

    fn load_cuda_function(ptx_code: &str, name: &str) -> Result<CudaFunctionHandle> {
        // In real implementation: cuModuleLoadDataEx + cuModuleGetFunction
        let _module_handle = Self::cuda_module_load_data(ptx_code)?;
        let function_handle = Self::cuda_module_get_function(name)?;
        Ok(function_handle)
    }

    fn query_kernel_properties(_function_handle: CudaFunctionHandle) -> Result<(u32, usize, u32)> {
        // In real implementation: cuFuncGetAttribute
        let register_count = 32; // Example values
        let shared_memory_size = 1024;
        let max_threads_per_block = 1024;
        Ok((register_count, shared_memory_size, max_threads_per_block))
    }

    fn validate_launch_parameters(&self, grid_size: usize, block_size: usize) -> Result<()> {
        if block_size == 0 {
            return Err(SimulatorError::InvalidInput(
                "Block size cannot be zero".to_string(),
            ));
        }
        if grid_size == 0 {
            return Err(SimulatorError::InvalidInput(
                "Grid size cannot be zero".to_string(),
            ));
        }
        if block_size > self.max_threads_per_block as usize {
            return Err(SimulatorError::InvalidInput(format!(
                "Block size {} exceeds maximum {}",
                block_size, self.max_threads_per_block
            )));
        }
        Ok(())
    }

    fn calculate_max_blocks_per_sm(&self, block_size: usize) -> Result<usize> {
        // Simplified calculation based on register and shared memory usage
        let max_blocks_by_registers = 65_536 / (self.register_count as usize * block_size);
        let max_blocks_by_shared_memory = if self.shared_memory_size > 0 {
            98_304 / self.shared_memory_size // 96KB shared memory per SM
        } else {
            usize::MAX
        };
        Ok(max_blocks_by_registers
            .min(max_blocks_by_shared_memory)
            .min(32)) // Max 32 blocks per SM
    }

    // Placeholder CUDA functions
    fn cuda_module_load_data(_ptx_code: &str) -> Result<usize> {
        Ok(1) // Mock module handle
    }

    fn cuda_module_get_function(_name: &str) -> Result<CudaFunctionHandle> {
        Ok(1) // Mock function handle
    }

    fn cuda_launch_kernel(
        _function: CudaFunctionHandle,
        _grid_size: usize,
        _block_size: usize,
        _params: &[*const std::ffi::c_void],
        _stream: Option<super::streams::CudaStreamHandle>,
    ) -> Result<()> {
        // In real implementation: cuLaunchKernel
        Ok(())
    }
}

impl CudaQuantumKernels {
    /// Create new CUDA quantum kernels
    pub fn new(config: CudaKernelConfig) -> Result<Self> {
        let mut kernels = Self {
            config,
            #[cfg(feature = "advanced_math")]
            context: None,
            #[cfg(feature = "advanced_math")]
            streams: Vec::new(),
            #[cfg(feature = "advanced_math")]
            kernels: HashMap::new(),
            #[cfg(feature = "advanced_math")]
            memory_pool: Arc::new(Mutex::new(GpuMemory::new())),
            backend: None,
            stats: CudaKernelStats::default(),
        };

        kernels.initialize_cuda()?;
        Ok(kernels)
    }

    /// Initialize with SciRS2 backend
    pub fn with_backend(mut self) -> Result<Self> {
        self.backend = Some(SciRS2Backend::new());
        Ok(self)
    }

    /// Initialize CUDA context and kernels
    fn initialize_cuda(&mut self) -> Result<()> {
        #[cfg(feature = "advanced_math")]
        {
            // Initialize CUDA context
            self.context = Some(CudaContext::new(self.config.device_id)?);

            // Create CUDA streams
            for _ in 0..self.config.num_streams {
                self.streams.push(CudaStream::new()?);
            }

            // Initialize memory pool
            {
                let mut pool = self.memory_pool.lock().unwrap_or_else(|e| e.into_inner());
                pool.allocate_pool(self.config.memory_pool_size)?;
            }

            // Compile and load kernels
            self.compile_kernels()?;
        }

        #[cfg(not(feature = "advanced_math"))]
        {
            // Placeholder initialization for when CUDA is not available
        }

        Ok(())
    }

    /// Compile all quantum kernels
    #[cfg(feature = "advanced_math")]
    fn compile_kernels(&mut self) -> Result<()> {
        // Compile standard quantum gate kernels
        let hadamard_source = include_str!("kernels/hadamard.cu");
        let hadamard_kernel =
            CudaKernel::compile(hadamard_source, "hadamard_kernel", &self.config)?;
        self.kernels.insert("hadamard".to_string(), hadamard_kernel);

        let pauli_x_source = include_str!("kernels/pauli_x.cu");
        let pauli_x_kernel = CudaKernel::compile(pauli_x_source, "pauli_x_kernel", &self.config)?;
        self.kernels.insert("pauli_x".to_string(), pauli_x_kernel);

        // Add more kernels as needed...

        Ok(())
    }

    #[cfg(not(feature = "advanced_math"))]
    fn compile_kernels(&mut self) -> Result<()> {
        // No-op when CUDA is not available
        Ok(())
    }

    /// Apply Hadamard gate using CUDA kernel
    pub fn apply_hadamard(&mut self, state: &mut Array1<Complex64>, qubit: usize) -> Result<()> {
        #[cfg(feature = "advanced_math")]
        {
            let kernel = self.kernels.get("hadamard").ok_or_else(|| {
                SimulatorError::UnsupportedOperation("Hadamard kernel not available".to_string())
            })?;

            let stream = &self.streams[0]; // Use first stream

            // Prepare kernel parameters
            let params = vec![
                state.as_ptr() as *const std::ffi::c_void,
                &qubit as *const _ as *const std::ffi::c_void,
                &state.len() as *const _ as *const std::ffi::c_void,
            ];

            // Calculate grid and block sizes
            let block_size = self.config.block_size;
            let grid_size = state.len().div_ceil(block_size);

            // Launch kernel
            kernel.launch(grid_size, block_size, &params, stream)?;
            stream.synchronize()?;

            self.stats.total_kernels_launched += 1;
        }

        #[cfg(not(feature = "advanced_math"))]
        {
            // Fallback CPU implementation
            self.apply_hadamard_cpu(state, qubit)?;
        }

        Ok(())
    }

    /// CPU fallback for Hadamard gate
    fn apply_hadamard_cpu(&self, state: &mut Array1<Complex64>, qubit: usize) -> Result<()> {
        let n = state.len();
        let mask = 1 << qubit;

        for i in 0..n {
            if i & mask == 0 {
                let j = i | mask;
                let temp = state[i];
                state[i] = (state[i] + state[j]) / Complex64::new(2.0_f64.sqrt(), 0.0);
                state[j] = (temp - state[j]) / Complex64::new(2.0_f64.sqrt(), 0.0);
            }
        }

        Ok(())
    }

    /// Get performance statistics
    pub fn get_stats(&self) -> &CudaKernelStats {
        &self.stats
    }

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

    /// Synchronize all streams
    pub fn synchronize_all(&self) -> Result<()> {
        #[cfg(feature = "advanced_math")]
        {
            for stream in &self.streams {
                stream.synchronize()?;
            }
        }
        Ok(())
    }
}