ronn-providers 0.1.0

//! CPU kernel implementations for compiled subgraphs.
//!
//! This module provides CPU-specific implementations of compiled kernels
//! with SIMD optimizations and operator fusion.

use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Instant;

use anyhow::{Result, anyhow};
use rayon::prelude::*;
use ronn_core::tensor::Tensor;
use ronn_core::{CompiledKernel, DataType, KernelStats, MemoryUsage, SubGraph, TensorLayout};
use tracing::{debug, warn};

use crate::cpu::simd::{SimdCapabilities, simd_add_f32, simd_matmul_f32, simd_mul_f32};

/// CPU-compiled kernel for subgraph execution.
#[derive(Debug)]
pub struct CpuKernel {
    /// Original subgraph this kernel was compiled from.
    subgraph: SubGraph,
    /// Execution plan with fused operations.
    execution_plan: ExecutionPlan,
    /// SIMD capabilities for optimized execution.
    simd_capabilities: SimdCapabilities,
    /// Performance statistics tracking.
    stats: Arc<Mutex<KernelStatsInternal>>,
    /// Memory usage tracking.
    memory_stats: Arc<Mutex<MemoryStatsInternal>>,
}

/// Internal execution plan for the kernel.
#[derive(Debug, Clone)]
struct ExecutionPlan {
    /// Sequence of fused operations to execute.
    operations: Vec<FusedOperation>,
    /// Memory layout plan for intermediate tensors.
    memory_plan: MemoryPlan,
}

/// A fused operation combining multiple graph nodes.
#[derive(Debug, Clone)]
struct FusedOperation {
    /// Type of the fused operation.
    op_type: FusedOpType,
    /// Input tensor indices.
    inputs: Vec<usize>,
    /// Output tensor indices.
    outputs: Vec<usize>,
    /// Operation-specific parameters.
    params: HashMap<String, f64>,
}

/// Types of fused operations supported.
#[derive(Debug, Clone)]
enum FusedOpType {
    /// Element-wise addition.
    Add,
    /// Element-wise multiplication.
    Multiply,
    /// Matrix multiplication.
    MatMul,
    /// Fused Add + ReLU.
    AddRelu,
    /// Fused MatMul + Add (bias).
    MatMulAdd,
    /// Fused Conv + BatchNorm + ReLU (placeholder for future).
    ConvBnRelu,
    /// Copy operation.
    Copy,
    /// Reshape operation.
    Reshape,
}

/// Memory layout plan for kernel execution.
#[derive(Debug, Clone)]
struct MemoryPlan {
    /// Total number of intermediate tensors needed.
    tensor_count: usize,
    /// Size requirements for each tensor.
    tensor_sizes: Vec<usize>,
    /// Data types for each tensor.
    tensor_dtypes: Vec<DataType>,
    /// Layout preferences for each tensor.
    tensor_layouts: Vec<TensorLayout>,
}

/// Internal performance statistics.
#[derive(Debug, Default)]
struct KernelStatsInternal {
    execution_count: u64,
    total_time_us: u64,
    min_time_us: u64,
    max_time_us: u64,
}

/// Internal memory usage statistics.
#[derive(Debug, Default)]
struct MemoryStatsInternal {
    peak_bytes: usize,
    current_bytes: usize,
    allocation_count: usize,
}

impl CpuKernel {
    /// Compile a subgraph into a CPU kernel.
    pub fn compile(subgraph: SubGraph, simd_capabilities: SimdCapabilities) -> Result<Self> {
        let execution_plan = Self::analyze_and_plan(&subgraph)?;

        Ok(Self {
            subgraph,
            execution_plan,
            simd_capabilities,
            stats: Arc::new(Mutex::new(KernelStatsInternal::default())),
            memory_stats: Arc::new(Mutex::new(MemoryStatsInternal::default())),
        })
    }

    /// Analyze subgraph and create optimized execution plan.
    fn analyze_and_plan(subgraph: &SubGraph) -> Result<ExecutionPlan> {
        let mut operations = Vec::new();
        let mut tensor_count = 0;
        let mut tensor_sizes = Vec::new();
        let mut tensor_dtypes = Vec::new();
        let mut tensor_layouts = Vec::new();

        // Simple operation mapping (no fusion for now)
        for (i, node) in subgraph.nodes.iter().enumerate() {
            let fused_op = match node.op_type.as_str() {
                "Add" => FusedOperation {
                    op_type: FusedOpType::Add,
                    inputs: vec![i * 2, i * 2 + 1], // Simplified input mapping
                    outputs: vec![tensor_count],
                    params: HashMap::new(),
                },
                "Mul" => FusedOperation {
                    op_type: FusedOpType::Multiply,
                    inputs: vec![i * 2, i * 2 + 1],
                    outputs: vec![tensor_count],
                    params: HashMap::new(),
                },
                "MatMul" => FusedOperation {
                    op_type: FusedOpType::MatMul,
                    inputs: vec![i * 2, i * 2 + 1],
                    outputs: vec![tensor_count],
                    params: HashMap::new(),
                },
                "Reshape" => FusedOperation {
                    op_type: FusedOpType::Reshape,
                    inputs: vec![i],
                    outputs: vec![tensor_count],
                    params: HashMap::new(),
                },
                _ => {
                    warn!("Unsupported operation type: {}", node.op_type);
                    continue;
                }
            };

            operations.push(fused_op);

            // Estimate tensor requirements (simplified)
            tensor_sizes.push(1024); // Default 1KB tensors
            tensor_dtypes.push(DataType::F32); // Default F32
            tensor_layouts.push(TensorLayout::RowMajor); // Default row-major

            tensor_count += 1;
        }

        let memory_plan = MemoryPlan {
            tensor_count,
            tensor_sizes,
            tensor_dtypes,
            tensor_layouts,
        };

        Ok(ExecutionPlan {
            operations,
            memory_plan,
        })
    }

    /// Execute a single fused operation.
    fn execute_operation(
        &self,
        operation: &FusedOperation,
        tensors: &mut [ronn_core::tensor::Tensor],
    ) -> Result<()> {
        match operation.op_type {
            FusedOpType::Add => {
                if operation.inputs.len() != 2 || operation.outputs.len() != 1 {
                    return Err(anyhow!("Add operation requires 2 inputs and 1 output"));
                }

                let a = &tensors[operation.inputs[0]];
                let b = &tensors[operation.inputs[1]];
                let output_idx = operation.outputs[0];

                // Get tensor data
                let a_data = a.to_vec()?;
                let b_data = b.to_vec()?;

                if a_data.len() != b_data.len() {
                    return Err(anyhow!("Input tensors must have the same size for Add"));
                }

                let mut result_data = vec![0.0; a_data.len()];

                // Use SIMD-optimized addition
                simd_add_f32(&a_data, &b_data, &mut result_data, &self.simd_capabilities);

                // Create result tensor
                let result_tensor =
                    Tensor::from_data(result_data, a.shape(), a.dtype(), a.layout())?;

                tensors[output_idx] = result_tensor;
            }

            FusedOpType::Multiply => {
                if operation.inputs.len() != 2 || operation.outputs.len() != 1 {
                    return Err(anyhow!("Multiply operation requires 2 inputs and 1 output"));
                }

                let a = &tensors[operation.inputs[0]];
                let b = &tensors[operation.inputs[1]];
                let output_idx = operation.outputs[0];

                let a_data = a.to_vec()?;
                let b_data = b.to_vec()?;

                if a_data.len() != b_data.len() {
                    return Err(anyhow!(
                        "Input tensors must have the same size for Multiply"
                    ));
                }

                let mut result_data = vec![0.0; a_data.len()];

                // Use SIMD-optimized multiplication
                simd_mul_f32(&a_data, &b_data, &mut result_data, &self.simd_capabilities);

                let result_tensor =
                    Tensor::from_data(result_data, a.shape(), a.dtype(), a.layout())?;

                tensors[output_idx] = result_tensor;
            }

            FusedOpType::MatMul => {
                if operation.inputs.len() != 2 || operation.outputs.len() != 1 {
                    return Err(anyhow!("MatMul operation requires 2 inputs and 1 output"));
                }

                let a = &tensors[operation.inputs[0]];
                let b = &tensors[operation.inputs[1]];
                let output_idx = operation.outputs[0];

                let a_shape = a.shape();
                let b_shape = b.shape();

                if a_shape.len() != 2 || b_shape.len() != 2 {
                    return Err(anyhow!("MatMul currently only supports 2D tensors"));
                }

                if a_shape[1] != b_shape[0] {
                    return Err(anyhow!(
                        "Matrix dimensions incompatible: {}x{} and {}x{}",
                        a_shape[0],
                        a_shape[1],
                        b_shape[0],
                        b_shape[1]
                    ));
                }

                let a_data = a.to_vec()?;
                let b_data = b.to_vec()?;
                let mut result_data = vec![0.0; a_shape[0] * b_shape[1]];

                // Use SIMD-optimized matrix multiplication
                simd_matmul_f32(
                    &a_data,
                    a_shape[0],
                    a_shape[1],
                    &b_data,
                    b_shape[0],
                    b_shape[1],
                    &mut result_data,
                    &self.simd_capabilities,
                );

                let result_tensor = Tensor::from_data(
                    result_data,
                    vec![a_shape[0], b_shape[1]],
                    a.dtype(),
                    a.layout(),
                )?;

                tensors[output_idx] = result_tensor;
            }

            FusedOpType::AddRelu => {
                // Fused Add + ReLU operation
                if operation.inputs.len() != 2 || operation.outputs.len() != 1 {
                    return Err(anyhow!("AddRelu operation requires 2 inputs and 1 output"));
                }

                let a = &tensors[operation.inputs[0]];
                let b = &tensors[operation.inputs[1]];
                let output_idx = operation.outputs[0];

                let a_data = a.to_vec()?;
                let b_data = b.to_vec()?;

                if a_data.len() != b_data.len() {
                    return Err(anyhow!("Input tensors must have the same size for AddRelu"));
                }

                let mut result_data = vec![0.0; a_data.len()];

                // SIMD addition followed by ReLU
                simd_add_f32(&a_data, &b_data, &mut result_data, &self.simd_capabilities);

                // Apply ReLU (could be SIMD-optimized)
                for value in &mut result_data {
                    *value = value.max(0.0);
                }

                let result_tensor =
                    Tensor::from_data(result_data, a.shape(), a.dtype(), a.layout())?;

                tensors[output_idx] = result_tensor;
            }

            FusedOpType::Copy => {
                if operation.inputs.len() != 1 || operation.outputs.len() != 1 {
                    return Err(anyhow!("Copy operation requires 1 input and 1 output"));
                }

                let input_tensor = &tensors[operation.inputs[0]];
                let output_idx = operation.outputs[0];

                // Simple copy - in a real implementation, this might be optimized
                tensors[output_idx] = input_tensor.clone();
            }

            FusedOpType::Reshape => {
                if operation.inputs.len() != 1 || operation.outputs.len() != 1 {
                    return Err(anyhow!("Reshape operation requires 1 input and 1 output"));
                }

                // Reshape implementation would go here
                // For now, just copy the tensor
                let input_tensor = &tensors[operation.inputs[0]];
                let output_idx = operation.outputs[0];
                tensors[output_idx] = input_tensor.clone();
            }

            _ => {
                return Err(anyhow!(
                    "Unsupported fused operation: {:?}",
                    operation.op_type
                ));
            }
        }

        Ok(())
    }

    /// Parallel execution of independent operations.
    fn execute_parallel(
        &self,
        inputs: &[ronn_core::tensor::Tensor],
    ) -> Result<Vec<ronn_core::tensor::Tensor>> {
        let plan = &self.execution_plan;

        // Allocate workspace tensors
        let mut tensors = Vec::with_capacity(plan.memory_plan.tensor_count + inputs.len());

        // Add input tensors
        tensors.extend_from_slice(inputs);

        // Add workspace tensors (initialized with zeros)
        for i in 0..plan.memory_plan.tensor_count {
            let size = plan.memory_plan.tensor_sizes[i];
            let dtype = plan.memory_plan.tensor_dtypes[i];
            let layout = plan.memory_plan.tensor_layouts[i];

            // Create a dummy shape based on size (simplified)
            let elements = size / 4; // Assuming F32
            let shape = vec![elements];

            let tensor = Tensor::zeros(shape, dtype, layout)?;
            tensors.push(tensor);
        }

        // Execute operations sequentially (could be parallelized for independent ops)
        for operation in &plan.operations {
            self.execute_operation(operation, &mut tensors)?;
        }

        // Return output tensors (last N tensors where N is number of operations)
        let output_start = tensors.len() - plan.operations.len();
        Ok(tensors[output_start..].to_vec())
    }
}

impl CompiledKernel for CpuKernel {
    fn execute(
        &self,
        inputs: &[ronn_core::tensor::Tensor],
    ) -> Result<Vec<ronn_core::tensor::Tensor>> {
        let start_time = Instant::now();

        // Update memory statistics
        {
            let mut memory_stats = self.memory_stats.lock().unwrap();
            memory_stats.allocation_count += 1;
            // Memory tracking would be more sophisticated in practice
        }

        // Execute the kernel
        let result = if inputs.len() > 1 {
            // Use parallel execution for multiple inputs
            self.execute_parallel(inputs)
        } else if !inputs.is_empty() {
            // Single input execution
            self.execute_parallel(inputs)
        } else {
            Ok(vec![])
        };

        // Update performance statistics
        let execution_time = start_time.elapsed();
        {
            let mut stats = self.stats.lock().unwrap();
            stats.execution_count += 1;
            stats.total_time_us += execution_time.as_micros() as u64;

            if stats.execution_count == 1 {
                stats.min_time_us = execution_time.as_micros() as u64;
                stats.max_time_us = execution_time.as_micros() as u64;
            } else {
                stats.min_time_us = stats.min_time_us.min(execution_time.as_micros() as u64);
                stats.max_time_us = stats.max_time_us.max(execution_time.as_micros() as u64);
            }
        }

        debug!("CPU kernel executed in {:?}", execution_time);

        result
    }

    fn get_memory_usage(&self) -> MemoryUsage {
        let memory_stats = self.memory_stats.lock().unwrap();
        MemoryUsage {
            peak_bytes: memory_stats.peak_bytes,
            current_bytes: memory_stats.current_bytes,
            allocation_count: memory_stats.allocation_count,
        }
    }

    fn get_performance_stats(&self) -> KernelStats {
        let stats = self.stats.lock().unwrap();

        let average_time_us = if stats.execution_count > 0 {
            stats.total_time_us as f64 / stats.execution_count as f64
        } else {
            0.0
        };

        KernelStats {
            execution_count: stats.execution_count,
            average_time_us,
            min_time_us: stats.min_time_us as f64,
            max_time_us: stats.max_time_us as f64,
        }
    }
}

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

    fn create_test_subgraph() -> SubGraph {
        let node1 = GraphNode {
            id: 0,
            op_type: "Add".to_string(),
            attributes: HashMap::new(),
            inputs: vec!["input1".to_string(), "input2".to_string()],
            outputs: vec!["output1".to_string()],
            name: Some("add_node".to_string()),
        };

        SubGraph {
            nodes: vec![node1],
            edges: vec![],
            inputs: vec!["input1".to_string(), "input2".to_string()],
            outputs: vec!["output1".to_string()],
        }
    }

    #[test]
    fn test_kernel_compilation() -> Result<()> {
        let subgraph = create_test_subgraph();
        let simd_caps = crate::cpu::simd::detect_simd_capabilities();

        let kernel = CpuKernel::compile(subgraph, simd_caps)?;

        // Should have compiled successfully
        assert!(kernel.execution_plan.operations.len() > 0);

        Ok(())
    }

    #[test]
    fn test_kernel_execution() -> Result<()> {
        let subgraph = create_test_subgraph();
        let simd_caps = crate::cpu::simd::detect_simd_capabilities();
        let kernel = CpuKernel::compile(subgraph, simd_caps)?;

        // Create test inputs
        let input1 = Tensor::from_data(
            vec![1.0, 2.0, 3.0, 4.0],
            vec![4],
            DataType::F32,
            TensorLayout::RowMajor,
        )?;

        let input2 = Tensor::from_data(
            vec![5.0, 6.0, 7.0, 8.0],
            vec![4],
            DataType::F32,
            TensorLayout::RowMajor,
        )?;

        let inputs = vec![input1, input2];

        // Execute kernel
        let outputs = kernel.execute(&inputs)?;

        // Should produce outputs
        assert!(outputs.len() > 0);

        // Check performance stats
        let stats = kernel.get_performance_stats();
        assert_eq!(stats.execution_count, 1);
        assert!(stats.average_time_us > 0.0);

        Ok(())
    }

    #[test]
    fn test_multiple_executions() -> Result<()> {
        let subgraph = create_test_subgraph();
        let simd_caps = crate::cpu::simd::detect_simd_capabilities();
        let kernel = CpuKernel::compile(subgraph, simd_caps)?;

        let input1 = Tensor::ones(vec![100], DataType::F32, TensorLayout::RowMajor)?;
        let input2 = Tensor::ones(vec![100], DataType::F32, TensorLayout::RowMajor)?;
        let inputs = vec![input1, input2];

        // Execute multiple times
        for _ in 0..5 {
            let _outputs = kernel.execute(&inputs)?;
        }

        let stats = kernel.get_performance_stats();
        assert_eq!(stats.execution_count, 5);
        assert!(stats.min_time_us <= stats.average_time_us);
        assert!(stats.average_time_us <= stats.max_time_us);

        Ok(())
    }

    #[test]
    fn test_memory_statistics() -> Result<()> {
        let subgraph = create_test_subgraph();
        let simd_caps = crate::cpu::simd::detect_simd_capabilities();
        let kernel = CpuKernel::compile(subgraph, simd_caps)?;

        let input1 = Tensor::zeros(vec![1000], DataType::F32, TensorLayout::RowMajor)?;
        let input2 = Tensor::zeros(vec![1000], DataType::F32, TensorLayout::RowMajor)?;
        let inputs = vec![input1, input2];

        let _outputs = kernel.execute(&inputs)?;

        let memory_usage = kernel.get_memory_usage();
        assert!(memory_usage.allocation_count > 0);

        Ok(())
    }
}