torsh-fx 0.1.2

//! Core infrastructure for FX graph code generation
//!
//! This module provides the fundamental traits, types, and abstractions
//! used by all code generation backends.

use crate::{FxGraph, Node};
use std::collections::{HashMap, HashSet};
use std::sync::{Arc, RwLock};
use torsh_core::Result;

/// Code generation backend trait
///
/// This trait defines the interface that all code generation backends must implement.
/// Each backend is responsible for translating FX graphs into executable code
/// for a specific target language or runtime.
pub trait CodeGenBackend {
    /// Generate code for the given graph
    ///
    /// Takes an FX graph and produces executable code as a string.
    /// The specific format and language depend on the backend implementation.
    fn generate(&self, graph: &FxGraph) -> Result<String>;

    /// Get the file extension for generated code
    ///
    /// Returns the appropriate file extension for code generated by this backend.
    /// For example: "py" for Python, "cpp" for C++, etc.
    fn file_extension(&self) -> &'static str;

    /// Get the language name
    ///
    /// Returns a human-readable name for the target language or runtime.
    fn language_name(&self) -> &'static str;
}

/// Target device enumeration
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum TargetDevice {
    CPU,
    CUDA,
    Metal,
    Vulkan,
    ROCm,
    WebGPU,
    TPU,
}

/// SIMD support levels
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SimdSupport {
    None,
    SSE,
    AVX,
    AVX2,
    AVX512,
    NEON,
    SVE,
}

/// Backend type enumeration
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum BackendType {
    CPU,
    CUDA,
    Metal,
    Vulkan,
    WebGL,
    TensorRT,
    DirectML,
    CoreML,
    ONNX,
    OpenVINO,
    XLA,
}

/// Target specification for code generation
#[derive(Debug, Clone)]
pub struct TargetSpecification {
    pub device: TargetDevice,
    pub simd_support: SimdSupport,
    pub optimization_level: OptimizationLevel,
    pub precision: Precision,
    pub memory_layout: MemoryLayout,
}

/// Optimization level enumeration
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum OptimizationLevel {
    Debug,
    Release,
    Aggressive,
    Size,
}

/// Precision specification
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum Precision {
    Float16,
    Float32,
    Float64,
    BFloat16,
    Int8,
    Int16,
    Int32,
    Mixed,
}

/// Memory layout specification
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MemoryLayout {
    RowMajor,
    ColumnMajor,
    Blocked,
    Packed,
    Strided,
}

/// Optimized kernel representation
#[derive(Debug, Clone)]
pub struct OptimizedKernel {
    pub name: String,
    pub source_code: String,
    pub target_device: TargetDevice,
    pub performance_hint: PerformanceHint,
}

/// Performance hint for kernel optimization
#[derive(Debug, Clone)]
pub struct PerformanceHint {
    pub vectorization_factor: usize,
    pub unroll_factor: usize,
    pub memory_bound: bool,
    pub compute_bound: bool,
}

/// Lowered graph representation
#[derive(Debug, Clone)]
pub struct LoweredGraph {
    pub nodes: Vec<LoweredNode>,
    pub edges: Vec<(usize, usize)>,
    pub backend_type: BackendType,
}

/// Lowered node representation
#[derive(Debug, Clone)]
pub struct LoweredNode {
    pub id: usize,
    pub operation: String,
    pub inputs: Vec<usize>,
    pub outputs: Vec<usize>,
    pub backend_specific_data: HashMap<String, String>,
}

/// Common code generation utilities
pub struct CodeGenUtils;

impl CodeGenUtils {
    /// Generate a unique variable name
    pub fn generate_var_name(prefix: &str, index: usize) -> String {
        format!("{}_{}", prefix, index)
    }

    /// Sanitize a name for use in generated code
    pub fn sanitize_name(name: &str) -> String {
        name.chars()
            .map(|c| if c.is_alphanumeric() || c == '_' { c } else { '_' })
            .collect()
    }

    /// Generate indentation string
    pub fn indent(level: usize, size: usize) -> String {
        " ".repeat(level * size)
    }

    /// Check if a graph is valid for code generation
    pub fn validate_graph(graph: &FxGraph) -> Result<()> {
        // Basic validation - ensure no cycles, all inputs defined, etc.
        // This is a simplified implementation
        Ok(())
    }

    /// Perform topological sort on graph nodes
    pub fn topological_sort(graph: &FxGraph) -> Result<Vec<usize>> {
        // Simplified topological sort implementation
        // In practice, this would use the petgraph crate's algorithms
        let node_count = graph.node_count();
        Ok((0..node_count).collect())
    }

    /// Generate header comment for generated code
    pub fn generate_header_comment(backend_name: &str) -> String {
        format!(
            "// Generated by ToRSh FX {} backend\n// Do not modify this file directly\n",
            backend_name
        )
    }
}

/// Error types specific to code generation
#[derive(Debug, thiserror::Error)]
pub enum CodeGenError {
    #[error("Unsupported operation: {operation}")]
    UnsupportedOperation { operation: String },

    #[error("Invalid target specification: {message}")]
    InvalidTargetSpec { message: String },

    #[error("Backend not available: {backend}")]
    BackendNotAvailable { backend: String },

    #[error("Code generation failed: {reason}")]
    GenerationFailed { reason: String },

    #[error("Optimization failed: {reason}")]
    OptimizationFailed { reason: String },
}

/// Result type alias for code generation operations
pub type CodeGenResult<T> = std::result::Result<T, CodeGenError>;

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

    #[test]
    fn test_generate_var_name() {
        assert_eq!(CodeGenUtils::generate_var_name("var", 0), "var_0");
        assert_eq!(CodeGenUtils::generate_var_name("tensor", 42), "tensor_42");
    }

    #[test]
    fn test_sanitize_name() {
        assert_eq!(CodeGenUtils::sanitize_name("valid_name"), "valid_name");
        assert_eq!(CodeGenUtils::sanitize_name("invalid-name!"), "invalid_name_");
        assert_eq!(CodeGenUtils::sanitize_name("123abc"), "123abc");
    }

    #[test]
    fn test_indent() {
        assert_eq!(CodeGenUtils::indent(0, 4), "");
        assert_eq!(CodeGenUtils::indent(1, 4), "    ");
        assert_eq!(CodeGenUtils::indent(2, 2), "    ");
    }

    #[test]
    fn test_generate_header_comment() {
        let header = CodeGenUtils::generate_header_comment("Python");
        assert!(header.contains("ToRSh FX Python backend"));
        assert!(header.contains("Do not modify"));
    }

    #[test]
    fn test_target_specification_creation() {
        let spec = TargetSpecification {
            device: TargetDevice::CUDA,
            simd_support: SimdSupport::AVX2,
            optimization_level: OptimizationLevel::Release,
            precision: Precision::Float32,
            memory_layout: MemoryLayout::RowMajor,
        };

        assert_eq!(spec.device, TargetDevice::CUDA);
        assert_eq!(spec.precision, Precision::Float32);
    }

    #[test]
    fn test_optimized_kernel_creation() {
        let kernel = OptimizedKernel {
            name: "test_kernel".to_string(),
            source_code: "// kernel code".to_string(),
            target_device: TargetDevice::CPU,
            performance_hint: PerformanceHint {
                vectorization_factor: 4,
                unroll_factor: 2,
                memory_bound: false,
                compute_bound: true,
            },
        };

        assert_eq!(kernel.name, "test_kernel");
        assert_eq!(kernel.target_device, TargetDevice::CPU);
        assert!(kernel.performance_hint.compute_bound);
    }
}