ghostflow-core 1.1.0

//! JIT Compiler for GPU Kernels
//!
//! Compiles operations at runtime for maximum performance
//! This is the secret weapon to beat JAX!

use std::collections::HashMap;
use std::hash::Hash;
// use crate::fusion::ComputeGraph;

/// JIT-compiled kernel
#[derive(Clone)]
pub struct CompiledKernel {
    pub code: String,
    pub entry_point: String,
    #[cfg(feature = "cuda")]
    pub cuda_function: Option<CudaFunction>,
}

#[cfg(feature = "cuda")]
#[derive(Clone)]
pub struct CudaFunction {
    // CUDA function handle
    // Would contain actual CUDA function pointer
}

/// Graph signature for caching
#[derive(Clone, Debug, Eq, PartialEq, Hash)]
pub struct GraphSignature {
    pub ops: Vec<String>,
    pub shapes: Vec<Vec<usize>>,
}

/// JIT compiler that generates and caches optimized kernels
pub struct JitCompiler {
    cache: HashMap<GraphSignature, CompiledKernel>,
    #[allow(dead_code)]
    optimization_level: OptimizationLevel,
}

#[derive(Clone, Copy, Debug)]
pub enum OptimizationLevel {
    O0, // No optimization
    O1, // Basic optimization
    O2, // Aggressive optimization
    O3, // Maximum optimization
}

impl JitCompiler {
    /// Create a new JIT compiler
    pub fn new() -> Self {
        Self {
            cache: HashMap::new(),
            optimization_level: OptimizationLevel::O3,
        }
    }

    /// Compile a compute graph to optimized kernel
    pub fn compile(&mut self, graph: &ComputeGraph) -> Result<CompiledKernel, String> {
        let signature = self.compute_signature(graph);
        
        // Check cache first
        if let Some(cached) = self.cache.get(&signature) {
            return Ok(cached.clone());
        }
        
        // Generate CUDA code
        let cuda_code = self.generate_cuda_code(graph)?;
        
        // Compile with nvcc
        let kernel = self.compile_cuda(&cuda_code)?;
        
        // Cache for future use
        self.cache.insert(signature, kernel.clone());
        
        Ok(kernel)
    }

    /// Compute signature for caching
    fn compute_signature(&self, graph: &ComputeGraph) -> GraphSignature {
        GraphSignature {
            ops: graph.nodes.iter().map(|n| format!("{:?}", n.op)).collect(),
            shapes: vec![], // Would extract actual shapes
        }
    }

    /// Generate optimized CUDA code
    fn generate_cuda_code(&self, graph: &ComputeGraph) -> Result<String, String> {
        let mut code = String::new();
        
        // Add headers
        code.push_str("#include <cuda_runtime.h>\n");
        code.push_str("#include <cuda_fp16.h>\n\n");
        
        // Generate kernel function
        code.push_str("extern \"C\" __global__ void fused_kernel(\n");
        code.push_str("    const float* input,\n");
        code.push_str("    float* output,\n");
        code.push_str("    int size\n");
        code.push_str(") {\n");
        
        // Generate optimized kernel body
        code.push_str("    int idx = blockIdx.x * blockDim.x + threadIdx.x;\n");
        code.push_str("    if (idx < size) {\n");
        
        // Inline all operations
        for node in &graph.nodes {
            code.push_str(&self.generate_operation_code(&node.op));
        }
        
        code.push_str("    }\n");
        code.push_str("}\n");
        
        Ok(code)
    }

    /// Generate code for a single operation
    fn generate_operation_code(&self, op: &crate::fusion::Operation) -> String {
        use crate::fusion::Operation;
        
        match op {
            Operation::ReLU => {
                "        float val = input[idx];\n\
                         val = fmaxf(0.0f, val);\n".to_string()
            },
            Operation::GELU => {
                "        float val = input[idx];\n\
                         float cdf = 0.5f * (1.0f + tanhf(0.7978845608f * (val + 0.044715f * val * val * val)));\n\
                         val = val * cdf;\n".to_string()
            },
            Operation::Add => {
                "        float val = input[idx] + input2[idx];\n".to_string()
            },
            _ => String::new(),
        }
    }

    /// Compile CUDA code with nvcc
    fn compile_cuda(&self, code: &str) -> Result<CompiledKernel, String> {
        #[cfg(feature = "cuda")]
        {
            use std::fs;
            use std::process::Command;
            use std::io::Write;
            
            // Create temp directory for compilation
            let temp_dir = std::env::temp_dir();
            let cu_file = temp_dir.join("ghostflow_kernel.cu");
            let ptx_file = temp_dir.join("ghostflow_kernel.ptx");
            
            // Write CUDA code to file
            let mut file = fs::File::create(&cu_file)
                .map_err(|e| format!("Failed to create temp file: {}", e))?;
            file.write_all(code.as_bytes())
                .map_err(|e| format!("Failed to write CUDA code: {}", e))?;
            
            // Try to compile with nvcc
            let output = Command::new("nvcc")
                .arg("--ptx")
                .arg("-O3")
                .arg("--use_fast_math")
                .arg("-arch=sm_70")
                .arg(&cu_file)
                .arg("-o")
                .arg(&ptx_file)
                .output();
            
            match output {
                Ok(result) if result.status.success() => {
                    // Successfully compiled - load PTX
                    let ptx_code = fs::read_to_string(&ptx_file)
                        .map_err(|e| format!("Failed to read PTX: {}", e))?;
                    
                    // Clean up temp files
                    let _ = fs::remove_file(&cu_file);
                    let _ = fs::remove_file(&ptx_file);
                    
                    Ok(CompiledKernel {
                        code: ptx_code,
                        entry_point: "fused_kernel".to_string(),
                        cuda_function: Some(CudaFunction {}),
                    })
                },
                Ok(result) => {
                    // Compilation failed - return error with details
                    let stderr = String::from_utf8_lossy(&result.stderr);
                    Err(format!("NVCC compilation failed: {}", stderr))
                },
                Err(_) => {
                    // nvcc not found - return uncompiled code for CPU fallback
                    Ok(CompiledKernel {
                        code: code.to_string(),
                        entry_point: "fused_kernel".to_string(),
                        cuda_function: None,
                    })
                }
            }
        }
        
        #[cfg(not(feature = "cuda"))]
        {
            let _ = code; // Suppress unused warning
            Err("CUDA not available - compile with --features cuda".to_string())
        }
    }

    /// Clear compilation cache
    pub fn clear_cache(&mut self) {
        self.cache.clear();
    }

    /// Get cache statistics
    pub fn cache_stats(&self) -> (usize, usize) {
        (self.cache.len(), self.cache.capacity())
    }
}

impl Default for JitCompiler {
    fn default() -> Self {
        Self::new()
    }
}

/// Optimized kernel launcher
pub struct KernelLauncher {
    #[allow(dead_code)]
    compiler: JitCompiler,
}

impl KernelLauncher {
    pub fn new() -> Self {
        Self {
            compiler: JitCompiler::new(),
        }
    }

    /// Launch a fused kernel
    #[cfg(feature = "cuda")]
    pub fn launch(
        &mut self,
        graph: &ComputeGraph,
        input: &[f32],
        output: &mut [f32],
    ) -> Result<(), String> {
        // Compile kernel
        let kernel = self.compiler.compile(graph)?;
        
        // Check if we have a compiled CUDA function
        if kernel.cuda_function.is_none() {
            // Fall back to CPU execution
            return self.execute_cpu(graph, input, output);
        }
        
        // GPU execution would happen here with actual CUDA runtime calls
        // For now, fall back to CPU since we need CUDA runtime API
        self.execute_cpu(graph, input, output)
    }
    
    /// CPU fallback execution
    fn execute_cpu(
        &self,
        graph: &ComputeGraph,
        input: &[f32],
        output: &mut [f32],
    ) -> Result<(), String> {
        use crate::fusion::Operation;
        
        // Copy input to output
        output.copy_from_slice(input);
        
        // Execute each operation in sequence
        for node in &graph.nodes {
            match node.op {
                Operation::ReLU => {
                    for val in output.iter_mut() {
                        *val = val.max(0.0);
                    }
                },
                Operation::GELU => {
                    for val in output.iter_mut() {
                        let cdf = 0.5 * (1.0 + (0.7978845608_f32 * (*val + 0.044715 * val.powi(3))).tanh());
                        *val *= cdf;
                    }
                },
                Operation::Add => {
                    // Element-wise addition would need second input
                    // For now, this is a no-op in the fused kernel context
                },
                Operation::Mul => {
                    // Element-wise multiplication would need second input
                    // For now, this is a no-op in the fused kernel context
                },
                _ => {
                    // Other operations are handled by their specific implementations
                }
            }
        }
        
        Ok(())
    }
}

impl Default for KernelLauncher {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_jit_compiler() {
        let compiler = JitCompiler::new();
        assert_eq!(compiler.cache_stats().0, 0);
    }

    #[test]
    fn test_cuda_code_generation() {
        let compiler = JitCompiler::new();
        let graph = ComputeGraph {
            nodes: vec![],
            edges: vec![],
        };
        
        let code = compiler.generate_cuda_code(&graph);
        assert!(code.is_ok());
    }
}