1207 lines
36 KiB
Rust
1207 lines
36 KiB
Rust
//! Code generation tests for RTX Compiler
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//! Tests compilation to various target backends and platforms
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//!
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//! NOTE: Disabled until RTX Compiler API is fully implemented
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#![cfg(feature = "disabled_tests")]
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use rtx_compiler::codegen::*;
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use rtx_compiler::ir::*;
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use rtx_compiler::*;
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use std::collections::HashMap;
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/// Test CUDA code generation
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#[test]
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fn test_cuda_codegen() {
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let graph = create_simple_conv_graph();
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let mut codegen = CudaCodegen::new(CudaConfig {
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compute_capability: (8, 0), // Ampere
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max_threads_per_block: 1024,
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shared_memory_kb: 48,
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use_tensor_cores: true,
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});
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify CUDA kernel generation
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assert!(cuda_code.contains("__global__ void"));
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assert!(cuda_code.contains("__device__ __forceinline__"));
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assert!(cuda_code.contains("blockIdx"));
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assert!(cuda_code.contains("threadIdx"));
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// Verify tensor core usage for appropriate operations
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if codegen.config.use_tensor_cores {
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assert!(cuda_code.contains("wmma::"));
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assert!(cuda_code.contains("fragment"));
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}
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// Verify memory coalescing optimizations
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assert!(cuda_code.contains("__shared__"));
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// Test compilation
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let compiled_kernel = codegen.compile(&cuda_code).unwrap();
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assert!(!compiled_kernel.is_empty());
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println!("Generated CUDA code:\n{}", cuda_code);
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}
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/// Test CPU code generation with vectorization
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#[test]
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fn test_cpu_codegen() {
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let graph = create_simple_conv_graph();
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let mut codegen = CpuCodegen::new(CpuConfig {
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target_arch: "x86_64".to_string(),
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use_avx512: true,
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use_fma: true,
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optimization_level: OptimizationLevel::O3,
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target_features: vec!["avx512f".to_string(), "avx512dq".to_string()],
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});
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let cpu_code = codegen.generate(&graph).unwrap();
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// Verify vectorization
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assert!(cpu_code.contains("__m512"));
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assert!(cpu_code.contains("_mm512_"));
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assert!(cpu_code.contains("_mm512_fmadd_ps"));
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// Verify loop optimizations
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assert!(cpu_code.contains("#pragma omp parallel"));
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assert!(cpu_code.contains("#pragma omp simd"));
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// Verify cache optimization
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assert!(cpu_code.contains("// Cache blocking"));
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let compiled_lib = codegen.compile(&cpu_code).unwrap();
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assert!(!compiled_lib.is_empty());
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println!("Generated CPU code:\n{}", cpu_code);
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}
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/// Test WebAssembly code generation
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#[test]
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fn test_wasm_codegen() {
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let graph = create_simple_linear_graph();
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let mut codegen = WasmCodegen::new(WasmConfig {
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use_simd: true,
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memory_pages: 256,
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stack_size_kb: 64,
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optimization_level: OptimizationLevel::O2,
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});
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let wasm_code = codegen.generate(&graph).unwrap();
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// Verify WASM structure
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assert!(wasm_code.contains("(module"));
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assert!(wasm_code.contains("(memory"));
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assert!(wasm_code.contains("(export"));
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assert!(wasm_code.contains("(func"));
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// Verify SIMD usage if enabled
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if codegen.config.use_simd {
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assert!(wasm_code.contains("v128"));
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assert!(wasm_code.contains("f32x4"));
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}
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let compiled_wasm = codegen.compile(&wasm_code).unwrap();
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assert!(!compiled_wasm.is_empty());
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println!("Generated WASM code:\n{}", wasm_code);
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}
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/// Test Metal code generation for Apple GPUs
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#[test]
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fn test_metal_codegen() {
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let graph = create_simple_conv_graph();
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let mut codegen = MetalCodegen::new(MetalConfig {
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metal_version: MetalVersion::V3_0,
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max_threads_per_threadgroup: 1024,
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use_metal_performance_shaders: true,
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});
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let metal_code = codegen.generate(&graph).unwrap();
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// Verify Metal shader structure
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assert!(metal_code.contains("#include <metal_stdlib>"));
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assert!(metal_code.contains("using namespace metal;"));
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assert!(metal_code.contains("kernel void"));
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assert!(metal_code.contains("threadgroup"));
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assert!(metal_code.contains("thread_position_in_grid"));
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// Verify MPS integration if enabled
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if codegen.config.use_metal_performance_shaders {
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assert!(metal_code.contains("// MPS optimized"));
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}
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let compiled_metallib = codegen.compile(&metal_code).unwrap();
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assert!(!compiled_metallib.is_empty());
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println!("Generated Metal code:\n{}", metal_code);
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}
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/// Test OpenCL code generation
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#[test]
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fn test_opencl_codegen() {
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let graph = create_simple_conv_graph();
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let mut codegen = OpenClCodegen::new(OpenClConfig {
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opencl_version: "2.0".to_string(),
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device_type: OpenClDeviceType::GPU,
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local_memory_kb: 32,
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max_work_group_size: 256,
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});
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let opencl_code = codegen.generate(&graph).unwrap();
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// Verify OpenCL kernel structure
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assert!(opencl_code.contains("__kernel void"));
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assert!(opencl_code.contains("__global"));
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assert!(opencl_code.contains("__local"));
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assert!(opencl_code.contains("get_global_id"));
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assert!(opencl_code.contains("get_local_id"));
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// Verify work group optimizations
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assert!(opencl_code.contains("barrier(CLK_LOCAL_MEM_FENCE)"));
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let compiled_kernel = codegen.compile(&opencl_code).unwrap();
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assert!(!compiled_kernel.is_empty());
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println!("Generated OpenCL code:\n{}", opencl_code);
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}
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/// Test multi-target code generation
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#[test]
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fn test_multi_target_codegen() {
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let graph = create_simple_conv_graph();
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let mut multi_codegen = MultiTargetCodegen::new();
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// Add multiple targets
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multi_codegen.add_target(CodegenTarget::Cuda(CudaConfig {
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compute_capability: (8, 0),
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max_threads_per_block: 1024,
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shared_memory_kb: 48,
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use_tensor_cores: true,
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}));
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multi_codegen.add_target(CodegenTarget::Cpu(CpuConfig {
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target_arch: "x86_64".to_string(),
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use_avx512: true,
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use_fma: true,
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optimization_level: OptimizationLevel::O3,
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target_features: vec!["avx512f".to_string()],
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}));
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multi_codegen.add_target(CodegenTarget::Wasm(WasmConfig {
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use_simd: true,
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memory_pages: 256,
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stack_size_kb: 64,
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optimization_level: OptimizationLevel::O2,
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}));
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let multi_output = multi_codegen.generate(&graph).unwrap();
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// Verify all targets generated
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assert!(multi_output.targets.len() == 3);
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assert!(multi_output.targets.contains_key(&"cuda"));
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assert!(multi_output.targets.contains_key(&"cpu"));
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assert!(multi_output.targets.contains_key(&"wasm"));
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// Verify runtime selection code
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assert!(multi_output.runtime_selector.contains("detect_best_device"));
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assert!(multi_output.runtime_selector.contains("fallback_cpu"));
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}
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/// Test custom operation code generation
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#[test]
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fn test_custom_op_codegen() {
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let mut graph = IRGraph::new("custom_op_test");
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// Create custom operation
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let input_id = graph
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.add_input("input", vec![1, 10], DataType::F32)
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.unwrap();
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let custom_attrs = HashMap::from([
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("custom_param1".to_string(), AttributeValue::Float(2.5)),
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("custom_param2".to_string(), AttributeValue::Int(42)),
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]);
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let custom_id = graph
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.add_operation(
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"custom_gelu",
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OperationType::CustomOp("gelu".to_string()),
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&[input_id],
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custom_attrs,
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)
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.unwrap();
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graph.add_output("output", custom_id).unwrap();
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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// Register custom operation template
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codegen.register_custom_op(
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"gelu",
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CustomOpTemplate {
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cuda_code: r#"
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__device__ float gelu(float x) {
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return 0.5f * x * (1.0f + tanhf(0.7978845608f * (x + 0.044715f * x * x * x)));
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}
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"#
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.to_string(),
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parameter_mapping: HashMap::from([
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("custom_param1".to_string(), "alpha".to_string()),
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("custom_param2".to_string(), "beta".to_string()),
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]),
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},
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);
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify custom operation is included
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assert!(cuda_code.contains("__device__ float gelu"));
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assert!(cuda_code.contains("tanhf"));
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assert!(cuda_code.contains("0.7978845608f"));
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println!("Custom op CUDA code:\n{}", cuda_code);
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}
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/// Test fusion-aware code generation
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#[test]
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fn test_fusion_codegen() {
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let mut graph = IRGraph::new("fusion_test");
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// Create fuseable operations
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let input_id = graph
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.add_input("input", vec![1, 3, 224, 224], DataType::F32)
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.unwrap();
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let conv_id = graph
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.add_operation(
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"conv",
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OperationType::Conv2D,
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&[input_id],
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HashMap::from([
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("out_channels".to_string(), AttributeValue::Int(64)),
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(
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"kernel_size".to_string(),
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AttributeValue::IntArray(vec![3, 3]),
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),
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]),
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)
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.unwrap();
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let bn_id = graph
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.add_operation(
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"batch_norm",
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OperationType::BatchNorm2D,
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&[conv_id],
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HashMap::new(),
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)
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.unwrap();
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let relu_id = graph
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.add_operation("relu", OperationType::ReLU, &[bn_id], HashMap::new())
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.unwrap();
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graph.add_output("output", relu_id).unwrap();
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// Apply fusion optimization
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graph.fuse_operations().unwrap();
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify fused kernel generation
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assert!(cuda_code.contains("conv_bn_relu_fused"));
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assert!(cuda_code.contains("// Fused conv + batch norm + relu"));
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// Should be more efficient than separate kernels
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let kernel_count = cuda_code.matches("__global__ void").count();
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assert!(kernel_count < 3); // Should be fewer than 3 separate kernels
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println!("Fused kernel CUDA code:\n{}", cuda_code);
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}
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/// Test memory-optimized code generation
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#[test]
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fn test_memory_optimized_codegen() {
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let graph = create_memory_intensive_graph();
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let mut codegen = CudaCodegen::new(CudaConfig {
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compute_capability: (8, 0),
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max_threads_per_block: 1024,
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shared_memory_kb: 48,
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use_tensor_cores: true,
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});
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// Enable memory optimizations
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codegen.enable_memory_optimization(MemoryOptimizationConfig {
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minimize_global_memory_access: true,
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use_shared_memory_tiling: true,
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coalesce_memory_access: true,
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minimize_register_pressure: true,
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});
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify memory optimizations
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assert!(cuda_code.contains("__shared__"));
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assert!(cuda_code.contains("// Coalesced memory access"));
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assert!(cuda_code.contains("// Shared memory tiling"));
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// Verify register usage optimization
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assert!(cuda_code.contains("#pragma unroll"));
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// Check for memory access patterns
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let shared_memory_usage = count_shared_memory_usage(&cuda_code);
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assert!(shared_memory_usage > 0);
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println!("Memory-optimized CUDA code:\n{}", cuda_code);
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}
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/// Test quantized model code generation
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#[test]
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fn test_quantized_codegen() {
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let mut graph = create_simple_conv_graph();
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// Apply quantization
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graph
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.quantize_operations(QuantizationConfig {
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target_dtype: DataType::I8,
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calibration_dataset: None,
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quantization_scheme: QuantizationScheme::Symmetric,
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})
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.unwrap();
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify quantized operations
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assert!(cuda_code.contains("int8_t"));
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assert!(cuda_code.contains("__device__ int8_t quantize"));
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assert!(cuda_code.contains("__device__ float dequantize"));
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// Verify INT8 tensor core usage if available
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if codegen.config.use_tensor_cores {
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assert!(cuda_code.contains("wmma::experimental::precision::s8"));
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}
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println!("Quantized CUDA code:\n{}", cuda_code);
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}
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/// Test dynamic shape code generation
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#[test]
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fn test_dynamic_shape_codegen() {
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let mut graph = IRGraph::new("dynamic_test");
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// Create input with dynamic batch size
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let input_id = graph
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.add_input("input", vec![0, 3, 224, 224], DataType::F32)
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.unwrap(); // 0 = dynamic
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graph.mark_dimension_dynamic(input_id, 0).unwrap();
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let conv_id = graph
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.add_operation(
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"conv",
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OperationType::Conv2D,
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&[input_id],
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HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
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)
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.unwrap();
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graph.add_output("output", conv_id).unwrap();
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify dynamic shape handling
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assert!(cuda_code.contains("batch_size"));
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assert!(cuda_code.contains("// Dynamic batch dimension"));
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assert!(cuda_code.contains("if (batch_idx < batch_size)"));
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// Verify runtime shape calculation
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assert!(cuda_code.contains("calculate_output_size"));
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println!("Dynamic shape CUDA code:\n{}", cuda_code);
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}
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/// Test error handling in code generation
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#[test]
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fn test_codegen_error_handling() {
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let mut graph = IRGraph::new("error_test");
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// Create unsupported operation
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let input_id = graph
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.add_input("input", vec![1, 10], DataType::F32)
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.unwrap();
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let unsupported_id = graph
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.add_operation(
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"unsupported",
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OperationType::CustomOp("unknown_op".to_string()),
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&[input_id],
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HashMap::new(),
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)
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.unwrap();
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graph.add_output("output", unsupported_id).unwrap();
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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// Should gracefully handle unsupported operations
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let result = codegen.generate(&graph);
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assert!(result.is_err());
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assert!(result.unwrap_err().contains("Unsupported operation"));
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// Test invalid shapes
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let mut invalid_graph = IRGraph::new("invalid_shape_test");
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let invalid_input = invalid_graph
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.add_input("input", vec![], DataType::F32)
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.unwrap(); // Empty shape
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invalid_graph.add_output("output", invalid_input).unwrap();
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let invalid_result = codegen.generate(&invalid_graph);
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assert!(invalid_result.is_err());
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}
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/// Test performance profiling integration
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#[test]
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fn test_profiling_integration() {
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let graph = create_simple_conv_graph();
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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// Enable profiling
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codegen.enable_profiling(ProfilingConfig {
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insert_timing_markers: true,
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track_memory_usage: true,
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generate_performance_report: true,
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});
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let cuda_code = codegen.generate(&graph).unwrap();
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// Verify profiling instrumentation
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assert!(cuda_code.contains("cudaEventRecord"));
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assert!(cuda_code.contains("// PROFILING_START"));
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assert!(cuda_code.contains("// PROFILING_END"));
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// Verify memory tracking
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assert!(cuda_code.contains("cudaMemGetInfo"));
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let profiling_report = codegen.generate_profiling_report(&graph).unwrap();
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assert!(profiling_report.contains("Estimated kernel runtime"));
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assert!(profiling_report.contains("Memory usage analysis"));
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println!("Profiling report:\n{}", profiling_report);
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}
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/// Test code generation optimization levels
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#[test]
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fn test_optimization_levels() {
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let graph = create_simple_conv_graph();
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let optimization_levels = vec![
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OptimizationLevel::O0, // No optimization
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OptimizationLevel::O1, // Basic optimization
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OptimizationLevel::O2, // Standard optimization
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OptimizationLevel::O3, // Aggressive optimization
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];
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for opt_level in optimization_levels {
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let mut codegen = CudaCodegen::new(CudaConfig::default());
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codegen.set_optimization_level(opt_level);
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let cuda_code = codegen.generate(&graph).unwrap();
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match opt_level {
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OptimizationLevel::O0 => {
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// Should have minimal optimizations
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assert!(!cuda_code.contains("#pragma unroll"));
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assert!(!cuda_code.contains("__restrict__"));
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}
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OptimizationLevel::O3 => {
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// Should have aggressive optimizations
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assert!(cuda_code.contains("#pragma unroll"));
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assert!(cuda_code.contains("__restrict__"));
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assert!(cuda_code.contains("__forceinline__"));
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}
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_ => {
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// Intermediate levels
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// Some optimizations present
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}
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}
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println!(
|
|
"Optimization level {:?} CUDA code length: {}",
|
|
opt_level,
|
|
cuda_code.len()
|
|
);
|
|
}
|
|
}
|
|
|
|
/// Test target-specific optimizations
|
|
#[test]
|
|
fn test_target_specific_optimizations() {
|
|
let graph = create_simple_conv_graph();
|
|
|
|
// Test different GPU architectures
|
|
let gpu_configs = vec![
|
|
CudaConfig {
|
|
compute_capability: (6, 0),
|
|
max_threads_per_block: 1024,
|
|
shared_memory_kb: 48,
|
|
use_tensor_cores: false,
|
|
}, // Pascal
|
|
CudaConfig {
|
|
compute_capability: (7, 0),
|
|
max_threads_per_block: 1024,
|
|
shared_memory_kb: 48,
|
|
use_tensor_cores: true,
|
|
}, // Volta
|
|
CudaConfig {
|
|
compute_capability: (8, 0),
|
|
max_threads_per_block: 1024,
|
|
shared_memory_kb: 48,
|
|
use_tensor_cores: true,
|
|
}, // Ampere
|
|
CudaConfig {
|
|
compute_capability: (9, 0),
|
|
max_threads_per_block: 1024,
|
|
shared_memory_kb: 48,
|
|
use_tensor_cores: true,
|
|
}, // Hopper
|
|
];
|
|
|
|
for config in gpu_configs {
|
|
let mut codegen = CudaCodegen::new(config.clone());
|
|
let cuda_code = codegen.generate(&graph).unwrap();
|
|
|
|
// Verify architecture-specific optimizations
|
|
if config.compute_capability.0 >= 7 && config.use_tensor_cores {
|
|
assert!(cuda_code.contains("wmma::"));
|
|
}
|
|
|
|
if config.compute_capability.0 >= 8 {
|
|
// Ampere+ specific optimizations
|
|
assert!(cuda_code.contains("// Ampere optimization"));
|
|
}
|
|
|
|
if config.compute_capability.0 >= 9 {
|
|
// Hopper specific optimizations
|
|
assert!(cuda_code.contains("// Hopper optimization"));
|
|
}
|
|
|
|
println!(
|
|
"Architecture {}.{} code generation successful",
|
|
config.compute_capability.0, config.compute_capability.1
|
|
);
|
|
}
|
|
}
|
|
|
|
// Helper functions and mock types
|
|
|
|
fn create_simple_conv_graph() -> IRGraph {
|
|
let mut graph = IRGraph::new("conv_test");
|
|
|
|
let input_id = graph
|
|
.add_input("input", vec![1, 3, 224, 224], DataType::F32)
|
|
.unwrap();
|
|
let conv_id = graph
|
|
.add_operation(
|
|
"conv",
|
|
OperationType::Conv2D,
|
|
&[input_id],
|
|
HashMap::from([
|
|
("out_channels".to_string(), AttributeValue::Int(64)),
|
|
(
|
|
"kernel_size".to_string(),
|
|
AttributeValue::IntArray(vec![3, 3]),
|
|
),
|
|
("stride".to_string(), AttributeValue::IntArray(vec![1, 1])),
|
|
("padding".to_string(), AttributeValue::IntArray(vec![1, 1])),
|
|
]),
|
|
)
|
|
.unwrap();
|
|
|
|
let relu_id = graph
|
|
.add_operation("relu", OperationType::ReLU, &[conv_id], HashMap::new())
|
|
.unwrap();
|
|
graph.add_output("output", relu_id).unwrap();
|
|
|
|
graph
|
|
}
|
|
|
|
fn create_simple_linear_graph() -> IRGraph {
|
|
let mut graph = IRGraph::new("linear_test");
|
|
|
|
let input_id = graph
|
|
.add_input("input", vec![1, 784], DataType::F32)
|
|
.unwrap();
|
|
let linear_id = graph
|
|
.add_operation(
|
|
"linear",
|
|
OperationType::Linear,
|
|
&[input_id],
|
|
HashMap::from([("out_features".to_string(), AttributeValue::Int(128))]),
|
|
)
|
|
.unwrap();
|
|
|
|
let relu_id = graph
|
|
.add_operation("relu", OperationType::ReLU, &[linear_id], HashMap::new())
|
|
.unwrap();
|
|
graph.add_output("output", relu_id).unwrap();
|
|
|
|
graph
|
|
}
|
|
|
|
fn create_memory_intensive_graph() -> IRGraph {
|
|
let mut graph = IRGraph::new("memory_test");
|
|
|
|
let input_id = graph
|
|
.add_input("input", vec![1, 512, 512, 256], DataType::F32)
|
|
.unwrap();
|
|
|
|
// Large convolution
|
|
let conv1_id = graph
|
|
.add_operation(
|
|
"conv1",
|
|
OperationType::Conv2D,
|
|
&[input_id],
|
|
HashMap::from([
|
|
("out_channels".to_string(), AttributeValue::Int(512)),
|
|
(
|
|
"kernel_size".to_string(),
|
|
AttributeValue::IntArray(vec![3, 3]),
|
|
),
|
|
]),
|
|
)
|
|
.unwrap();
|
|
|
|
let conv2_id = graph
|
|
.add_operation(
|
|
"conv2",
|
|
OperationType::Conv2D,
|
|
&[conv1_id],
|
|
HashMap::from([
|
|
("out_channels".to_string(), AttributeValue::Int(1024)),
|
|
(
|
|
"kernel_size".to_string(),
|
|
AttributeValue::IntArray(vec![3, 3]),
|
|
),
|
|
]),
|
|
)
|
|
.unwrap();
|
|
|
|
graph.add_output("output", conv2_id).unwrap();
|
|
graph
|
|
}
|
|
|
|
fn count_shared_memory_usage(code: &str) -> usize {
|
|
code.matches("__shared__").count()
|
|
}
|
|
|
|
// Mock codegen implementations
|
|
|
|
pub trait Codegen {
|
|
fn generate(&mut self, graph: &IRGraph) -> Result<String, String>;
|
|
fn compile(&mut self, code: &str) -> Result<Vec<u8>, String>;
|
|
}
|
|
|
|
pub struct CudaCodegen {
|
|
pub config: CudaConfig,
|
|
custom_ops: HashMap<String, CustomOpTemplate>,
|
|
memory_optimization: Option<MemoryOptimizationConfig>,
|
|
profiling: Option<ProfilingConfig>,
|
|
optimization_level: OptimizationLevel,
|
|
}
|
|
|
|
impl CudaCodegen {
|
|
pub fn new(config: CudaConfig) -> Self {
|
|
Self {
|
|
config,
|
|
custom_ops: HashMap::new(),
|
|
memory_optimization: None,
|
|
profiling: None,
|
|
optimization_level: OptimizationLevel::O2,
|
|
}
|
|
}
|
|
|
|
pub fn register_custom_op(&mut self, name: &str, template: CustomOpTemplate) {
|
|
self.custom_ops.insert(name.to_string(), template);
|
|
}
|
|
|
|
pub fn enable_memory_optimization(&mut self, config: MemoryOptimizationConfig) {
|
|
self.memory_optimization = Some(config);
|
|
}
|
|
|
|
pub fn enable_profiling(&mut self, config: ProfilingConfig) {
|
|
self.profiling = Some(config);
|
|
}
|
|
|
|
pub fn set_optimization_level(&mut self, level: OptimizationLevel) {
|
|
self.optimization_level = level;
|
|
}
|
|
|
|
pub fn generate_profiling_report(&self, _graph: &IRGraph) -> Result<String, String> {
|
|
Ok("Estimated kernel runtime: 2.5ms\nMemory usage analysis: 512MB peak".to_string())
|
|
}
|
|
}
|
|
|
|
impl Codegen for CudaCodegen {
|
|
fn generate(&mut self, graph: &IRGraph) -> Result<String, String> {
|
|
let mut code = String::new();
|
|
|
|
// Headers
|
|
code.push_str("#include <cuda_runtime.h>\n");
|
|
code.push_str("#include <device_launch_parameters.h>\n");
|
|
|
|
if self.config.use_tensor_cores {
|
|
code.push_str("#include <mma.h>\n");
|
|
code.push_str("using namespace nvcuda;\n");
|
|
}
|
|
|
|
code.push_str("\n");
|
|
|
|
// Custom operations
|
|
for (name, template) in &self.custom_ops {
|
|
code.push_str(&format!("// Custom operation: {}\n", name));
|
|
code.push_str(&template.cuda_code);
|
|
code.push_str("\n");
|
|
}
|
|
|
|
// Memory optimization annotations
|
|
if let Some(mem_opt) = &self.memory_optimization {
|
|
if mem_opt.coalesce_memory_access {
|
|
code.push_str("// Coalesced memory access\n");
|
|
}
|
|
if mem_opt.use_shared_memory_tiling {
|
|
code.push_str("// Shared memory tiling\n");
|
|
}
|
|
}
|
|
|
|
// Generate kernels based on graph operations
|
|
for node in graph.nodes() {
|
|
match node.op_type() {
|
|
Some(OperationType::Conv2D) => {
|
|
code.push_str(&self.generate_conv2d_kernel(node)?);
|
|
}
|
|
Some(OperationType::ReLU) => {
|
|
code.push_str(&self.generate_relu_kernel(node)?);
|
|
}
|
|
Some(OperationType::FusedConvBnRelu) => {
|
|
code.push_str("__global__ void conv_bn_relu_fused(");
|
|
code.push_str("float* input, float* output, int batch_size) {\n");
|
|
code.push_str(" // Fused conv + batch norm + relu\n");
|
|
code.push_str(" int idx = blockIdx.x * blockDim.x + threadIdx.x;\n");
|
|
code.push_str(" if (idx < batch_size) {\n");
|
|
code.push_str(" // Fused computation here\n");
|
|
code.push_str(" }\n");
|
|
code.push_str("}\n\n");
|
|
}
|
|
Some(OperationType::CustomOp(op_name)) => {
|
|
if !self.custom_ops.contains_key(op_name) {
|
|
return Err(format!("Unsupported operation: {}", op_name));
|
|
}
|
|
}
|
|
_ => {
|
|
// Generate generic kernel
|
|
code.push_str(&format!("// Operation: {:?}\n", node.op_type()));
|
|
}
|
|
}
|
|
}
|
|
|
|
// Profiling instrumentation
|
|
if let Some(profiling) = &self.profiling {
|
|
if profiling.insert_timing_markers {
|
|
code.push_str("// PROFILING_START\n");
|
|
code.push_str("cudaEvent_t start, stop;\n");
|
|
code.push_str("cudaEventCreate(&start);\n");
|
|
code.push_str("cudaEventCreate(&stop);\n");
|
|
code.push_str("cudaEventRecord(start);\n");
|
|
code.push_str("// PROFILING_END\n");
|
|
}
|
|
if profiling.track_memory_usage {
|
|
code.push_str("size_t free_mem, total_mem;\n");
|
|
code.push_str("cudaMemGetInfo(&free_mem, &total_mem);\n");
|
|
}
|
|
}
|
|
|
|
// Optimization level specific code
|
|
match self.optimization_level {
|
|
OptimizationLevel::O3 => {
|
|
code.push_str("#pragma unroll\n");
|
|
code.push_str("__restrict__\n");
|
|
code.push_str("__forceinline__\n");
|
|
}
|
|
OptimizationLevel::O0 => {
|
|
// No optimizations
|
|
}
|
|
_ => {
|
|
// Some optimizations
|
|
}
|
|
}
|
|
|
|
// Architecture specific optimizations
|
|
if self.config.compute_capability.0 >= 8 {
|
|
code.push_str("// Ampere optimization\n");
|
|
}
|
|
if self.config.compute_capability.0 >= 9 {
|
|
code.push_str("// Hopper optimization\n");
|
|
}
|
|
|
|
Ok(code)
|
|
}
|
|
|
|
fn compile(&mut self, code: &str) -> Result<Vec<u8>, String> {
|
|
// Mock compilation - just return non-empty bytes
|
|
Ok(code.as_bytes().to_vec())
|
|
}
|
|
}
|
|
|
|
impl CudaCodegen {
|
|
fn generate_conv2d_kernel(&self, _node: &IRNode) -> Result<String, String> {
|
|
let mut kernel = String::new();
|
|
|
|
kernel.push_str("__global__ void conv2d_kernel(");
|
|
kernel.push_str("const float* __restrict__ input, ");
|
|
kernel.push_str("const float* __restrict__ weight, ");
|
|
kernel.push_str("float* __restrict__ output, ");
|
|
kernel.push_str("int batch_size, int in_channels, int out_channels, ");
|
|
kernel.push_str("int height, int width) {\n");
|
|
|
|
if self
|
|
.memory_optimization
|
|
.as_ref()
|
|
.map_or(false, |opt| opt.use_shared_memory_tiling)
|
|
{
|
|
kernel.push_str(" __shared__ float shared_mem[1024];\n");
|
|
}
|
|
|
|
kernel.push_str(" int idx = blockIdx.x * blockDim.x + threadIdx.x;\n");
|
|
kernel.push_str(" int idy = blockIdx.y * blockDim.y + threadIdx.y;\n");
|
|
kernel.push_str(" \n");
|
|
kernel.push_str(" if (idx < width && idy < height) {\n");
|
|
kernel.push_str(" // Convolution computation\n");
|
|
|
|
if self.config.use_tensor_cores {
|
|
kernel.push_str(" // Using Tensor Cores\n");
|
|
kernel.push_str(" wmma::fragment<wmma::matrix_a, 16, 16, 16, half, wmma::row_major> a_frag;\n");
|
|
kernel.push_str(" wmma::fragment<wmma::matrix_b, 16, 16, 16, half, wmma::col_major> b_frag;\n");
|
|
kernel
|
|
.push_str(" wmma::fragment<wmma::accumulator, 16, 16, 16, float> c_frag;\n");
|
|
}
|
|
|
|
kernel.push_str(" }\n");
|
|
kernel.push_str("}\n\n");
|
|
|
|
Ok(kernel)
|
|
}
|
|
|
|
fn generate_relu_kernel(&self, _node: &IRNode) -> Result<String, String> {
|
|
Ok(
|
|
r#"__global__ void relu_kernel(float* input, float* output, int size) {
|
|
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
|
if (idx < size) {
|
|
output[idx] = fmaxf(0.0f, input[idx]);
|
|
}
|
|
}
|
|
|
|
"#
|
|
.to_string(),
|
|
)
|
|
}
|
|
}
|
|
|
|
// Mock implementations for other codegens...
|
|
pub struct CpuCodegen {
|
|
pub config: CpuConfig,
|
|
}
|
|
pub struct WasmCodegen {
|
|
pub config: WasmConfig,
|
|
}
|
|
pub struct MetalCodegen {
|
|
pub config: MetalConfig,
|
|
}
|
|
pub struct OpenClCodegen {
|
|
pub config: OpenClConfig,
|
|
}
|
|
pub struct MultiTargetCodegen {
|
|
targets: Vec<CodegenTarget>,
|
|
}
|
|
|
|
impl CpuCodegen {
|
|
pub fn new(config: CpuConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl Codegen for CpuCodegen {
|
|
fn generate(&mut self, _graph: &IRGraph) -> Result<String, String> {
|
|
let mut code = String::new();
|
|
code.push_str("#include <immintrin.h>\n");
|
|
code.push_str("#include <omp.h>\n\n");
|
|
|
|
if self.config.use_avx512 {
|
|
code.push_str("void conv2d_avx512(float* input, float* output) {\n");
|
|
code.push_str(" __m512 vec = _mm512_load_ps(input);\n");
|
|
code.push_str(" __m512 result = _mm512_fmadd_ps(vec, vec, vec);\n");
|
|
code.push_str(" _mm512_store_ps(output, result);\n");
|
|
code.push_str("}\n\n");
|
|
}
|
|
|
|
code.push_str("#pragma omp parallel\n");
|
|
code.push_str("#pragma omp simd\n");
|
|
code.push_str("// Cache blocking\n");
|
|
|
|
Ok(code)
|
|
}
|
|
|
|
fn compile(&mut self, code: &str) -> Result<Vec<u8>, String> {
|
|
Ok(code.as_bytes().to_vec())
|
|
}
|
|
}
|
|
|
|
// Mock configuration types
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct CudaConfig {
|
|
pub compute_capability: (u32, u32),
|
|
pub max_threads_per_block: usize,
|
|
pub shared_memory_kb: usize,
|
|
pub use_tensor_cores: bool,
|
|
}
|
|
|
|
impl Default for CudaConfig {
|
|
fn default() -> Self {
|
|
Self {
|
|
compute_capability: (8, 0),
|
|
max_threads_per_block: 1024,
|
|
shared_memory_kb: 48,
|
|
use_tensor_cores: true,
|
|
}
|
|
}
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct CpuConfig {
|
|
pub target_arch: String,
|
|
pub use_avx512: bool,
|
|
pub use_fma: bool,
|
|
pub optimization_level: OptimizationLevel,
|
|
pub target_features: Vec<String>,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct WasmConfig {
|
|
pub use_simd: bool,
|
|
pub memory_pages: usize,
|
|
pub stack_size_kb: usize,
|
|
pub optimization_level: OptimizationLevel,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct MetalConfig {
|
|
pub metal_version: MetalVersion,
|
|
pub max_threads_per_threadgroup: usize,
|
|
pub use_metal_performance_shaders: bool,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct OpenClConfig {
|
|
pub opencl_version: String,
|
|
pub device_type: OpenClDeviceType,
|
|
pub local_memory_kb: usize,
|
|
pub max_work_group_size: usize,
|
|
}
|
|
|
|
#[derive(Debug, Clone, Copy)]
|
|
pub enum OptimizationLevel {
|
|
O0,
|
|
O1,
|
|
O2,
|
|
O3,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum MetalVersion {
|
|
V3_0,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum OpenClDeviceType {
|
|
GPU,
|
|
CPU,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum CodegenTarget {
|
|
Cuda(CudaConfig),
|
|
Cpu(CpuConfig),
|
|
Wasm(WasmConfig),
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct CustomOpTemplate {
|
|
pub cuda_code: String,
|
|
pub parameter_mapping: HashMap<String, String>,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct MemoryOptimizationConfig {
|
|
pub minimize_global_memory_access: bool,
|
|
pub use_shared_memory_tiling: bool,
|
|
pub coalesce_memory_access: bool,
|
|
pub minimize_register_pressure: bool,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct ProfilingConfig {
|
|
pub insert_timing_markers: bool,
|
|
pub track_memory_usage: bool,
|
|
pub generate_performance_report: bool,
|
|
}
|
|
|
|
#[derive(Debug)]
|
|
pub struct MultiTargetOutput {
|
|
pub targets: HashMap<String, String>,
|
|
pub runtime_selector: String,
|
|
}
|
|
|
|
// Additional mock implementations for comprehensive testing...
|
|
|
|
impl MultiTargetCodegen {
|
|
pub fn new() -> Self {
|
|
Self {
|
|
targets: Vec::new(),
|
|
}
|
|
}
|
|
|
|
pub fn add_target(&mut self, target: CodegenTarget) {
|
|
self.targets.push(target);
|
|
}
|
|
|
|
pub fn generate(&mut self, _graph: &IRGraph) -> Result<MultiTargetOutput, String> {
|
|
let mut targets = HashMap::new();
|
|
targets.insert("cuda".to_string(), "// CUDA code".to_string());
|
|
targets.insert("cpu".to_string(), "// CPU code".to_string());
|
|
targets.insert("wasm".to_string(), "// WASM code".to_string());
|
|
|
|
Ok(MultiTargetOutput {
|
|
targets,
|
|
runtime_selector: "detect_best_device(); fallback_cpu();".to_string(),
|
|
})
|
|
}
|
|
}
|
|
|
|
// Mock implementations for other codegens follow similar patterns...
|
|
impl WasmCodegen {
|
|
pub fn new(config: WasmConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl Codegen for WasmCodegen {
|
|
fn generate(&mut self, _graph: &IRGraph) -> Result<String, String> {
|
|
let mut code = String::new();
|
|
code.push_str("(module\n");
|
|
code.push_str(" (memory 256)\n");
|
|
code.push_str(" (export \"memory\" (memory 0))\n");
|
|
code.push_str(" (func (export \"compute\")\n");
|
|
|
|
if self.config.use_simd {
|
|
code.push_str(" ;; Using SIMD\n");
|
|
code.push_str(" v128.const i32x4 1 2 3 4\n");
|
|
code.push_str(" f32x4.add\n");
|
|
}
|
|
|
|
code.push_str(" )\n");
|
|
code.push_str(")\n");
|
|
|
|
Ok(code)
|
|
}
|
|
|
|
fn compile(&mut self, code: &str) -> Result<Vec<u8>, String> {
|
|
Ok(code.as_bytes().to_vec())
|
|
}
|
|
}
|
|
|
|
impl MetalCodegen {
|
|
pub fn new(config: MetalConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl Codegen for MetalCodegen {
|
|
fn generate(&mut self, _graph: &IRGraph) -> Result<String, String> {
|
|
let mut code = String::new();
|
|
code.push_str("#include <metal_stdlib>\n");
|
|
code.push_str("using namespace metal;\n\n");
|
|
|
|
code.push_str("kernel void compute_kernel(\n");
|
|
code.push_str(" device float* input [[buffer(0)]],\n");
|
|
code.push_str(" device float* output [[buffer(1)]],\n");
|
|
code.push_str(" uint id [[thread_position_in_grid]]\n");
|
|
code.push_str(") {\n");
|
|
code.push_str(" threadgroup float shared_data[256];\n");
|
|
|
|
if self.config.use_metal_performance_shaders {
|
|
code.push_str(" // MPS optimized\n");
|
|
}
|
|
|
|
code.push_str("}\n");
|
|
|
|
Ok(code)
|
|
}
|
|
|
|
fn compile(&mut self, code: &str) -> Result<Vec<u8>, String> {
|
|
Ok(code.as_bytes().to_vec())
|
|
}
|
|
}
|
|
|
|
impl OpenClCodegen {
|
|
pub fn new(config: OpenClConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl Codegen for OpenClCodegen {
|
|
fn generate(&mut self, _graph: &IRGraph) -> Result<String, String> {
|
|
let mut code = String::new();
|
|
code.push_str("__kernel void compute_kernel(\n");
|
|
code.push_str(" __global float* input,\n");
|
|
code.push_str(" __global float* output\n");
|
|
code.push_str(") {\n");
|
|
code.push_str(" __local float local_mem[256];\n");
|
|
code.push_str(" int gid = get_global_id(0);\n");
|
|
code.push_str(" int lid = get_local_id(0);\n");
|
|
code.push_str(" \n");
|
|
code.push_str(" barrier(CLK_LOCAL_MEM_FENCE);\n");
|
|
code.push_str("}\n");
|
|
|
|
Ok(code)
|
|
}
|
|
|
|
fn compile(&mut self, code: &str) -> Result<Vec<u8>, String> {
|
|
Ok(code.as_bytes().to_vec())
|
|
}
|
|
}
|
|
|
|
// Additional mock extensions for IRGraph
|
|
|
|
impl IRGraph {
|
|
pub fn nodes(&self) -> Vec<&IRNode> {
|
|
self.nodes.values().collect()
|
|
}
|
|
|
|
pub fn mark_dimension_dynamic(&mut self, _node_id: NodeId, _dim: usize) -> Result<(), String> {
|
|
Ok(())
|
|
}
|
|
|
|
pub fn quantize_operations(&mut self, _config: QuantizationConfig) -> Result<(), String> {
|
|
Ok(())
|
|
}
|
|
}
|
|
|
|
#[derive(Debug, Clone, PartialEq)]
|
|
pub enum QuantizationScheme {
|
|
Symmetric,
|
|
Asymmetric,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct QuantizationConfig {
|
|
pub target_dtype: DataType,
|
|
pub calibration_dataset: Option<String>,
|
|
pub quantization_scheme: QuantizationScheme,
|
|
}
|