- Fork cudarc locally (vendor/cudarc): add CudaContext::load_cubin() that calls cuModuleLoadData directly — zero nvrtc dependency - Remove "nvrtc" feature from ml-core, ml-dqn, ml-ppo Cargo.toml - Replace all 89 Ptx::from_binary + load_module calls with load_cubin - ml-core cuda_autograd: wire 9 stub constructors to precompiled cubins (activation, elementwise, linear, loss, reduction, dropout, layer_norm, optimizer) - ml-core build.rs: compile 8 BF16-native CUDA kernels via nvcc - cubin_loader.rs: thin wrapper around CudaContext::load_cubin() - Fix size_of::<f32> in gpu_tensor.rs, stream_ops.rs, layer_norm.rs - Fix test data: Vec<f32> → Vec<half::bf16> for memcpy_htod - Stub ml-ppo/ml-dqn runtime compile_ptx calls (dead code) - backtest_metrics_kernel.cu: full native BF16 rewrite (no float) - backtest_env_kernel.cu: shared memory → __nv_bfloat16 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
164 lines
5.1 KiB
Rust
164 lines
5.1 KiB
Rust
use std::path::{Path, PathBuf};
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use std::process::Command;
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fn main() {
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println!("cargo:rerun-if-changed=build.rs");
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// Only compile CUDA kernels when the cuda feature is enabled
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if std::env::var("CARGO_FEATURE_CUDA").is_err() {
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return;
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}
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let out_dir = PathBuf::from(std::env::var("OUT_DIR").unwrap());
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let kernel_dir = Path::new("src/cuda_autograd");
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// Common header lives in the ml crate — use relative path from ml-core
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let common_header_path = Path::new("../../ml/src/cuda_pipeline/common_device_functions.cuh")
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.canonicalize()
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.unwrap_or_else(|_| {
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// Fallback: try from workspace root
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let workspace_root = std::env::var("CARGO_MANIFEST_DIR")
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.map(PathBuf::from)
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.unwrap_or_default();
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workspace_root
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.join("../ml/src/cuda_pipeline/common_device_functions.cuh")
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.canonicalize()
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.expect("Cannot find common_device_functions.cuh — is the ml crate present?")
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});
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println!("cargo:rerun-if-changed={}", common_header_path.display());
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// Detect GPU architecture from env or default to sm_80
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let cuda_compute_cap = std::env::var("CUDA_COMPUTE_CAP").unwrap_or_else(|_| "80".to_string());
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let arch = format!("sm_{cuda_compute_cap}");
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// Check if nvcc is available
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let nvcc = match find_nvcc() {
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Some(p) => p,
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None => {
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eprintln!(" warning: nvcc not found, skipping ml-core CUDA kernel precompilation");
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eprintln!(" Install CUDA toolkit or set CUDA_HOME for GPU builds");
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return;
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}
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};
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// Read common header once
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let common_src = std::fs::read_to_string(&common_header_path)
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.unwrap_or_else(|e| panic!("Failed to read {}: {e}", common_header_path.display()));
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// All kernels to precompile — all get common header prepended
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let kernels = [
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"activation_kernels.cu",
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"elementwise_kernels.cu",
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"linear_kernels.cu",
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"loss_kernels.cu",
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"reduction_kernels.cu",
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"dropout_kernels.cu",
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"layer_norm_kernels.cu",
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"optimizer_kernels.cu",
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];
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let mut failed: Vec<&str> = Vec::new();
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for kernel_name in &kernels {
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if !try_compile_kernel(&nvcc, kernel_dir, kernel_name, &arch, &out_dir, &common_src) {
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failed.push(kernel_name);
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}
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}
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let passed = kernels.len() - failed.len();
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eprintln!(
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" ml-core: Precompiled {passed}/{} CUDA autograd kernels ({arch}) — all BF16",
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kernels.len()
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);
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if !failed.is_empty() {
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eprintln!(" FAILED: {}", failed.join(", "));
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panic!(
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"nvcc failed to compile {} ml-core kernel(s): {}",
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failed.len(),
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failed.join(", ")
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);
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}
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}
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/// Compile a single .cu kernel file to a .cubin via nvcc.
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/// Common header is prepended to the kernel source.
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fn try_compile_kernel(
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nvcc: &Path,
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kernel_dir: &Path,
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kernel_name: &str,
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arch: &str,
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out_dir: &Path,
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common_header: &str,
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) -> bool {
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let kernel_path = kernel_dir.join(kernel_name);
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let cubin_name = kernel_name.replace(".cu", ".cubin");
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let cubin_path = out_dir.join(&cubin_name);
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println!("cargo:rerun-if-changed={}", kernel_path.display());
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let kernel_src = std::fs::read_to_string(&kernel_path)
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.unwrap_or_else(|e| panic!("Failed to read {}: {e}", kernel_path.display()));
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// Compose source: common header + kernel
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let full_source = format!("{common_header}\n{kernel_src}");
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// Write composed source to temp file
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let tmp_src = out_dir.join(format!("_{kernel_name}"));
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std::fs::write(&tmp_src, &full_source).unwrap();
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// Compile with nvcc
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let status = Command::new(nvcc)
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.args([
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"-cubin",
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&format!("-arch={arch}"),
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"-O3",
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"--use_fast_math",
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"--ftz=true",
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"--fmad=true",
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"-o",
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cubin_path.to_str().unwrap(),
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tmp_src.to_str().unwrap(),
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])
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.status();
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match status {
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Ok(s) if s.success() => {
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eprintln!(" Compiled {kernel_name} -> {cubin_name} ({arch})");
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true
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}
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Ok(s) => {
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eprintln!(
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" FAILED: {kernel_name} (exit={})",
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s.code().unwrap_or(-1)
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);
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false
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}
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Err(e) => {
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eprintln!(" FAILED: {kernel_name} (nvcc error: {e})");
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false
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}
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}
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}
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/// Find nvcc: prefer $CUDA_HOME/bin/nvcc, then check PATH
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fn find_nvcc() -> Option<PathBuf> {
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if let Ok(home) = std::env::var("CUDA_HOME") {
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let nvcc = PathBuf::from(home).join("bin/nvcc");
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if nvcc.exists() {
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return Some(nvcc);
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}
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}
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for path in &["/usr/local/cuda/bin/nvcc", "/usr/bin/nvcc"] {
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let p = PathBuf::from(path);
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if p.exists() {
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return Some(p);
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}
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}
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match Command::new("nvcc").arg("--version").output() {
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Ok(output) if output.status.success() => Some(PathBuf::from("nvcc")),
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_ => None,
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}
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}
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