Files
foxhunt/crates/ml-core/build.rs
jgrusewski 07d0e60fe4 feat(bf16): remove nvrtc from entire workspace + wire ml-core precompiled cubins
- 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>
2026-03-28 10:11:46 +01:00

164 lines
5.1 KiB
Rust

use std::path::{Path, PathBuf};
use std::process::Command;
fn main() {
println!("cargo:rerun-if-changed=build.rs");
// Only compile CUDA kernels when the cuda feature is enabled
if std::env::var("CARGO_FEATURE_CUDA").is_err() {
return;
}
let out_dir = PathBuf::from(std::env::var("OUT_DIR").unwrap());
let kernel_dir = Path::new("src/cuda_autograd");
// Common header lives in the ml crate — use relative path from ml-core
let common_header_path = Path::new("../../ml/src/cuda_pipeline/common_device_functions.cuh")
.canonicalize()
.unwrap_or_else(|_| {
// Fallback: try from workspace root
let workspace_root = std::env::var("CARGO_MANIFEST_DIR")
.map(PathBuf::from)
.unwrap_or_default();
workspace_root
.join("../ml/src/cuda_pipeline/common_device_functions.cuh")
.canonicalize()
.expect("Cannot find common_device_functions.cuh — is the ml crate present?")
});
println!("cargo:rerun-if-changed={}", common_header_path.display());
// Detect GPU architecture from env or default to sm_80
let cuda_compute_cap = std::env::var("CUDA_COMPUTE_CAP").unwrap_or_else(|_| "80".to_string());
let arch = format!("sm_{cuda_compute_cap}");
// Check if nvcc is available
let nvcc = match find_nvcc() {
Some(p) => p,
None => {
eprintln!(" warning: nvcc not found, skipping ml-core CUDA kernel precompilation");
eprintln!(" Install CUDA toolkit or set CUDA_HOME for GPU builds");
return;
}
};
// Read common header once
let common_src = std::fs::read_to_string(&common_header_path)
.unwrap_or_else(|e| panic!("Failed to read {}: {e}", common_header_path.display()));
// All kernels to precompile — all get common header prepended
let kernels = [
"activation_kernels.cu",
"elementwise_kernels.cu",
"linear_kernels.cu",
"loss_kernels.cu",
"reduction_kernels.cu",
"dropout_kernels.cu",
"layer_norm_kernels.cu",
"optimizer_kernels.cu",
];
let mut failed: Vec<&str> = Vec::new();
for kernel_name in &kernels {
if !try_compile_kernel(&nvcc, kernel_dir, kernel_name, &arch, &out_dir, &common_src) {
failed.push(kernel_name);
}
}
let passed = kernels.len() - failed.len();
eprintln!(
" ml-core: Precompiled {passed}/{} CUDA autograd kernels ({arch}) — all BF16",
kernels.len()
);
if !failed.is_empty() {
eprintln!(" FAILED: {}", failed.join(", "));
panic!(
"nvcc failed to compile {} ml-core kernel(s): {}",
failed.len(),
failed.join(", ")
);
}
}
/// Compile a single .cu kernel file to a .cubin via nvcc.
/// Common header is prepended to the kernel source.
fn try_compile_kernel(
nvcc: &Path,
kernel_dir: &Path,
kernel_name: &str,
arch: &str,
out_dir: &Path,
common_header: &str,
) -> bool {
let kernel_path = kernel_dir.join(kernel_name);
let cubin_name = kernel_name.replace(".cu", ".cubin");
let cubin_path = out_dir.join(&cubin_name);
println!("cargo:rerun-if-changed={}", kernel_path.display());
let kernel_src = std::fs::read_to_string(&kernel_path)
.unwrap_or_else(|e| panic!("Failed to read {}: {e}", kernel_path.display()));
// Compose source: common header + kernel
let full_source = format!("{common_header}\n{kernel_src}");
// Write composed source to temp file
let tmp_src = out_dir.join(format!("_{kernel_name}"));
std::fs::write(&tmp_src, &full_source).unwrap();
// Compile with nvcc
let status = Command::new(nvcc)
.args([
"-cubin",
&format!("-arch={arch}"),
"-O3",
"--use_fast_math",
"--ftz=true",
"--fmad=true",
"-o",
cubin_path.to_str().unwrap(),
tmp_src.to_str().unwrap(),
])
.status();
match status {
Ok(s) if s.success() => {
eprintln!(" Compiled {kernel_name} -> {cubin_name} ({arch})");
true
}
Ok(s) => {
eprintln!(
" FAILED: {kernel_name} (exit={})",
s.code().unwrap_or(-1)
);
false
}
Err(e) => {
eprintln!(" FAILED: {kernel_name} (nvcc error: {e})");
false
}
}
}
/// Find nvcc: prefer $CUDA_HOME/bin/nvcc, then check PATH
fn find_nvcc() -> Option<PathBuf> {
if let Ok(home) = std::env::var("CUDA_HOME") {
let nvcc = PathBuf::from(home).join("bin/nvcc");
if nvcc.exists() {
return Some(nvcc);
}
}
for path in &["/usr/local/cuda/bin/nvcc", "/usr/bin/nvcc"] {
let p = PathBuf::from(path);
if p.exists() {
return Some(p);
}
}
match Command::new("nvcc").arg("--version").output() {
Ok(output) if output.status.success() => Some(PathBuf::from("nvcc")),
_ => None,
}
}