Three root causes of sporadic NaN during training: 1. --use_fast_math (nvcc) breaks IEEE 754 NaN semantics: fmaxf(NaN,x) returns NaN instead of x, isnan()/isinf() compile to false. Replaced with --ftz=true --fmad=true --prec-div=true --prec-sqrt=true across all 4 build.rs (ml, ml-dqn, ml-ppo, ml-core). 2. Cross-stream race: replay buffer wrote batch data on the device's original stream while the trainer read it on a forked stream. Fixed by passing the forked stream to the DQN agent via agent_device, so all GPU components share a single CUDA stream (zero sync overhead). 3. Rewards/dones stored as bf16 in replay buffer caused done=0xFFFF NaN. Converted entire rewards/dones pipeline to f32: experience collector, replay buffer storage, nstep kernel, loss/grad kernels. Also: - Removed fast_isnan/fast_isinf/fast_isfinite wrappers — standard isnan/isinf/isfinite work correctly without --use_fast_math - Updated dqn-smoketest.toml: lr=1e-4, epsilon=1e-8 (f32 Adam values) - Removed debug printfs from gather kernels - Added curiosity_weight to training profile system - Cleaned up smoke_params() inline overrides 11/11 smoke tests pass, 5/5 stress runs of 50-epoch test pass, 359/359 ml-dqn + 895/895 ml unit tests pass. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
168 lines
5.1 KiB
Rust
168 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");
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// Common header lives in the ml crate — use relative path from ml-dqn
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let manifest_dir = PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap());
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let common_header_path = manifest_dir
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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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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-dqn 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 get the common header (BF16 types + math wrappers)
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let bf16_kernels = [
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"rmsnorm_kernels.cu",
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"noisy_kernels.cu",
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"residual_kernels.cu",
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"cast_kernels.cu",
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"replay_buffer_kernels.cu",
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"seg_tree_kernel.cu",
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];
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let standalone_kernels: [&str; 0] = [];
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let mut failed: Vec<&str> = Vec::new();
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for kernel_name in &bf16_kernels {
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if !try_compile_kernel(&nvcc, kernel_dir, kernel_name, &arch, &out_dir, Some(&common_src)) {
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failed.push(kernel_name);
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}
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}
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for kernel_name in &standalone_kernels {
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if !try_compile_kernel(&nvcc, kernel_dir, kernel_name, &arch, &out_dir, None) {
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failed.push(kernel_name);
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}
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}
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let total = bf16_kernels.len() + standalone_kernels.len();
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let passed = total - failed.len();
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eprintln!(
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" ml-dqn: Precompiled {passed}/{total} CUDA kernels ({arch})",
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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-dqn 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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/// If common_header is Some, it 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: Option<&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: optional common header + kernel
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let full_source = match common_header {
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Some(hdr) => format!("{hdr}\n{kernel_src}"),
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None => kernel_src,
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};
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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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"--ftz=true",
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"--fmad=true",
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"--prec-div=true",
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"--prec-sqrt=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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