data: gpt2 bpe via xserv-tokenizer + TinyStories corpus + lr schedule + grad clip

New xtrain-train crate scaffold. Data pipeline reuses xserv's from-scratch
GPT-2/Qwen BPE via a path-dep (../../../xserv/crates/xserv-tokenizer, resolves
on both ~/projects and dash5 /opt/wjh/projects): Corpus::load tokenizes the
corpus into one id stream and samples fixed-length (input, target) next-token
windows (LCG-seeded, reproducible). Trims a range-downloaded file to whole
stories (<|endoftext|> boundaries).

Also the host-only training math: LrSchedule (linear warmup + cosine decay)
and global L2 grad-norm + clip scale, each with a local unit test.

Corpus: data/tinystories-valid-3mb.txt — first ~3MB of TinyStories-valid
(fetched on dash5 via hf-mirror.com; HF direct unreachable). Substitution
noted: a real TinyStories subset, not the full set.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

crates/xtrain-train/build.rs

@@ -0,0 +1,26 @@
+use std::env;
+use std::path::Path;
+use std::process::Command;
+
+// Per-crate convention: the training loop / sampler / checkpoint all drive GPU
+// ops through the model + tensor layers, so the bulk of this crate is gated
+// behind `not(no_cuda)`. The LR schedule and the grad-clip *math* are host-only
+// and always compile. cfg does not propagate across crates, so re-detect nvcc.
+fn main() {
+    println!("cargo:rustc-check-cfg=cfg(no_cuda)");
+
+    let cuda_path = env::var("CUDA_HOME")
+        .or_else(|_| env::var("CUDA_PATH"))
+        .unwrap_or_else(|_| "/usr/local/cuda".to_string());
+
+    if !nvcc_available(&cuda_path) {
+        println!("cargo:rustc-cfg=no_cuda");
+    }
+}
+
+fn nvcc_available(cuda_path: &str) -> bool {
+    if Command::new("nvcc").arg("--version").output().is_ok() {
+        return true;
+    }
+    Path::new(&format!("{cuda_path}/bin/nvcc")).exists()
+}