Initial K3 snapshot: 0.5B KDA/MLA/MoE train path

Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias
backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests.

kda/training/train_tokenizer.py

@@ -0,0 +1,51 @@
+"""Train a SentencePiece tokenizer on a Chinese Wikipedia subset.
+
+用法:
+    uv run python kda/training/train_tokenizer.py \
+        --out data/spm_4k --vocab-size 4096 --limit 20000
+
+产出:
+    data/spm_4k.model / data/spm_4k.vocab  (BPE/unigram, 中文小语料)
+"""
+from __future__ import annotations
+
+import argparse
+
+import sentencepiece as spm
+
+from .data import fetch_wiki_texts
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--out", default="data/spm_4k", help="输出前缀 (model/vocab 文件)")
+    p.add_argument("--vocab-size", type=int, default=8192)
+    p.add_argument("--limit", type=int, default=20000, help="用于训练的 wiki 文章数")
+    p.add_argument("--model-type", default="unigram", choices=["unigram", "bpe"])
+    p.add_argument("--character-coverage", type=float, default=0.9995)
+    args = p.parse_args()
+
+    texts = fetch_wiki_texts(args.limit)
+    corpus = "".join(texts)
+    tmp = args.out + ".corpus.txt"
+    with open(tmp, "w", encoding="utf-8") as f:
+        f.write(corpus)
+    print(f"corpus: {len(corpus):,} chars from {len(texts)} articles")
+
+    spm.SentencePieceTrainer.train(
+        input=tmp,
+        model_prefix=args.out,
+        vocab_size=args.vocab_size,
+        model_type=args.model_type,
+        character_coverage=args.character_coverage,
+        unk_id=0,
+        pad_id=1,
+        bos_id=-1,
+        eos_id=-1,
+        num_threads=4,
+    )
+    print(f"tokenizer saved: {args.out}.model / {args.out}.vocab")
+
+
+if __name__ == "__main__":
+    main()