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.

tests/integration/test_pretrain_data.py

@@ -0,0 +1,53 @@
+import json
+import sys
+
+import torch
+
+from kda.training.data import (
+    chunk_ids,
+    fetch_wiki_texts,
+    interleave_balanced,
+    split_heldout,
+)
+
+
+def test_interleave_is_one_to_one_monolingual_blocks():
+    a = list(range(10))
+    b = list(range(100, 112))
+    out = interleave_balanced(a, b, block=4)
+    assert out == [0, 1, 2, 3, 100, 101, 102, 103, 4, 5, 6, 7, 104, 105, 106, 107]
+
+
+def test_split_heldout_keeps_at_least_one_train():
+    chunks = torch.arange(10).view(10, 1, 1)
+    train, held = split_heldout(chunks, frac=0.01, min_heldout=1)
+    assert train.size(0) == 9
+    assert held.size(0) == 1
+    empty_train, empty_held = split_heldout(chunks[:1], frac=0.5)
+    assert empty_train.size(0) == 1
+    assert empty_held.size(0) == 0
+
+
+def test_chunk_ids_drops_tail():
+    ids = list(range(10))
+    chunks = chunk_ids(ids, batch=2, seq_len=4)
+    assert chunks.shape == (1, 2, 4)
+
+
+def test_wiki_cache_roundtrip(tmp_path, monkeypatch):
+    cache = tmp_path / "pretrain"
+    cache.mkdir()
+    path = cache / "wiki-zh-n2-limit3.jsonl"
+    path.write_text(
+        "\n".join(json.dumps({"text": f"article {i}"}) for i in range(3)) + "\n",
+        encoding="utf-8",
+    )
+
+    def _boom(*_a, **_k):
+        raise AssertionError("must not hit the network")
+
+    fake = type(sys)("datasets")
+    fake.load_dataset = _boom
+    monkeypatch.setitem(sys.modules, "datasets", fake)
+    texts = fetch_wiki_texts(3, lang="zh", cache_dir=cache)
+    assert texts == ["article 0", "article 1", "article 2"]