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_sft_data.py

@@ -0,0 +1,52 @@
+from kda.training.data import IGNORE_INDEX, collate_sft, encode_sft_row, load_sft_rows
+from kda.training.prompts import instruction_prompt
+
+
+class _Tok:
+    vocab_size = 32
+
+    def encode(self, text: str) -> list[int]:
+        return [min((ord(c) % 30) + 1, 31) for c in text[:12]] or [1]
+
+    def decode(self, ids: list[int]) -> str:
+        return "x" * len(ids)
+
+
+def test_instruction_matches_eval_template():
+    assert instruction_prompt("你好", "en") == "Translate to English:\n你好"
+    assert instruction_prompt("Hello", "zh") == "Translate to Chinese:\nHello"
+
+
+def test_prompt_tokens_are_ignored():
+    tok = _Tok()
+    src, tgt = "ab", "cd"
+    ids, labels = encode_sft_row(tok, src, tgt, "en", max_len=64)
+    prompt_n = len(tok.encode(instruction_prompt(src, "en")))
+    assert labels[:prompt_n] == [IGNORE_INDEX] * prompt_n
+    assert all(v != IGNORE_INDEX for v in labels[prompt_n:])
+    assert ids[prompt_n:] == tok.encode(tgt)
+
+
+def test_collate_and_jsonl(tmp_path):
+    path = tmp_path / "tiny.jsonl"
+    path.write_text(
+        '{"src": "a", "tgt": "b", "target_lang": "en"}\n'
+        '{"src": "c", "tgt": "d", "target_lang": "zh"}\n',
+        encoding="utf-8",
+    )
+    rows = load_sft_rows(path)
+    assert len(rows) == 2
+    x, y = collate_sft(rows, _Tok(), max_len=32)
+    assert x.shape == y.shape
+    assert x.size(0) == 2
+    assert (y == IGNORE_INDEX).any()
+
+
+def test_toy_sft_file_parses():
+    from pathlib import Path
+
+    path = Path(__file__).resolve().parents[2] / "data" / "sft" / "toy.jsonl"
+    rows = load_sft_rows(path)
+    assert len(rows) >= 20
+    langs = {r["target_lang"] for r in rows}
+    assert langs == {"en", "zh"}