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/kernels/test_gate.py

@@ -0,0 +1,20 @@
+"""The vendored FLA fused gate must match the PyTorch reference."""
+import pytest
+import torch
+
+from kda.ops.reference.gate import kda_gate_reference
+from kda.ops.triton.gate import kda_gate_fwd
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+
+
+def test_triton_gate_matches_reference():
+    B, T, HV, K = 1, 32, 4, 8
+    torch.manual_seed(10)
+    device = "cuda"
+    g = torch.randn(B, T, HV, K, device=device, dtype=torch.float32)
+    A_log = torch.randn(HV, device=device, dtype=torch.float32) * 0.5
+    dt_bias = torch.randn(HV, K, device=device, dtype=torch.float32) * 0.1
+    expected = kda_gate_reference(g, A_log, dt_bias)
+    actual = kda_gate_fwd(g, A_log, dt_bias, lower_bound=None)
+    torch.testing.assert_close(actual, expected, rtol=1e-4, atol=1e-4)