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_triton_fwd.py

@@ -0,0 +1,29 @@
+"""L3: vendored FLA Triton fwd vs naive chunked."""
+import pytest
+import torch
+import torch.nn.functional as F
+
+from kda.ops.reference.chunkwise import naive_chunk_kda
+from kda.ops.triton.chunk_fwd import chunk_kda_fwd
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+
+
+def test_triton_fwd_matches_naive():
+    B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32
+    torch.manual_seed(3)
+    device = "cuda"
+    q = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1)
+    k = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1)
+    v = torch.randn(B, T, HV, V, device=device)
+    g = -torch.rand(B, T, HV, K, device=device) * 2
+    beta = torch.rand(B, T, HV, device=device)
+
+    o_ref, S_ref = naive_chunk_kda(
+        q, k, v, g, beta, output_final_state=True, chunk_size=64
+    )
+    o_tr, S_tr = chunk_kda_fwd(
+        q, k, v, g, beta, output_final_state=True, chunk_size=64
+    )
+    torch.testing.assert_close(o_tr.float(), o_ref.float(), rtol=2e-3, atol=2e-3)
+    torch.testing.assert_close(S_tr.float(), S_ref.float(), rtol=2e-3, atol=2e-3)