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/ops/recurrent/fused.py

@@ -0,0 +1,73 @@
+"""L6: FLA fused recurrent KDA decode with optional step cache."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import torch
+
+from kda._fla.ops.kda.fused_recurrent import fused_recurrent_kda as _fused_recurrent_kda
+
+
+@dataclass
+class KDAState:
+    """Mutable recurrent state cache: ``S`` is ``[B, HV, K, V]``."""
+
+    S: torch.Tensor
+    pos: int = 0
+
+    def reset(self):
+        self.S.zero_()
+        self.pos = 0
+
+
+def fused_recurrent_kda_step(
+    state: KDAState,
+    q_t: torch.Tensor,
+    k_t: torch.Tensor,
+    v_t: torch.Tensor,
+    g_t: torch.Tensor,
+    beta_t: torch.Tensor,
+    scale: float | None = None,
+):
+    """Single-token step. Inputs are ``[B, H|HV, ...]`` (no time dim)."""
+    o, ht = _fused_recurrent_kda(
+        q_t.unsqueeze(1),
+        k_t.unsqueeze(1),
+        v_t.unsqueeze(1),
+        g_t.unsqueeze(1),
+        beta_t.unsqueeze(1),
+        scale=scale,
+        initial_state=state.S,
+        output_final_state=True,
+    )
+    state.S = ht
+    state.pos += 1
+    return o.squeeze(1)
+
+
+def fused_recurrent_kda(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    g: torch.Tensor,
+    beta: torch.Tensor,
+    scale: float | None = None,
+    initial_state: torch.Tensor | None = None,
+    output_final_state: bool = False,
+    **kwargs,
+):
+    return _fused_recurrent_kda(
+        q,
+        k,
+        v,
+        g,
+        beta,
+        scale=scale,
+        initial_state=initial_state,
+        output_final_state=output_final_state,
+        **kwargs,
+    )
+
+
+__all__ = ["KDAState", "fused_recurrent_kda", "fused_recurrent_kda_step"]