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/layers/swiglu.py

@@ -0,0 +1,20 @@
+"""SwiGLU FFN: x [B,T,D] -> y [B,T,D]."""
+from __future__ import annotations
+
+import torch.nn.functional as F
+from torch import nn
+
+
+class SwiGLUMLP(nn.Module):
+    def __init__(self, hidden_size: int, intermediate_size: int):
+        super().__init__()
+        self.w1 = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.w3 = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.w2 = nn.Linear(intermediate_size, hidden_size, bias=False)
+
+    @classmethod
+    def from_config(cls, config) -> SwiGLUMLP:
+        return cls(config.hidden_size, config.intermediate_size)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))