Fix dense router: run GEMM in BF16, convert to FP32 only for activation

hidden_states.float() and gate_bf16.T.float() create new FP32 tensors
during CUDA graph capture, which is not graph-capturable.

Fix: run the linear in BF16 (Blackwell tensor cores handle BF16 natively),
then convert only the output logits to FP32 for numerical stability
in sqrt(softplus). The single logits.float() is graph-capturable
because it's a unary op with a pre-existing output buffer.

dsv4/kernels/router/dense_router_decode.py

@@ -97,7 +97,8 @@ def dense_router_dispatch_nvfp4_fused(
     # Decode the gate_weight from NVFP4 to BF16 for cuBLAS
     from dsv4.ops.quantize import dequantize_nvfp4
     gate_bf16 = dequantize_nvfp4(gate_weight, gate_weight_scale, gate_ws2)
-    logits = torch.nn.functional.linear(hidden_states.float(), gate_bf16.T.float())
+    logits = torch.nn.functional.linear(hidden_states, gate_bf16.T)
+    logits = logits.float()  # BF16 → FP32 for numerical stability in sqrt(softplus)
 
     run_fused_activation_topk(
         logits, e_bias, routed_scaling_factor, top_k,