vulkan/cuda: fix topk_moe with exp_probs_b (#18071)

I updated test_topk_moe to more closely match llm_graph_context::build_moe_ffn
and added coverage for exp_probs_b and some other missing combinations. This
exposed a bug in both CUDA and Vulkan backends where they were assuming the
input to argsort and the input to get_rows are the same. I'd like to optimize
this graph in another change, but for now just get it functional.

CUDA also had a bug where it got n_experts from the wrong place, leading to
GGML_ASSERT failures in some of the new tests.

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -12941,24 +12941,43 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
 
     const ggml_tensor * softmax;
     const ggml_tensor * weights;
+    const ggml_tensor * get_rows;
+    const ggml_tensor * argsort;
 
     switch (mode) {
     case TOPK_MOE_EARLY_SOFTMAX_NORM:
         softmax = cgraph->nodes[node_idx + 0];
         weights = cgraph->nodes[node_idx + 9];
+        get_rows = cgraph->nodes[node_idx + 4];
+        argsort = cgraph->nodes[node_idx + 2];
         break;
     case TOPK_MOE_EARLY_SOFTMAX:
         softmax = cgraph->nodes[node_idx + 0];
         weights = cgraph->nodes[node_idx + 4];
+        get_rows = cgraph->nodes[node_idx + 4];
+        argsort = cgraph->nodes[node_idx + 2];
         break;
     case TOPK_MOE_LATE_SOFTMAX:
         softmax = cgraph->nodes[node_idx + 4];
         weights = cgraph->nodes[node_idx + 5];
+        get_rows = cgraph->nodes[node_idx + 2];
+        argsort = cgraph->nodes[node_idx + 0];
         break;
     default:
         return false;
     }
 
+    ggml_tensor * probs = get_rows->src[0];
+    if (probs->op != GGML_OP_RESHAPE) {
+        return false;
+    }
+    probs = probs->src[0];
+    ggml_tensor * selection_probs = argsort->src[0];
+
+    if (probs != selection_probs) {
+        return false;
+    }
+
     const float * op_params = (const float *)softmax->op_params;
 
     float scale = op_params[0];