common : fractional -ncmoe for tensor-granularity expert placement

Extends --n-cpu-moe to accept a fractional layer count. The integer part
offloads whole layers as before; the fractional part offloads a subset of the
boundary layer expert tensors (gate, then up), keeping down_proj resident.
This realizes ATSInfer tensor-granularity static placement, giving sub-layer
control over expert VRAM residency. Placement-only, lossless.

Assisted-by: Claude

common/arg.cpp

@@ -2606,15 +2606,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_CPU_MOE"));
     add_opt(common_arg(
         {"-ncmoe", "--n-cpu-moe"}, "N",
-        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
-        [](common_params & params, int value) {
-            if (value < 0) {
+        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU; "
+        "fractional N offloads part of the boundary layer at tensor granularity",
+        [](common_params & params, const std::string & value) {
+            const double n = std::stod(value);
+            if (n < 0) {
                 throw std::invalid_argument("invalid value");
             }
-            for (int i = 0; i < value; ++i) {
+            for (const std::string & re : llm_ffn_exps_cpu_block_regexes(n)) {
                 // keep strings alive and avoid leaking memory by storing them in a static vector
                 static std::list<std::string> buft_overrides;
-                buft_overrides.push_back(llm_ffn_exps_block_regex(i));
+                buft_overrides.push_back(re);
                 params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
             }
         }