llama : add --n-cpu-ffn option (#26622)

* common : dedupe --n-cpu-moe / --spec-draft-n-cpu-moe override loops

* common : add --n-cpu-ffn to CPU-offload dense FFN weights of first N layers

* common : generalize llm_ffn_block_regex over the FFN regex, drop TODO

common/arg.cpp

@@ -2750,14 +2750,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             if (value < 0) {
                 throw std::invalid_argument("invalid value");
             }
-            for (int i = 0; i < value; ++i) {
+            llm_add_n_cpu_ffn_overrides(value, LLM_FFN_EXPS_REGEX, params.tensor_buft_overrides);
-                // 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));
-                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
-            }
         }
     ).set_env("LLAMA_ARG_N_CPU_MOE"));
+    add_opt(common_arg(
+        {"-ncffn", "--n-cpu-ffn"}, "N",
+        "keep the dense FFN weights of the first N layers in the CPU\n"
+        "(dense models; for MoE expert weights use --n-cpu-moe)",
+        [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
+            llm_add_n_cpu_ffn_overrides(value, LLM_FFN_DENSE_REGEX, params.tensor_buft_overrides);
+        }
+    ).set_env("LLAMA_ARG_N_CPU_FFN"));
     GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
@@ -4084,11 +4090,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             if (value < 0) {
                 throw std::invalid_argument("invalid value");
             }
-            for (int i = 0; i < value; ++i) {
+            llm_add_n_cpu_ffn_overrides(value, LLM_FFN_EXPS_REGEX, params.speculative.draft.tensor_buft_overrides);
-                static std::list<std::string> buft_overrides_draft;
-                buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
-                params.speculative.draft.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
-            }
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE"));
 

common/common.h

@@ -8,6 +8,7 @@
 #include "ggml.h"
 #include "llama.h"
 
+#include <list>
 #include <set>
 #include <sstream>
 #include <string>
@@ -1108,19 +1109,30 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 }
 
 //
-// MoE utils
+// FFN offload utils
 //
 
 const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps";
 
-inline std::string llm_ffn_exps_block_regex(int idx) {
+const char * const LLM_FFN_DENSE_REGEX = "\\.ffn_(up|down|gate)\\.";
-    return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);
+
+inline std::string llm_ffn_block_regex(int idx, const char * ffn_regex) {
+    return string_format("blk\\.%d%s", idx, ffn_regex);
 }
 
 inline llama_model_tensor_buft_override llm_ffn_exps_cpu_override() {
     return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() };
 }
 
+inline void llm_add_n_cpu_ffn_overrides(int n, const char * ffn_regex, std::vector<llama_model_tensor_buft_override> & overrides) {
+    // keep strings alive and avoid leaking memory by storing them in a static list
+    static std::list<std::string> buft_override_strings;
+    for (int i = 0; i < n; ++i) {
+        buft_override_strings.push_back(llm_ffn_block_regex(i, ffn_regex));
+        overrides.push_back({buft_override_strings.back().c_str(), ggml_backend_cpu_buffer_type()});
+    }
+}
+
 //
 // training utils
 //

tools/llama-bench/llama-bench.cpp

@@ -1254,7 +1254,7 @@ struct cmd_params_instance {
             merged.reserve(merged.size() + (size_t) n_cpu_moe + 1);
 
             for (int i = 0; i < n_cpu_moe; ++i) {
-                patterns.push_back(llm_ffn_exps_block_regex(i));
+                patterns.push_back(llm_ffn_block_regex(i, LLM_FFN_EXPS_REGEX));
                 merged.push_back({ patterns.back().c_str(),
                                 ggml_backend_cpu_buffer_type() });
             }