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llama : add --n-cpu-moe option (#15077)

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* llama : add --n-cpu-moe option

Keeps the MoE weights of the first N layers in the CPU
This commit is contained in:
Diego Devesa
2025-08-04 16:05:36 -07:00
committed by GitHub
parent 19f68fa5a4
commit ec428b02c3
1 changed files with 23 additions and 7 deletions
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30
common/arg.cpp
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@@ -24,6 +24,7 @@
#include <cstdarg>
#include <filesystem>
#include <fstream>
#include <list>
#include <regex>
#include <set>
#include <string>
@@ -2375,20 +2376,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
throw std::invalid_argument("unknown buffer type");
}
// FIXME: this leaks memory
params.tensor_buft_overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
// 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(tensor_name);
params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)});
}
}
));
add_opt(common_arg(
{"--cpu-moe"},
"use CPU for Mixture of Experts (MoE) weights",
{"--cpu-moe", "-cmoe"},
"keep all Mixture of Experts (MoE) weights in the CPU",
[](common_params & params) {
params.tensor_buft_overrides.push_back({"\\.ffn_up_exps\\.weight$", ggml_backend_cpu_buffer_type()});
params.tensor_buft_overrides.push_back({"\\.ffn_down_exps\\.weight$", ggml_backend_cpu_buffer_type()});
params.tensor_buft_overrides.push_back({"\\.ffn_gate_exps\\.weight$", ggml_backend_cpu_buffer_type()});
params.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
}
).set_env("LLAMA_ARG_CPU_MOE"));
add_opt(common_arg(
{"--n-cpu-moe", "-ncmoe"}, "N",
"keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
[](common_params & params, int value) {
if (value < 0) {
throw std::invalid_argument("invalid value");
}
for (int i = 0; i < value; ++i) {
// 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(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", 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(
{"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
"number of layers to store in VRAM",