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llama-bench: add --n-cpu-moe support (#15952)
* llama-bench: add --n-cpu-moe support Support --n-cpu-moe in llama-bench the same way it is supported by llama-server.
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@@ -2548,7 +2548,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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{"--cpu-moe", "-cmoe"},
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"keep all Mixture of Experts (MoE) weights in the CPU",
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[](common_params & params) {
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params.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
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params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
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}
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).set_env("LLAMA_ARG_CPU_MOE"));
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add_opt(common_arg(
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@@ -2561,7 +2561,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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for (int i = 0; i < value; ++i) {
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// keep strings alive and avoid leaking memory by storing them in a static vector
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static std::list<std::string> buft_overrides;
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buft_overrides.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
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buft_overrides.push_back(llm_ffn_exps_block_regex(i));
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params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
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}
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}
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@@ -2570,7 +2570,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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{"--cpu-moe-draft", "-cmoed"},
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"keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
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[](common_params & params) {
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params.speculative.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
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params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
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}
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).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
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add_opt(common_arg(
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@@ -2582,7 +2582,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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}
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for (int i = 0; i < value; ++i) {
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static std::list<std::string> buft_overrides_draft;
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buft_overrides_draft.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
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buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
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params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
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}
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}
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@@ -734,6 +734,20 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
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}
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//
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// MoE utils
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//
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const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_exps";
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static std::string llm_ffn_exps_block_regex(int idx) {
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return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);
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}
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static llama_model_tensor_buft_override llm_ffn_exps_cpu_override() {
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return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() };
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}
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//
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// training utils
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//
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