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
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+15
-3
@@ -8,6 +8,7 @@
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#include "ggml.h"
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#include "llama.h"
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#include <list>
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#include <set>
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#include <sstream>
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#include <string>
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@@ -1108,19 +1109,30 @@ 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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// FFN offload utils
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//
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const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps";
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inline 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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const char * const LLM_FFN_DENSE_REGEX = "\\.ffn_(up|down|gate)\\.";
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inline std::string llm_ffn_block_regex(int idx, const char * ffn_regex) {
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return string_format("blk\\.%d%s", idx, ffn_regex);
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}
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inline 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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inline void llm_add_n_cpu_ffn_overrides(int n, const char * ffn_regex, std::vector<llama_model_tensor_buft_override> & overrides) {
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// keep strings alive and avoid leaking memory by storing them in a static list
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static std::list<std::string> buft_override_strings;
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for (int i = 0; i < n; ++i) {
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buft_override_strings.push_back(llm_ffn_block_regex(i, ffn_regex));
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overrides.push_back({buft_override_strings.back().c_str(), ggml_backend_cpu_buffer_type()});
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}
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}
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//
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// training utils
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//
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