llama: cv eval: move cb eval field in common gpt_params
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3 changed files with 13 additions and 15 deletions
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@ -1674,6 +1674,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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cparams.yarn_orig_ctx = params.yarn_orig_ctx;
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cparams.pooling_type = params.pooling_type;
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cparams.defrag_thold = params.defrag_thold;
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cparams.cb_eval = params.cb_eval;
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cparams.cb_eval_user_data = params.cb_eval_user_data;
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cparams.offload_kqv = !params.no_kv_offload;
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cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
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@ -80,6 +80,9 @@ struct gpt_params {
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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float defrag_thold = -1.0f; // KV cache defragmentation threshold
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ggml_backend_sched_eval_callback cb_eval;
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void * cb_eval_user_data;
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ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
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llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
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@ -596,24 +596,17 @@ int main(int argc, char ** argv) {
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llama_backend_init();
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llama_numa_init(params.numa);
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llama_model_params mparams = llama_model_params_from_gpt_params(params);
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llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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llama_context_params cparams = llama_context_params_from_gpt_params(params);
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// pass the callback to the backend scheduler
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// it will be executed for each node during the graph computation
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cparams.cb_eval = ik_collect_imatrix;
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cparams.cb_eval_user_data = NULL;
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params.cb_eval = ik_collect_imatrix;
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params.cb_eval_user_data = NULL;
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llama_context * ctx = llama_new_context_with_model(model, cparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: unable to create context\n", __func__);
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// init
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llama_model * model;
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llama_context * ctx;
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == nullptr || ctx == nullptr) {
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fprintf(stderr, "%s : failed to init\n", __func__);
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return 1;
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}
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