mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
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dbfd151594
* Better argsort (CPU) * Attemt at grouped topk * This seems to do the trick for grouped experts routing * Cleanup * Trying to merge, something is not right * Working merged grouped top_k (CPU) * Add command line option to enable grouped expert routing * Add grouped expert routing option to llama-bench --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
44 lines
1.1 KiB
C++
44 lines
1.1 KiB
C++
#pragma once
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#include "llama-impl.h"
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#include <cstdint>
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struct llama_cparams {
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uint32_t n_ctx; // context size used during inference
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uint32_t n_batch;
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uint32_t n_ubatch;
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uint32_t n_seq_max;
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uint32_t n_threads; // number of threads to use for generation
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uint32_t n_threads_batch; // number of threads to use for batch processing
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float rope_freq_base;
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float rope_freq_scale;
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uint32_t n_ctx_orig_yarn;
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// These hyperparameters are not exposed in GGUF, because all
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// existing YaRN models use the same values for them.
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float yarn_ext_factor;
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float yarn_attn_factor;
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float yarn_beta_fast;
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float yarn_beta_slow;
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float defrag_thold;
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bool embeddings;
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bool causal_attn;
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bool offload_kqv;
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bool flash_attn;
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int mla_attn;
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int attn_max_batch;
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bool fused_moe_up_gate;
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bool grouped_expert_routing;
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bool fused_up_gate;
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int min_experts;
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float thresh_experts;
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enum llama_pooling_type pooling_type;
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ggml_backend_sched_eval_callback cb_eval;
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void * cb_eval_user_data;
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};
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