mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-03-14 07:48:16 +00:00
c77ec4b8b8
* Revive fused delta-net * Add command line argument for fused delta net * Simplify/improve CUDA delta-net * Add -fdn to llama-bench * More CUDA fused delta net optimizations * CPU optimizations * Much faster fused delta-net on the CPU It seems it is faster than the chunked implementation! * Change meaning of fdn from bool flag to threshold value * Use eps = 1e-6 * Give some nodes a name
59 lines
1.5 KiB
C++
59 lines
1.5 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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std::vector<std::string> devices;
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std::vector<std::string> devices_draft;
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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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bool fused_mmad;
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bool rope_cache;
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bool graph_reuse;
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bool k_cache_hadamard;
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bool split_mode_graph_scheduling;
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//bool split_mode_f16;
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bool scheduler_async;
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int fused_delta_net;
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int min_experts;
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float thresh_experts;
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bool mtp;
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enum ggml_type reduce_type;
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enum llama_pooling_type pooling_type;
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enum llama_mtp_op_type mtp_op_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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void * cuda_params;
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};
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