mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-05-11 11:34:10 +00:00
994118a183
* git-friendly migration * add build_graph * nits * exclude old code from build * wip * add llm_arch_model_i * prepare downstream functions * nits * nits * wip * wip * add back create_tensor_qkv * fix files missing include * enforce one llm_build per arch * cmake: use glob * missing model params * nits * wip * wip (2) * wip (3) * test-llama-archs is happy * improve switch case * move more stuff into llm_arch_model_i * fix downstream code * nits * nits (2) * fix order * llama_model_base * LLAMA_LOAD_LOCALS * small fix * fix build errors * auto * rm migration script and ifdef
708 lines
25 KiB
C++
708 lines
25 KiB
C++
#pragma once
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#include "llama.h"
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#include "llama-arch.h"
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#include "llama-graph.h"
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#include "llama-hparams.h"
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#include "llama-memory.h"
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#include "llama-vocab.h"
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#include <map>
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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struct llama_cparams;
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struct llama_ubatch;
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struct llama_model_loader;
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// available models
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enum llm_type {
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LLM_TYPE_UNKNOWN,
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LLM_TYPE_14M,
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LLM_TYPE_17M,
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LLM_TYPE_22M,
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LLM_TYPE_33M,
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LLM_TYPE_47M,
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LLM_TYPE_60M,
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LLM_TYPE_70M,
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LLM_TYPE_80M,
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LLM_TYPE_109M,
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LLM_TYPE_137M,
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LLM_TYPE_140M,
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LLM_TYPE_149M,
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LLM_TYPE_160M,
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LLM_TYPE_190M,
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LLM_TYPE_220M,
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LLM_TYPE_250M,
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LLM_TYPE_256M,
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LLM_TYPE_270M,
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LLM_TYPE_335M,
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LLM_TYPE_350M,
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LLM_TYPE_360M,
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LLM_TYPE_395M,
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LLM_TYPE_410M,
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LLM_TYPE_450M,
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LLM_TYPE_475M,
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LLM_TYPE_558M,
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LLM_TYPE_700M,
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LLM_TYPE_770M,
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LLM_TYPE_780M,
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LLM_TYPE_950M,
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LLM_TYPE_0_3B,
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LLM_TYPE_0_5B,
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LLM_TYPE_0_6B,
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LLM_TYPE_0_8B,
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LLM_TYPE_1B,
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LLM_TYPE_1_2B,
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LLM_TYPE_1_3B,
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LLM_TYPE_1_4B,
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LLM_TYPE_1_5B,
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LLM_TYPE_1_6B,
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LLM_TYPE_1_7B,
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LLM_TYPE_1_8B,
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LLM_TYPE_2B,
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LLM_TYPE_2_6B,
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LLM_TYPE_2_8B,
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LLM_TYPE_2_9B,
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LLM_TYPE_3B,
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LLM_TYPE_4B,
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LLM_TYPE_6B,
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LLM_TYPE_6_9B,
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LLM_TYPE_7B,
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LLM_TYPE_8B,
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LLM_TYPE_9B,
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LLM_TYPE_11B,
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LLM_TYPE_12B,
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LLM_TYPE_13B,
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LLM_TYPE_14B,
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LLM_TYPE_15B,
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LLM_TYPE_16B,
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LLM_TYPE_20B,
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LLM_TYPE_26B,
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LLM_TYPE_27B,
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LLM_TYPE_30B,
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LLM_TYPE_31B,
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LLM_TYPE_32B,
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LLM_TYPE_34B,
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LLM_TYPE_35B,
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LLM_TYPE_36B,
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LLM_TYPE_40B,
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LLM_TYPE_65B,
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LLM_TYPE_70B,
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LLM_TYPE_120B,
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LLM_TYPE_142B,
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LLM_TYPE_236B,
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LLM_TYPE_290B,
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LLM_TYPE_314B,
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LLM_TYPE_405B,
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LLM_TYPE_671B,
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LLM_TYPE_SMALL,
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LLM_TYPE_MEDIUM,
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LLM_TYPE_LARGE,
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LLM_TYPE_XL,
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LLM_TYPE_A1_7B,
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LLM_TYPE_A2_7B,
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LLM_TYPE_8x7B,
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LLM_TYPE_8x22B,
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LLM_TYPE_16x12B,
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LLM_TYPE_16x3_8B,
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LLM_TYPE_10B_128x3_66B,
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LLM_TYPE_57B_A14B,
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LLM_TYPE_17B_16E, // llama4 Scout
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LLM_TYPE_17B_128E, // llama4 Maverick
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LLM_TYPE_A13B,
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LLM_TYPE_7B_A1B,
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LLM_TYPE_8B_A1B, // lfm2moe
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LLM_TYPE_16B_A1B,
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LLM_TYPE_21B_A3B, // Ernie MoE small
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LLM_TYPE_24B_A2B, // lfm2moe
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LLM_TYPE_26B_A4B, // Gemma4
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LLM_TYPE_30B_A3B,
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LLM_TYPE_31B_A3_5B,
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LLM_TYPE_35B_A3B, // Qwen3.5
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LLM_TYPE_48B_A3B, // Kimi Linear
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LLM_TYPE_80B_A3B, // Qwen3 Next
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LLM_TYPE_100B_A6B,
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LLM_TYPE_102B_A12B, // Solar-Open
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LLM_TYPE_106B_A12B, // GLM-4.5-Air
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LLM_TYPE_120B_A12B, // Nemotron 3 Super
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LLM_TYPE_122B_A10B, // Qwen3.5
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LLM_TYPE_196B_A11B, // Step3.5-Flash
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LLM_TYPE_230B_A10B, // Minimax M2
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LLM_TYPE_235B_A22B,
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LLM_TYPE_300B_A47B, // Ernie MoE big
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LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
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LLM_TYPE_355B_A32B, // GLM-4.5
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LLM_TYPE_397B_A17B, // Qwen3.5
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LLM_TYPE_744B_A40B, // GLM-5
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LLM_TYPE_E2B,
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LLM_TYPE_E4B,
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};
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std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type);
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struct llama_layer_posnet {
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// resnet
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struct ggml_tensor * norm1 = nullptr;
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struct ggml_tensor * norm1_b = nullptr;
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struct ggml_tensor * conv1 = nullptr;
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struct ggml_tensor * conv1_b = nullptr;
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struct ggml_tensor * norm2 = nullptr;
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struct ggml_tensor * norm2_b = nullptr;
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struct ggml_tensor * conv2 = nullptr;
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struct ggml_tensor * conv2_b = nullptr;
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// attention
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struct ggml_tensor * attn_norm = nullptr;
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struct ggml_tensor * attn_norm_b = nullptr;
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struct ggml_tensor * attn_q = nullptr;
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struct ggml_tensor * attn_q_b = nullptr;
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struct ggml_tensor * attn_k = nullptr;
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struct ggml_tensor * attn_k_b = nullptr;
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struct ggml_tensor * attn_v = nullptr;
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struct ggml_tensor * attn_v_b = nullptr;
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struct ggml_tensor * attn_o = nullptr;
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struct ggml_tensor * attn_o_b = nullptr;
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// normalize
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struct ggml_tensor * norm = nullptr;
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struct ggml_tensor * norm_b = nullptr;
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};
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struct llama_layer_convnext {
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struct ggml_tensor * dw = nullptr;
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struct ggml_tensor * dw_b = nullptr;
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struct ggml_tensor * norm = nullptr;
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struct ggml_tensor * norm_b = nullptr;
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struct ggml_tensor * pw1 = nullptr;
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struct ggml_tensor * pw1_b = nullptr;
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struct ggml_tensor * pw2 = nullptr;
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struct ggml_tensor * pw2_b = nullptr;
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struct ggml_tensor * gamma = nullptr;
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};
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struct llama_layer_shortconv {
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struct ggml_tensor * in_proj = nullptr;
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struct ggml_tensor * conv = nullptr;
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struct ggml_tensor * out_proj = nullptr;
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};
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struct llama_layer_nextn {
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struct ggml_tensor * eh_proj = nullptr;
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struct ggml_tensor * embed_tokens = nullptr;
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struct ggml_tensor * enorm = nullptr;
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struct ggml_tensor * hnorm = nullptr;
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struct ggml_tensor * shared_head_head = nullptr;
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struct ggml_tensor * shared_head_norm = nullptr;
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};
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struct llama_layer {
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// normalization
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struct ggml_tensor * attn_norm = nullptr;
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struct ggml_tensor * attn_norm_b = nullptr;
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struct ggml_tensor * attn_norm_2 = nullptr;
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struct ggml_tensor * attn_norm_2_b = nullptr;
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struct ggml_tensor * attn_q_norm = nullptr;
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struct ggml_tensor * attn_q_norm_b = nullptr;
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struct ggml_tensor * attn_k_norm = nullptr;
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struct ggml_tensor * attn_k_norm_b = nullptr;
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struct ggml_tensor * attn_out_norm = nullptr;
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struct ggml_tensor * attn_out_norm_b = nullptr;
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struct ggml_tensor * attn_q_a_norm = nullptr;
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struct ggml_tensor * attn_kv_a_norm = nullptr;
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struct ggml_tensor * attn_sub_norm = nullptr;
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struct ggml_tensor * attn_post_norm = nullptr;
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struct ggml_tensor * ffn_sub_norm = nullptr;
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struct ggml_tensor * attn_norm_cross = nullptr;
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struct ggml_tensor * attn_norm_enc = nullptr;
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struct ggml_tensor * ssm_norm = nullptr;
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struct ggml_tensor * ssm_dt_norm = nullptr;
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struct ggml_tensor * ssm_b_norm = nullptr;
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struct ggml_tensor * ssm_c_norm = nullptr;
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// attention
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struct ggml_tensor * wq = nullptr;
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struct ggml_tensor * wk = nullptr;
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struct ggml_tensor * wv = nullptr;
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struct ggml_tensor * wo = nullptr;
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struct ggml_tensor * wqkv = nullptr;
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struct ggml_tensor * wq_a = nullptr;
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struct ggml_tensor * wq_b = nullptr;
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struct ggml_tensor * wkv_a_mqa = nullptr;
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struct ggml_tensor * wkv_b = nullptr;
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struct ggml_tensor * wk_b = nullptr;
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struct ggml_tensor * wv_b = nullptr;
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struct ggml_tensor * wqkv_b = nullptr;
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struct ggml_tensor * wo_b = nullptr;
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struct ggml_tensor * wq_cross = nullptr;
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struct ggml_tensor * wk_cross = nullptr;
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struct ggml_tensor * wv_cross = nullptr;
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struct ggml_tensor * wo_cross = nullptr;
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struct ggml_tensor * wq_enc = nullptr;
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struct ggml_tensor * wk_enc = nullptr;
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struct ggml_tensor * wv_enc = nullptr;
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struct ggml_tensor * wo_enc = nullptr;
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struct ggml_tensor * wqkv_gate = nullptr;
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// relative position bias
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struct ggml_tensor * attn_rel_b = nullptr;
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struct ggml_tensor * attn_rel_b_enc = nullptr;
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struct ggml_tensor * attn_rel_b_cross = nullptr;
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// normalization
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struct ggml_tensor * ffn_norm = nullptr;
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struct ggml_tensor * ffn_norm_b = nullptr;
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struct ggml_tensor * ffn_post_norm = nullptr;
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struct ggml_tensor * ffn_post_norm_1 = nullptr; // gemma4
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struct ggml_tensor * ffn_post_norm_2 = nullptr; // gemma4
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struct ggml_tensor * ffn_pre_norm_2 = nullptr; // gemma4
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struct ggml_tensor * layer_out_norm = nullptr;
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struct ggml_tensor * layer_out_norm_b = nullptr;
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struct ggml_tensor * ffn_norm_exps = nullptr;
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struct ggml_tensor * ffn_norm_enc = nullptr;
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// ff
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struct ggml_tensor * ffn_gate = nullptr; // w1
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struct ggml_tensor * ffn_down = nullptr; // w2
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struct ggml_tensor * ffn_up = nullptr; // w3
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struct ggml_tensor * ffn_gate_enc = nullptr;
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struct ggml_tensor * ffn_down_enc = nullptr;
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struct ggml_tensor * ffn_up_enc = nullptr;
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// ff MoE
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struct ggml_tensor * ffn_gate_inp = nullptr;
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struct ggml_tensor * ffn_gate_inp_s = nullptr; // gemma4
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struct ggml_tensor * ffn_gate_exps = nullptr;
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struct ggml_tensor * ffn_down_exps = nullptr;
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struct ggml_tensor * ffn_up_exps = nullptr;
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struct ggml_tensor * ffn_gate_up_exps = nullptr;
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struct ggml_tensor * ffn_gate_inp_b = nullptr;
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struct ggml_tensor * ffn_gate_exps_b = nullptr;
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struct ggml_tensor * ffn_down_exps_b = nullptr;
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struct ggml_tensor * ffn_up_exps_b = nullptr;
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struct ggml_tensor * ffn_gate_up_exps_b = nullptr;
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// ff MoE per-expert scales (NVFP4 per-tensor scale2)
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struct ggml_tensor * ffn_gate_exps_s = nullptr;
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struct ggml_tensor * ffn_down_exps_s = nullptr;
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struct ggml_tensor * ffn_up_exps_s = nullptr;
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// ff MoE latent proj
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struct ggml_tensor * ffn_latent_down = nullptr;
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struct ggml_tensor * ffn_latent_up = nullptr;
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// ff shared expert (shexp)
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struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
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struct ggml_tensor * ffn_gate_shexp = nullptr;
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struct ggml_tensor * ffn_down_shexp = nullptr;
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struct ggml_tensor * ffn_up_shexp = nullptr;
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// ff adjugate experts (chexps)
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struct ggml_tensor * ffn_gate_chexps = nullptr;
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struct ggml_tensor * ffn_down_chexps = nullptr;
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struct ggml_tensor * ffn_up_chexps = nullptr;
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// ff bias
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struct ggml_tensor * ffn_gate_b = nullptr;
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struct ggml_tensor * ffn_down_b = nullptr; // b2
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struct ggml_tensor * ffn_up_b = nullptr; // b3
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struct ggml_tensor * ffn_act = nullptr;
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struct ggml_tensor * ffn_exp_probs_b = nullptr;
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// mamba proj
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struct ggml_tensor * ssm_in = nullptr;
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struct ggml_tensor * ssm_x = nullptr;
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struct ggml_tensor * ssm_dt = nullptr;
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struct ggml_tensor * ssm_out = nullptr;
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// mamba
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struct ggml_tensor * ssm_conv1d = nullptr;
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struct ggml_tensor * ssm_a = nullptr;
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struct ggml_tensor * ssm_d = nullptr;
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// mamba bias
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struct ggml_tensor * ssm_conv1d_b = nullptr;
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struct ggml_tensor * ssm_dt_b = nullptr;
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// qwen3next
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struct ggml_tensor * ssm_beta_alpha = nullptr;
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// qwen3.5
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struct ggml_tensor * ssm_alpha = nullptr;
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// rwkv
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struct ggml_tensor * time_mix_w1 = nullptr;
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struct ggml_tensor * time_mix_w2 = nullptr;
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struct ggml_tensor * time_mix_lerp_x = nullptr;
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struct ggml_tensor * time_mix_lerp_w = nullptr;
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struct ggml_tensor * time_mix_lerp_k = nullptr;
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struct ggml_tensor * time_mix_lerp_v = nullptr;
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struct ggml_tensor * time_mix_lerp_r = nullptr;
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struct ggml_tensor * time_mix_lerp_g = nullptr;
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struct ggml_tensor * time_mix_lerp_fused = nullptr;
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struct ggml_tensor * time_mix_first = nullptr;
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struct ggml_tensor * time_mix_decay = nullptr;
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struct ggml_tensor * time_mix_decay_w1 = nullptr;
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struct ggml_tensor * time_mix_decay_w2 = nullptr;
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struct ggml_tensor * time_mix_key = nullptr;
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struct ggml_tensor * time_mix_key_b = nullptr;
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struct ggml_tensor * time_mix_value = nullptr;
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struct ggml_tensor * time_mix_value_b = nullptr;
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struct ggml_tensor * time_mix_receptance = nullptr;
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struct ggml_tensor * time_mix_receptance_b = nullptr;
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struct ggml_tensor * time_mix_gate = nullptr;
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// rwkv7
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struct ggml_tensor * time_mix_w0 = nullptr;
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struct ggml_tensor * time_mix_a0 = nullptr;
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struct ggml_tensor * time_mix_a1 = nullptr;
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struct ggml_tensor * time_mix_a2 = nullptr;
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struct ggml_tensor * time_mix_v0 = nullptr;
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struct ggml_tensor * time_mix_v1 = nullptr;
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struct ggml_tensor * time_mix_v2 = nullptr;
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struct ggml_tensor * time_mix_g1 = nullptr;
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struct ggml_tensor * time_mix_g2 = nullptr;
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struct ggml_tensor * time_mix_k_k = nullptr;
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struct ggml_tensor * time_mix_k_a = nullptr;
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struct ggml_tensor * time_mix_r_k = nullptr;
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struct ggml_tensor * time_mix_ln = nullptr;
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struct ggml_tensor * time_mix_ln_b = nullptr;
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struct ggml_tensor * time_mix_output = nullptr;
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struct ggml_tensor * channel_mix_lerp_k = nullptr;
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struct ggml_tensor * channel_mix_lerp_r = nullptr;
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struct ggml_tensor * channel_mix_key = nullptr;
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struct ggml_tensor * channel_mix_receptance = nullptr;
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struct ggml_tensor * channel_mix_value = nullptr;
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// long rope factors
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struct ggml_tensor * rope_long = nullptr;
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struct ggml_tensor * rope_short = nullptr;
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struct ggml_tensor * rope_freqs = nullptr;
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// bitnet scale
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struct ggml_tensor * wq_s = nullptr;
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struct ggml_tensor * wk_s = nullptr;
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struct ggml_tensor * wv_s = nullptr;
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struct ggml_tensor * wo_s = nullptr;
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struct ggml_tensor * wqkv_s = nullptr;
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struct ggml_tensor * wqkv_gate_s = nullptr;
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struct ggml_tensor * ffn_gate_s = nullptr;
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struct ggml_tensor * ffn_up_s = nullptr;
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struct ggml_tensor * ffn_down_s = nullptr;
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struct ggml_tensor * ffn_gate_shexp_s = nullptr;
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struct ggml_tensor * ffn_up_shexp_s = nullptr;
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struct ggml_tensor * ffn_down_shexp_s = nullptr;
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struct ggml_tensor * ssm_in_s = nullptr;
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struct ggml_tensor * ssm_out_s = nullptr;
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struct ggml_tensor * ssm_alpha_s = nullptr;
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struct ggml_tensor * ssm_beta_s = nullptr;
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// input scales
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struct ggml_tensor * wq_in_s = nullptr;
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struct ggml_tensor * wk_in_s = nullptr;
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struct ggml_tensor * wv_in_s = nullptr;
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struct ggml_tensor * wo_in_s = nullptr;
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struct ggml_tensor * wqkv_in_s = nullptr;
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struct ggml_tensor * wqkv_gate_in_s = nullptr;
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|
struct ggml_tensor * ffn_gate_in_s = nullptr;
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struct ggml_tensor * ffn_up_in_s = nullptr;
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|
struct ggml_tensor * ffn_down_in_s = nullptr;
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struct ggml_tensor * ffn_gate_exps_in_s = nullptr;
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struct ggml_tensor * ffn_down_exps_in_s = nullptr;
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struct ggml_tensor * ffn_up_exps_in_s = nullptr;
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struct ggml_tensor * ffn_gate_shexp_in_s= nullptr;
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struct ggml_tensor * ffn_up_shexp_in_s = nullptr;
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struct ggml_tensor * ffn_down_shexp_in_s= nullptr;
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|
struct ggml_tensor * ssm_in_in_s = nullptr;
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|
struct ggml_tensor * ssm_out_in_s = nullptr;
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struct ggml_tensor * ssm_alpha_in_s = nullptr;
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struct ggml_tensor * ssm_beta_in_s = nullptr;
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|
|
|
// altup & laurel
|
|
struct ggml_tensor * per_layer_inp_gate = nullptr;
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struct ggml_tensor * per_layer_proj = nullptr;
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struct ggml_tensor * per_layer_post_norm = nullptr;
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struct ggml_tensor * altup_correct_coef = nullptr;
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struct ggml_tensor * altup_correct_scale = nullptr;
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|
struct ggml_tensor * altup_predict_coef = nullptr;
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struct ggml_tensor * altup_router = nullptr;
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|
struct ggml_tensor * altup_router_norm = nullptr;
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struct ggml_tensor * laurel_l = nullptr;
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|
struct ggml_tensor * laurel_r = nullptr;
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|
struct ggml_tensor * laurel_post_norm = nullptr;
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|
|
|
// openai-moe
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|
struct ggml_tensor * attn_sinks = nullptr;
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|
|
|
// cogvlm
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|
struct ggml_tensor * visexp_attn_wqkv = nullptr;
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struct ggml_tensor * visexp_attn_wo = nullptr;
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|
struct ggml_tensor * visexp_ffn_gate = nullptr;
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|
struct ggml_tensor * visexp_ffn_down = nullptr;
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|
struct ggml_tensor * visexp_ffn_up = nullptr;
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|
|
|
// xIELU activation parameters for Apertus
|
|
struct ggml_tensor * ffn_act_alpha_n = nullptr;
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|
struct ggml_tensor * ffn_act_alpha_p = nullptr;
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|
struct ggml_tensor * ffn_act_beta = nullptr;
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|
struct ggml_tensor * ffn_act_eps = nullptr;
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|
|
|
// Kimi Linear KDA (using ssm_ prefix for consistency)
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|
// Note: ssm_dt_b already exists above (mamba bias), reused for Kimi dt_bias
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|
struct ggml_tensor * ssm_q_conv = nullptr;
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|
struct ggml_tensor * ssm_k_conv = nullptr;
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|
struct ggml_tensor * ssm_v_conv = nullptr;
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|
struct ggml_tensor * ssm_f_a = nullptr;
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|
struct ggml_tensor * ssm_f_b = nullptr;
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|
struct ggml_tensor * ssm_beta = nullptr;
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|
struct ggml_tensor * ssm_g_a = nullptr;
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|
struct ggml_tensor * ssm_g_b = nullptr;
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|
struct ggml_tensor * ssm_o_norm = nullptr;
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|
|
|
// DSA (deepseek sparse attention)
|
|
struct ggml_tensor * indexer_k_norm = nullptr;
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|
struct ggml_tensor * indexer_k_norm_b = nullptr;
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|
struct ggml_tensor * indexer_proj = nullptr;
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|
struct ggml_tensor * indexer_attn_k = nullptr;
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|
struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias
|
|
|
|
// gemma4 layer output scale
|
|
struct ggml_tensor * out_scale = nullptr;
|
|
|
|
struct llama_layer_posnet posnet;
|
|
|
|
struct llama_layer_convnext convnext;
|
|
|
|
struct llama_layer_shortconv shortconv;
|
|
|
|
struct llama_layer_nextn nextn;
|
|
};
|
|
|
|
struct llama_device {
|
|
bool is_meta;
|
|
|
|
ggml_backend_dev_t dev;
|
|
};
|
|
|
|
struct llama_meta_device_get_split_state_userdata {
|
|
size_t n_devices;
|
|
const struct llama_model * model;
|
|
};
|
|
|
|
struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const struct ggml_tensor * tensor, void * userdata);
|
|
|
|
struct llama_model {
|
|
llm_type type = LLM_TYPE_UNKNOWN;
|
|
llm_arch arch = LLM_ARCH_UNKNOWN;
|
|
|
|
std::string name = "n/a";
|
|
|
|
llama_hparams hparams = {};
|
|
llama_vocab vocab;
|
|
|
|
// for classifier models
|
|
std::vector<std::string> classifier_labels;
|
|
|
|
struct ggml_tensor * tok_embd = nullptr;
|
|
struct ggml_tensor * type_embd = nullptr;
|
|
struct ggml_tensor * pos_embd = nullptr;
|
|
struct ggml_tensor * tok_norm = nullptr;
|
|
struct ggml_tensor * tok_norm_b = nullptr;
|
|
|
|
struct ggml_tensor * output_norm = nullptr;
|
|
struct ggml_tensor * output_norm_b = nullptr;
|
|
struct ggml_tensor * output = nullptr;
|
|
struct ggml_tensor * output_b = nullptr;
|
|
struct ggml_tensor * output_norm_enc = nullptr;
|
|
|
|
// classifier
|
|
struct ggml_tensor * cls = nullptr;
|
|
struct ggml_tensor * cls_b = nullptr;
|
|
struct ggml_tensor * cls_out = nullptr;
|
|
struct ggml_tensor * cls_out_b = nullptr;
|
|
struct ggml_tensor * cls_norm = nullptr;
|
|
|
|
struct ggml_tensor * conv1d = nullptr;
|
|
struct ggml_tensor * conv1d_b = nullptr;
|
|
|
|
// gemma3n altup
|
|
struct ggml_tensor * altup_proj = nullptr;
|
|
struct ggml_tensor * altup_unembd_proj = nullptr;
|
|
struct ggml_tensor * per_layer_tok_embd = nullptr;
|
|
struct ggml_tensor * per_layer_model_proj = nullptr;
|
|
struct ggml_tensor * per_layer_proj_norm = nullptr;
|
|
|
|
std::vector<llama_layer> layers;
|
|
|
|
//Dense linear projections for SentenceTransformers models like embeddinggemma
|
|
// For Sentence Transformers models structure see
|
|
// https://sbert.net/docs/sentence_transformer/usage/custom_models.html#structure-of-sentence-transformer-models
|
|
struct ggml_tensor * dense_2_out_layers = nullptr;
|
|
struct ggml_tensor * dense_2_out_layers_b = nullptr;
|
|
struct ggml_tensor * dense_3_out_layers = nullptr;
|
|
|
|
// gguf metadata
|
|
std::unordered_map<std::string, std::string> gguf_kv;
|
|
|
|
// list of devices used in this model
|
|
std::vector<llama_device> devices;
|
|
|
|
// for quantize-stats only
|
|
std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
|
|
|
|
// for keeping track of associated LoRA adapters
|
|
std::unordered_set<llama_adapter_lora *> loras;
|
|
|
|
// statically allocated context for assigning
|
|
struct llama_meta_device_get_split_state_userdata get_split_state_ud;
|
|
|
|
int64_t t_load_us = 0;
|
|
int64_t t_start_us = 0;
|
|
|
|
explicit llama_model(const llama_model_params & params);
|
|
virtual ~llama_model();
|
|
|
|
std::string arch_name() const;
|
|
std::string type_name() const;
|
|
|
|
std::string desc() const;
|
|
|
|
size_t size() const; // file size
|
|
size_t n_tensors() const;
|
|
size_t n_devices() const;
|
|
const float * tensor_split() const;
|
|
|
|
uint32_t n_gpu_layers() const;
|
|
llama_split_mode split_mode() const;
|
|
|
|
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const;
|
|
|
|
// total number of parameters in the model
|
|
uint64_t n_elements() const;
|
|
|
|
void print_info() const;
|
|
|
|
ggml_backend_dev_t dev_layer(int il) const;
|
|
ggml_backend_dev_t dev_output() const;
|
|
|
|
ggml_backend_buffer_type_t select_buft(int il) const;
|
|
|
|
bool has_tensor_overrides() const;
|
|
|
|
const struct ggml_tensor * get_tensor(const char * name) const;
|
|
|
|
float get_rope_freq_base (const llama_cparams & cparams, int il) const;
|
|
float get_rope_freq_scale(const llama_cparams & cparams, int il) const;
|
|
|
|
ggml_tensor * get_rope_factors(const llama_cparams & cparams, int il) const;
|
|
|
|
llama_memory_i * create_memory(const llama_memory_params & params, const llama_cparams & cparams) const;
|
|
|
|
ggml_cgraph * build_graph(const llm_graph_params & params) const;
|
|
|
|
virtual void load_stats (llama_model_loader & ml) = 0;
|
|
virtual void load_hparams(llama_model_loader & ml) = 0;
|
|
virtual void load_vocab (llama_model_loader & ml) = 0;
|
|
virtual bool load_tensors(llama_model_loader & ml) = 0; // returns false if cancelled by progress_callback
|
|
|
|
// model must define these
|
|
virtual void load_arch_hparams(llama_model_loader & ml) = 0;
|
|
virtual void load_arch_tensors(llama_model_loader & ml) = 0;
|
|
virtual std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const = 0;
|
|
|
|
protected:
|
|
llama_model_params params;
|
|
|
|
struct impl;
|
|
std::unique_ptr<impl> pimpl;
|
|
};
|
|
|
|
llama_model * llama_model_create(llm_arch arch, const llama_model_params & params);
|
|
llama_model * llama_model_create(llama_model_loader & ml, const llama_model_params & params);
|
|
|
|
// model must inherit from this
|
|
struct llama_model_base : public llama_model {
|
|
friend struct llama_model;
|
|
|
|
llama_model * model;
|
|
llama_model_loader * ml = nullptr;
|
|
const LLM_TN tn;
|
|
|
|
// llama_model_loader is not yet defined at this point, so we will set it after construction
|
|
const int TENSOR_DUPLICATED;
|
|
const int TENSOR_NOT_REQUIRED;
|
|
const int TENSOR_SKIP;
|
|
const int TENSOR_SKIP_IF_VIRTUAL;
|
|
|
|
explicit llama_model_base(const llama_model_params & params);
|
|
virtual ~llama_model_base() = default;
|
|
|
|
ggml_tensor * create_tensor(llama_model_loader & ml, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
|
|
|
|
// convenience overload of create_tensor that doesn't require llama_model_loader
|
|
ggml_tensor * create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
|
|
|
|
// helper: try merged gate_up_exps first, fall back to separate gate and up
|
|
void create_tensor_gate_up_exps(llama_layer & layer, int bid, int64_t n_embd_,
|
|
int64_t n_ff_, int64_t n_expert_, int flags);
|
|
|
|
// helper: try to load merged qkv first, fall back to separate q, k, v
|
|
void create_tensor_qkv(llama_layer & layer, int bid,
|
|
int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
|
|
int flags);
|
|
|
|
void load_stats (llama_model_loader & ml) override;
|
|
void load_hparams(llama_model_loader & ml) override;
|
|
void load_vocab (llama_model_loader & ml) override;
|
|
bool load_tensors(llama_model_loader & ml) override;
|
|
|
|
// model must define these
|
|
void load_arch_hparams(llama_model_loader & ml) override = 0;
|
|
void load_arch_tensors(llama_model_loader & ml) override = 0;
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override = 0;
|
|
};
|
|
|
|
const char * llm_type_name(llm_type type);
|
|
|
|
// convenience macro for loading local variables for load_tensors() in llama_model_base
|
|
// note: cast to int64_t since we will use these for the tensor dimensions
|
|
#define LLAMA_LOAD_LOCALS \
|
|
const int n_layer = hparams.n_layer; GGML_UNUSED(n_layer); \
|
|
const int64_t n_head = hparams.n_head(); GGML_UNUSED(n_head); \
|
|
const int64_t n_head_kv = hparams.n_head_kv(); GGML_UNUSED(n_head_kv); \
|
|
const int64_t n_embd = hparams.n_embd; GGML_UNUSED(n_embd); \
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); GGML_UNUSED(n_embd_k_gqa); \
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); GGML_UNUSED(n_embd_v_gqa); \
|
|
const int64_t n_embd_head_k = hparams.n_embd_head_k(); GGML_UNUSED(n_embd_head_k); \
|
|
const int64_t n_embd_head_v = hparams.n_embd_head_v(); GGML_UNUSED(n_embd_head_v); \
|
|
const int64_t n_ff = hparams.n_ff(); GGML_UNUSED(n_ff); \
|
|
const int64_t n_embd_gqa = n_embd_v_gqa; GGML_UNUSED(n_embd_gqa); \
|
|
const int64_t n_vocab = vocab.n_tokens(); GGML_UNUSED(n_vocab); \
|
|
const int64_t n_token_types = vocab.n_token_types(); GGML_UNUSED(n_token_types); \
|
|
const int64_t n_rot = hparams.n_rot(); GGML_UNUSED(n_rot); \
|
|
const int64_t n_expert = hparams.n_expert; GGML_UNUSED(n_expert); \
|
|
const int64_t n_expert_used = hparams.n_expert_used; GGML_UNUSED(n_expert_used); \
|
|
const int64_t n_ctx_train = hparams.n_ctx_train; GGML_UNUSED(n_ctx_train);
|
|
|
|
// For internal test use
|
|
// TODO: remove
|
|
const std::vector<std::pair<std::string, ggml_tensor *>> & llama_internal_get_tensor_map(const llama_model * model);
|