1#pragma once
 2
 3#include "llama.h"
 4
 5#include "ggml-cpp.h"
 6
 7#include <string>
 8#include <unordered_map>
 9#include <vector>
10
11// TODO: pimpl
12
13//
14// llama_adapter_cvec
15//
16
17struct llama_adapter_cvec {
18    ggml_tensor * tensor_for(int il) const;
19
20    ggml_tensor * apply_to(ggml_context * ctx, ggml_tensor * cur, int  il) const;
21
22    bool apply(
23            const llama_model & model,
24            const float * data,
25            size_t len,
26            int32_t n_embd,
27            int32_t il_start,
28            int32_t il_end);
29
30private:
31    bool init(const llama_model & model);
32
33    int32_t layer_start = -1;
34    int32_t layer_end   = -1;
35
36    std::vector<ggml_context_ptr> ctxs;
37    std::vector<ggml_backend_buffer_ptr> bufs;
38
39    std::vector<ggml_tensor *> tensors; // per layer
40};
41
42//
43// llama_adapter_lora
44//
45
46struct llama_adapter_lora_weight {
47    ggml_tensor * a = nullptr;
48    ggml_tensor * b = nullptr;
49
50    // get actual scale based on rank and alpha
51    float get_scale(float alpha, float adapter_scale) const {
52        const float rank  = (float) b->ne[0];
53        const float scale = alpha ? adapter_scale * alpha / rank : adapter_scale;
54        return scale;
55    }
56
57    llama_adapter_lora_weight() = default;
58    llama_adapter_lora_weight(ggml_tensor * a, ggml_tensor * b) : a(a), b(b) {}
59};
60
61struct llama_adapter_lora {
62    // map tensor name to lora_a_b
63    std::unordered_map<std::string, llama_adapter_lora_weight> ab_map;
64
65    std::vector<ggml_context_ptr> ctxs;
66    std::vector<ggml_backend_buffer_ptr> bufs;
67
68    float alpha;
69
70    // gguf metadata
71    std::unordered_map<std::string, std::string> gguf_kv;
72
73    // activated lora (aLoRA)
74    std::vector<llama_token> alora_invocation_tokens;
75
76    llama_adapter_lora() = default;
77    ~llama_adapter_lora() = default;
78
79    llama_adapter_lora_weight * get_weight(ggml_tensor * w);
80
81    uint32_t get_n_nodes() const {
82        return ab_map.size() * 6u; // a, b, scale, add, 2 x mul_mat
83    }
84};
85
86using llama_adapter_loras = std::unordered_map<llama_adapter_lora *, float>;