File size: 1,831 Bytes
9b19fd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | #pragma once
#include "llama.h"
#include "ggml-cpp.h"
#include <string>
#include <unordered_map>
#include <vector>
// TODO: pimpl
//
// llama_adapter_cvec
//
struct llama_adapter_cvec {
ggml_tensor * tensor_for(int il) const;
ggml_tensor * apply_to(ggml_context * ctx, ggml_tensor * cur, int il) const;
bool apply(
const llama_model & model,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end);
private:
bool init(const llama_model & model);
int32_t layer_start = -1;
int32_t layer_end = -1;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
std::vector<ggml_tensor *> tensors; // per layer
};
//
// llama_adapter_lora
//
struct llama_adapter_lora_weight {
ggml_tensor * a = nullptr;
ggml_tensor * b = nullptr;
// get actual scale based on rank and alpha
float get_scale(float alpha, float adapter_scale) const {
const float rank = (float) b->ne[0];
const float scale = alpha ? adapter_scale * alpha / rank : adapter_scale;
return scale;
}
llama_adapter_lora_weight() = default;
llama_adapter_lora_weight(ggml_tensor * a, ggml_tensor * b) : a(a), b(b) {}
};
struct llama_adapter_lora {
// map tensor name to lora_a_b
std::unordered_map<std::string, llama_adapter_lora_weight> ab_map;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
float alpha;
llama_adapter_lora() = default;
~llama_adapter_lora() = default;
llama_adapter_lora_weight * get_weight(ggml_tensor * w);
};
using llama_adapter_loras = std::unordered_map<llama_adapter_lora *, float>;
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