#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
};
using llama_adapter_cvec_ptr = std::shared_ptr<llama_adapter_cvec>;
//
// 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 {
llama_model * model = nullptr;
// 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;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
// activated lora (aLoRA)
std::vector<llama_token> alora_invocation_tokens;
explicit llama_adapter_lora(llama_model * model) : model(model) {}
~llama_adapter_lora() = default;
llama_adapter_lora_weight * get_weight(ggml_tensor * w);
uint32_t get_n_nodes() const {
return ab_map.size() * 6u; // a, b, scale, add, 2 x mul_mat
}
};
using llama_adapter_loras = std::unordered_map<llama_adapter_lora *, float>;
using llama_adapter_loras_ptr = std::unique_ptr<llama_adapter_loras>;