koboldcpp/expose.cpp

//This is Concedo's shitty adapter for adding python bindings for llama

//Considerations:
//Don't want to use pybind11 due to dependencies on MSVCC
//ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations here!
//Leave main.cpp UNTOUCHED, We want to be able to update the repo and pull any changes automatically.
//No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields
//Python will ALWAYS provide the memory, we just write to it.

#include <cassert>
#include <cstring>
#include <fstream>
#include <regex>
#include <iostream>
#include <iterator>
#include <queue>
#include <string>
#include <math.h>

#include "expose.h"
#include "model_adapter.cpp"

extern "C"
{

    //return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
    static FileFormat file_format = FAIL;

    bool load_model(const load_model_inputs inputs)
    {
        std::string model = inputs.model_filename;
        file_format = check_file_format(model.c_str());

        if(file_format==GPTJ1 || file_format==GPTJ2)
        {
            printf("\n---\nIdentified as GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);   
            return gptj_load_model(inputs, file_format);
        }
        else
        {
            printf("\n---\nIdentified as LLAMA model: (ver %d)\nAttempting to Load...\n---\n", file_format);   
            return llama_load_model(inputs, file_format);
        }
    }

    generation_outputs generate(const generation_inputs inputs, generation_outputs &output)
    {
        if (file_format == GPTJ1 || file_format == GPTJ2)
        {
            return gptj_generate(inputs, output);
        }
        else
        {
            return llama_generate(inputs, output);
        }
    }
}