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 <cstdint>
#include "expose.h"
#include "model_adapter.cpp"

extern "C"
{

    std::string platformenv, deviceenv, vulkandeviceenv;

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

    bool load_model(const load_model_inputs inputs)
    {
        std::string model = inputs.model_filename;
        lora_filename = inputs.lora_filename;
        lora_base = inputs.lora_base;
        mmproj_filename = inputs.mmproj_filename;

        int forceversion = inputs.forceversion;

        file_format = check_file_format(model.c_str(),&file_format_meta);

        if(forceversion!=0)
        {
            printf("\nWARNING: FILE FORMAT FORCED TO VER %d\nIf incorrect, loading may fail or crash.\n",forceversion);
            file_format = (FileFormat)forceversion;
        }

        //first digit is whether configured, second is platform, third is devices
        int cl_parseinfo = inputs.clblast_info;

        std::string usingclblast = "GGML_OPENCL_CONFIGURED="+std::to_string(cl_parseinfo>0?1:0);
        putenv((char*)usingclblast.c_str());

        cl_parseinfo = cl_parseinfo%100; //keep last 2 digits
        int platform = cl_parseinfo/10;
        int devices = cl_parseinfo%10;
        platformenv = "GGML_OPENCL_PLATFORM="+std::to_string(platform);
        deviceenv = "GGML_OPENCL_DEVICE="+std::to_string(devices);
        putenv((char*)platformenv.c_str());
        putenv((char*)deviceenv.c_str());

        std::string vulkan_info_raw = inputs.vulkan_info;
        std::string vulkan_info_str = "";
        for (size_t i = 0; i < vulkan_info_raw.length(); ++i) {
            vulkan_info_str += vulkan_info_raw[i];
            if (i < vulkan_info_raw.length() - 1) {
                vulkan_info_str += ",";
            }
        }
        if(vulkan_info_str!="")
        {
            vulkandeviceenv = "GGML_VK_VISIBLE_DEVICES="+vulkan_info_str;
            putenv((char*)vulkandeviceenv.c_str());
        }

        executable_path = inputs.executable_path;

        if(file_format==FileFormat::GPTJ_1 || file_format==FileFormat::GPTJ_2 || file_format==FileFormat::GPTJ_3 || file_format==FileFormat::GPTJ_4  || file_format==FileFormat::GPTJ_5)
        {
            printf("\n---\nIdentified as Legacy GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
            ModelLoadResult lr = gpttype_load_model(inputs, file_format, file_format_meta);
            if (lr == ModelLoadResult::RETRY_LOAD)
            {
                if(file_format==FileFormat::GPTJ_1)
                {
                    //if we tried 1 first, then try 3 and lastly 2
                    //otherwise if we tried 3 first, then try 2
                    file_format = FileFormat::GPTJ_4;
                    printf("\n---\nRetrying as Legacy GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                    lr = gpttype_load_model(inputs, file_format, file_format_meta);
                }

                if (lr == ModelLoadResult::RETRY_LOAD)
                {
                    file_format = FileFormat::GPTJ_3;
                    printf("\n---\nRetrying as Legacy GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                    lr = gpttype_load_model(inputs, file_format, file_format_meta);
                }

                //lastly try format 2
                if (lr == ModelLoadResult::RETRY_LOAD)
                {
                    file_format = FileFormat::GPTJ_2;
                    printf("\n---\nRetrying as Legacy GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                    lr = gpttype_load_model(inputs, file_format, file_format_meta);
                }
            }

            if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
            {
                return false;
            }
            else
            {
                return true;
            }
        }
        else if(file_format==FileFormat::GPT2_1||file_format==FileFormat::GPT2_2||file_format==FileFormat::GPT2_3||file_format==FileFormat::GPT2_4)
        {
            printf("\n---\nIdentified as Legacy GPT-2 model: (ver %d)\nAttempting to Load...\n---\n", file_format);
            ModelLoadResult lr = gpttype_load_model(inputs, file_format, file_format_meta);
            if (lr == ModelLoadResult::RETRY_LOAD)
            {
                file_format = FileFormat::GPT2_3;
                printf("\n---\nRetrying as Legacy GPT-2 model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                lr = gpttype_load_model(inputs, file_format, file_format_meta);
            }
            if (lr == ModelLoadResult::RETRY_LOAD)
            {
                file_format = FileFormat::GPT2_2;
                printf("\n---\nRetrying as Legacy GPT-2 model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                lr = gpttype_load_model(inputs, file_format, file_format_meta);
            }
            if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
            {
                return false;
            }
            else
            {
                return true;
            }
        }
        else if(file_format==FileFormat::NEOX_1 || file_format==FileFormat::NEOX_2 || file_format==FileFormat::NEOX_3 || file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5 || file_format==FileFormat::NEOX_6 || file_format==FileFormat::NEOX_7)
        {
            printf("\n---\nIdentified as Legacy GPT-NEO-X model: (ver %d)\nAttempting to Load...\n---\n", file_format);
            ModelLoadResult lr = gpttype_load_model(inputs, file_format, file_format_meta);
            if (lr == ModelLoadResult::RETRY_LOAD)
            {
                if(file_format==FileFormat::NEOX_2)
                {
                    file_format = FileFormat::NEOX_3;
                    printf("\n---\nRetrying as Legacy GPT-NEO-X model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                    lr = gpttype_load_model(inputs, file_format, file_format_meta);
                }
                else
                {
                    file_format = FileFormat::NEOX_5;
                    printf("\n---\nRetrying as Legacy GPT-NEO-X model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                    lr = gpttype_load_model(inputs, file_format, file_format_meta);
                }
            }
            if (lr == ModelLoadResult::RETRY_LOAD)
            {
                file_format = FileFormat::NEOX_1;
                printf("\n---\nRetrying as Legacy GPT-NEO-X model: (ver %d)\nAttempting to Load...\n---\n", file_format);
                lr = gpttype_load_model(inputs, file_format, file_format_meta);
            }
            if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
            {
                return false;
            }
            else
            {
                return true;
            }
        }
        else
        {
            if(file_format==FileFormat::MPT_1)
            {
                printf("\n---\nIdentified as Legacy MPT model: (ver %d)\nAttempting to Load...\n---\n", file_format);
            }
            else if(file_format==FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
            {
                printf("\n---\nIdentified as Legacy RWKV model: (ver %d)\nAttempting to Load...\n---\n", file_format);
            }
            else if(file_format==FileFormat::GGUF_GENERIC)
            {
                printf("\n---\nIdentified as GGUF model: (ver %d)\nAttempting to Load...\n---\n", file_format);
            }
            else if(file_format==FileFormat::GGML || file_format==FileFormat::GGHF || file_format==FileFormat::GGJT || file_format==FileFormat::GGJT_2 || file_format==FileFormat::GGJT_3)
            {
                printf("\n---\nIdentified as Legacy GGML model: (ver %d)\n======\nGGML Models are Outdated: You are STRONGLY ENCOURAGED to obtain a newer GGUF model!\n======\nAttempting to Load...\n---\n", file_format);
            }
            else
            {
                printf("\n---\nUnidentified Model Encountered: (ver %d)\n---\n", file_format);
            }
            ModelLoadResult lr = gpttype_load_model(inputs, file_format, file_format_meta);
            if(file_format==FileFormat::GGML || file_format==FileFormat::GGHF || file_format==FileFormat::GGJT || file_format==FileFormat::GGJT_2 || file_format==FileFormat::GGJT_3)
            {
                //warn a second time
                printf("\n======\nGGML Models are Outdated: You are STRONGLY ENCOURAGED to obtain a newer GGUF model!\n======\n");
            }
            if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
            {
                return false;
            }
            else
            {
                return true;
            }
        }
    }

    generation_outputs generate(const generation_inputs inputs)
    {
        return gpttype_generate(inputs);
    }

    bool sd_load_model(const sd_load_model_inputs inputs)
    {
        return sdtype_load_model(inputs);
    }
    sd_generation_outputs sd_generate(const sd_generation_inputs inputs)
    {
        return sdtype_generate(inputs);
    }

    bool whisper_load_model(const whisper_load_model_inputs inputs)
    {
        return whispertype_load_model(inputs);
    }
    whisper_generation_outputs whisper_generate(const whisper_generation_inputs inputs)
    {
        return whispertype_generate(inputs);
    }

    const char * new_token(int idx) {
        if (generated_tokens.size() <= idx || idx < 0) return nullptr;

        return generated_tokens[idx].c_str();
    }

    int get_stream_count() {
        return generated_tokens.size();
    }

    bool has_finished() {
        return generation_finished;
    }

    float get_last_eval_time() {
        return last_eval_time;
    }
    float get_last_process_time() {
        return last_process_time;
    }
    int get_last_token_count() {
        return last_token_count;
    }
    int get_last_seed()
    {
        return last_seed;
    }
    int get_total_gens() {
        return total_gens;
    }
    int get_total_img_gens()
    {
        return total_img_gens;
    }
    int get_last_stop_reason() {
        return (int)last_stop_reason;
    }

    const char* get_pending_output() {
       return gpttype_get_pending_output().c_str();
    }

    bool abort_generate() {
        return gpttype_generate_abort();
    }

    static std::vector<int> toks; //just share a static object for token counting
    token_count_outputs token_count(const char * input, bool addbos)
    {
        std::string inputstr = input;
        token_count_outputs output;
        toks = gpttype_get_token_arr(inputstr,addbos);
        output.count = toks.size();
        output.ids = toks.data(); //this may be slightly unsafe
        return output;
    }

    static std::vector<TopPicksData> last_logprob_toppicks;
    static std::vector<logprob_item> last_logprob_items;
    last_logprobs_outputs last_logprobs()
    {
        last_logprobs_outputs output;
        last_logprob_items.clear();
        last_logprob_toppicks.clear();
        last_logprob_toppicks = gpttype_get_top_picks_data(); //copy top picks
        for(int i=0;i<last_logprob_toppicks.size();++i)
        {
            logprob_item itm;
            itm.option_count = last_logprob_toppicks[i].tokenid.size();
            itm.selected_token = last_logprob_toppicks[i].selected_token.c_str();
            itm.selected_logprob = last_logprob_toppicks[i].selected_logprob;
            itm.logprobs = last_logprob_toppicks[i].logprobs.data();
            for(int j=0;j<itm.option_count && j<logprobs_max;++j)
            {
                itm.tokens[j] = last_logprob_toppicks[i].tokens[j].c_str();
            }
            last_logprob_items.push_back(itm);
        }
        output.count = last_logprob_items.size();
        output.logprob_items = last_logprob_items.data();
        return output;
    }


}