model_adapter.cpp

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

#include "model_adapter.h"
#include "ggml.h"

#include <chrono>

static auto bench_timer = std::chrono::high_resolution_clock().now();

void timer_start()
{
    bench_timer = std::chrono::high_resolution_clock().now();
}
double timer_check()
{
    auto endtime = std::chrono::high_resolution_clock().now();
    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(endtime - bench_timer);
    double time_taken = duration.count()/1000.0;
    return time_taken;
}

void print_vec(std::vector<std::string> &embd)
{
    std::cout << "[";
    bool first = true;
    for (auto i : embd)
    {
        if (!first)
        {
            std::cout << ',';
        }
        first = false;
        std::cout << i;
    }
    std::cout << "]\n";
}
void print_tok_vec(std::vector<int> &embd)
{
    std::cout << "[";
    bool first = true;
    for (auto i : embd)
    {
        if (!first)
        {
            std::cout << ',';
        }
        first = false;
        std::cout << i;
    }
    std::cout << "]\n";
}
void print_tok_vec(std::vector<float> &embd)
{
    std::cout << "[";
    bool first = true;
    int n = 0;
    for (auto i : embd)
    {
        if (!first)
        {
            std::cout << ',';
        }
        first = false;
        std::cout << i;
        if(++n>20)
        {
            break;
        }
    }
    std::cout << "]\n";
}

//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
 FileFormat check_file_format(const std::string & fname, FileFormatExtraMeta * fileformatmeta)
 {
    std::vector<char> f_buf(1024*1024);

    auto fin = std::ifstream(fname, std::ios::binary);
    fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
    if (!fin) {
        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
        return FileFormat::BADFORMAT;
    }

    FileFormat fileformat = FileFormat::BADFORMAT;
    uint32_t magic;
    fin.read((char *) &magic, sizeof(magic));
    if (magic == 0x67676d6c) {  //v1 format ggml, alpaca, old gptj and gpt2 models
       fileformat = FileFormat::GGML;
       //we need to read more to determine
       int32_t vocabsiz = 0;
       fin.read((char *) &vocabsiz, sizeof(int32_t));
       if(vocabsiz==4096 || vocabsiz==7168) //actually the d_model for mpt
       {
           fileformat = FileFormat::MPT_1;
       }
       else if(vocabsiz==50400) //know GPT-J vocab size
       {
           fileformat = FileFormat::GPTJ_1;
           uint32_t temp;
           fin.read((char *)&temp, sizeof(temp)); //ctx
           fin.read((char *)&temp, sizeof(temp)); //n_embd
           fin.read((char *)&temp, sizeof(temp)); //n_head
           fin.read((char *)&temp, sizeof(temp)); //n_layer
           fin.read((char *)&temp, sizeof(temp)); //n_rot
           fin.read((char *)&temp, sizeof(temp)); //f16
           const int32_t qntvr = temp / 1000;
           temp %= 1000;
           if (qntvr != 0)
           {
               if (qntvr == 1)
               {
                   fileformat = FileFormat::GPTJ_4;
               }
               else
               {
                   fileformat = FileFormat::GPTJ_5;
               }
           }
           else if (temp != 0 && temp != 1)
           {
               fileformat = FileFormat::GPTJ_3; //quantized format cannot be legacy type
           }
       }
       else if(vocabsiz==50257 || (vocabsiz>=49152&&vocabsiz<=49157)) //49152-6 is starcoder
       {
           fileformat = FileFormat::GPT2_1;
           uint32_t temp, v1,v2,v3;
           fin.read((char *)&v1, sizeof(temp)); //ctx
           fin.read((char *)&v2, sizeof(temp)); //n_embd
           fin.read((char *)&v3, sizeof(temp)); //n_head
           fin.read((char *)&temp, sizeof(temp)); //n_layer
           if(vocabsiz==49152 && v1==4096 && v2==2560 && v3==32 && temp==32)
           {
                //special case, Stablecode Completion Alpha 3B
               fileformat = FileFormat::NEOX_6;
           }
           else
           {
                fin.read((char *)&temp, sizeof(temp)); //f16
                const int32_t qntvr = temp / 1000;
                temp %= 1000;
                if (qntvr != 0)
                {
                    if (qntvr == 1)
                    {
                        fileformat = FileFormat::GPT2_3;
                    }
                    else
                    {
                        fileformat = FileFormat::GPT2_4;
                    }
                }
                else if (temp != 0 && temp != 1)
                {
                    fileformat = FileFormat::GPT2_2; //quantized format cannot be legacy type
                }
           }
       }
       else if(vocabsiz < 31998 || vocabsiz > 33000)
       {
           //anything outside the llama v1 range is assumed to be NeoX
           fileformat = FileFormat::NEOX_6;
           uint32_t temp,temp2;
           fin.read((char *)&temp, sizeof(temp)); //ctx
           fin.read((char *)&temp, sizeof(temp)); //n_embd
           fin.read((char *)&temp, sizeof(temp)); //n_head
           fin.read((char *)&temp, sizeof(temp)); //n_layer
           fin.read((char *)&temp, sizeof(temp)); //n_rot
           fin.read((char *)&temp, sizeof(temp)); //either par_res or ftype (for older ver)

           if(temp!=0 && temp!=1){
               //must be ftype, means its an older model. par_res will be undefined
               fileformat = FileFormat::NEOX_2;
           }
           else
           {
                //it could be a newer model, or an old f16/f32 model
                fin.read((char *)&temp2, sizeof(temp2)); //if previous was par_res, this is ftype. else unknown

                //if it is new ftype, then it must have these properties: > 1000, low multiple of 1k and small remaineder
                bool isNewFtype = (temp2>=1000 && temp2<=9000 && temp2%1000<20);

                if(!isNewFtype)
                {
                    fileformat = FileFormat::NEOX_2;
                    if((temp==0||temp==1)&&(temp2==0||temp2==1))//special case: par_res and ftype are both 1 or 0
                    {
                        //its a f16/f32 model in the new format
                        fileformat = temp==0?FileFormat::NEOX_7:FileFormat::NEOX_6;
                    }
                }
                else
                {
                    const int32_t qntvr = temp2 / 1000; //for future use
                    //then temp was par_res, use_parallel_residual is false in RedPajama
                    if(qntvr==1)
                    {
                        fileformat = (temp==0?FileFormat::NEOX_5:FileFormat::NEOX_4);
                    }
                    else
                    {
                        fileformat = (temp==0?FileFormat::NEOX_7:FileFormat::NEOX_6);
                    }
                }
           }

       }
    }
    else if(magic == 0x67676d66) //v2 format ggmf
    {
        fileformat = FileFormat::GGHF;
        uint32_t temp;
        fin.read((char *)&temp, sizeof(temp)); //file version
        if(temp==100)
        {
            fileformat = FileFormat::RWKV_1;
        }
        else if(temp==101)
        {
            fileformat = FileFormat::RWKV_2;
        }
    }
    else if(magic == 0x67676a74) //v3 format ggjt
    {
        fileformat = FileFormat::GGJT_3; //ggjt by default
        uint32_t ver, temp, ftype;
        fin.read((char *)&ver, sizeof(ver)); //file version
        fin.read((char *)&temp, sizeof(temp));//vocab
        fin.read((char *)&temp, sizeof(temp)); //embd
        fin.read((char *)&temp, sizeof(temp)); //mult
        fin.read((char *)&temp, sizeof(temp));//head
        fin.read((char *)&temp, sizeof(temp));//layer
        fin.read((char *)&temp, sizeof(temp));//rot
        fin.read((char *)&ftype, sizeof(ftype));//filetype

        if(ver==1)
        {
            fileformat = FileFormat::GGJT;
        }
        else if(ver==2)
        {
            fileformat = FileFormat::GGJT_2;
        }
    }
    else if(magic == 0x46554747)
    {
        fin.close();
        fileformat = FileFormat::GGUF_GENERIC;

        struct gguf_init_params ggufparams;
        ggufparams.no_alloc = true;
        ggufparams.ctx = NULL;

        auto ctx  = gguf_init_from_file(fname.c_str(), ggufparams);

        auto keyidx = gguf_find_key(ctx, "general.architecture");
        std::string modelarch = "";
        if (keyidx != -1) { modelarch = gguf_get_val_str(ctx, keyidx); }

        printf("\nThe reported GGUF Arch is: %s\n",(modelarch==""?"unknown":modelarch.c_str()));

        if(modelarch!="" && fileformatmeta!=nullptr)
        {
            int n_tensors = gguf_get_n_tensors(ctx);
            float freq_base_train = 0;

            std::string fkey = modelarch+".context_length";
            int keyidx = gguf_find_key(ctx, fkey.c_str());
            if (keyidx != -1) {
                fileformatmeta->n_ctx_train = gguf_get_val_u32(ctx, keyidx);
            }
            fkey = modelarch+".expert_count";
            keyidx = gguf_find_key(ctx, fkey.c_str());
            if (keyidx != -1) {
                fileformatmeta->n_expert_count = gguf_get_val_u32(ctx, keyidx);
            }
            fkey = modelarch+".rope.freq_base";
            keyidx = gguf_find_key(ctx, fkey.c_str());
            if (keyidx != -1) {
                freq_base_train = gguf_get_val_f32(ctx, keyidx);
            }

            int filever = gguf_get_version(ctx);

            fileformatmeta->fileversion = filever;
            fileformatmeta->model_architecture = GGUFArch::ARCH_DEFAULT;
            if(modelarch=="phi2")
            {
                fileformatmeta->model_architecture = GGUFArch::ARCH_PHI;
            }
            else if(modelarch=="falcon")
            {
                fileformatmeta->model_architecture = GGUFArch::ARCH_FALCON;
            }
            else if(modelarch=="mamba")
            {
                fileformatmeta->model_architecture = GGUFArch::ARCH_MAMBA;
            }
            else if(modelarch=="llama" && freq_base_train==10000.0f && (n_tensors==435 || n_tensors==611))
            {
                fileformatmeta->model_architecture = GGUFArch::ARCH_SOLAR;
            }
            else if(modelarch=="qwen2")
            {
                fileformatmeta->model_architecture = GGUFArch::ARCH_QWEN2;
            }
            else if(modelarch=="rwkv6")
            {
                fileformatmeta->model_architecture = GGUFArch::ARCH_RWKV;
            }
            printf("Arch Category: %d\n",fileformatmeta->model_architecture);

        }

        gguf_free(ctx);
    }

    if(fin.is_open())
    {
        fin.close();
    }


    return fileformat;
 }

 bool ArrStartWith(const std::vector<int> targetArray, const std::vector<int> searchSeq)
 {
     int ss = searchSeq.size();
     if(targetArray.size()<ss)
     {
         return false;
     }
     for(int i=0;i<ss;++i)
     {
         if(targetArray[i]!=searchSeq[i])
         {
             return false;
         }
     }
     return true;
 }

 int ArrFindIndexOf(const std::vector<int> targetArray, const std::vector<int> searchSeq)
 {
     int ss = searchSeq.size();
     int tas = targetArray.size();
     if(tas<ss)
     {
         return -1;
     }
     for(int i=0;i<tas;++i)
     {
         int srch = 0;
         bool fail = false;
         for(int srch=0;srch<ss;++srch)
         {
             if ((i + srch) >= tas || targetArray[i + srch] != searchSeq[srch])
             {
                 fail = true;
                 break;
             }
         }
         if(!fail)
         {
             return i;
         }
     }
     return -1;
 }

 std::vector<int> LongestCommonSubseq(const std::vector<int> x, const std::vector<int> y)
 {
     int m = x.size(), n = y.size();

     //int LCSuff[m+1][n+1];
     std::vector<std::vector<int>> LCSuff(m+1, std::vector<int>(n+1));

     for (int j = 0; j <= n; j++)
         LCSuff[0][j] = 0;
     for (int i = 0; i <= m; i++)
         LCSuff[i][0] = 0;

     for (int i = 1; i <= m; i++)
     {
         for (int j = 1; j <= n; j++)
         {
             if (x[i - 1] == y[j - 1])
                 LCSuff[i][j] = LCSuff[i - 1][j - 1] + 1;
             else
                 LCSuff[i][j] = 0;
         }
     }

     std::vector<int> longest;
     for (int i = 1; i <= m; i++)
     {
         for (int j = 1; j <= n; j++)
         {
             if (LCSuff[i][j] > longest.size())
             {
                 auto off1 = ((i - LCSuff[i][j] + 1) - 1);
                 auto off2 = off1 + LCSuff[i][j];
                 longest.clear();
                //  std::vector<int>().swap(longest);
                 longest = std::vector<int>(x.begin() + off1, x.begin() + off2);
                // x.substr((i - LCSuff[i][j] + 1) - 1, LCSuff[i][j]);
             }
         }
     }
     return longest;
 }

 void ContextFastForward(std::vector<int> &current_context_tokens, std::vector<int> &embd_inp,
 int &n_past, std::vector<int> &last_n_tokens, const int nctx, std::vector<int> &smartcontext,
 bool useSmartContext, const bool requireFullSubset)
 {
     const int SCCtxLenThreshold = nctx * 0.8; //how much context length must be reach to trigger smartcontext
     const int SCInpLenThreshold = nctx * 0.6; //how big must the input array be to trigger smartcontext
     const int SCPastLenThreshold = nctx * 0.5; //how wide of a gap between the fast forwarded past and the present to trigger smart context
     const float SCTruncationRatio = 0.5; //ratio for how many tokens to fast forward
     const int SCTokThreshold = 32 + (nctx*0.05); //how many tokens of similarity triggers smartcontext


    //fast forward the past based on identical tokens, stop once a divergence is noted
    int embd_inp_len = embd_inp.size();
    bool fastforwardok = true;

    for (int i = 0; i < current_context_tokens.size(); ++i)
    {
        if (current_context_tokens[i] == embd_inp[i])
        {
            n_past += 1;
            last_n_tokens.push_back(current_context_tokens[i]);
        }
        else
        {
            if(requireFullSubset) //RWKV can only do this if embd_inp contains everything in current context
            {
                last_n_tokens.erase(last_n_tokens.end() - n_past, last_n_tokens.end());
                n_past = 0;
                fastforwardok = false;
            }
            break;
        }

        if (requireFullSubset) //RWKV can only do this if embd_inp contains everything in current context
        {
            if (i >= embd_inp_len)
            {
                last_n_tokens.erase(last_n_tokens.end() - n_past, last_n_tokens.end());
                n_past = 0;
                fastforwardok = false;
                break;
            }
        }
        else
        {
            if ((i + 2) >= embd_inp_len)
            {
                break;
            }
        }
    }

    if(fastforwardok)
    {
        last_n_tokens.erase(last_n_tokens.begin(), last_n_tokens.begin() + n_past);
        embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_past);
        embd_inp_len = embd_inp.size();
    }

    //smart context mode, detect if we have a shifted context at max length
    //requirement: previous context was at least nctx/2 longer than current,
    //mode is on, and current context already maxed.

    if (fastforwardok && useSmartContext && smartcontext.size() > 0 && embd_inp_len >= SCInpLenThreshold)
    {
        //see if smartcontext is still usable
        auto shared = LongestCommonSubseq(smartcontext, embd_inp);
        if (shared.size() > SCTokThreshold && ArrStartWith(smartcontext, shared)) //at least 32 tokens in common
        {
            int found = ArrFindIndexOf(embd_inp,shared);
            if(found>=0)
            {
                auto trimmed = std::vector<int>(embd_inp.begin() + found, embd_inp.end());
                embd_inp = trimmed;
                embd_inp_len = embd_inp.size();
                printf("\n[Reusing Smart Context: %d allowance remaining]", found);

                int old_n_past = n_past;
                int offset_fix = old_n_past;
                if (current_context_tokens[n_past] != embd_inp[0])
                {
                    offset_fix = 0;
                }

                for (int i = n_past; i < current_context_tokens.size(); ++i)
                {
                    if (current_context_tokens[i] == embd_inp[i-offset_fix])
                    {
                        n_past += 1;
                        last_n_tokens.push_back(current_context_tokens[i]);
                    }
                    else
                    {
                        break;
                    }
                    if ((i + 2 - offset_fix) >= embd_inp_len)
                    {
                        break;
                    }
                }

                last_n_tokens.erase(last_n_tokens.begin(), last_n_tokens.begin() + (n_past-old_n_past));
                embd_inp.erase(embd_inp.begin(), embd_inp.begin() + (n_past-old_n_past));

            }else{
                smartcontext.clear();
            }
        }
        else
        {
            smartcontext.clear();
        }
    }
    else
    {
        smartcontext.clear();
    }

    if(fastforwardok && useSmartContext
    && smartcontext.size()==0 && current_context_tokens.size() >= SCCtxLenThreshold
    && embd_inp_len >= SCInpLenThreshold
    && current_context_tokens.size() - n_past > SCPastLenThreshold)
    {
        //determine longest common substring after removing start part
        int shiftamt = embd_inp.size() * SCTruncationRatio;
        smartcontext = std::vector<int>(embd_inp.begin() + shiftamt, embd_inp.end());
         printf("\n[New Smart Context Triggered! Buffered Token Allowance: %d]",shiftamt);

        embd_inp = smartcontext;
        //if max ctx length is exceeded, chop the prompt in half after the start part, and memorize it. The memorized part becomes LCS marker.
        //when a future prompt comes in, find the LCS again. If LCS > a length and LCS starts with memorized LCS
        //remove all tokens between start part and start of LCS in new prompt, thus avoiding shift
        //if LCS not found or mismatched, regenerate. chop new prompt and repeat from step B
    }
 }