llama.cpp/main.cpp

#include "ggml.h"

#include "utils.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
#include <map>
#include <string>
#include <vector>

#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#include <signal.h>
#endif

#define ANSI_COLOR_RED     "\x1b[31m"
#define ANSI_COLOR_GREEN   "\x1b[32m"
#define ANSI_COLOR_YELLOW  "\x1b[33m"
#define ANSI_COLOR_BLUE    "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN    "\x1b[36m"
#define ANSI_COLOR_RESET   "\x1b[0m"
#define ANSI_BOLD          "\x1b[1m"

static const int EOS_TOKEN_ID = 2;

// determine number of model parts based on the dimension
static const std::map<int, int> LLAMA_N_PARTS = {
    { 4096, 1 },
    { 5120, 2 },
    { 6656, 4 },
    { 8192, 8 },
};

// default hparams (LLaMA 7B)
struct llama_hparams {
    int32_t n_vocab = 32000;
    int32_t n_ctx   = 512;   // this is provided as user input?
    int32_t n_embd  = 4096;
    int32_t n_mult  = 256;
    int32_t n_head  = 32;
    int32_t n_layer = 32;
    int32_t n_rot   = 64;
    int32_t f16     = 1;
};

struct llama_layer {
    // normalization
    struct ggml_tensor * attention_norm;

    // attention
    struct ggml_tensor * wq;
    struct ggml_tensor * wk;
    struct ggml_tensor * wv;
    struct ggml_tensor * wo;

    // normalization
    struct ggml_tensor * ffn_norm;

    // ff
    struct ggml_tensor * w1;
    struct ggml_tensor * w2;
    struct ggml_tensor * w3;
};

struct llama_model {
    llama_hparams hparams;

    struct ggml_tensor * tok_embeddings;

    struct ggml_tensor * norm;
    struct ggml_tensor * output;

    std::vector<llama_layer> layers;

    // key + value memory
    struct ggml_tensor * memory_k;
    struct ggml_tensor * memory_v;

    //
    struct ggml_context * ctx;
    std::map<std::string, struct ggml_tensor *> tensors;
};

// load the model's weights from a file
bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) {
    fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

    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 false;
    }

    // verify magic
    {
        uint32_t magic;
        fin.read((char *) &magic, sizeof(magic));
        if (magic != 0x67676d6c) {
            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
            return false;
        }
    }

    int n_ff = 0;
    int n_parts = 0;

    // load hparams
    {
        auto & hparams = model.hparams;

        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
        //fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
        fin.read((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
        fin.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));

        hparams.n_ctx = n_ctx;

        n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
        n_parts = LLAMA_N_PARTS.at(hparams.n_embd);

        fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
        fprintf(stderr, "%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
        fprintf(stderr, "%s: n_embd  = %d\n", __func__, hparams.n_embd);
        fprintf(stderr, "%s: n_mult  = %d\n", __func__, hparams.n_mult);
        fprintf(stderr, "%s: n_head  = %d\n", __func__, hparams.n_head);
        fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
        fprintf(stderr, "%s: n_rot   = %d\n", __func__, hparams.n_rot);
        fprintf(stderr, "%s: f16     = %d\n", __func__, hparams.f16);
        fprintf(stderr, "%s: n_ff    = %d\n", __func__, n_ff);
        fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
    }

    // load vocab
    {
        std::string word;
        for (int i = 0; i < model.hparams.n_vocab; i++) {
            uint32_t len;
            fin.read((char *) &len, sizeof(len));

            word.resize(len);
            fin.read((char *) word.data(), len);

            vocab.token_to_id[word] = i;
            vocab.id_to_token[i] = word;

            //if (i < 30000) {
            //    fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
            //}
        }
    }

    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
    // in order to save memory and also to speed up the computation
    ggml_type wtype = GGML_TYPE_COUNT;
    switch (model.hparams.f16) {
        case 0: wtype = GGML_TYPE_F32;  break;
        case 1: wtype = GGML_TYPE_F16;  break;
        case 2: wtype = GGML_TYPE_Q4_0; break;
        case 3: wtype = GGML_TYPE_Q4_1; break;
        default:
                {
                    fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
                            __func__, fname.c_str(), model.hparams.f16);
                    return false;
                }
    }

    auto & ctx = model.ctx;

    size_t ctx_size = 0;

    {
        const auto & hparams = model.hparams;

        const int n_embd  = hparams.n_embd;
        const int n_layer = hparams.n_layer;
        const int n_ctx   = hparams.n_ctx;
        const int n_vocab = hparams.n_vocab;

        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // tok_embeddings

        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm

        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // output

        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm

        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq
        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk
        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv
        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo

        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm

        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3

        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v

        ctx_size += (5 + 10*n_layer)*256; // object overhead

        fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
    }

    // create the ggml context
    {
        struct ggml_init_params params = {
            /*.mem_size   =*/ ctx_size,
            /*.mem_buffer =*/ NULL,
        };

        model.ctx = ggml_init(params);
        if (!model.ctx) {
            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
            return false;
        }
    }

    // prepare memory for the weights
    {
        const auto & hparams = model.hparams;

        const int n_embd  = hparams.n_embd;
        const int n_layer = hparams.n_layer;
        const int n_vocab = hparams.n_vocab;

        model.layers.resize(n_layer);

        model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

        model.norm   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.output = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);

        // map by name
        model.tensors["tok_embeddings.weight"] = model.tok_embeddings;

        model.tensors["norm.weight"]   = model.norm;
        model.tensors["output.weight"] = model.output;

        for (int i = 0; i < n_layer; ++i) {
            auto & layer = model.layers[i];

            layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

            layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);
            layer.w2 = ggml_new_tensor_2d(ctx, wtype,   n_ff, n_embd);
            layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);

            // map by name
            model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;

            model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;
            model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;
            model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;
            model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;

            model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;

            model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;
        }
    }

    // key + value memory
    {
        const auto & hparams = model.hparams;

        const int n_embd  = hparams.n_embd;
        const int n_layer = hparams.n_layer;
        const int n_ctx   = hparams.n_ctx;

        const int n_mem      = n_layer*n_ctx;
        const int n_elements = n_embd*n_mem;

        model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);

        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);

        fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
    }

    const size_t file_offset = fin.tellg();

    fin.close();

    std::vector<uint8_t> tmp;

    for (int i = 0; i < n_parts; ++i) {
        const int part_id = i;
        //const int part_id = n_parts - i - 1;

        std::string fname_part = fname;
        if (i > 0) {
            fname_part += "." + std::to_string(i);
        }

        fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());

        fin = std::ifstream(fname_part, std::ios::binary);
        fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
        fin.seekg(file_offset);

        // load weights
        {
            int n_tensors = 0;
            size_t total_size = 0;

            fprintf(stderr, "%s: ", __func__);

            while (true) {
                int32_t n_dims;
                int32_t length;
                int32_t ftype;

                fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
                fin.read(reinterpret_cast<char *>(&length), sizeof(length));
                fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));

                if (fin.eof()) {
                    break;
                }

                int32_t nelements = 1;
                int32_t ne[2] = { 1, 1 };
                for (int i = 0; i < n_dims; ++i) {
                    fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
                    nelements *= ne[i];
                }

                std::string name(length, 0);
                fin.read(&name[0], length);

                if (model.tensors.find(name.data()) == model.tensors.end()) {
                    fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
                    return false;
                }

                // split_type = 0: split by columns
                // split_type = 1: split by rows
                int split_type = 0;

                // split_type = 0:
                // regex:
                //   - tok_embeddings.*
                //   - layers.*.attention.wo.weight
                //   - layers.*.feed_forward.w2.weight

                // split_type = 1:
                // regex:
                //   - output.*
                //   - layers.*.attention.wq.weight
                //   - layers.*.attention.wk.weight
                //   - layers.*.attention.wv.weight
                //   - layers.*.feed_forward.w1.weight
                //   - layers.*.feed_forward.w3.weight
                if (name.find("tok_embeddings") != std::string::npos) {
                    split_type = 0;
                } else if (name.find("layers") != std::string::npos) {
                    if (name.find("attention.wo.weight") != std::string::npos) {
                        split_type = 0;
                    } else if (name.find("feed_forward.w2.weight") != std::string::npos) {
                        split_type = 0;
                    } else {
                        split_type = 1;
                    }
                } else if (name.find("output") != std::string::npos) {
                    split_type = 1;
                }

                auto tensor = model.tensors[name.data()];

                if (n_dims == 1) {
                    if (ggml_nelements(tensor) != nelements) {
                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                        return false;
                    }
                } else {
                    if (ggml_nelements(tensor)/n_parts != nelements) {
                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                        return false;
                    }
                }

                if (n_dims == 1) {
                    if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
                        fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
                                __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
                        return false;
                    }
                } else {
                    if (split_type == 0) {
                        if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
                                    __func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
                            return false;
                        }
                    } else {
                        if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
                                    __func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
                            return false;
                        }
                    }
                }

                if (0) {
                    static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
                    fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
                }

                size_t bpe = 0;

                switch (ftype) {
                    case 0: bpe = ggml_type_size(GGML_TYPE_F32);  break;
                    case 1: bpe = ggml_type_size(GGML_TYPE_F16);  break;
                    case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
                    case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
                    default:
                            {
                                fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
                                return false;
                            }
                };

                if (n_dims == 1 || n_parts == 1) {
                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
                                __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
                        return false;
                    }

                    if (part_id == 0) {
                        fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
                    } else {
                        fin.seekg(ggml_nbytes(tensor), std::ios::cur);
                    }

                    total_size += ggml_nbytes(tensor);
                } else {
                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
                                __func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
                        return false;
                    }

                    if (split_type == 0) {
                        const int np0 = ne[0];

                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
                        assert(row_size == tensor->nb[1]);

                        for (int i1 = 0; i1 < ne[1]; ++i1) {
                            const size_t offset_row = i1*row_size;
                            const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
                            fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
                        }
                    } else {
                        const int np1 = ne[1];

                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);

                        for (int i1 = 0; i1 < ne[1]; ++i1) {
                            const size_t offset_row = (i1 + part_id*np1)*row_size;
                            fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
                        }
                    }

                    total_size += ggml_nbytes(tensor)/n_parts;
                }

                //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
                if (++n_tensors % 8 == 0) {
                    fprintf(stderr, ".");
                    fflush(stderr);
                }
            }

            fprintf(stderr, " done\n");

            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
        }

        fin.close();
    }

    return true;
}

// evaluate the transformer
//
//   - model:     the model
//   - n_threads: number of threads to use
//   - n_past:    the context size so far
//   - embd_inp:  the embeddings of the tokens in the context
//   - embd_w:    the predicted logits for the next token
//
// The GPT-J model requires about 16MB of memory per input token.
//
bool llama_eval(
        const llama_model & model,
        const int n_threads,
        const int n_past,
        const std::vector<gpt_vocab::id> & embd_inp,
              std::vector<float>         & embd_w,
              size_t                     & mem_per_token) {
    const int N = embd_inp.size();

    const auto & hparams = model.hparams;

    const int n_embd  = hparams.n_embd;
    const int n_layer = hparams.n_layer;
    const int n_ctx   = hparams.n_ctx;
    const int n_head  = hparams.n_head;
    const int n_vocab = hparams.n_vocab;
    const int n_rot   = hparams.n_embd/hparams.n_head;

    // TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
    // static size_t buf_size = hparams.n_ctx*1024*1024;
    static size_t buf_size = 512u*1024*1024;
    static void * buf = malloc(buf_size);

    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
        //fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);

        // reallocate
        buf_size = buf_size_new;
        buf = realloc(buf, buf_size);
        if (buf == nullptr) {
            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
            return false;
        }
    }

    struct ggml_init_params params = {
        /*.mem_size   =*/ buf_size,
        /*.mem_buffer =*/ buf,
    };

    struct ggml_context * ctx0 = ggml_init(params);
    ggml_cgraph gf = {};
    gf.n_threads = n_threads;

    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));

    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);

    for (int il = 0; il < n_layer; ++il) {
        struct ggml_tensor * inpSA = inpL;

        struct ggml_tensor * cur;

        // norm
        {
            cur = ggml_rms_norm(ctx0, inpL);

            // cur = attention_norm*cur
            cur = ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
                        cur);
        }

        // self-attention
        {
            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);

            // store key and value to memory
            if (N >= 1) {
                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));

                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
            }

            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
            struct ggml_tensor * Q =
                ggml_permute(ctx0,
                        ggml_rope(ctx0,
                            ggml_cpy(ctx0,
                                Qcur,
                                ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
                            n_past, n_rot, 0),
                        0, 2, 1, 3);

            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
            struct ggml_tensor * K =
                ggml_permute(ctx0,
                        ggml_rope(ctx0,
                            ggml_reshape_3d(ctx0,
                                ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
                                n_embd/n_head, n_head, n_past + N),
                            n_past, n_rot, 1),
                        0, 2, 1, 3);

            // K * Q
            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

            // KQ_scaled = KQ / sqrt(n_embd/n_head)
            struct ggml_tensor * KQ_scaled =
                ggml_scale(ctx0,
                        KQ,
                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
                        );

            // KQ_masked = mask_past(KQ_scaled)
            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);

            // KQ = soft_max(KQ_masked)
            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);

            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
            struct ggml_tensor * V_trans =
                ggml_permute(ctx0,
                        ggml_reshape_3d(ctx0,
                            ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
                            n_embd/n_head, n_head, n_past + N),
                        1, 2, 0, 3);

            // KQV = transpose(V) * KQ_soft_max
            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

            // KQV_merged = KQV.permute(0, 2, 1, 3)
            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

            // cur = KQV_merged.contiguous().view(n_embd, N)
            cur = ggml_cpy(ctx0,
                    KQV_merged,
                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));

            // projection (no bias)
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].wo,
                    cur);
        }

        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);

        // feed-forward network
        {
            // norm
            {
                cur = ggml_rms_norm(ctx0, inpFF);

                // cur = ffn_norm*cur
                cur = ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
                        cur);
            }

            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
                    model.layers[il].w3,
                    cur);


            cur = ggml_mul_mat(ctx0,
                    model.layers[il].w1,
                    cur);

            // SILU activation
            cur = ggml_silu(ctx0, cur);

            cur = ggml_mul(ctx0, cur, tmp);

            cur = ggml_mul_mat(ctx0,
                    model.layers[il].w2,
                    cur);
        }

        cur  = ggml_add(ctx0, cur, inpFF);

        // input for next layer
        inpL = cur;
    }

    // norm
    {
        inpL = ggml_rms_norm(ctx0, inpL);

        // inpL = norm*inpL
        inpL = ggml_mul(ctx0,
                    ggml_repeat(ctx0, model.norm, inpL),
                    inpL);
    }

    // lm_head
    {
        inpL = ggml_mul_mat(ctx0, model.output, inpL);
    }

    // logits -> probs
    //inpL = ggml_soft_max(ctx0, inpL);

    // run the computation
    ggml_build_forward_expand(&gf, inpL);
    ggml_graph_compute       (ctx0, &gf);

    //if (n_past%100 == 0) {
    //    ggml_graph_print   (&gf);
    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
    //}

    //embd_w.resize(n_vocab*N);
    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);

    // return result for just the last token
    embd_w.resize(n_vocab);
    memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);

    if (mem_per_token == 0) {
        mem_per_token = ggml_used_mem(ctx0)/N;
    }
    //fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

static bool is_interacting = false;

#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
void sigint_handler(int signo) {
    printf(ANSI_COLOR_RESET);
    printf("\n"); // this also force flush stdout.
    if (signo == SIGINT) {
        if (!is_interacting) {
            is_interacting=true;
        } else {
            _exit(130);
        }
    }
}
#endif

const char * llama_print_system_info(void) {
    static std::string s;

    s  = "";
    s += "AVX = "       + std::to_string(ggml_cpu_has_avx())       + " | ";
    s += "AVX2 = "      + std::to_string(ggml_cpu_has_avx2())      + " | ";
    s += "AVX512 = "    + std::to_string(ggml_cpu_has_avx512())    + " | ";
    s += "FMA = "       + std::to_string(ggml_cpu_has_fma())       + " | ";
    s += "NEON = "      + std::to_string(ggml_cpu_has_neon())      + " | ";
    s += "ARM_FMA = "   + std::to_string(ggml_cpu_has_arm_fma())   + " | ";
    s += "F16C = "      + std::to_string(ggml_cpu_has_f16c())      + " | ";
    s += "FP16_VA = "   + std::to_string(ggml_cpu_has_fp16_va())   + " | ";
    s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
    s += "BLAS = "      + std::to_string(ggml_cpu_has_blas())      + " | ";
    s += "SSE3 = "      + std::to_string(ggml_cpu_has_sse3())      + " | ";
    s += "VSX = "       + std::to_string(ggml_cpu_has_vsx())       + " | ";

    return s.c_str();
}

int main(int argc, char ** argv) {
    ggml_time_init();
    const int64_t t_main_start_us = ggml_time_us();

    gpt_params params;
    params.model = "models/llama-7B/ggml-model.bin";

    if (gpt_params_parse(argc, argv, params) == false) {
        return 1;
    }

    if (params.n_ctx > 2048) {
        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
                "expect poor results\n", __func__, params.n_ctx);
    }

    if (params.seed < 0) {
        params.seed = time(NULL);
    }

    fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);

    std::mt19937 rng(params.seed);
    if (params.random_prompt) {
        params.prompt = gpt_random_prompt(rng);
    }

//    params.prompt = R"(// this function checks if the number n is prime
//bool is_prime(int n) {)";

    int64_t t_load_us = 0;

    gpt_vocab vocab;
    llama_model model;

    // load the model
    {
        const ggml_type memory_type = params.memory_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
        const int64_t t_start_us = ggml_time_us();
        if (!llama_model_load(params.model, model, vocab, params.n_ctx, memory_type)) {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
            return 1;
        }

        t_load_us = ggml_time_us() - t_start_us;
    }

    // print system information
    {
        fprintf(stderr, "\n");
        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
                params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
    }

    int n_past = 0;

    int64_t t_sample_us  = 0;
    int64_t t_predict_us = 0;

    std::vector<float> logits;

    // Add a space in front of the first character to match OG llama tokenizer behavior
    params.prompt.insert(0, 1, ' ');
    // tokenize the prompt
    std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);

    params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());

    // prefix & suffix for instruct mode
    const std::vector<gpt_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
    const std::vector<gpt_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);

    // in instruct mode, we inject a prefix and a suffix to each input by the user
    if (params.instruct) {
        params.interactive = true;
        params.antiprompt.push_back("### Instruction:\n\n");
    }

    // tokenize the reverse prompt
    std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
    
    for (auto antiprompt : params.antiprompt) {
        antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
    }

    // enable interactive mode if reverse prompt is specified
    if (!antipromptv_inp.size()) {
        params.interactive = true;
    }

    fprintf(stderr, "\n");
    fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
    fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
    for (int i = 0; i < (int) embd_inp.size(); i++) {
        fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
    }
    fprintf(stderr, "\n");
    if (params.interactive) {
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
        struct sigaction sigint_action;
        sigint_action.sa_handler = sigint_handler;
        sigemptyset (&sigint_action.sa_mask);
        sigint_action.sa_flags = 0;
        sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
        signal(SIGINT, sigint_handler);
#endif

        fprintf(stderr, "%s: interactive mode on.\n", __func__);

        if(antipromptv_inp.size()) {
            for (size_t apindex = 0; apindex < antipromptv_inp.size(); ++apindex) {
                auto antiprompt_inp = antipromptv_inp.at(apindex);
                fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.at(apindex).c_str());
                fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
                for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
                    fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
                }
                fprintf(stderr, "\n");
            }
        }
    }
    fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
    fprintf(stderr, "\n\n");

    std::vector<gpt_vocab::id> embd;

    // determine the required inference memory per token:
    size_t mem_per_token = 0;
    llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);

    int last_n_size = params.repeat_last_n;
    std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
    std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);

    if (params.interactive) {
        fprintf(stderr, "== Running in interactive mode. ==\n"
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
               " - Press Ctrl+C to interject at any time.\n"
#endif
               " - Press Return to return control to LLaMa.\n"
               " - If you want to submit another line, end your input in '\\'.\n\n");
        is_interacting = true;
    }

    int input_consumed = 0;
    bool input_noecho = false;

    int remaining_tokens = params.n_predict;

    // set the color for the prompt which will be output initially
    if (params.use_color) {
        printf(ANSI_COLOR_YELLOW);
    }

    while (remaining_tokens > 0 || params.interactive) {
        // predict
        if (embd.size() > 0) {
            const int64_t t_start_us = ggml_time_us();

            if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
                fprintf(stderr, "Failed to predict\n");
                return 1;
            }

            t_predict_us += ggml_time_us() - t_start_us;
        }

        n_past += embd.size();
        embd.clear();

        if (embd_inp.size() <= input_consumed) {
            // out of user input, sample next token
            const float top_k = params.top_k;
            const float top_p = params.top_p;
            const float temp  = params.temp;
            const float repeat_penalty = params.repeat_penalty;

            const int n_vocab = model.hparams.n_vocab;

            gpt_vocab::id id = 0;

            {
                const int64_t t_start_sample_us = ggml_time_us();

                if (params.ignore_eos) {
                    // set the logit of the eos token to zero to avoid sampling it
                    logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
                }

                id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);

                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(id);

                t_sample_us += ggml_time_us() - t_start_sample_us;
            }

            // add it to the context
            embd.push_back(id);

            // echo this to console
            input_noecho = false;

            // decrement remaining sampling budget
            --remaining_tokens;
        } else {
            // some user input remains from prompt or interaction, forward it to processing
            while (embd_inp.size() > input_consumed) {
                embd.push_back(embd_inp[input_consumed]);
                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(embd_inp[input_consumed]);
                ++input_consumed;
                if ((int) embd.size() >= params.n_batch) {
                    break;
                }
            }
        }

        // display text
        if (!input_noecho) {
            for (auto id : embd) {
                printf("%s", vocab.id_to_token[id].c_str());
            }
            fflush(stdout);
        }
        // reset color to default if we there is no pending user input
        if (!input_noecho && params.use_color && (int)embd_inp.size() == input_consumed) {
            printf(ANSI_COLOR_RESET);
        }

        // in interactive mode, and not currently processing queued inputs;
        // check if we should prompt the user for more
        if (params.interactive && embd_inp.size() <= input_consumed) {
            // check for reverse prompt
            for (auto antiprompt_inp : antipromptv_inp) {
                if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
                    // reverse prompt found
                    is_interacting = true;
                    break;
                }
            }
            if (is_interacting) {
                if (params.instruct) {
                    input_consumed = embd_inp.size();
                    embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());

                    printf("\n> ");
                }

                // currently being interactive
                if (params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
                std::string buffer;
                std::string line;
                bool another_line = true;
                do {
                    std::getline(std::cin, line);
                    if (line.empty() || line.back() != '\\') {
                        another_line = false;
                    } else {
                        line.pop_back(); // Remove the continue character
                    }
                    buffer += line + '\n'; // Append the line to the result
                } while (another_line);
                if (params.use_color) printf(ANSI_COLOR_RESET);

                std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
                embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());

                if (params.instruct) {
                    embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
                }

                remaining_tokens -= line_inp.size();

                input_noecho = true; // do not echo this again
            }
            is_interacting = false;
        }

        // end of text token
        if (embd.back() == EOS_TOKEN_ID) {
            if (params.interactive) {
                is_interacting = true;
            } else {
                fprintf(stderr, " [end of text]\n");
                break;
            }
        }

        // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
        if (params.interactive && remaining_tokens <= 0) {
            remaining_tokens = params.n_predict;
            is_interacting = true;
        }
    }

#if defined (_WIN32)
    signal(SIGINT, SIG_DFL);
#endif

    // report timing
    {
        const int64_t t_main_end_us = ggml_time_us();

        fprintf(stderr, "\n\n");
        fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
        fprintf(stderr, "%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
        fprintf(stderr, "%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
        fprintf(stderr, "%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
        fprintf(stderr, "%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
    }

    ggml_free(model.ctx);

    if (params.use_color) {
        printf(ANSI_COLOR_RESET);
    }

    return 0;
}