#include "ggml.h"

#include "utils.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#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

////////////////////////////////////////////////////////
//////////////////////   BLOOM    //////////////////////
////////////////////////////////////////////////////////

struct bloom_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 f16     = 1;
};

struct bloom_layer {
    // normalization
    struct ggml_tensor * attention_norm;
    struct ggml_tensor * attention_norm_b;

    // attention
    struct ggml_tensor * query_key_value;
    struct ggml_tensor * query_key_value_b;
    struct ggml_tensor * wo;
    struct ggml_tensor * wo_b;

    // normalization
    struct ggml_tensor * ffn_norm;
    struct ggml_tensor * ffn_norm_b;

    // ff
    struct ggml_tensor * w1;
    struct ggml_tensor * w1_b;
    struct ggml_tensor * w2;
    struct ggml_tensor * w2_b;
};

struct bloom_model {
    bloom_hparams hparams;

    struct ggml_tensor * tok_embeddings;
    struct ggml_tensor * norm;
    struct ggml_tensor * norm_b;

    struct ggml_tensor * output_norm;
    struct ggml_tensor * output_norm_b;
    struct ggml_tensor * output;
    

    std::vector<bloom_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 bloom_model_load(const std::string & fname, bloom_model & model, gpt_vocab & vocab, int n_ctx) {
    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);
    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.f16,     sizeof(hparams.f16));

        hparams.n_ctx = n_ctx;

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

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

    // load vocab
    {
        const int32_t n_vocab = model.hparams.n_vocab;

        if (n_vocab != model.hparams.n_vocab) {
            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
            return false;
        }

        std::string word;
        for (int i = 0; i < 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) {
            //    printf("%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;
                }
    }

    const ggml_type wtype2 = GGML_TYPE_F32;

    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*ggml_type_sizef(GGML_TYPE_F32); // norm_b

        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // output_norm
        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // output_norm_b

        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*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm_b

        ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // query_key_value
        ctx_size += n_layer*(3*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // query_key_value_b
        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)); // wo_b

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

        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
        ctx_size += n_layer*(n_ff*ggml_type_sizef(GGML_TYPE_F32)); // w1_b
        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
        ctx_size += n_layer*(n_ff*ggml_type_sizef(GGML_TYPE_F32)); // w2_b

        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v

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

        printf("%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_ctx   = hparams.n_ctx;
        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.norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

        model.output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.output_norm_b = 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["norm.bias"]   = model.norm_b;
        
        model.tensors["output_norm.weight"] = model.output_norm;
        model.tensors["output_norm.bias"] = model.output_norm_b;
        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.attention_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.query_key_value = ggml_new_tensor_2d(ctx, wtype, n_embd, 3*n_embd);
            layer.query_key_value_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
            layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wo_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

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

            layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);
            layer.w1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_ff);
            layer.w2 = ggml_new_tensor_2d(ctx, wtype,   n_ff, n_embd);
            layer.w2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

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

            model.tensors["layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.query_key_value;
            model.tensors["layers." + std::to_string(i) + ".attention.query_key_value.bias"] = layer.query_key_value_b;
            model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;
            model.tensors["layers." + std::to_string(i) + ".attention.wo.bias"] = layer.wo_b;

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

            model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.bias"] = layer.w1_b;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.bias"] = layer.w2_b;
        }
    }

    // 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, GGML_TYPE_F32, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

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

        printf("%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);
        }

        printf("%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.seekg(file_offset);

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

            printf("%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", };
                    printf("%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;
                }

                //printf("%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) {
                    printf(".");
                    fflush(stdout);
                }
            }

            printf(" done\n");

            printf("%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 bloom_eval(
        const bloom_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 d_key = n_embd/n_head;

    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
        //printf("\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);

    // word embeddings norm
    {
        inpL = ggml_norm(ctx0, inpL);
        inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm, inpL), inpL);
        inpL = ggml_add(ctx0, ggml_repeat(ctx0, model.norm_b, inpL), inpL);
    }

    for (int il = 0; il < n_layer; ++il) {
        struct ggml_tensor * inpSA = inpL; //TODO: copy?

        struct ggml_tensor * cur;

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

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

        // attn
        {
            cur = ggml_mul_mat(ctx0,model.layers[il].query_key_value, cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].query_key_value_b, cur),
                    cur);
        }

        // cur = ggml_debug(ctx0, cur);

        // self-attention
        {
            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd); //TODO: float or fp16?
            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);

            // 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_cpy(ctx0, Qcur,
                                ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
                        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_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),
                        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))
                        );

            // Alibi
            // KQ_scaled_alibi = KQ_scaled + alibi_bias //TODO: optimize
            struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, n_past, n_head);

            // KQ_masked = mask_past(KQ_scaled)
            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, 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
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].wo,
                    cur);
            cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].wo_b, cur), cur);
        }

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

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

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

            cur = ggml_mul_mat(ctx0,
                    model.layers[il].w1,
                    cur);
            cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].w1_b, cur), cur);

            cur = ggml_gelu(ctx0, cur);

            cur = ggml_mul_mat(ctx0,
                    model.layers[il].w2,
                    cur);
            cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].w2_b, cur), cur);
        }

        cur  = ggml_add(ctx0, cur, inpFF);

        // input for next layer
        inpL = cur;
    }

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

        // inpL = norm*inpL
        inpL = ggml_mul(ctx0,
                    ggml_repeat(ctx0, model.output_norm, inpL),
                    inpL);
        
        inpL = ggml_add(ctx0, ggml_repeat(ctx0, model.output_norm_b, 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;
    }
    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

int main_bloom(gpt_params params) {
    ggml_time_init();
    const int64_t t_main_start_us = ggml_time_us();

    int64_t t_load_us = 0;

    std::mt19937 rng(params.seed);

    gpt_vocab vocab;
    bloom_model model;

    // load the model
    {
        const int64_t t_start_us = ggml_time_us();
        const int n_ctx = 512; // TODO: set context from user input ??
        if (!bloom_model_load(params.model, model, vocab, n_ctx)) {  // TODO: set context from user input ??
            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;
    }

    int n_past = 0;

    int64_t t_sample_us  = 0;
    int64_t t_predict_us = 0;

    std::vector<float> logits;

    // tokenize the prompt
    std::vector<gpt_vocab::id> embd_inp = ::whitespace_tokenize(params.prompt); //TODO: set bos to true?

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

    printf("\n");
    printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
    for (int i = 0; i < (int) embd_inp.size(); i++) {
        printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
    }
    printf("\n");
    printf("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);
    printf("\n\n");

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

    // determine the required inference memory per token:
    size_t mem_per_token = 0;
    bloom_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);

    printf("\n<|BEGIN> ");
    for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
        // predict
        if (embd.size() > 0) {
            const int64_t t_start_us = ggml_time_us();

            if (!bloom_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { // update logits
                printf("Failed to predict\n");
                return 1;
            }

            t_predict_us += ggml_time_us() - t_start_us;
        }

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

        if (i >= embd_inp.size()) {
            // 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;
            if (params.return_logits) {
                printf("logits: ");
                for (int i = 0; i < n_vocab; i++) {
                    // Upto 8 decimal places
                    printf("%.8f ", logits[i]);
                }
                printf(" <END|>\n");
                // Stdout should flush before returning
                fflush(stdout);
                return 0;
            }

            {
                const int64_t t_start_sample_us = ggml_time_us();

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

                // // print
                // printf("\ngenerated token: '%s' (%d)\n", vocab.id_to_token[id].c_str(), id);

                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);
        } else {
            // if here, it means we are still processing the input prompt
            for (int k = i; k < embd_inp.size(); k++) {
                if (params.return_logits) {
                    printf("logits: ");
                    for (int i = 0; i < model.hparams.n_vocab; i++) {
                        // Upto 8 decimal places
                        printf("%.8f ", logits[i]);
                    }
                    printf("\n");
                    // Stdout should flush before returning
                    fflush(stdout);
                }
                embd.push_back(embd_inp[k]);
                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(embd_inp[k]);
                if (embd.size() > params.n_batch) {
                    break;
                }
            }
            i += embd.size() - 1;
        }

        // display text
        for (auto id : embd) {
            if (!params.return_logits) {
                printf(" %d ", id);
            }
            // printf("%s", vocab.id_to_token[id].c_str());
        }
        fflush(stdout);

        // end of text token
        if (embd.back() == 2) {
            break;
        }
    }
    printf(" <END|>\n");

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

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

    ggml_free(model.ctx);

    return 0;
}

////////////////////////////////////////////////////////
////////////////////// GPT-J ///////////////////////////
////////////////////////////////////////////////////////

// default hparams (GPT-J 6B)
struct gptj_hparams {
    int32_t n_vocab = 50400;
    int32_t n_ctx   = 2048;
    int32_t n_embd  = 4096;
    int32_t n_head  = 16;
    int32_t n_layer = 28;
    int32_t n_rot   = 64;
    int32_t f16     = 1;
};

struct gptj_layer {
    // normalization
    struct ggml_tensor * ln_1_g;
    struct ggml_tensor * ln_1_b;

    // attention
    struct ggml_tensor * c_attn_q_proj_w;
    struct ggml_tensor * c_attn_k_proj_w;
    struct ggml_tensor * c_attn_v_proj_w;

    struct ggml_tensor * c_attn_proj_w;

    // ff
    struct ggml_tensor * c_mlp_fc_w;
    struct ggml_tensor * c_mlp_fc_b;

    struct ggml_tensor * c_mlp_proj_w_trans;
    struct ggml_tensor * c_mlp_proj_b;
};

struct gptj_model {
    gptj_hparams hparams;

    // normalization
    struct ggml_tensor * ln_f_g;
    struct ggml_tensor * ln_f_b;

    struct ggml_tensor * wte; // position embedding

    struct ggml_tensor * lmh_g; // language model head
    struct ggml_tensor * lmh_b; // language model bias

    std::vector<gptj_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 gptj_model_load(const std::string & fname, gptj_model & model, gpt_vocab & vocab, int n_ctx) {
    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);
    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;
        }

        else{
            printf("%s: valid model file '%s' (good magic)\n", __func__, fname.c_str());
        }
    }

    // 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_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;

        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
        printf("%s: n_rot   = %d\n", __func__, hparams.n_rot);
        printf("%s: f16     = %d\n", __func__, hparams.f16);
    }

    // load vocab
    {
        int32_t n_vocab = 0;
        fin.read((char *) &n_vocab, sizeof(n_vocab));

        if (n_vocab != model.hparams.n_vocab) {
            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
            return false;
        }

        std::string word;
        for (int i = 0; i < 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;
        }
    }

    // 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;
                }
    }

    const ggml_type wtype2 = GGML_TYPE_F32;

    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*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b

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

        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype);         // lmh_g
        ctx_size +=        n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b

        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b

        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w

        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w

        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_fc_w
        ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b

        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_proj_w_trans
        ctx_size += n_layer*(         n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b

        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v

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

        printf("%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_ctx   = hparams.n_ctx;
        const int n_vocab = hparams.n_vocab;

        model.layers.resize(n_layer);

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

        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

        model.lmh_g  = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
        model.lmh_b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);

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

        model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
        model.tensors["transformer.ln_f.bias"]   = model.ln_f_b;

        model.tensors["lm_head.weight"] = model.lmh_g;
        model.tensors["lm_head.bias"]   = model.lmh_b;

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

            layer.ln_1_g                = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.ln_1_b                = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            layer.c_attn_q_proj_w       = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
            layer.c_attn_k_proj_w       = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
            layer.c_attn_v_proj_w       = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);

            layer.c_attn_proj_w         = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);

            layer.c_mlp_fc_w            = ggml_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
            layer.c_mlp_fc_b            = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);

            layer.c_mlp_proj_w_trans    = ggml_new_tensor_2d(ctx, wtype,         4*n_embd,   n_embd);
            layer.c_mlp_proj_b          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            // map by name
            model.tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"]          = layer.ln_1_g;
            model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"]            = layer.ln_1_b;

            model.tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"]   = layer.c_attn_q_proj_w;
            model.tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"]   = layer.c_attn_k_proj_w;
            model.tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"]   = layer.c_attn_v_proj_w;

            model.tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;

            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"]     = layer.c_mlp_fc_w;
            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"]       = layer.c_mlp_fc_b;

            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"]    = layer.c_mlp_proj_w_trans;
            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"]      = layer.c_mlp_proj_b;
        }
    }

    // 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, GGML_TYPE_F32, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

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

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

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

        printf("%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;
            }

            auto tensor = model.tensors[name.data()];
            if (ggml_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                return false;
            }

            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;
            }

            if (0) {
                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
            }

            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 ((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;
            }

            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));

            //printf("%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);
            total_size += ggml_nbytes(tensor);
            if (++n_tensors % 8 == 0) {
                printf(".");
                fflush(stdout);
            }
        }

        printf(" done\n");

        printf("%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 gptj_eval(
        const gptj_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_rot;

    const int d_key = n_embd/n_head;

    static size_t buf_size = 256u*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
        //printf("\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);
    struct ggml_cgraph 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));

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

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

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

            // cur = ln_1_g*cur + ln_1_b
            cur = ggml_add(ctx0,
                    ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
                        cur),
                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
        }

        struct ggml_tensor * inpSA = cur;

        // self-attention
        {
            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur);
            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur);
            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, 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].c_attn_proj_w,
                    cur);
        }

        struct ggml_tensor * inpFF = cur;

        // feed-forward network
        // this is independent of the self-attention result, so it could be done in parallel to the self-attention
        {
            // note here we pass inpSA instead of cur
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].c_mlp_fc_w,
                    inpSA);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
                    cur);

            // GELU activation
            cur = ggml_gelu(ctx0, cur);

            // projection
            // cur = proj_w*cur + proj_b
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].c_mlp_proj_w_trans,
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
                    cur);
        }

        // self-attention + FF
        cur  = ggml_add(ctx0, cur, inpFF);

        // input for next layer
        inpL = ggml_add(ctx0, cur, inpL);
    }

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

        // inpL = ln_f_g*inpL + ln_f_b
        inpL = ggml_add(ctx0,
                ggml_mul(ctx0,
                    ggml_repeat(ctx0, model.ln_f_g, inpL),
                    inpL),
                ggml_repeat(ctx0, model.ln_f_b, inpL));
    }

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

        inpL = ggml_add(ctx0,
                ggml_repeat(ctx0, model.lmh_b, inpL),
                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;
    }
    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

int main_gptj(gpt_params params) {
    ggml_time_init();
    const int64_t t_main_start_us = ggml_time_us();
    int64_t t_load_us = 0;

    std::mt19937 rng(params.seed); //TODO: understand this

    gpt_vocab vocab;
    gptj_model model;
    // load the model
    {
        const int64_t t_start_us = ggml_time_us();
        const int n_ctx = 512; // TODO: set context from user input ??
        if (!gptj_model_load(params.model, model, vocab, n_ctx)) {  // TODO: set context from user input ??
            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;
    }

    int n_past = 0;

    int64_t t_sample_us  = 0;
    int64_t t_predict_us = 0;

    std::vector<float> logits;

    // tokenize the prompt
    std::vector<gpt_vocab::id> embd_inp = ::whitespace_tokenize(params.prompt); //TODO: set bos to true?

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

    printf("\n");
    printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
    // for (int i = 0; i < (int) embd_inp.size(); i++) {
    //     printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
    // }
    printf("\n");
    printf("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);
    printf("\n\n");

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

    // determine the required inference memory per token:
    size_t mem_per_token = 0;
    gptj_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);

    printf("\n<|BEGIN> ");
    for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
        // predict
        if (embd.size() > 0) {
            const int64_t t_start_us = ggml_time_us();

            if (!gptj_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { // update logits
                printf("Failed to predict\n");
                return 1;
            }

            t_predict_us += ggml_time_us() - t_start_us;
        }

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

        if (i >= embd_inp.size()) {
            // 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;

            if (params.return_logits) {
                printf("logits: ");
                for (int i = 0; i < n_vocab; i++) {
                    // Upto 8 decimal places
                    printf("%.8f ", logits[i]);
                }
                printf(" <END|>\n");
                // Stdout should flush before returning
                fflush(stdout);
                return 0;
            }
            {
                const int64_t t_start_sample_us = ggml_time_us();

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

                // // print
                // printf("\ngenerated token: '%s' (%d)\n", vocab.id_to_token[id].c_str(), id);

                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);
        } else {
            // if here, it means we are still processing the input prompt
            for (int k = i; k < embd_inp.size(); k++) {
                if (params.return_logits) {
                    printf("logits: ");
                    for (int i = 0; i < model.hparams.n_vocab; i++) {
                        // Upto 8 decimal places
                        printf("%.8f ", logits[i]);
                    }
                    printf("\n");
                }
                embd.push_back(embd_inp[k]);
                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(embd_inp[k]);
                if (embd.size() > params.n_batch) {
                    break;
                }
            }
            i += embd.size() - 1;
        }

        // display text
        for (auto id : embd) {
            if (!params.return_logits) {
                printf(" %d ", id);
            }
            // printf("%s", vocab.id_to_token[id].c_str());
        }
        fflush(stdout);

        // end of text token
        if (embd.back() == 2) {
            break;
        }
    }
    printf(" <END|>\n");

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

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

    ggml_free(model.ctx);

    return 0;
}


////////////////////////////////////////////////////////
////////////////////// GPT-J ///////////////////////////
////////////////////////////////////////////////////////

// default hparams (GPT-J 6B)
struct gptneox_hparams {
    int32_t n_vocab = 50400;
    int32_t n_ctx   = 2048;
    int32_t n_embd  = 4096;
    int32_t n_head  = 16;
    int32_t n_layer = 28;
    int32_t n_rot   = 64;
    int32_t use_parallel_residual = 1; // 1 = true, 0 = false
    int32_t f16     = 1;
};

struct gptneox_layer {
    // input_layernorm
    struct ggml_tensor * input_layernorm_weight;
    struct ggml_tensor * input_layernorm_bias;

    // post_attention_layernorm
    struct ggml_tensor * post_attention_layernorm_weight;
    struct ggml_tensor * post_attention_layernorm_bias;

    // attention
    struct ggml_tensor * c_attn_q_proj_w;
    struct ggml_tensor * c_attn_k_proj_w;
    struct ggml_tensor * c_attn_v_proj_w;

    struct ggml_tensor * c_attn_q_proj_bias;
    struct ggml_tensor * c_attn_k_proj_bias;
    struct ggml_tensor * c_attn_v_proj_bias;

    struct ggml_tensor * c_attn_proj_w;
    struct ggml_tensor * c_attn_proj_bias;

    // ff
    struct ggml_tensor * c_mlp_fc_w;
    struct ggml_tensor * c_mlp_fc_b;

    struct ggml_tensor * c_mlp_proj_w_trans;
    struct ggml_tensor * c_mlp_proj_b;
};

struct gptneox_model {
    gptneox_hparams hparams;

    // final normalization
    struct ggml_tensor * ln_f_g;
    struct ggml_tensor * ln_f_b;

    struct ggml_tensor * wte; // word embedding

    struct ggml_tensor * lmh_g; // language model head
    // struct ggml_tensor * lmh_b; // language model bias

    std::vector<gptneox_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 gptneox_model_load(const std::string & fname, gptneox_model & model, gpt_vocab & vocab, int n_ctx) {
    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);
    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;
        }
    }

    // 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_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.use_parallel_residual,     sizeof(hparams.use_parallel_residual));
        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));

        hparams.n_ctx = n_ctx;

        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
        printf("%s: n_rot   = %d\n", __func__, hparams.n_rot);
        printf("%s: use_parallel_residual = %d\n", __func__, hparams.use_parallel_residual);
        printf("%s: f16     = %d\n", __func__, hparams.f16);
    }

    // load vocab
    {
        int32_t n_vocab = model.hparams.n_vocab;
        // fin.read((char *) &n_vocab, sizeof(n_vocab));

        if (n_vocab != model.hparams.n_vocab) {
            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
            return false;
        }

        std::string word;
        for (int i = 0; i < 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;
        }
    }

    // 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;
                }
    }

    const ggml_type wtype2 = GGML_TYPE_F32;

    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*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b

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

        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype);         // lmh_g
        // ctx_size +=        n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b

        { // Transformer layers
            { // Layernorms
                ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // input_layernorm_weight
                ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // input_layernorm_bias

                ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // post_attention_layernorm_weight
                ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // post_attention_layernorm_bias
            }

            { // Attention layer
                ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
                ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
                ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w

                ctx_size += n_layer*(n_embd*       ggml_type_sizef(GGML_TYPE_F32)); // c_attn_q_proj_bias
                ctx_size += n_layer*(n_embd*       ggml_type_sizef(GGML_TYPE_F32)); // c_attn_k_proj_bias
                ctx_size += n_layer*(n_embd*       ggml_type_sizef(GGML_TYPE_F32)); // c_attn_v_proj_bias

                ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
                ctx_size += n_layer*(n_embd*       ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_bias
            }

            { // Feedforward layer
                ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_fc_w
                ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b

                ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype));         // c_mlp_proj_w_trans
                ctx_size += n_layer*(         n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
            }
        }

        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v

        ctx_size += (6 + 16*n_layer)*256; // object overhead

        printf("%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_ctx   = hparams.n_ctx;
        const int n_vocab = hparams.n_vocab;

        model.layers.resize(n_layer);

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

        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

        model.lmh_g  = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
        // model.lmh_b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);

        // map by name
        model.tensors["gpt_neox.embed_in.weight"] = model.wte;

        model.tensors["gpt_neox.final_layer_norm.weight"] = model.ln_f_g;
        model.tensors["gpt_neox.final_layer_norm.bias"]   = model.ln_f_b;

        model.tensors["embed_out.weight"] = model.lmh_g;
        // model.tensors["lm_head.bias"]   = model.lmh_b;

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

            // Layernorms
            layer.input_layernorm_weight          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.input_layernorm_bias            = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.post_attention_layernorm_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.post_attention_layernorm_bias   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            // Attention
            layer.c_attn_q_proj_w       = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
            layer.c_attn_k_proj_w       = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
            layer.c_attn_v_proj_w       = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);

            layer.c_attn_q_proj_bias    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.c_attn_k_proj_bias    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.c_attn_v_proj_bias    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            layer.c_attn_proj_w         = ggml_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
            layer.c_attn_proj_bias      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            // Feedforward
            layer.c_mlp_fc_w            = ggml_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
            layer.c_mlp_fc_b            = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);

            layer.c_mlp_proj_w_trans    = ggml_new_tensor_2d(ctx, wtype,         4*n_embd,   n_embd);
            layer.c_mlp_proj_b          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            // map by name
            // Layernorms
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"]          = layer.input_layernorm_weight;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"]            = layer.input_layernorm_bias;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.post_attention_layernorm_weight;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"]   = layer.post_attention_layernorm_bias;

            // Attention
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query.weight"]   = layer.c_attn_q_proj_w;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query.bias"]     = layer.c_attn_q_proj_bias;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.key.weight"]     = layer.c_attn_k_proj_w;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.key.bias"]       = layer.c_attn_k_proj_bias;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.value.weight"]   = layer.c_attn_v_proj_w;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.value.bias"]     = layer.c_attn_v_proj_bias;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"]   = layer.c_attn_proj_w;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"]     = layer.c_attn_proj_bias;

            // Feedforward
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"]    = layer.c_mlp_fc_w;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"]      = layer.c_mlp_fc_b;

            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"]    = layer.c_mlp_proj_w_trans;
            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"]      = layer.c_mlp_proj_b;
        }
    }

    // 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, GGML_TYPE_F32, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

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

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

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

        printf("%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;
            }

            auto tensor = model.tensors[name.data()];
            if (ggml_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                return false;
            }

            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;
            }

            if (0) {
                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
            }

            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 ((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;
            }

            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));

            //printf("%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);
            total_size += ggml_nbytes(tensor);
            if (++n_tensors % 8 == 0) {
                printf(".");
                fflush(stdout);
            }
        }

        printf(" done\n");

        printf("%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-NeoX model requires about 16MB of memory per input token.
//
bool gptneox_eval(
        const gptneox_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_rot;

    const int d_key = n_embd/n_head;

    static size_t buf_size = 256u*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
        //printf("\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);
    struct ggml_cgraph 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));

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

    // for (int il = 0; il < 1; ++il) {
    for (int il = 0; il < n_layer; ++il) {
        struct ggml_tensor * cur;

        // input norm
        {
            cur = ggml_norm(ctx0, inpL);

            // cur = input_layernorm_weight*cur + input_layernorm_bias
            cur = ggml_add(ctx0,
                    ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].input_layernorm_weight, cur),
                        cur),
                    ggml_repeat(ctx0, model.layers[il].input_layernorm_bias, cur));

        }

        // self-attention
        {
            // Weight
            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur);
            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur);
            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur);

            // Add bias
            Qcur = ggml_add(ctx0, Qcur, ggml_repeat(ctx0, model.layers[il].c_attn_q_proj_bias, Qcur));
            Kcur = ggml_add(ctx0, Kcur, ggml_repeat(ctx0, model.layers[il].c_attn_k_proj_bias, Kcur));
            Vcur = ggml_add(ctx0, Vcur, ggml_repeat(ctx0, model.layers[il].c_attn_v_proj_bias, Vcur));

            // // // // cur = ggml_add(ctx0, cur, Qcur);
            // // // // cur = ggml_add(ctx0, cur, Kcur);
            // // // // cur = ggml_add(ctx0, cur, Vcur);

            // 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_gptneox_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_gptneox_rope(ctx0,// "change Qcur" in line 2270.)
                            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 (first weight)
            cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_proj_w, cur);

            // projection (then bias)
            cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_attn_proj_bias, cur), cur);
        }

        struct ggml_tensor * inpFF;

        if (hparams.use_parallel_residual == 0) {
            printf("use_parallel_residual == 0\n");
            // This takes the self-attention residual output as input to Feedforward
            inpFF = ggml_add(ctx0, cur, inpL);

            // post attention layer norm
            {
                inpFF = ggml_norm(ctx0, inpFF);

                // inpFF = input_layernorm_weight*inpFF + input_layernorm_bias
                inpFF = ggml_add(ctx0,
                    ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].post_attention_layernorm_weight, inpFF),
                        inpFF),
                    ggml_repeat(ctx0, model.layers[il].post_attention_layernorm_bias, inpFF));
            }

            // feed-forward network
            {
                // note here we pass inpFF instead of cur
                inpFF = ggml_mul_mat(ctx0, model.layers[il].c_mlp_fc_w, inpFF);

                inpFF = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, inpFF), inpFF);

                inpFF = ggml_gelu(ctx0, inpFF);

                inpFF = ggml_mul_mat(ctx0, model.layers[il].c_mlp_proj_w_trans, inpFF);

                inpFF = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, inpFF), inpFF);
            }

            // inpL = inpFF + inpL
            inpL = ggml_add(ctx0, inpFF, inpL);

        } else if (hparams.use_parallel_residual == 1) {
            // printf("use_parallel_residual == 1\n");
            // This is independent of the self-attention result, so it could be done in parallel to the self-attention

            // post attention layer norm
            {
                inpFF = ggml_norm(ctx0, inpL);

                // inpFF = input_layernorm_weight*inpFF + input_layernorm_bias
                inpFF = ggml_add(ctx0,
                    ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].post_attention_layernorm_weight, inpFF),
                        inpFF),
                    ggml_repeat(ctx0, model.layers[il].post_attention_layernorm_bias, inpFF));
            }


            // feed-forward network
            {
                // note here we pass inpFF instead of cur
                inpFF = ggml_mul_mat(ctx0, model.layers[il].c_mlp_fc_w, inpFF);

                inpFF = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, inpFF), inpFF);

                // GELU activation
                inpFF = ggml_gelu(ctx0, inpFF);

                // projection
                // inpFF = proj_w*inpFF + proj_b
                inpFF = ggml_mul_mat(ctx0, model.layers[il].c_mlp_proj_w_trans, inpFF);

                inpFF = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, inpFF), inpFF);
            }

            // inpL = inpL + inpFF + cur
            inpFF = ggml_add(ctx0, cur, inpFF);
            inpL = ggml_add(ctx0, inpL, inpFF);
        } else {
            printf("use_parallel_residual == %d\n", hparams.use_parallel_residual);
            assert(0);
        }
    }

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

        // inpL = ln_f_g*inpL + ln_f_b
        inpL = ggml_add(ctx0,
                ggml_mul(ctx0,
                    ggml_repeat(ctx0, model.ln_f_g, inpL),
                    inpL),
                ggml_repeat(ctx0, model.ln_f_b, inpL));
    }

    // lm_head
    {
        inpL = ggml_mul_mat(ctx0, model.lmh_g, 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;
    }
    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

int main_gptneox(gpt_params params) {
    ggml_time_init();
    const int64_t t_main_start_us = ggml_time_us();
    int64_t t_load_us = 0;

    std::mt19937 rng(params.seed);

    gpt_vocab vocab;
    gptneox_model model;
    // load the model
    {
        const int64_t t_start_us = ggml_time_us();
        const int n_ctx = 512; // TODO: set context from user input ??
        if (!gptneox_model_load(params.model, model, vocab, n_ctx)) {  // TODO: set context from user input ??
            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;
    }

    int n_past = 0;

    int64_t t_sample_us  = 0;
    int64_t t_predict_us = 0;

    std::vector<float> logits;

    // tokenize the prompt
    std::vector<gpt_vocab::id> embd_inp = ::whitespace_tokenize(params.prompt); //TODO: set bos to true?

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

    printf("\n");
    printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
    // for (int i = 0; i < (int) embd_inp.size(); i++) {
    //     printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
    // }
    printf("\n");
    printf("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);
    printf("\n\n");

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

    // determine the required inference memory per token:
    size_t mem_per_token = 0;
    gptneox_eval(model, params.n_threads, 0, { 1, 2, 3, 4, 5 }, 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);

    printf("\n<|BEGIN> ");
    for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
        // predict
        if (embd.size() > 0) {
            const int64_t t_start_us = ggml_time_us();

            if (!gptneox_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { // update logits
                printf("Failed to predict\n");
                return 1;
            }

            t_predict_us += ggml_time_us() - t_start_us;
        }

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

        if (i >= embd_inp.size()) {
            // 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;

            if (params.return_logits) {
                printf("logits: ");
                for (int i = 0; i < n_vocab; i++) {
                    // Upto 8 decimal places
                    printf("%.8f ", logits[i]);
                }
                printf(" <END|>\n");
                // Stdout should flush before returning
                fflush(stdout);
                return 0;
            }

            {
                const int64_t t_start_sample_us = ggml_time_us();

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

                // // print
                // printf("\ngenerated token: '%s' (%d)\n", vocab.id_to_token[id].c_str(), id);

                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);
        } else {
            // if here, it means we are still processing the input prompt
            for (int k = i; k < embd_inp.size(); k++) {
                if (params.return_logits) {
                    printf("logits: ");
                    for (int i = 0; i < model.hparams.n_vocab; i++) {
                        // Upto 8 decimal places
                        printf("%.8f ", logits[i]);
                    }
                    printf("\n");
                }
                embd.push_back(embd_inp[k]);
                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(embd_inp[k]);
                if (embd.size() > params.n_batch) {
                    break;
                }
            }
            i += embd.size() - 1;
        }

        // display text
        for (auto id : embd) {
            if (!params.return_logits) {
                printf(" %d ", id);
            }
            // printf("%s", vocab.id_to_token[id].c_str());
        }
        fflush(stdout);

        // end of text token
        if (embd.back() == 2) {
            break;
        }
    }
    printf(" <END|>\n");

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

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

    ggml_free(model.ctx);

    return 0;
}

////////////////////////////////////////////////////////
//////////////////////   GPT2    //////////////////////
////////////////////////////////////////////////////////

struct gpt2_hparams {
    int32_t n_vocab = 50257;
    int32_t n_ctx   = 1024;
    int32_t n_embd  = 768;
    int32_t n_head  = 12;
    int32_t n_layer = 12;
    int32_t n_rot   = 0;
    int32_t f16     = 1;
};

struct gpt2_layer {
    // normalization
    struct ggml_tensor * ln_1_g;
    struct ggml_tensor * ln_1_b;

    // attention
    struct ggml_tensor * attn_c_attn_w;
    struct ggml_tensor * attn_c_attn_b;
    struct ggml_tensor * attn_c_proj_w;
    struct ggml_tensor * attn_c_proj_b;

    // normalization
    struct ggml_tensor * ln_2_g;
    struct ggml_tensor * ln_2_b;

    // mlp
    struct ggml_tensor * mlp_c_fc_w;
    struct ggml_tensor * mlp_c_fc_b;
    struct ggml_tensor * mlp_c_proj_w;
    struct ggml_tensor * mlp_c_proj_b;

};

struct gpt2_model {
    struct gpt2_hparams hparams;
    struct ggml_context * ctx;

    std::map<std::string, struct ggml_tensor *>tensors;

    std::vector<struct gpt2_layer> layers;

    struct ggml_tensor * wpe;
    struct ggml_tensor * wte;

    struct ggml_tensor * ln_f_g;
    struct ggml_tensor * ln_f_b;

    struct ggml_tensor * memory_k;
    struct ggml_tensor * memory_v;

};

bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab, int n_ctx) {
    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);
    if (!fin) {
        printf("%s: failed to open file '%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;
        }

        else{
            printf("%s: valid model file '%s' (good magic)\n", __func__, fname.c_str());
        }
    }

    // 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_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;

        printf("%s: hparams: n_vocab = %d\n", __func__, hparams.n_vocab);
        printf("%s: hparams: n_ctx   = %d\n", __func__, hparams.n_ctx);
        printf("%s: hparams: n_embd  = %d\n", __func__, hparams.n_embd);
        printf("%s: hparams: n_head  = %d\n", __func__, hparams.n_head);
        printf("%s: hparams: n_layer = %d\n", __func__, hparams.n_layer);
        printf("%s: hparams: n_rot   = %d\n", __func__, hparams.n_rot);
        printf("%s: hparams: f16     = %d\n", __func__, hparams.f16);
    }

    // load vocab
    {
        int32_t n_vocab = 0;
        fin.read((char *)&n_vocab, sizeof(n_vocab));

        if (n_vocab != model.hparams.n_vocab) {
            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
            return false;
        }

        std::string word;
        for (int i = 0; i < 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;
        }
    }

    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;
                }
    }

    const ggml_type wtype2 = GGML_TYPE_F32;

    auto & ctx = model.ctx;

    size_t ctx_size = 0;

    {
        const auto & hparams = model.hparams;

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

        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b

        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // wte
        ctx_size += n_ctx*n_embd*ggml_type_sizef(wtype); // wpe

        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b

        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b

        ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // attn_c_attn_w
        ctx_size += n_layer*(3*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attn_c_attn_b
        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // attn_c_proj_w
        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attn_c_proj_b

        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // mlp_c_fc_w
        ctx_size += n_layer*(4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // mlp_c_fc_b

        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // mlp_c_proj_w
        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // mlp_c_proj_b

        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k TODO: check
        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v TODO: check

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

        printf("%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;
        }

        else {
            printf("ggml context created successfully\n");
        }
    }

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

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

        model.layers.resize(n_layer);

        model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
        model.wpe = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ctx);

        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

        model.tensors["ln_f.weight"] = model.ln_f_g;
        model.tensors["ln_f.bias"] = model.ln_f_b;

        model.tensors["wte.weight"] = model.wte;
        model.tensors["wpe.weight"] = model.wpe;

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

            layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
            layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
            layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.attn_c_attn_w = ggml_new_tensor_2d(ctx, wtype, 3*n_embd, n_embd);
            layer.attn_c_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);

            layer.attn_c_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.attn_c_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.mlp_c_fc_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
            layer.mlp_c_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);

            layer.mlp_c_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
            layer.mlp_c_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            // map by name
            model.tensors["h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g;
            model.tensors["h." + std::to_string(i) + ".ln_1.bias"] = layer.ln_1_b;

            model.tensors["h." + std::to_string(i) + ".attn.c_attn.weight"] = layer.attn_c_attn_w;
            model.tensors["h." + std::to_string(i) + ".attn.c_attn.bias"] = layer.attn_c_attn_b;

            model.tensors["h." + std::to_string(i) + ".attn.c_proj.weight"] = layer.attn_c_proj_w;
            model.tensors["h." + std::to_string(i) + ".attn.c_proj.bias"] = layer.attn_c_proj_b;

            model.tensors["h." + std::to_string(i) + ".ln_2.weight"] = layer.ln_2_g;
            model.tensors["h." + std::to_string(i) + ".ln_2.bias"] = layer.ln_2_b;

            model.tensors["h." + std::to_string(i) + ".mlp.c_fc.weight"] = layer.mlp_c_fc_w;
            model.tensors["h." + std::to_string(i) + ".mlp.c_fc.bias"] = layer.mlp_c_fc_b;

            model.tensors["h." + std::to_string(i) + ".mlp.c_proj.weight"] = layer.mlp_c_proj_w;
            model.tensors["h." + std::to_string(i) + ".mlp.c_proj.bias"] = layer.mlp_c_proj_b;

        }
    }

    // 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, GGML_TYPE_F32, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

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

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

    }

    // load weights

    {
        int n_tensors = 0;
        size_t total_size = 0;

        printf("%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;
            }

            auto tensor = model.tensors[name.data()];
            if (ggml_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                // print the actual value and in the file
                printf("Actual dim = %d\n", ggml_nelements(tensor));
                printf("In file dim = %d\n", nelements);
                return false;
            }

            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;
            }

            if (0) {
                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
            }

            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 ((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;
            }

            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));

            //printf("%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);
            total_size += ggml_nbytes(tensor);
            if (++n_tensors % 8 == 0) {
                printf(".");
                fflush(stdout);
            }
        }

        printf(" done\n");

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


    fin.close();

    return true;
}


bool gpt2_eval(
        const gpt2_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;

    static size_t buf_size = 256u*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
        //printf("\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);
    struct ggml_cgraph 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 * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    for (int i = 0; i < N; ++i) {
        ((int32_t *) position->data)[i] = n_past + i;
    }

    // wte + wpe
    struct ggml_tensor * inpL =
        ggml_add(ctx0,
                ggml_get_rows(ctx0, model.wte, embd),
                ggml_get_rows(ctx0, model.wpe, position));

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

        // norm
        {
            // [ 768, N]
            cur = ggml_norm(ctx0, inpL);

            // cur = ln_1_g*cur + ln_1_b
            // [ 768, N]
            cur = ggml_add(ctx0,
                    ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
                        cur),
                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
        }

        // attn
        // [2304, 768] - model.layers[il].c_attn_attn_w
        // [2304,   1] - model.layers[il].c_attn_attn_b
        // [ 768,   N] - cur (in)
        // [2304,   N] - cur (out)
        //
        // cur = attn_w*cur + attn_b
        // [2304, N]
        {
            cur = ggml_mul_mat(ctx0,
                    ggml_transpose(ctx0, model.layers[il].attn_c_attn_w),
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].attn_c_attn_b, cur),
                    cur);
        }

        // self-attention
        {
            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);

            // 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)
            // [64, N, 12]
            struct ggml_tensor * Q =
                ggml_permute(ctx0,
                        ggml_cpy(ctx0,
                            Qcur,
                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
                        0, 2, 1, 3);

            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
            // [64, n_past + N, 12]
            struct ggml_tensor * K =
                ggml_permute(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),
                        0, 2, 1, 3);

            // GG: flash attention
            //struct ggml_tensor * V =
            //    ggml_cpy(ctx0,
            //            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),
            //            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));

            //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);

            // K * Q
            // [n_past + N, N, 12]
            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

            // KQ_scaled = KQ / sqrt(n_embd/n_head)
            // [n_past + N, N, 12]
            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)
            // [n_past + N, N, 12]
            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);

            // KQ = soft_max(KQ_masked)
            // [n_past + N, N, 12]
            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()
            // [n_past + N, 64, 12]
            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
            // [64, N, 12]
            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

            // KQV_merged = KQV.permute(0, 2, 1, 3)
            // [64, 12, N]
            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

            // cur = KQV_merged.contiguous().view(n_embd, N)
            // [768, N]
            cur = ggml_cpy(ctx0,
                    KQV_merged,
                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
        }

        // projection
        // [ 768, 768] - model.layers[il].c_attn_proj_w
        // [ 768,   1] - model.layers[il].c_attn_proj_b
        // [ 768,   N] - cur (in)
        // [ 768,   N] - cur (out)
        //
        // cur = proj_w*cur + proj_b
        // [768, N]
        {
            cur = ggml_mul_mat(ctx0,
                    ggml_transpose(ctx0, model.layers[il].attn_c_proj_w),
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].attn_c_proj_b, cur),
                    cur);
        }

        // add the input
        cur = ggml_add(ctx0, cur, inpL);

        struct ggml_tensor * inpFF = cur;

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

                // cur = ln_2_g*cur + ln_2_b
                // [ 768, N]
                cur = ggml_add(ctx0,
                        ggml_mul(ctx0,
                            ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
                            cur),
                        ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
            }

            // fully connected
            // [3072, 768] - model.layers[il].c_mlp_fc_w
            // [3072,   1] - model.layers[il].c_mlp_fc_b
            // [ 768,   N] - cur (in)
            // [3072,   N] - cur (out)
            //
            // cur = fc_w*cur + fc_b
            // [3072, N]


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

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].mlp_c_fc_b, cur),
                    cur);

            // GELU activation
            // [3072, N]
            cur = ggml_gelu(ctx0, cur);

            // projection
            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
            // [ 768,    1] - model.layers[il].c_mlp_proj_b
            // [3072,    N] - cur (in)
            // [ 768,    N] - cur (out)
            //
            // cur = proj_w*cur + proj_b
            // [768, N]
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].mlp_c_proj_w,
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].mlp_c_proj_b, cur),
                    cur);
        }

        // input for next layer
        inpL = ggml_add(ctx0, cur, inpFF);
    }

    // norm
    {
        // [ 768, N]
        inpL = ggml_norm(ctx0, inpL);

        // inpL = ln_f_g*inpL + ln_f_b
        // [ 768, N]
        inpL = ggml_add(ctx0,
                ggml_mul(ctx0,
                    ggml_repeat(ctx0, model.ln_f_g, inpL),
                    inpL),
                ggml_repeat(ctx0, model.ln_f_b, inpL));
    }

    // inpL = WTE * inpL
    // [ 768, 50257] - model.wte
    // [ 768, N]     - inpL
    inpL = ggml_mul_mat(ctx0, model.wte, 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 just for 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;
    }
    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

int main_gpt2(gpt_params params){
    ggml_time_init();
    const int64_t t_main_start_us = ggml_time_us();
    int64_t t_load_us = 0;

    std::mt19937 rng(params.seed);

    gpt_vocab vocab;
    gpt2_model model;

    // load model
    {
        const int64_t t_main_start_us = ggml_time_us();
        const int n_ctx = 1024;

        if (!gpt2_model_load(params.model, model, vocab, n_ctx)) {
            return 1;
        }

        t_load_us = ggml_time_us() - t_main_start_us;
    }

    int n_past = 0;

    int64_t t_sample_us = 0;
    int64_t t_predict_us = 0;

    std::vector<float> logits;

    // tokenize the prompt
    std::vector<gpt_vocab::id> embd_inp = ::whitespace_tokenize(params.prompt); //TODO: set bos to true?

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

    printf("\n");
    printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
    for (int i = 0; i < (int) embd_inp.size(); i++) {
        printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
    }
    printf("\n");
    printf("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);
    printf("\n\n");

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

    // determine the required inference memory per token:
    size_t mem_per_token = 0;
    gpt2_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);

    printf("\n<|BEGIN> ");
    for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
        // predict
        if (embd.size() > 0) {
            const int64_t t_start_us = ggml_time_us();

            if (!gpt2_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { // update logits
                printf("Failed to predict\n");
                return 1;
            }

            t_predict_us += ggml_time_us() - t_start_us;
        }

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

        if (i >= embd_inp.size()) {
            // 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;
            if (params.return_logits) {
                printf("logits: ");
                for (int i = 0; i < n_vocab; i++) {
                    // Upto 8 decimal places
                    printf("%.8f ", logits[i]);
                }
                printf(" <END|>\n");
                // Stdout should flush before returning
                fflush(stdout);
                return 0;
            }

            {
                const int64_t t_start_sample_us = ggml_time_us();

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

                // // print
                // printf("\ngenerated token: '%s' (%d)\n", vocab.id_to_token[id].c_str(), id);

                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);
        } else {
            // if here, it means we are still processing the input prompt
            for (int k = i; k < embd_inp.size(); k++) {
                if (params.return_logits) {
                    printf("logits: ");
                    for (int i = 0; i < model.hparams.n_vocab; i++) {
                        // Upto 8 decimal places
                        printf("%.8f ", logits[i]);
                    }
                    printf("\n");
                }

                embd.push_back(embd_inp[k]);
                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(embd_inp[k]);
                if (embd.size() > params.n_batch) {
                    break;
                }
            }
            i += embd.size() - 1;
        }

        // display text
        for (auto id : embd) {
            if (!params.return_logits) {
                printf(" %d ", id);
            }
            // printf("%s", vocab.id_to_token[id].c_str());
        }
        fflush(stdout);

        // end of text token
        if (embd.back() == 50256) {
            printf(" <END|>\n");
            break;
        }
    }

    printf(" <END|>\n");

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

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

    ggml_free(model.ctx);

    return 0;
}

////////////////////////////////////////////////////////
////////////////////////////////////////////////////////
////////////////////////////////////////////////////////

int main(int argc, char ** argv) {
    gpt_params params; // We say "gpt", but it's actually any LLM
    // params.model = "models/ggml-model-bloomz-7b1-f16-q4_0.bin";
    // params.prompt = "Je vais";

    // loop through argv and print all the arguments, one per line
    for (int i = 0; i < argc; i++) {
        printf("argv[%d] = %s\n", i, argv[i]);
    }

    if (gpt_params_parse(argc, argv, params) == false) {
        return 1;
    }
    // return if params.model or params.prompt are empty
    if (params.model.empty() || params.prompt.empty()) {
        return 1;
    }

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

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

    if (params.prompt.empty()) {
        return 1;
    }

    // Get the model type from argv[1]
    std::string model_type = argv[1];
    printf("model_type: %s\n", model_type.c_str());

    if (params.return_logits) {
        printf("********************************\n");
        printf("*** return_logits mode ***\n");
        printf("*** setting sampling to greedy ***\n");
        printf("********************************\n");
        // model_type should be either gptj or gptneox or bloom
        // if (model_type != "gptj" && model_type != "gptneox" && model_type != "bloom") {
        //     printf("model_type: %s, should be either gptj or gptneox or bloom\n", model_type.c_str());
        //     assert(false);
        // }
    }

    if (model_type == "bloom") {
        return main_bloom(params);
    } else if (model_type == "gptj") {
        return main_gptj(params);
    } else if (model_type == "gpt2") {
        return main_gpt2(params);
    } else if (model_type == "gptneox") {
        return main_gptneox(params);
    } else {
        printf("Unknown model type: %s\n", model_type.c_str());
        return 1;
    }
}
