seamless_communication/ggml/examples/unity/unity.cpp

#include "ggml/ggml.h"
#include "ggml/ggml-alloc.h"

#include "common.h"
#include "common-ggml.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <iostream>
// default hparams
struct unity_hparams {
    int32_t n_text_vocab = 256206; 
    int32_t n_unit_vocab = 10084; 
    int32_t n_audio_enc_dim = 1024;
    int32_t n_audio_enc_ffn_dim = 4096;
    int32_t n_audio_enc_feat_dim = 160;
    int32_t n_audio_enc_layer = 24;
    int32_t n_audio_enc_head = 16;
    int32_t ftype   = 1;
    float   eps     = 1e-5f;
};
// layer def
struct audio_enc_layer {
    struct ggml_tensor * self_attn_layer_norm_w;
    struct ggml_tensor * self_attn_layer_norm_b;

    struct ggml_tensor * self_attn_linear_k_w;
    struct ggml_tensor * self_attn_linear_k_b;
    struct ggml_tensor * self_attn_linear_q_w;
    struct ggml_tensor * self_attn_linear_q_b;
    struct ggml_tensor * self_attn_linear_v_w;
    struct ggml_tensor * self_attn_linear_v_b;
    struct ggml_tensor * self_attn_linear_out_w;
    struct ggml_tensor * self_attn_linear_out_b;
    struct ggml_tensor * self_attn_linear_pos_w;

    struct ggml_tensor * self_attn_pos_bias_u;
    struct ggml_tensor * self_attn_pos_bias_v;

    struct ggml_tensor * conv_layer_norm_w;
    struct ggml_tensor * conv_layer_norm_b;

    struct ggml_tensor * conv_pointwise_conv1_w;
    struct ggml_tensor * conv_depthwise_conv_w;
    struct ggml_tensor * conv_batch_norm_w;
    struct ggml_tensor * conv_batch_norm_b;
    struct ggml_tensor * conv_batch_norm_running_mean;
    struct ggml_tensor * conv_batch_norm_running_var;
    struct ggml_tensor * conv_batch_norm_num_batches_tracked;
    struct ggml_tensor * conv_pointwise_conv2_w;

    struct ggml_tensor * ffn1_layer_norm_w;
    struct ggml_tensor * ffn1_layer_norm_b;
    struct ggml_tensor * ffn1_w1;
    struct ggml_tensor * ffn1_b1;
    struct ggml_tensor * ffn1_w2;
    struct ggml_tensor * ffn1_b2;

    struct ggml_tensor * ffn2_layer_norm_w;
    struct ggml_tensor * ffn2_layer_norm_b;
    struct ggml_tensor * ffn2_w1;
    struct ggml_tensor * ffn2_b1;
    struct ggml_tensor * ffn2_w2;
    struct ggml_tensor * ffn2_b2;

    struct ggml_tensor * final_layer_norm_w;
    struct ggml_tensor * final_layer_norm_b;
};

// struct ggml_tensor * conv_ln;
// struct ggml_tensor * conv_pool_1d;

// model def
struct unity_model {
    unity_hparams hparams;
    // audio encoder
    struct ggml_tensor * post_extract_proj;
    struct ggml_tensor * audio_enc_pos_conv;
    std::vector<audio_enc_layer> audio_enc_layers;

    // text encoder
    // std::vector<text_enc_layer> text_enc_layers;

    // adaptor
    // std::vector<adapter_layer> adapter_layers;

    // text decoder
    // std::vector<text_dec_layer> text_dec_layers;

    // unit decoder
    // std::vector<unit_dec_layer> unit_dec_layers;

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

// model load
bool unity_model_load(const std::string & fname, unity_model & model, gpt_vocab & vocab) {
    printf("%s: loading model from '%s'\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 != GGML_FILE_MAGIC) {
            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_text_vocab, sizeof(hparams.n_text_vocab));
        fin.read((char *) &hparams.n_audio_enc_dim,  sizeof(hparams.n_audio_enc_dim));
        fin.read((char *) &hparams.n_audio_enc_ffn_dim,  sizeof(hparams.n_audio_enc_ffn_dim));
        fin.read((char *) &hparams.n_audio_enc_feat_dim,  sizeof(hparams.n_audio_enc_feat_dim));
        fin.read((char *) &hparams.n_audio_enc_layer, sizeof(hparams.n_audio_enc_layer));
        fin.read((char *) &hparams.n_audio_enc_head, sizeof(hparams.n_audio_enc_head));
        fin.read((char *) &hparams.ftype,   sizeof(hparams.ftype));

        const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;

        printf("%s: n_text_vocab = %d\n", __func__, hparams.n_text_vocab);
        printf("%s: n_audio_enc_dim   = %d\n", __func__, hparams.n_audio_enc_dim);
        printf("%s: n_audio_enc_ffn_dim  = %d\n", __func__, hparams.n_audio_enc_ffn_dim);
        printf("%s: n_audio_enc_feat_dim = %d\n", __func__, hparams.n_audio_enc_feat_dim);
        printf("%s: n_audio_enc_layer = %d\n", __func__, hparams.n_audio_enc_layer);
        printf("%s: n_audio_enc_head = %d\n", __func__, hparams.n_audio_enc_head);
        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
        printf("%s: qntvr   = %d\n", __func__, qntvr);

        hparams.ftype %= GGML_QNT_VERSION_FACTOR;
    }

    // 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_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
    if (wtype == GGML_TYPE_COUNT) {
        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
                __func__, fname.c_str(), model.hparams.ftype);
        return false;
    }

    auto & ctx = model.ctx;

    size_t ctx_size = 0;

    {
        const auto & hparams = model.hparams;

        const int n_audio_enc_dim  = hparams.n_audio_enc_dim;
        const int n_audio_enc_ffn_dim  = hparams.n_audio_enc_ffn_dim;
        const int n_audio_enc_layer = hparams.n_audio_enc_layer;
        // const int n_text_vocab = hparams.n_text_vocab;
        const int kernel_size = 31;

        ctx_size += n_audio_enc_layer*n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32); // self_attn_layer_norm_w
        ctx_size += n_audio_enc_layer*n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32); // self_attn_layer_norm_b

        ctx_size += n_audio_enc_layer*(5*n_audio_enc_dim*n_audio_enc_dim*ggml_type_sizef(wtype));         // self_attn_w
        ctx_size += n_audio_enc_layer*(4*n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // self_attn_b

        ctx_size += n_audio_enc_layer*n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32); // conv_layer_norm_w
        ctx_size += n_audio_enc_layer*n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32); // conv_layer_norm_b

        ctx_size += n_audio_enc_layer*(n_audio_enc_dim*n_audio_enc_dim*2*ggml_type_sizef(wtype));           // conv_pointwise_conv1_w
        ctx_size += n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32); // conv_batch_norm_w
        ctx_size += n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32); // conv_batch_norm_b
        ctx_size += n_audio_enc_layer*(n_audio_enc_dim*n_audio_enc_dim*kernel_size*ggml_type_sizef(wtype));         // conv_depthwise_conv_w
        ctx_size += n_audio_enc_layer*(n_audio_enc_dim*n_audio_enc_dim*ggml_type_sizef(wtype));           // conv_pointwise_conv2_w

        ctx_size += 2 * n_audio_enc_layer * (n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // ffn{1,2}_layer_norm_w
        ctx_size += 2 * n_audio_enc_layer * (n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // ffn{1,2}_layer_norm_b
        ctx_size += 2 * n_audio_enc_layer * (2 * n_audio_enc_dim * n_audio_enc_ffn_dim * ggml_type_sizef(wtype));  // ffn{1,2}_w{1,2}
        ctx_size += 2 * n_audio_enc_layer * (2 * n_audio_enc_dim * ggml_type_sizef(GGML_TYPE_F32));  // ffn{1,2}_b{1,2}

        ctx_size += n_audio_enc_layer*(n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // final_layer_norm_w
        ctx_size += n_audio_enc_layer*(n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // final_layer_norm_b

        // Adaptor
        // ctx_size += n_audio_enc_layer*(n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // conv_ln
        // ctx_size += n_audio_enc_layer*(n_audio_enc_dim*ggml_type_sizef(GGML_TYPE_F32)); // conv_pool_1d

        // object overhead might differ depending on the structure and other miscellaneous factors
        ctx_size += (6 + 12*n_audio_enc_layer)*512; // updated object overhead

        printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
        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,
            /*.no_alloc   =*/ false,
        };

        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_audio_enc_dim  = hparams.n_audio_enc_dim;
        const int n_audio_enc_ffn_dim  = hparams.n_audio_enc_ffn_dim;
        // const int n_audio_enc_feat_dim = hparams.n_audio_enc_feat_dim;
        const int n_audio_enc_layer = hparams.n_audio_enc_layer;
        const int n_audio_enc_head = hparams.n_audio_enc_head;
        // const int n_text_vocab = hparams.n_text_vocab;

        model.audio_enc_layers.resize(n_audio_enc_layer);
        // model.post_extract_proj_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_feat_dim);
        // model.post_extract_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
        // model.tensors["model/post_extract_proj/w"] = model.post_extract_proj_w
        // model.tensors["model/post_extract_proj/b"] = model.post_extract_proj_b
        // model.audio_enc_pos_conv_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim, 1);
        // model.tensors["model/audio_enc_pos_conv/w"] = model.audio_enc_pos_conv_w;
        // model.audio_enc_pos_conv_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
        // model.tensors["model/audio_enc_pos_conv/b"] = model.audio_enc_pos_conv_b;

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

            layer.self_attn_layer_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.self_attn_layer_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            layer.self_attn_linear_k_w   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim);
            layer.self_attn_linear_k_b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.self_attn_linear_q_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim);
            layer.self_attn_linear_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.self_attn_linear_v_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim);
            layer.self_attn_linear_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.self_attn_linear_out_w   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim);
            layer.self_attn_linear_out_b   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.self_attn_linear_pos_w   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim);

            layer.self_attn_pos_bias_u = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_head, n_audio_enc_dim / n_audio_enc_head);
            layer.self_attn_pos_bias_v = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_head, n_audio_enc_dim / n_audio_enc_head);

            layer.conv_layer_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.conv_layer_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            layer.conv_pointwise_conv1_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2*n_audio_enc_dim, n_audio_enc_dim);
            layer.conv_depthwise_conv_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, 31);

            layer.conv_batch_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.conv_batch_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.conv_batch_norm_running_mean = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.conv_batch_norm_running_var = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.conv_batch_norm_num_batches_tracked = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); 

            layer.conv_pointwise_conv2_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_dim);

            layer.ffn1_layer_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.ffn1_layer_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            layer.ffn1_w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_ffn_dim, n_audio_enc_dim);
            layer.ffn1_b1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_ffn_dim);

            layer.ffn1_w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_ffn_dim);
            layer.ffn1_b2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            layer.ffn2_layer_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.ffn2_layer_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            layer.ffn2_w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_ffn_dim, n_audio_enc_dim);
            layer.ffn2_b1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_ffn_dim);

            layer.ffn2_w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_enc_dim, n_audio_enc_ffn_dim);
            layer.ffn2_b2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            layer.final_layer_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);
            layer.final_layer_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_enc_dim);

            // map by name

            model.tensors["model/h" + std::to_string(i) + "/self_attn_layer_norm/w"] = layer.self_attn_layer_norm_w;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_layer_norm/b"] = layer.self_attn_layer_norm_b;

            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_k/w"] = layer.self_attn_linear_k_w;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_k/b"] = layer.self_attn_linear_k_b;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_q/w"] = layer.self_attn_linear_q_w;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_q/b"] = layer.self_attn_linear_q_b;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_v/w"] = layer.self_attn_linear_v_w;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_v/b"] = layer.self_attn_linear_v_b;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_out/w"] = layer.self_attn_linear_out_w;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_out/b"] = layer.self_attn_linear_out_b;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_linear_pos/w"] = layer.self_attn_linear_pos_w;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_pos_bias/u"] = layer.self_attn_pos_bias_u;
            model.tensors["model/h" + std::to_string(i) + "/self_attn_pos_bias/v"] = layer.self_attn_pos_bias_v;

            model.tensors["model/h" + std::to_string(i) + "/conv_layer_norm/w"]        = layer.conv_layer_norm_w;
            model.tensors["model/h" + std::to_string(i) + "/conv_layer_norm/b"]        = layer.conv_layer_norm_b;

            model.tensors["model/h" + std::to_string(i) + "/conv_pointwise_conv1/w"] = layer.conv_pointwise_conv1_w;
            model.tensors["model/h" + std::to_string(i) + "/conv_depthwise_conv/w"] = layer.conv_depthwise_conv_w;
            model.tensors["model/h" + std::to_string(i) + "/conv_batch_norm/w"] = layer.conv_batch_norm_w;
            model.tensors["model/h" + std::to_string(i) + "/conv_batch_norm/b"] = layer.conv_batch_norm_b;
            model.tensors["model/h" + std::to_string(i) + "/conv_batch_norm/m"] = layer.conv_batch_norm_running_mean;
            model.tensors["model/h" + std::to_string(i) + "/conv_batch_norm/v"] = layer.conv_batch_norm_running_var;
            model.tensors["model/h" + std::to_string(i) + "/conv_batch_norm/n"] = layer.conv_batch_norm_num_batches_tracked;
            model.tensors["model/h" + std::to_string(i) + "/conv_pointwise_conv2/w"] = layer.conv_pointwise_conv2_w;

            model.tensors["model/h" + std::to_string(i) + "/ffn1_layer_norm/w"] = layer.ffn1_layer_norm_w;
            model.tensors["model/h" + std::to_string(i) + "/ffn1_layer_norm/b"] = layer.ffn1_layer_norm_b;
            model.tensors["model/h" + std::to_string(i) + "/ffn1_w_1/w"] = layer.ffn1_w1;
            model.tensors["model/h" + std::to_string(i) + "/ffn1_w_1/b"] = layer.ffn1_b1;
            model.tensors["model/h" + std::to_string(i) + "/ffn1_w_2/w"] = layer.ffn1_w2;
            model.tensors["model/h" + std::to_string(i) + "/ffn1_w_2/b"] = layer.ffn1_b2;

            model.tensors["model/h" + std::to_string(i) + "/ffn2_layer_norm/w"] = layer.ffn2_layer_norm_w;
            model.tensors["model/h" + std::to_string(i) + "/ffn2_layer_norm/b"] = layer.ffn2_layer_norm_b;
            model.tensors["model/h" + std::to_string(i) + "/ffn2_w_1/w"] = layer.ffn2_w1;
            model.tensors["model/h" + std::to_string(i) + "/ffn2_w_1/b"] = layer.ffn2_b1;
            model.tensors["model/h" + std::to_string(i) + "/ffn2_w_2/w"] = layer.ffn2_w2;
            model.tensors["model/h" + std::to_string(i) + "/ffn2_w_2/b"] = layer.ffn2_b2;

            model.tensors["model/h" + std::to_string(i) + "/final_layer_norm/w"] = layer.final_layer_norm_w;
            model.tensors["model/h" + std::to_string(i) + "/final_layer_norm/b"] = layer.final_layer_norm_b;
        }
    }

   
    // load weights
    {
        size_t total_size = 0;
        while (true) {
            int32_t n_dims;
            int32_t length;
            int32_t ttype;

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

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

            int32_t nelements = 1;
            int32_t ne[3] = { 1, 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);

            std::cout << "loading " << name << " " << n_dims << std::endl;

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

            auto tensor = model.tensors[name];
            if (ggml_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.c_str());
                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.c_str(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
                return false;
            }

            // for debugging
            if (0) {
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.c_str(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
            }

            const size_t bpe = ggml_type_size(ggml_type(ttype));

            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.c_str(), ggml_nbytes(tensor), nelements*bpe);
                return false;
            }

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

            total_size += ggml_nbytes(tensor);
        }

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

    fin.close();

    return true;
}

int main(int argc, char ** argv) {
    // ggml_time_init();

    // const int64_t t_main_start_us = ggml_time_us();

    gpt_params params;
    params.model = "models/gpt-2-117M/ggml-model.bin";

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

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

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

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

    // int64_t t_load_us = 0;

    gpt_vocab vocab;
    unity_model model;

    // load the model
    {
        // const int64_t t_start_us = ggml_time_us();

        if (!unity_model_load(params.model, model, vocab)) {
            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;

        // test_gpt_tokenizer(vocab, params.token_test);
    }

    // keep this buffer alive while evaluating the model
    // std::vector<uint8_t> compute_buffer;

    // struct ggml_allocr * allocr = NULL;
    // // allocate the compute buffer
    // {
    //     allocr = ggml_allocr_new_measure(GGML_MEM_ALIGN);

    //     // create the worst case graph for memory usage estimation
    //     int n_tokens = std::min(model.hparams.n_ctx, params.n_batch);
    //     int n_past = model.hparams.n_ctx - n_tokens;
    //     struct ggml_cgraph * gf = gpt2_graph(model, allocr, n_past, std::vector<gpt_vocab::id>(n_tokens, 0));

    //     // compute the required memory
    //     size_t mem_size = ggml_allocr_alloc_graph(allocr, gf) + GGML_MEM_ALIGN;

    //     // recreate the allocator with the required memory
    //     ggml_allocr_free(allocr);
    //     compute_buffer.resize(mem_size);
    //     allocr = ggml_allocr_new(compute_buffer.data(), mem_size, GGML_MEM_ALIGN);

    //     fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0/1024.0);
    // }

    ggml_free(model.ctx);

    return 0;
}