clip.cpp

#include <cassert>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <iostream>
#include <map>
#include <pthread.h>
#include <regex>
#include <stdexcept>
#include <thread>
#include <vector>

#include "clip.h"
#include "ggml/ggml.h"

#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"

// #define CLIP_DEBUG

static std::string format(const char * fmt, ...) {
    va_list ap;
    va_list ap2;
    va_start(ap, fmt);
    va_copy(ap2, ap);
    int size = vsnprintf(NULL, 0, fmt, ap);
    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
    std::vector<char> buf(size + 1);
    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
    GGML_ASSERT(size2 == size);
    va_end(ap2);
    va_end(ap);
    return std::string(buf.data(), buf.size());
}

//
// key constants
//

#define KEY_FTYPE "general.file_type"
#define KEY_NAME "general.name"
#define KEY_DESCRIPTION "general.description"
#define KEY_HAS_TEXT_ENC "clip.has_text_encoder"
#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
#define KEY_USE_GELU "clip.use_gelu"
#define KEY_N_EMBD "clip.%s.embedding_length"
#define KEY_N_FF "clip.%s.feed_forward_length"
#define KEY_N_BLOCK "clip.%s.block_count"
#define KEY_N_HEAD "clip.%s.attention.head_count"
#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon"
#define KEY_PROJ_DIM "clip.%s.projection_dim"
#define KEY_TOKENS "tokenizer.ggml.tokens"
#define KEY_N_POSITIONS "clip.text.context_length"
#define KEY_IMAGE_SIZE "clip.vision.image_size"
#define KEY_PATCH_SIZE "clip.vision.patch_size"
#define KEY_IMAGE_MEAN "clip.vision.image_mean"
#define KEY_IMAGE_STD "clip.vision.image_std"

//
// tensor name constants
//

#define TN_TOKEN_EMBD "%s.token_embd.weight"
#define TN_POS_EMBD "%s.position_embd.weight"
#define TN_CLASS_EMBD "v.class_embd"
#define TN_PATCH_EMBD "v.patch_embd.weight"
#define TN_ATTN_K "%s.blk.%d.attn_k.%s"
#define TN_ATTN_Q "%s.blk.%d.attn_q.%s"
#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
#define TN_LN_1 "%s.blk.%d.ln1.%s"
#define TN_LN_2 "%s.blk.%d.ln2.%s"
#define TN_LN_PRE "%s.pre_ln.%s"
#define TN_LN_POST "%s.post_ln.%s"
#define TN_TEXT_PROJ "text_projection.weight"
#define TN_VIS_PROJ "visual_projection.weight"

//
// utilities to get data from a gguf file
//

int get_key_idx(const gguf_context * ctx, const char * key) {
    int i = gguf_find_key(ctx, key);
    if (i == -1) {
        fprintf(stderr, "key %s not found in file\n", key);
        throw std::runtime_error(format("Missing required key: %s", key));
    }

    return i;
}

const uint32_t get_u32(const gguf_context * ctx, std::string key) {
    const int i = get_key_idx(ctx, key.c_str());

    return gguf_get_val_u32(ctx, i);
}

const float get_f32(const gguf_context * ctx, std::string key) {
    const int i = get_key_idx(ctx, key.c_str());

    return gguf_get_val_f32(ctx, i);
}

struct ggml_tensor * get_tensor(struct ggml_context * ctx, std::string name) {
    struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
    if (!cur) {
        printf("unable to find tensor %s\n", name.c_str());
        throw std::runtime_error(format("unable to find tensor %s\n", name.c_str()));
    }

    return cur;
}

std::string get_ftype(int ftype) {
    switch (ftype) {
    case 0:
        return "f32";
        break;
    case 1:
        return "f16";
        break;
    case 2:
        return "q4_0";
        break;
    case 3:
        return "q4_1";
        break;
    case 6:
        return "q5_0";
        break;
    case 7:
        return "q5_1";
        break;
    case 8:
        return "q8_0";
        break;
    default:
        throw std::runtime_error(format("Unrecognized file type: %d\n", ftype));
    }
}

//
// Vocab utils
//

struct clip_vocab {
    using id = clip_vocab_id;
    using token = std::string;

    std::map<token, id> token_to_id;
    std::map<id, token> id_to_token;
    std::vector<std::string> special_tokens;

    //    void add_special_token(const std::string & token);
};

//
// clip layers
//

struct clip_layer {
    // attention
    struct ggml_tensor * k_w;
    struct ggml_tensor * k_b;
    struct ggml_tensor * q_w;
    struct ggml_tensor * q_b;
    struct ggml_tensor * v_w;
    struct ggml_tensor * v_b;

    struct ggml_tensor * o_w;
    struct ggml_tensor * o_b;

    // layernorm 1
    struct ggml_tensor * ln_1_w;
    struct ggml_tensor * ln_1_b;

    // ff
    struct ggml_tensor * ff_i_w;
    struct ggml_tensor * ff_i_b;

    struct ggml_tensor * ff_o_w;
    struct ggml_tensor * ff_o_b;

    // layernorm 2
    struct ggml_tensor * ln_2_w;
    struct ggml_tensor * ln_2_b;
};

struct clip_text_model {
    struct clip_text_hparams hparams;

    // embeddings
    struct ggml_tensor * token_embeddings;
    struct ggml_tensor * position_embeddings;

    std::vector<clip_layer> layers;

    struct ggml_tensor * post_ln_w;
    struct ggml_tensor * post_ln_b;

    struct ggml_tensor * projection;
};

struct clip_vision_model {
    struct clip_vision_hparams hparams;

    // embeddings
    struct ggml_tensor * class_embedding;
    struct ggml_tensor * patch_embeddings;
    struct ggml_tensor * position_embeddings;

    struct ggml_tensor * pre_ln_w;
    struct ggml_tensor * pre_ln_b;

    std::vector<clip_layer> layers;

    struct ggml_tensor * post_ln_w;
    struct ggml_tensor * post_ln_b;

    struct ggml_tensor * projection;
};

// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
struct clip_buffer {
    uint8_t * data = NULL;
    size_t size = 0;

    void resize(size_t size) {
        delete[] data;
        data = new uint8_t[size];
        this->size = size;
    }

    ~clip_buffer() { delete[] data; }
};

struct clip_ctx {
    bool has_text_encoder = false;
    bool has_vision_encoder = false;
    struct clip_text_model text_model;
    struct clip_vision_model vision_model;
    struct clip_vocab vocab;
    float image_mean[3];
    float image_std[3];
    bool use_gelu = false;
    int32_t ftype = 1;
    struct ggml_context * ctx;
    struct gguf_context * ctx_gguf;
    struct clip_buffer buf_compute;
};

//
// memory allocation and management
//

// utility function for a workaround until https://github.com/ggerganov/ggml/issues/260 is resolved
// after that, remove this and use the mechanism implemented in GGML directly
size_t get_mem_req_by_size(struct clip_ctx * ctx) {
    size_t mb = 1024 * 1024;
    const int n_tensors = gguf_get_n_tensors(ctx->ctx_gguf);
    const auto & vision_hparams = clip_get_vision_hparams(ctx);
    const int n_positions =
        ctx->has_vision_encoder ? vision_hparams->image_size * vision_hparams->image_size / vision_hparams->patch_size + 1 : 77;
    switch (n_tensors) {
    case 397:                    // base, two-tower
    case 200:                    // base, vision-only
        if (n_positions == 50) { // patch size = 32
            return 12 * mb;
        } else { // patch size = 16
            return 24 * mb;
        }
    case 197: // base or large, text-only
        return 12 * mb;
    case 589:                     // large, two-tower
    case 392:                     // large, vision-only
        if (n_positions == 257) { // input image size = 224
            return 24 * mb;
        } else { // input image size = 336
            return 60 * mb;
        }
    case 909: // huge, two-tower
    case 520: // huge, vision-only
        return 232 * mb;
    case 389: // huge, text-only
        return 120 * mb;
    default:
        fprintf(stderr, "%s: Unrecognized number of tensors: %d. Check if you pass the correct model file\n", __func__,
                n_tensors);
        exit(1);
    }
}

size_t get_scr_buf_req_by_size(struct clip_ctx * ctx) {
    size_t mb = 1024 * 1024;

    const int n_tensors = gguf_get_n_tensors(ctx->ctx_gguf);
    const auto & vision_hparams = clip_get_vision_hparams(ctx);
    const int n_positions =
        ctx->has_vision_encoder ? vision_hparams->image_size * vision_hparams->image_size / vision_hparams->patch_size + 1 : 77;

    switch (n_tensors) {
    case 397:
    case 200:
        if (n_positions <= 50) {
            return 32 * mb;
        } else {
            return 96 * mb;
        }
    case 197:
        return 32 * mb;
    case 589:
    case 392:
        if (n_positions <= 257) {
            return 96 * mb;
        } else {
            return 192 * mb;
        }
    case 909:
    case 520:
        return 144 * mb;
    case 389:
        return 60 * mb;
    default:
        fprintf(stderr, "%s: Unrecognized number of tensors: %d. Check if you pass the correct model file\n", __func__,
                n_tensors);
        exit(1);
    }
}

// read and create ggml_context containing the tensors and their data
struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {

    struct ggml_context * meta = NULL;

    struct gguf_init_params params = {
        /*.no_alloc = */ true,
        /*.ctx      = */ &meta,
    };

    struct gguf_context * ctx = gguf_init_from_file(fname, params);

    if (verbosity >= 1) {
        const int n_tensors = gguf_get_n_tensors(ctx);
        const int n_kv = gguf_get_n_kv(ctx);
        const int ftype = get_u32(ctx, KEY_FTYPE);
        const std::string ftype_str = get_ftype(ftype);
        const int idx_desc = get_key_idx(ctx, KEY_DESCRIPTION);
        const std::string description = gguf_get_val_str(ctx, idx_desc);
        const int idx_name = gguf_find_key(ctx, KEY_NAME);
        if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
            const std::string name = gguf_get_val_str(ctx, idx_name);
            printf("%s: model name:   %s\n", __func__, name.c_str());
        }
        printf("%s: description:  %s\n", __func__, description.c_str());
        printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
        printf("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
        printf("%s: n_tensors:    %d\n", __func__, n_tensors);
        printf("%s: n_kv:         %d\n", __func__, n_kv);
        printf("%s: ftype:        %s\n", __func__, ftype_str.c_str());
        printf("\n");
    }

    // kv
    if (verbosity >= 3) {
        const int n_kv = gguf_get_n_kv(ctx);

        for (int i = 0; i < n_kv; ++i) {
            const char * key = gguf_get_key(ctx, i);

            printf("%s: kv[%d]: key = %s\n", __func__, i, key);
        }
        printf("\n");
    }

    // data
    size_t ctx_size = 0;
    {
        const int n_tensors = gguf_get_n_tensors(ctx);

        for (int i = 0; i < n_tensors; ++i) {
            const char * name = gguf_get_tensor_name(ctx, i);
            const size_t offset = gguf_get_tensor_offset(ctx, i);

            struct ggml_tensor * cur = ggml_get_tensor(meta, name);
            ctx_size += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
            size_t tensor_size = ggml_nbytes(cur);
            size_t padded_size = ggml_nbytes_pad(cur);
            ctx_size += padded_size;
            if (verbosity >= 3) {
                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i,
                       cur->n_dims, cur->name, tensor_size, padded_size, offset);
            }
        }
    }

    clip_ctx * new_clip = new clip_ctx;

    // model size and capabilities
    {
        int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC);
        new_clip->has_text_encoder = gguf_get_val_bool(ctx, idx);

        idx = get_key_idx(ctx, KEY_HAS_VIS_ENC);
        new_clip->has_vision_encoder = gguf_get_val_bool(ctx, idx);

        idx = get_key_idx(ctx, KEY_USE_GELU);
        new_clip->use_gelu = gguf_get_val_bool(ctx, idx);

        if (verbosity >= 1) {
            printf("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
            printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
            printf("%s: model size:     %.2f MB\n", __func__, (ctx_size / 1024.0 / 1024.0));
            printf("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
        }
    }

    // load tensors
    {
        struct ggml_init_params params = {
            .mem_size = ctx_size,
            .mem_buffer = NULL,
            .no_alloc = false,
        };

        new_clip->ctx = ggml_init(params);
        if (!new_clip->ctx) {
            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
            clip_free(new_clip);
            return nullptr;
        }

        auto fin = std::ifstream(fname, std::ios::binary);
        if (!fin) {
            printf("cannot open model file for loading tensors\n");
            clip_free(new_clip);
            return nullptr;
        }

        const int n_tensors = gguf_get_n_tensors(ctx);
        for (int i = 0; i < n_tensors; ++i) {
            const char * name = gguf_get_tensor_name(ctx, i);
            struct ggml_tensor * t = ggml_get_tensor(meta, name);
            struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t);
            ggml_set_name(cur, name);

            const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
            fin.seekg(offset, std::ios::beg);
            if (!fin) {
                printf("%s: failed to seek for tensor %s\n", __func__, name);
                clip_free(new_clip);
                return nullptr;
            }

            fin.read(reinterpret_cast<char *>(cur->data), ggml_nbytes(t));
        }

        fin.close();
    }

    // text model
    if (new_clip->has_text_encoder) {
        // load text model
        auto & text_model = new_clip->text_model;
        auto & hparams = text_model.hparams;
        hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "text"));
        hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "text"));
        hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "text"));
        hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "text"));
        hparams.num_positions = get_u32(ctx, KEY_N_POSITIONS);
        hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "text"));
        hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "text"));

        const int idx_tokens = get_key_idx(ctx, KEY_TOKENS);
        hparams.n_vocab = gguf_get_arr_n(ctx, idx_tokens);
        auto & vocab = new_clip->vocab;
        for (int id = 0; id < hparams.n_vocab; ++id) {
            const std::string token = gguf_get_arr_str(ctx, idx_tokens, id);
            vocab.id_to_token[id] = token;
            vocab.token_to_id[token] = id;
        }

        if (verbosity >= 2) {
            printf("\n%s: text model hparams\n", __func__);
            printf("n_vocab            %d\n", hparams.n_vocab);
            printf("num_positions      %d\n", hparams.num_positions);
            printf("t_hidden_size      %d\n", hparams.hidden_size);
            printf("t_n_intermediate   %d\n", hparams.n_intermediate);
            printf("t_projection_dim   %d\n", hparams.projection_dim);
            printf("t_n_head           %d\n", hparams.n_head);
            printf("t_n_layer          %d\n", hparams.n_layer);
        }

        text_model.token_embeddings = get_tensor(new_clip->ctx, format(TN_TOKEN_EMBD, "t"));
        text_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "t"));
        text_model.post_ln_w = get_tensor(new_clip->ctx, format(TN_LN_POST, "t", "weight"));
        text_model.post_ln_b = get_tensor(new_clip->ctx, format(TN_LN_POST, "t", "bias"));
        text_model.projection = get_tensor(new_clip->ctx, TN_TEXT_PROJ);
        text_model.layers.resize(hparams.n_layer);
        for (int il = 0; il < hparams.n_layer; ++il) {
            auto & layer = text_model.layers[il];
            layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, "t", il, "weight"));
            layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "t", il, "weight"));
            layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "t", il, "weight"));
            layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "t", il, "weight"));
            layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "t", il, "weight"));
            layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "t", il, "weight"));
            layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "t", il, "weight"));
            layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "t", il, "weight"));
            layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "t", il, "bias"));
            layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "t", il, "bias"));
            layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "t", il, "bias"));
            layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "t", il, "bias"));
            layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "t", il, "bias"));
            layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "t", il, "bias"));
            layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "t", il, "bias"));
            layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "t", il, "bias"));
        }
    }

    // vision model
    if (new_clip->has_vision_encoder) {
        // load vision model
        auto & vision_model = new_clip->vision_model;
        auto & hparams = vision_model.hparams;
        hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision"));
        hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision"));
        hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision"));
        hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision"));
        hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE);
        hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE);
        hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision"));
        hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));

        int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
        int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);
        for (int i = 0; i < 3; ++i) {
            new_clip->image_mean[i] = *((float *)gguf_get_arr_data(ctx, idx_mean));
            new_clip->image_std[i] = *((float *)gguf_get_arr_data(ctx, idx_std));
        }

        if (verbosity >= 2) {
            printf("\n%s: vision model hparams\n", __func__);
            printf("image_size         %d\n", hparams.image_size);
            printf("patch_size         %d\n", hparams.patch_size);
            printf("v_hidden_size      %d\n", hparams.hidden_size);
            printf("v_n_intermediate   %d\n", hparams.n_intermediate);
            printf("v_projection_dim   %d\n", hparams.projection_dim);
            printf("v_n_head           %d\n", hparams.n_head);
            printf("v_n_layer          %d\n", hparams.n_layer);
        }

        vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD);
        vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD);
        vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "v"));
        vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "weight"));
        vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "bias"));
        vision_model.post_ln_w = get_tensor(new_clip->ctx, format(TN_LN_POST, "v", "weight"));
        vision_model.post_ln_b = get_tensor(new_clip->ctx, format(TN_LN_POST, "v", "bias"));
        vision_model.projection = get_tensor(new_clip->ctx, TN_VIS_PROJ);
        vision_model.layers.resize(hparams.n_layer);
        for (int il = 0; il < hparams.n_layer; ++il) {
            auto & layer = vision_model.layers[il];
            layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "weight"));
            layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "weight"));
            layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "weight"));
            layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "weight"));
            layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "weight"));
            layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "weight"));
            layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "weight"));
            layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "weight"));
            layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "bias"));
            layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "bias"));
            layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "bias"));
            layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "bias"));
            layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "bias"));
            layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "bias"));
            layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "bias"));
            layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "bias"));
        }
    }

    ggml_free(meta);

    new_clip->ctx_gguf = ctx;

    const size_t mem_req = get_mem_req_by_size(new_clip);
    new_clip->buf_compute.resize(mem_req);
    if (verbosity >= 1) {
        printf("\n%s: %zu MB of memory allocated\n", __func__, mem_req / 1024 / 1024);
    }

    return new_clip;
}

bool clip_tokenize(const clip_ctx * ctx, const char * text, struct clip_tokens * tokens) {
    if (!ctx->has_text_encoder) {
        printf("This GGUF file seems to have no text encoder\n");
        return false;
    }

    std::vector<std::string> words;

    // first split the text into words
    {
        std::string str = text;
        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";

        // Generate the subpattern from the special_tokens vector if it's not empty
        if (!ctx->vocab.special_tokens.empty()) {
            std::string special_tokens_subpattern;
            for (const auto & token : ctx->vocab.special_tokens) {
                if (!special_tokens_subpattern.empty()) {
                    special_tokens_subpattern += "|";
                }
                special_tokens_subpattern += token;
            }

            // Modify the regex pattern with the generated special tokens subpattern
            pat = special_tokens_subpattern + "|" + pat;
        }

        std::regex re(pat);
        std::smatch m;

        while (std::regex_search(str, m, re)) {
            for (auto x : m) {
                words.push_back(x);
            }
            str = m.suffix();
        }
    }

    std::vector<clip_vocab::id> v_tokens;
    v_tokens.push_back(49406); // startoftext

    for (const auto & word : words) {
        // feel lucky? let's try if it's a full word
        std::string full_word = "";
        if (word.find(" ") == 0) // starts_with for C++11
        {
            full_word += word.substr(1);
        } else {
            full_word += word;
        }
        full_word += "</w>";
        auto wit = ctx->vocab.token_to_id.find(full_word);
        if (wit != ctx->vocab.token_to_id.end()) {
            v_tokens.push_back(wit->second);
            continue;
        }

        for (int i = 0; i < word.size();) {
            for (int j = word.size() - 1; j >= i; j--) {
                auto cand = word.substr(i, j - i + 1);
                auto it = ctx->vocab.token_to_id.find(cand);
                if (it != ctx->vocab.token_to_id.end()) { // word.substr(i, j-i+1) in vocab
                    v_tokens.push_back(it->second);
                    i = j + 1;
                    break;
                } else if (j == i) { // word.substr(i, 1) has no matching
                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
                    i++;
                }
            }
        }
    }

    v_tokens.push_back(49407); // endoftext

    tokens->size = v_tokens.size();

    tokens->data = new int[v_tokens.size()];
    std::copy(v_tokens.begin(), v_tokens.end(), tokens->data);

    return true;
}

clip_image_u8 * make_clip_image_u8() { return new clip_image_u8(); }

clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); }

bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
    int nx, ny, nc;
    auto data = stbi_load(fname, &nx, &ny, &nc, 3);
    if (!data) {
        fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname);
        return false;
    }

    img->nx = nx;
    img->ny = ny;
    img->size = nx * ny * 3;
    img->data = new uint8_t[img->size]();
    memcpy(img->data, data, img->size);

    stbi_image_free(data);

    return true;
}

// normalize: x = (x - mean) / std
// TODO: implement bicubic interpolation instead of linear.
bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res) {
    if (!ctx->has_vision_encoder) {
        printf("This gguf file seems to have no vision encoder\n");
        return false;
    }

    const int nx = img->nx;
    const int ny = img->ny;

    const int nx2 = ctx->vision_model.hparams.image_size;
    const int ny2 = ctx->vision_model.hparams.image_size;

    res->nx = nx2;
    res->ny = ny2;
    res->size = 3 * nx2 * ny2;
    res->data = new float[res->size]();

    const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;

    const int nx3 = int(nx / scale + 0.5f);
    const int ny3 = int(ny / scale + 0.5f);

    const auto & m3 = ctx->image_mean; // {0.48145466f, 0.4578275f, 0.40821073f};
    const auto & s3 = ctx->image_std;  // {0.26862954f, 0.26130258f, 0.27577711f};

    for (int y = 0; y < ny3; y++) {
        for (int x = 0; x < nx3; x++) {
            for (int c = 0; c < 3; c++) {
                // linear interpolation
                const float sx = (x + 0.5f) * scale - 0.5f;
                const float sy = (y + 0.5f) * scale - 0.5f;

                const int x0 = std::max(0, (int)std::floor(sx));
                const int y0 = std::max(0, (int)std::floor(sy));

                const int x1 = std::min(x0 + 1, nx - 1);
                const int y1 = std::min(y0 + 1, ny - 1);

                const float dx = sx - x0;
                const float dy = sy - y0;

                const int j00 = 3 * (y0 * nx + x0) + c;
                const int j01 = 3 * (y0 * nx + x1) + c;
                const int j10 = 3 * (y1 * nx + x0) + c;
                const int j11 = 3 * (y1 * nx + x1) + c;

                const float v00 = img->data[j00];
                const float v01 = img->data[j01];
                const float v10 = img->data[j10];
                const float v11 = img->data[j11];

                const float v0 = v00 * (1.0f - dx) + v01 * dx;
                const float v1 = v10 * (1.0f - dx) + v11 * dx;

                const float v = v0 * (1.0f - dy) + v1 * dy;

                const uint8_t v2 = std::min(std::max(std::round(v), 0.0f), 255.0f);

                const int i = 3 * (y * nx3 + x) + c;

                res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
            }
        }
    }

    return true;
}

// Structure to hold the image data as an input to function to be executed for thread
typedef struct {
    const clip_image_u8 * input;
    clip_image_f32 * resized;
    const clip_ctx * ctx;
} ImageData;

// Function to preprocess a single image in a thread
void * preprocess_image(void * arg) {
    ImageData * imageData = static_cast<ImageData *>(arg);
    const clip_image_u8 * input = imageData->input;
    clip_image_f32 * resized = imageData->resized;
    const clip_ctx * ctx = imageData->ctx;

    // Call the original preprocess function on the image
    clip_image_preprocess(ctx, input, resized);

    pthread_exit(NULL);
}

// Function to batch-preprocess multiple images i
void clip_image_batch_preprocess(const clip_ctx * ctx, const int n_threads, const clip_image_u8_batch * img_inputs,
                                 clip_image_f32_batch * imgs_resized) {
    imgs_resized->size = img_inputs->size;

    int num_threads = std::min(n_threads, static_cast<int>(img_inputs->size));
    int i, t;

    // Divide the images among the threads
    int images_per_thread = img_inputs->size / num_threads;

    if (num_threads == 1) {
        // Single-threaded case
        for (i = 0; i < img_inputs->size; i++) {
            clip_image_preprocess(ctx, &img_inputs->data[i], &imgs_resized->data[i]);
        }
    } else {
        // Multi-threaded case

        std::vector<pthread_t> threads(num_threads);
        std::vector<ImageData> imageData(img_inputs->size);

        for (t = 0; t < num_threads; t++) {
            int start_index = t * images_per_thread;
            int end_index = (t == num_threads - 1) ? img_inputs->size : start_index + images_per_thread;

            // Create ImageData for each thread
            for (i = start_index; i < end_index; i++) {
                imageData[i].input = &img_inputs->data[i];
                imageData[i].resized = &imgs_resized->data[i];
                imageData[i].ctx = ctx;
            }

            // Create a thread for each batch of images
            pthread_create(&threads[t], NULL, preprocess_image, static_cast<void *>(&imageData[start_index]));
        }

        // Wait for all threads to finish
        for (t = 0; t < num_threads; t++) {
            pthread_join(threads[t], NULL);
        }
    }
}

void clip_free(clip_ctx * ctx) {
    ggml_free(ctx->ctx);
    gguf_free(ctx->ctx_gguf);
    delete ctx;
}

bool clip_text_encode(const clip_ctx * ctx, const int n_threads, const clip_tokens * tokens, float * vec,
                      const bool normalize) {
    if (!ctx->has_text_encoder) {
        printf("This GGUF file seems to have no text encoder\n");
        return false;
    }

    const auto & model = ctx->text_model;
    const auto & hparams = model.hparams;
    const size_t N = tokens->size;

    const int n_vocab = hparams.n_vocab;
    const int num_positions = hparams.num_positions;
    const int hidden_size = hparams.hidden_size;
    const int n_head = hparams.n_head;
    const int d_head = hidden_size / n_head;
    const int n_layer = hparams.n_layer;
    const int n_intermediate = hparams.n_intermediate;
    const int projection_dim = hparams.projection_dim;
    const float eps = hparams.eps;

    auto & buf_compute = ctx->buf_compute;

    struct ggml_init_params params = {
        .mem_size = buf_compute.size,
        .mem_buffer = buf_compute.data,
        .no_alloc = false,
    };

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

    static size_t scr0_size = get_scr_buf_req_by_size((struct clip_ctx *)ctx);
    static void * scr0 = malloc(scr0_size);

    struct ggml_tensor * input_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    memcpy(input_ids->data, tokens->data, N * ggml_element_size(input_ids));

    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    for (int i = 0; i < N; i++) {
        ggml_set_i32_1d(positions, i, i);
    }

    struct ggml_tensor * embeddings = ggml_get_rows(ctx0, model.token_embeddings, input_ids);

    embeddings = ggml_add(ctx0, ggml_get_rows(ctx0, model.position_embeddings, positions), embeddings);

    // loop over layers
    for (int il = 0; il < n_layer; il++) {
        struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states

        ggml_set_scratch(ctx0, {0, scr0_size, scr0});

        // layernorm1
        {
            cur = ggml_norm(ctx0, cur, eps);

            cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_w, cur), cur),
                           ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
        }

        // self-attention
        {
            struct ggml_tensor * Q =
                ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur));

            Q = ggml_scale_inplace(ctx0, Q, ggml_new_f32(ctx0, 1.0f / sqrt((float)d_head)));
            Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, N, 1);
            Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
            Q = ggml_reshape_3d(ctx0, Q, d_head, N, n_head);

            struct ggml_tensor * K =
                ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, cur), ggml_mul_mat(ctx0, model.layers[il].k_w, cur));

            K = ggml_reshape_4d(ctx0, K, d_head, n_head, N, 1);
            K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
            K = ggml_reshape_3d(ctx0, K, d_head, N, n_head);

            struct ggml_tensor * V =
                ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, cur), ggml_mul_mat(ctx0, model.layers[il].v_w, cur));
            V = ggml_reshape_4d(ctx0, V, d_head, n_head, N, 1);
            V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
            V = ggml_reshape_3d(ctx0, V, N, d_head, n_head);

            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
            KQ = ggml_diag_mask_inf_inplace(ctx0, KQ, 0); // causal masking
            KQ = ggml_soft_max_inplace(ctx0, KQ);

            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
            KQV = ggml_reshape_4d(ctx0, KQV, d_head, N, n_head, 1);
            KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3));

            cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hidden_size, N));
        }

        // attention output
        cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].o_b, cur), ggml_mul_mat(ctx0, model.layers[il].o_w, cur));

        // re-add the layer input, e.g., residual
        cur = ggml_add(ctx0, cur, embeddings);

        embeddings = cur; // embeddings = residual, cur = hidden_states

        // layernorm2
        {
            cur = ggml_norm(ctx0, cur, eps);

            cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_w, cur), cur),
                           ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
        }

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

        if (ctx->use_gelu) {
            cur = ggml_gelu_inplace(ctx0, cur);
        } else {
            cur = ggml_gelu_quick_inplace(ctx0, cur);
        }

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

        // residual 2
        cur = ggml_add(ctx0, embeddings, cur);

        embeddings = cur;
    }

    // final -layer_norm
    {
        embeddings = ggml_norm(ctx0, embeddings, eps);

        embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.post_ln_w, embeddings), embeddings),
                              ggml_repeat(ctx0, model.post_ln_b, embeddings));
    }

    // get the output of eot token, e.g., last index
    struct ggml_tensor * eot = ggml_new_i32(ctx0, N - 1);
    embeddings = ggml_get_rows(ctx0, embeddings, eot);

    ggml_set_scratch(ctx0, {0, 0, nullptr});

    // text projection
    embeddings = ggml_mul_mat(ctx0, model.projection, embeddings);

    // normalize output embeddings
    if (normalize) {
        ggml_tensor * length = ggml_sqrt(ctx0, ggml_sum(ctx0, ggml_sqr(ctx0, embeddings)));
        embeddings = ggml_scale_inplace(ctx0, embeddings, ggml_div(ctx0, ggml_new_f32(ctx0, 1.0f), length));
    }

    ggml_set_name(embeddings, "check");

    // run the computation

    ggml_build_forward_expand(&gf, embeddings);
    ggml_cplan cplan = ggml_graph_plan(&gf, n_threads);
    if (cplan.work_size != 0) {
        cplan.work_data = (uint8_t *)malloc(cplan.work_size);
    }
    ggml_graph_compute(&gf, &cplan);

// print
#ifdef CLIP_DEBUG
    {
        auto print_t_f32 = [&](struct ggml_tensor * t) {
            float * data = (float *)t->data;
            printf("dtype: f32, dims: %jd %jd %jd %jd, nb: %jd %jd %jd %jd\n", t->ne[0], t->ne[1], t->ne[2], t->ne[3], t->nb[0],
                   t->nb[1], t->nb[2], t->nb[3]);
            printf("data: ");
            for (int i = 0; i < std::min((int)t->ne[0], 20); i++) {
                printf("%f ", data[i]);
            }

            // printf("\n\n");
            double sum = 0.0;
            for (int i = 0; i < ggml_nelements(t); i++) {
                sum += data[i];
            }
            printf("sum:  %f\n", sum);
        };

        auto print_t_f16 = [&](struct ggml_tensor * t) {
            ggml_fp16_t * data = (ggml_fp16_t *)t->data;
            printf("dtype: f16, dims: %jd %jd %jd %jd\n", t->ne[0], t->ne[1], t->ne[2], t->ne[3]);
            printf("data: ");
            for (int i = 0; i < std::min((int)t->ne[0], 10); i++) {
                printf("%f ", ggml_fp16_to_fp32(data[i]));
            }
            printf("\n\n");
            double sum = 0.0;
            for (int i = 0; i < ggml_nelements(t); i++) {
                sum += ggml_fp16_to_fp32(data[i]);
            }
            printf("sum:  %f\n", sum);
        };

        auto * t = ggml_get_tensor(ctx0, "check");
        if (t->type == GGML_TYPE_F32) {
            print_t_f32(t);
        } else {
            print_t_f16(t);
        }
    }

    printf("used_mem = %zu\n", ggml_used_mem(ctx0));
#endif
    memcpy(vec, ggml_get_data_f32(embeddings), sizeof(float) * projection_dim);

    if (cplan.work_size != 0) {
        free(cplan.work_data);
    }

    ggml_free(ctx0);

    return true;
}

bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec, const bool normalize) {
    if (!ctx->has_vision_encoder) {
        printf("This gguf file seems to have no vision encoder\n");
        return false;
    }

    clip_image_f32_batch imgs{};
    imgs.size = 1;
    imgs.data = img;
    return clip_image_batch_encode(ctx, n_threads, &imgs, vec, normalize);
}

bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec,
                             const bool normalize) {

    if (!ctx->has_vision_encoder) {
        printf("This gguf file seems to have no vision encoder\n");
        return false;
    }

    const auto & model = ctx->vision_model;
    const auto & hparams = model.hparams;

    const int image_size = hparams.image_size;
    const int patch_size = hparams.patch_size;
    const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
    const int num_positions = num_patches + 1;
    const int hidden_size = hparams.hidden_size;
    const int n_head = hparams.n_head;
    const int d_head = hidden_size / n_head;
    const int n_layer = hparams.n_layer;
    const int n_intermediate = hparams.n_intermediate;
    const int projection_dim = hparams.projection_dim;
    const float eps = hparams.eps;
    int batch_size = imgs->size;

    auto & buf_compute = ctx->buf_compute;

    struct ggml_init_params params = {
        .mem_size = buf_compute.size,
        .mem_buffer = buf_compute.data,
        .no_alloc = false,
    };

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

    static size_t scr0_size = get_scr_buf_req_by_size((struct clip_ctx *)ctx);
    static void * scr0 = malloc(scr0_size);

    struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);

    {
        float * data = (float *)ggml_get_data(inp_raw);

        for (int b = 0; b < imgs->size; b++) {
            const int nx = imgs->data[b].nx;
            const int ny = imgs->data[b].ny;
            GGML_ASSERT(nx == image_size && ny == image_size);

            const int n = nx * ny;

            for (int b = 0; b < batch_size; b++) {
                for (int k = 0; k < 3; k++) {
                    for (int y = 0; y < ny; y++) {
                        for (int x = 0; x < nx; x++) {
                            data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].data[3 * (y * nx + x) + k];
                        }
                    }
                }
            }
        }
    }

    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);

    inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));

    // concat class_embeddings and patch_embeddings
    struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);

    ggml_set_zero(embeddings);
    struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size);

    embeddings = ggml_acc(ctx0, embeddings, ggml_repeat(ctx0, model.class_embedding, temp), embeddings->nb[1],
                          embeddings->nb[2], embeddings->nb[3], 0);
    embeddings =
        ggml_acc(ctx0, embeddings, inp, embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);

    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
    for (int i = 0; i < num_positions; i++) {
        ggml_set_i32_1d(positions, i, i);
    }

    embeddings =
        ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, model.position_embeddings, positions), embeddings));

    // pre-layernorm
    {
        embeddings = ggml_norm(ctx0, embeddings, eps);

        embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.pre_ln_w, embeddings), embeddings),
                              ggml_repeat(ctx0, model.pre_ln_b, embeddings));
    }

    // loop over layers
    for (int il = 0; il < n_layer; il++) {
        struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states

        const size_t nb_q_w = model.layers[il].q_w->nb[0];

        ggml_set_scratch(ctx0, {0, scr0_size, scr0});

        // layernorm1
        {
            cur = ggml_norm(ctx0, cur, eps);

            cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_w, cur), cur),
                           ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
        }

        // self-attention
        {

            struct ggml_tensor * Q =
                ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur));

            Q = ggml_scale_inplace(ctx0, Q, ggml_new_f32(ctx0, 1.0f / sqrt((float)d_head)));
            Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
            Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
            Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);

            struct ggml_tensor * K =
                ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, cur), ggml_mul_mat(ctx0, model.layers[il].k_w, cur));

            K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
            K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
            K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);

            struct ggml_tensor * V =
                ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, cur), ggml_mul_mat(ctx0, model.layers[il].v_w, cur));

            V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
            V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
            V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);

            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
            KQ = ggml_soft_max_inplace(ctx0, KQ);
            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
            KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
            KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3));

            cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size));
        }

        // attention output
        cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].o_b, cur), ggml_mul_mat(ctx0, model.layers[il].o_w, cur));

        // re-add the layer input, e.g., residual
        cur = ggml_add(ctx0, cur, embeddings);

        embeddings = cur; // embeddings = residual, cur = hidden_states

        // layernorm2
        {
            cur = ggml_norm(ctx0, cur, eps);

            cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_w, cur), cur),
                           ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
        }

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

        if (ctx->use_gelu) {
            cur = ggml_gelu_inplace(ctx0, cur);
        } else {
            cur = ggml_gelu_quick_inplace(ctx0, cur);
        }

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

        // residual 2
        cur = ggml_add(ctx0, embeddings, cur);

        embeddings = cur;
    }

    // get the output of cls token, e.g., 0th index
    struct ggml_tensor * cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, batch_size);
    for (int b = 0; b < batch_size; b++) {
        ggml_set_i32_1d(cls, b, b * num_positions);
    }
    embeddings = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, embeddings, hidden_size, num_positions * batch_size), cls);

    // post-layernorm
    {
        embeddings = ggml_norm(ctx0, embeddings, eps);

        embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.post_ln_w, embeddings), embeddings),
                              ggml_repeat(ctx0, model.post_ln_b, embeddings));
    }

    ggml_set_scratch(ctx0, {0, 0, nullptr});

    // final visual projection
    embeddings = ggml_mul_mat(ctx0, model.projection, embeddings);

    // normalize output embeddings
    struct ggml_tensor * output = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, projection_dim, batch_size);

    for (int b = 0; b < batch_size; b++) {
        struct ggml_tensor * embedding = ggml_get_rows(ctx0, embeddings, ggml_new_i32(ctx0, b));
        if (normalize) {
            ggml_tensor * length = ggml_sqrt(ctx0, ggml_sum(ctx0, ggml_sqr(ctx0, embedding)));
            embedding = ggml_scale_inplace(ctx0, embedding, ggml_div(ctx0, ggml_new_f32(ctx0, 1.0f), length));
        }
        output = ggml_acc(ctx0, output, embedding, output->nb[1], output->nb[2], output->nb[3], b * ggml_nbytes(embedding));
    }
    ggml_set_name(output, "check");

    // run the computation
    ggml_build_forward_expand(&gf, output);
    ggml_cplan cplan = ggml_graph_plan(&gf, n_threads);
    cplan.work_size *= batch_size;
    if (cplan.work_size != 0) {
        cplan.work_data = (uint8_t *)malloc(cplan.work_size);
    }
    ggml_graph_compute(&gf, &cplan);

// print
#ifdef CLIP_DEBUG
    {
        auto print_t_f32 = [&](struct ggml_tensor * t) {
            float * data = (float *)t->data;
            printf("dtype: f32, dims: %jd %jd %jd %jd, nb: %jd %jd %jd %jd\n", t->ne[0], t->ne[1], t->ne[2], t->ne[3], t->nb[0],
                   t->nb[1], t->nb[2], t->nb[3]);
            printf("data: ");
            for (int i = 0; i < std::min((int)t->ne[0], 20); i++) {
                printf("%f ", data[i]);
            }

            // printf("\n\n");
            double sum = 0.0;
            for (int i = 0; i < ggml_nelements(t); i++) {
                sum += data[i];
            }
            printf("sum:  %f\n", sum);
        };

        auto print_t_f16 = [&](struct ggml_tensor * t) {
            ggml_fp16_t * data = (ggml_fp16_t *)t->data;
            printf("dtype: f16, dims: %jd %jd %jd %jd\n", t->ne[0], t->ne[1], t->ne[2], t->ne[3]);
            printf("data: ");
            for (int i = 0; i < std::min((int)t->ne[0], 10); i++) {
                printf("%f ", ggml_fp16_to_fp32(data[i]));
            }
            printf("\n\n");
            double sum = 0.0;
            for (int i = 0; i < ggml_nelements(t); i++) {
                sum += ggml_fp16_to_fp32(data[i]);
            }
            printf("sum:  %f\n", sum);
        };

        auto * t = ggml_get_tensor(ctx0, "check");
        // auto t = inp_raw;
        if (t->type == GGML_TYPE_F32) {
            print_t_f32(t);
        } else {
            print_t_f16(t);
        }
    }

    printf("used_mem = %zu\n", ggml_used_mem(ctx0));
#endif

    memcpy(vec, ggml_get_data_f32(output), sizeof(float) * projection_dim * batch_size);

    if (cplan.work_size != 0) {
        free(cplan.work_data);
    }

    ggml_free(ctx0);

    return true;
}

float clip_similarity_score(const float * vec1, const float * vec2, const int vec_dim) {
    float dot_product = 0.0;
    for (int i = 0; i < vec_dim; i++) {
        dot_product += vec1[i] * vec2[i];
    }

    return dot_product;
}

bool clip_compare_text_and_image(const clip_ctx * ctx, const int n_threads, const char * text, const clip_image_u8 * image,
                                 float * score) {
    if (!(ctx->has_text_encoder && ctx->has_vision_encoder)) {
        printf("clip_compare_text_and_image function can only be used with two-tower models\n");
        return false;
    }

    // prepare image and text vectors
    const int projection_dim = ctx->vision_model.hparams.projection_dim;
    float img_vec[projection_dim];
    float txt_vec[projection_dim];

    // tokenize and encode text
    clip_tokens tokens;
    if (!clip_tokenize(ctx, text, &tokens)) {
        return false;
    }

    if (!clip_text_encode(ctx, n_threads, &tokens, txt_vec, true)) {
        return false;
    }

    // preprocess and encode image
    clip_image_f32 img_res;

    if (!clip_image_preprocess(ctx, image, &img_res)) {
        return false;
    }

    if (!clip_image_encode(ctx, n_threads, &img_res, img_vec, true)) {
        return false;
    }

    // compute similarity
    *score = clip_similarity_score(img_vec, txt_vec, projection_dim);

    return true;
}

typedef struct {
    float score;
    int index;
} ScoreIndexPair;

int compare_scores(const void * a, const void * b) {
    const ScoreIndexPair * pair1 = (const ScoreIndexPair *)a;
    const ScoreIndexPair * pair2 = (const ScoreIndexPair *)b;

    if (pair1->score < pair2->score) {
        return 1;
    } else if (pair1->score > pair2->score) {
        return -1;
    } else {
        return 0;
    }
}

bool softmax_with_sorting(float * arr, const int length, float * sorted_scores, int * indices) {
    ScoreIndexPair * score_index_pairs = (ScoreIndexPair *)malloc(length * sizeof(ScoreIndexPair));
    if (!score_index_pairs) {
        return false;
    }

    // Calculate softmax probabilities

    double sum = 0.0;
    for (int i = 0; i < length; i++) {
        arr[i] = exp(arr[i]) + 1e-9;
        sum += arr[i];
    }

    for (int i = 0; i < length; i++) {
        arr[i] /= sum;
        score_index_pairs[i].score = arr[i];
        score_index_pairs[i].index = i;
    }

    // Sort scores in descending order
    qsort(score_index_pairs, length, sizeof(ScoreIndexPair), compare_scores);

    // Copy sorted scores and indices to the respective arrays
    for (int i = 0; i < length; i++) {
        sorted_scores[i] = score_index_pairs[i].score;
        indices[i] = score_index_pairs[i].index;
    }

    free(score_index_pairs);
    return true;
}

bool clip_zero_shot_label_image(struct clip_ctx * ctx, const int n_threads, const struct clip_image_u8 * input_img,
                                const char ** labels, const size_t n_labels, float * scores, int * indices) {
    if (!(ctx->has_text_encoder && ctx->has_vision_encoder)) {
        printf("clip_zero_shot_label_image function can only be used with two-tower models\n");
        return false;
    }

    // load the image
    clip_image_f32 img_res;

    const int vec_dim = clip_get_vision_hparams(ctx)->projection_dim;

    clip_image_preprocess(ctx, input_img, &img_res);

    float img_vec[vec_dim];
    if (!clip_image_encode(ctx, n_threads, &img_res, img_vec, false)) {
        return false;
    }

    // encode texts and compute similarities
    float txt_vec[vec_dim];
    float similarities[n_labels];

    for (int i = 0; i < n_labels; i++) {
        const auto & text = labels[i];
        clip_tokens tokens;
        clip_tokenize(ctx, text, &tokens);
        clip_text_encode(ctx, n_threads, &tokens, txt_vec, false);
        similarities[i] = clip_similarity_score(img_vec, txt_vec, vec_dim);
    }

    // apply softmax and sort scores
    softmax_with_sorting(similarities, n_labels, scores, indices);

    return true;
}

bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {

    ggml_type type = GGML_TYPE_Q4_1;

    switch (itype) {
    case 2:
        type = GGML_TYPE_Q4_0;
        break;
    case 3:
        type = GGML_TYPE_Q4_1;
        break;
    case 6:
        type = GGML_TYPE_Q5_0;
        break;
    case 7:
        type = GGML_TYPE_Q5_1;
        break;
    case 8:
        type = GGML_TYPE_Q8_0;
        break;
    default:
        fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype);
        return false;
    };

    auto ctx_clip = clip_model_load(fname_inp, 2);
    const auto & ctx_src = ctx_clip->ctx_gguf;
    const auto & ctx_data = ctx_clip->ctx;

    auto ctx_out = gguf_init_empty();
    gguf_set_kv(ctx_out, ctx_src);
    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
    gguf_set_val_u32(ctx_out, "general.file_type", itype);

    auto fout = std::ofstream(fname_out, std::ios::binary);

    const int n_tensors = gguf_get_n_tensors(ctx_src);

    for (int i = 0; i < n_tensors; ++i) {
        const char * name = gguf_get_tensor_name(ctx_src, i);
        struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
        gguf_add_tensor(ctx_out, cur);
    }

    const size_t meta_size = gguf_get_meta_size(ctx_out);
    for (size_t i = 0; i < meta_size; ++i) {
        fout.put(0);
    }

    // regexes of tensor names to be quantized
    const std::vector<std::string> k_names = {
        ".*weight",
    };

    std::vector<uint8_t> read_data(512);
    std::vector<uint8_t> work(512);
    std::vector<float> conv_buf(512);
    std::vector<int64_t> hist_all(1 << 4, 0);
    size_t total_size_org = 0;
    size_t total_size_new = 0;

    for (int i = 0; i < n_tensors; ++i) {
        const std::string name = gguf_get_tensor_name(ctx_src, i);
        struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());

        enum ggml_type new_type;
        void * new_data;
        size_t new_size;

        bool quantize = false;
        for (const auto & s : k_names) {
            if (std::regex_match(name, std::regex(s))) {
                quantize = true;
                break;
            }
        }

        // quantize only 2D tensors
        quantize &= (cur->n_dims == 2);

        if (quantize) {
            new_type = type;
            const size_t n_elms = ggml_nelements(cur);
            float * f32_data;

            switch (cur->type) {
            case GGML_TYPE_F32:
                f32_data = (float *)cur->data;
                break;
            case GGML_TYPE_F16:
                if (conv_buf.size() < n_elms) {
                    conv_buf.resize(n_elms);
                }
                for (int j = 0; j < n_elms; ++j) {
                    conv_buf[j] = ggml_fp16_to_fp32(((ggml_fp16_t *)cur->data)[j]);
                }
                f32_data = (float *)conv_buf.data();
                break;
            default:
                printf("Please use an input file in f32 or f16\n");
                return false;
            }

            if (work.size() < n_elms * 4) {
                work.resize(n_elms * 4);
            }
            new_data = work.data();

            std::vector<int64_t> hist_cur(1 << 4, 0);

            switch (new_type) {
            case GGML_TYPE_Q4_0: {
                new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
            } break;
            case GGML_TYPE_Q4_1: {
                new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
            } break;
            case GGML_TYPE_Q5_0: {
                new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
            } break;
            case GGML_TYPE_Q5_1: {
                new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
            } break;
            case GGML_TYPE_Q8_0: {
                new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
            } break;
            default: {
                fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
                return false;
            }
            }

            for (int j = 0; j < hist_cur.size(); ++j) {
                hist_all[j] += hist_cur[j];
            }
        } else {
            new_type = cur->type;
            new_data = cur->data;
            new_size = ggml_nbytes(cur);
        }
        const size_t orig_size = ggml_nbytes(cur);
        total_size_org += orig_size;
        total_size_new += new_size;
        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
        fout.write((const char *)new_data, new_size);
        size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
        for (int j = 0; j < pad; ++j) {
            fout.put(0);
        }

        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), cur->n_dims, quantize,
               orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
    }

    // go back to beginning of file and write the updated metadata
    fout.seekp(0, std::ios::beg);
    std::vector<uint8_t> meta(meta_size);
    gguf_get_meta_data(ctx_out, meta.data());
    fout.write((const char *)meta.data(), meta_size);

    fout.close();

    clip_free(ctx_clip);
    gguf_free(ctx_out);

    {
        printf("%s: original size  = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
        printf("%s: quantized size  = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);

        int64_t sum_all = 0;
        for (size_t i = 0; i < hist_all.size(); ++i) {
            sum_all += hist_all[i];
        }

        printf("%s: hist: ", __func__);
        for (size_t i = 0; i < hist_all.size(); ++i) {
            printf("%5.3f ", hist_all[i] / (float)sum_all);
        }
        printf("\n");
    }

    return true;
}

struct clip_text_hparams * clip_get_text_hparams(struct clip_ctx * ctx) { return &ctx->text_model.hparams; }
struct clip_vision_hparams * clip_get_vision_hparams(struct clip_ctx * ctx) { return &ctx->vision_model.hparams; }