llama.cpp/tools/mtmd/mtmd.cpp

#include "clip.h"
#include "clip-impl.h"
#include "mtmd.h"
#include "mtmd-audio.h"

#include "llama.h"

#include <algorithm>
#include <cerrno>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <vector>

// represents raw image data, layout is RGBRGBRGB...
// length of data must be nx * ny * 3
struct mtmd_bitmap {
    uint32_t nx;
    uint32_t ny;
    std::vector<unsigned char> data;
    std::string id; // optional user-defined id, for ex: can be set to image hash, useful for KV cache tracking
    bool is_audio = false; // true if the bitmap is audio
};

struct mtmd_image_tokens {
    uint32_t nx; // number of tokens in x direction
    uint32_t ny; // number of tokens in y direction
    bool use_mrope_pos = false; // use M-RoPE position counting (the whole image is 1 temporal position)
    uint32_t n_tokens() const { return nx * ny; }
    clip_image_f32_batch batch_f32; // preprocessed image patches
    std::string id; // optional user-defined ID, useful for KV cache tracking

    mtmd_image_tokens clone() {
        return mtmd_image_tokens{
            nx,
            ny,
            use_mrope_pos,
            batch_f32.clone(),
            id
        };
    }
};
using mtmd_image_tokens_ptr = std::unique_ptr<mtmd_image_tokens>;

struct mtmd_audio_tokens {
    uint32_t n_tokens; // number of tokens
    clip_image_f32_batch batch_f32; // preprocessed image patches
    std::string id; // optional user-defined ID, useful for KV cache tracking

    mtmd_audio_tokens clone() {
        return mtmd_audio_tokens{
            n_tokens,
            batch_f32.clone(),
            id
        };
    }
};
using mtmd_audio_tokens_ptr = std::unique_ptr<mtmd_audio_tokens>;

struct mtmd_input_chunk {
    mtmd_input_chunk_type type;
    std::vector<llama_token> tokens_text;
    mtmd_image_tokens_ptr tokens_image;
    mtmd_audio_tokens_ptr tokens_audio;
};

struct mtmd_input_chunks {
    std::vector<mtmd_input_chunk> entries;
};

// slice template, used by some llava-uhd models to correctly place the special tokens around image embeddings
// models not having it (llava-1.6) will process embeddings without any special tokens in-between
enum mtmd_slice_tmpl {
    MTMD_SLICE_TMPL_NONE,
    MTMD_SLICE_TMPL_MINICPMV_2_5,
    MTMD_SLICE_TMPL_MINICPMV_2_6,
    MTMD_SLICE_TMPL_LLAMA4,
    // TODO @ngxson : add support for idefics (SmolVLM)
};

const char * mtmd_default_marker() {
    return "<__media__>";
}

mtmd_context_params mtmd_context_params_default() {
    mtmd_context_params params;
    params.use_gpu = true;
    params.print_timings = true;
    params.n_threads = 4;
    params.verbosity = GGML_LOG_LEVEL_INFO;
    params.image_marker = MTMD_DEFAULT_IMAGE_MARKER;
    params.media_marker = mtmd_default_marker();
    return params;
}

struct mtmd_context {
    struct clip_ctx * ctx_v; // vision
    struct clip_ctx * ctx_a; // audio
    const struct llama_model * text_model;
    std::vector<float> image_embd_v; // image embedding vector

    bool print_timings;
    int n_threads;
    std::string media_marker;
    const int n_embd_text;

    // these are not token, but strings used to mark the beginning and end of image/audio embeddings
    std::string img_beg;
    std::string img_end;
    std::string aud_beg;
    std::string aud_end;

    // for llava-uhd style models, we need special tokens in-between slices
    // minicpmv calls them "slices", llama 4 calls them "tiles"
    mtmd_slice_tmpl slice_tmpl    = MTMD_SLICE_TMPL_NONE;
    llama_token tok_ov_img_start  = LLAMA_TOKEN_NULL; // overview image
    llama_token tok_ov_img_end    = LLAMA_TOKEN_NULL; // overview image
    llama_token tok_slices_start  = LLAMA_TOKEN_NULL; // start of all slices
    llama_token tok_slices_end    = LLAMA_TOKEN_NULL; // end of all slices
    llama_token tok_sli_img_start = LLAMA_TOKEN_NULL; // single slice start
    llama_token tok_sli_img_end   = LLAMA_TOKEN_NULL; // single slice end
    llama_token tok_sli_img_mid   = LLAMA_TOKEN_NULL; // between 2 slices
    llama_token tok_row_end       = LLAMA_TOKEN_NULL; // end of row
    bool        tok_row_end_trail = false;
    bool        ov_img_first      = false;

    bool use_mrope = false; // for Qwen2VL, we need to use M-RoPE

    // for whisper, we pre-calculate the mel filter bank
    whisper_preprocessor::whisper_filters w_filters;

    // TODO @ngxson : add timings

    mtmd_context(const char * mmproj_fname,
                   const llama_model * text_model,
                   const mtmd_context_params & ctx_params) :
        text_model   (text_model),
        print_timings(ctx_params.print_timings),
        n_threads    (ctx_params.n_threads),
        media_marker (ctx_params.media_marker),
        n_embd_text  (llama_model_n_embd(text_model))
    {
        if (std::string(ctx_params.image_marker) != MTMD_DEFAULT_IMAGE_MARKER) {
            throw std::runtime_error("custom image_marker is not supported anymore, use media_marker instead");
        }

        if (media_marker.empty()) {
            throw std::runtime_error("media_marker must not be empty");
        }

        clip_context_params ctx_clip_params;
        ctx_clip_params.use_gpu   = ctx_params.use_gpu;
        ctx_clip_params.verbosity = ctx_params.verbosity;
        auto res = clip_init(mmproj_fname, ctx_clip_params);
        ctx_v = res.ctx_v;
        ctx_a = res.ctx_a;
        if (!ctx_v && !ctx_a) {
            throw std::runtime_error(string_format("Failed to load CLIP model from %s\n", mmproj_fname));
        }

        // if both vision and audio mmproj are present, we need to validate their n_embd
        if (ctx_v && ctx_a) {
            int n_embd_v = clip_n_mmproj_embd(ctx_v);
            int n_embd_a = clip_n_mmproj_embd(ctx_a);
            if (n_embd_v != n_embd_a) {
                throw std::runtime_error(string_format(
                    "mismatch between vision and audio mmproj (n_embd_v = %d, n_embd_a = %d)\n",
                    n_embd_v, n_embd_a));
            }
        }

        // since we already validate n_embd of vision and audio mmproj,
        // we can safely assume that they are the same
        int n_embd_clip = clip_n_mmproj_embd(ctx_v ? ctx_v : ctx_a);
        if (n_embd_text != n_embd_clip) {
            throw std::runtime_error(string_format(
                "mismatch between text model (n_embd = %d) and mmproj (n_embd = %d)\n"
                "hint: you may be using wrong mmproj\n",
                n_embd_text, n_embd_clip));
        }
        if (ctx_v) {
            init_vision();
        }
        if (ctx_a) {
            init_audio();
        }
    }

    void init_vision() {
        GGML_ASSERT(ctx_v != nullptr);
        use_mrope = clip_is_qwen2vl(ctx_v);

        projector_type proj = clip_get_projector_type(ctx_v);
        int minicpmv_version = clip_is_minicpmv(ctx_v);
        if (minicpmv_version == 2) {
            // minicpmv 2.5 format:
            // <image> (overview) </image><slice><image> (slice) </image><image> (slice) </image>\n ... </slice>
            slice_tmpl        = MTMD_SLICE_TMPL_MINICPMV_2_5;
            tok_ov_img_start  = lookup_token("<image>");
            tok_ov_img_end    = lookup_token("</image>");
            tok_slices_start  = lookup_token("<slice>");
            tok_slices_end    = lookup_token("</slice>");
            tok_sli_img_start = tok_ov_img_start;
            tok_sli_img_end   = tok_ov_img_end;
            tok_row_end       = lookup_token("\n");
            tok_row_end_trail = false; // no trailing end-of-row token
            ov_img_first      = true;

        } else if (minicpmv_version == 3 || minicpmv_version == 4) {
            // minicpmv 2.6 format:
            // <image> (overview) </image><slice> (slice) </slice><slice> (slice) </slice>\n ...
            slice_tmpl        = MTMD_SLICE_TMPL_MINICPMV_2_6;
            tok_ov_img_start  = lookup_token("<image>");
            tok_ov_img_end    = lookup_token("</image>");
            tok_sli_img_start = lookup_token("<slice>");
            tok_sli_img_end   = lookup_token("</slice>");
            tok_row_end       = lookup_token("\n");
            tok_row_end_trail = false; // no trailing end-of-row token
            ov_img_first      = true;

        } else if (minicpmv_version != 0) {
            GGML_ASSERT(false && "unsupported minicpmv version");
        } else if (proj == PROJECTOR_TYPE_LLAMA4) {
            // llama 4 format:
            // <|image_start|>
            //     (slice) <|tile_x_separator|> (slice) <|tile_x_separator|> ... <|tile_y_separator|>
            //     (slice) <|tile_x_separator|> (slice) <|tile_x_separator|> ... <|tile_y_separator|>
            //     ... <|tile_y_separator|>   <-- trailing end-of-row token
            // <|image|> (overview)           <-- overview image is last
            // <|image_end|>
            slice_tmpl        = MTMD_SLICE_TMPL_LLAMA4;
            tok_ov_img_start  = lookup_token("<|image|>");
            tok_sli_img_mid   = lookup_token("<|tile_x_separator|>");
            tok_row_end       = lookup_token("<|tile_y_separator|>");
            tok_row_end_trail = true; // add trailing end-of-row token
            ov_img_first      = false; // overview image is last
        }

        // set boi/eoi
        if (proj == PROJECTOR_TYPE_GEMMA3) {
            // <start_of_image> ... (image embeddings) ... <end_of_image>
            img_beg = "<start_of_image>";
            img_end = "<end_of_image>";

        } else if (proj == PROJECTOR_TYPE_IDEFICS3) {
            // https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
            img_beg = "<fake_token_around_image><global-img>";
            img_end = "<fake_token_around_image>";

        } else if (proj == PROJECTOR_TYPE_PIXTRAL) {
            // https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
            img_end = "[IMG_END]";

        } else if (proj == PROJECTOR_TYPE_QWEN2VL || proj == PROJECTOR_TYPE_QWEN25VL) {
            // <|vision_start|> ... (image embeddings) ... <|vision_end|>
            img_beg = "<|vision_start|>";
            img_end = "<|vision_end|>";

        } else if (proj == PROJECTOR_TYPE_LLAMA4) {
            // (more details in mtmd_context constructor)
            img_beg = "<|image_start|>";
            img_end = "<|image_end|>";
            LOG_WRN("%s: llama 4 vision is known to have degraded quality:\n"
                    "    https://github.com/ggml-org/llama.cpp/pull/13282\n", __func__);

        } else if (proj == PROJECTOR_TYPE_INTERNVL) {
            // <img> ... (image embeddings) ... </img>
            img_beg = "<img>";
            img_end = "</img>";

        }
    }

    void init_audio() {
        GGML_ASSERT(ctx_a != nullptr);
        projector_type proj = clip_get_projector_type(ctx_a);

        if (clip_has_whisper_encoder(ctx_a)) {
            // TODO @ngxson : check if model n_mel is 128 or 80
            w_filters = whisper_precalc_filters::get_128_bins();
        }

        LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n"
                "    https://github.com/ggml-org/llama.cpp/discussions/13759\n", __func__);

        if (proj == PROJECTOR_TYPE_QWEN2A) {
            // <|audio_bos|> ... (embeddings) ... <|audio_eos|>
            aud_beg = "<|audio_bos|>";
            aud_end = "<|audio_eos|>";

        }
    }

    // get clip ctx based on chunk type
    clip_ctx * get_clip_ctx(const mtmd_input_chunk * chunk) const {
        if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
            return ctx_v;
        } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
            return ctx_a;
        }
        GGML_ABORT("unknown chunk type");
    }

    projector_type proj_type_v() const {
        return ctx_v ? clip_get_projector_type(ctx_v) : PROJECTOR_TYPE_UNKNOWN;
    }

    projector_type proj_type_a() const {
        return ctx_a ? clip_get_projector_type(ctx_a) : PROJECTOR_TYPE_UNKNOWN;
    }

    ~mtmd_context() {
        clip_free(ctx_a);
        clip_free(ctx_v);
    }

private:
    llama_token lookup_token(const std::string & token_text) {
        const llama_vocab * vocab = llama_model_get_vocab(text_model);
        const int n_vocab = llama_vocab_n_tokens(vocab);
        for (int i = 0; i < n_vocab; i++) {
            if (token_to_piece(vocab, i, true) == token_text) {
                return i;
            }
        }
        return LLAMA_TOKEN_NULL;
    }

    std::string token_to_piece(const llama_vocab * vocab, llama_token token, bool special) {
        std::string piece;
        piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
        const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
        if (n_chars < 0) {
            piece.resize(-n_chars);
            int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
            GGML_ASSERT(check == -n_chars);
        } else {
            piece.resize(n_chars);
        }
        return piece;
    }
};

mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
        const struct llama_model * text_model,
        const struct mtmd_context_params ctx_params) {
    try {
        return new mtmd_context(mmproj_fname, text_model, ctx_params);
    } catch (const std::exception & e) {
        LOG_ERR("%s: error: %s\n", __func__, e.what());
        return nullptr;
    }
}

void mtmd_free(mtmd_context * ctx) {
    if (ctx) {
        delete ctx;
    }
}

struct mtmd_tokenizer {
    mtmd_context * ctx;
    std::vector<const mtmd_bitmap *> bitmaps;

    std::string input_text;
    bool add_special;
    bool parse_special;
    const llama_vocab * vocab;

    mtmd_input_chunks cur;

    mtmd_tokenizer(mtmd_context * ctx,
            const mtmd_input_text * text,
            const mtmd_bitmap ** bitmaps,
            size_t n_bitmaps) : ctx(ctx), bitmaps(bitmaps, bitmaps + n_bitmaps) {
        add_special   = text->add_special;
        parse_special = text->parse_special;
        input_text    = text->text;
        vocab         = llama_model_get_vocab(ctx->text_model);

        // for compatibility, we convert image marker to media marker
        string_replace_all(input_text, MTMD_DEFAULT_IMAGE_MARKER, ctx->media_marker);
    }

    int32_t tokenize(mtmd_input_chunks * output) {
        cur.entries.clear();
        std::vector<std::string> parts = split_text(input_text, ctx->media_marker);
        size_t i_bm = 0; // index of the current bitmap
        for (auto & part : parts) {
            if (part == ctx->media_marker) {
                // this is a marker, we should add the next bitmap
                if (i_bm >= bitmaps.size()) {
                    LOG_ERR("%s: error: number of bitmaps (%zu) does not match number of markers (%zu)\n",
                            __func__, bitmaps.size(), parts.size() - 1);
                    return 1;
                }
                const mtmd_bitmap * bitmap = bitmaps[i_bm++];
                int32_t res = add_media(bitmap);
                if (res != 0) {
                    return res;
                }
            } else {
                // this is a text part, we should add it as text
                add_text(part, parse_special);
            }
        }

        if (add_special && llama_vocab_get_add_bos(vocab)) {
            // if first chunk is text, we add BOS token to first text chunk
            // otherwise, create a new text chunk with BOS token
            if (!cur.entries.empty() && cur.entries[0].type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
                // add BOS token to the beginning of first text chunk
                cur.entries[0].tokens_text.insert(cur.entries[0].tokens_text.begin(), llama_vocab_bos(vocab));
            } else {
                // create a new text chunk with BOS token at the beginning
                mtmd_input_chunk bos_chunk{
                    MTMD_INPUT_CHUNK_TYPE_TEXT,
                    {llama_vocab_bos(vocab)},
                    nullptr, // image tokens
                    nullptr, // audio tokens
                };
                cur.entries.insert(cur.entries.begin(), std::move(bos_chunk));
            }
        }

        if (add_special && llama_vocab_get_add_eos(vocab)) {
            // if last chunk is text, we add EOS token to it
            add_text({llama_vocab_eos(vocab)});
        }

        if (i_bm != bitmaps.size()) {
            LOG_ERR("%s: error: number of bitmaps (%zu) does not match number of markers (%zu)\n",
                    __func__, bitmaps.size(), parts.size() - 1);
            return 1;
        }

        *output = std::move(cur);

        return 0;
    }

    void add_text(const std::string & txt, bool parse_special) {
        LOG_DBG("%s: %s\n", __func__, txt.c_str());
        auto tokens = mtmd_tokenize_text_internal(vocab, txt, /* add_special */ false, parse_special);
        add_text(tokens);
    }

    void add_text(const std::vector<llama_token> & tokens) {
        if (tokens.empty()) {
            return;
        }
        // if last entry is also a text chunk, add tokens to it instead of creating new chunk
        if (!cur.entries.empty() && cur.entries.back().type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
            cur.entries.back().tokens_text.insert(
                                            cur.entries.back().tokens_text.end(),
                                            tokens.begin(),
                                            tokens.end());
        } else {
            mtmd_input_chunk chunk{
                MTMD_INPUT_CHUNK_TYPE_TEXT,
                tokens,
                nullptr, // image tokens
                nullptr, // audio tokens
            };
            cur.entries.emplace_back(std::move(chunk));
        }
    }

    int32_t add_media(const mtmd_bitmap * bitmap) {
        if (!bitmap->is_audio) {
            // handle image

            if (!ctx->ctx_v) {
                LOG_ERR("%s: error: model does not support vision input\n", __func__);
                return 2;
            }

            if (!ctx->img_beg.empty()) {
                add_text(ctx->img_beg, true); // add image begin token
            }

            // convert mtmd_bitmap to clip_image_u8
            clip_image_u8_ptr img_u8(clip_image_u8_init());
            img_u8->nx = bitmap->nx;
            img_u8->ny = bitmap->ny;
            img_u8->buf.resize(bitmap->data.size());
            std::memcpy(img_u8->buf.data(), bitmap->data.data(), img_u8->nx * img_u8->ny * 3);

            // preprocess image
            clip_image_f32_batch batch_f32;
            bool ok = clip_image_preprocess(ctx->ctx_v, img_u8.get(), &batch_f32);
            if (!ok) {
                LOG_ERR("Unable to preprocess image\n");
                return 2;
            }

            // handle llava-uhd style preprocessing
            if (
                ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
                || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
                || ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
            ) {
                // split batch into chunks of single images
                auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmap->id);
                GGML_ASSERT(chunks.size() > 0);

                auto ov_chunk = std::move(chunks.front());
                chunks.erase(chunks.begin());

                // add overview image (first)
                if (ctx->ov_img_first) {
                    if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
                        add_text({ctx->tok_ov_img_start});
                    }
                    cur.entries.emplace_back(std::move(ov_chunk));
                    if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
                        add_text({ctx->tok_ov_img_end});
                    }
                }

                // add slices (or tiles)
                if (!chunks.empty()) {
                    const int n_col = batch_f32.grid_x;
                    const int n_row = batch_f32.grid_y;
                    if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
                        add_text({ctx->tok_slices_start});
                    }
                    for (int y = 0; y < n_row; y++) {
                        for (int x = 0; x < n_col; x++) {
                            const bool is_last_in_row = (x == n_col - 1);
                            if (ctx->tok_sli_img_start != LLAMA_TOKEN_NULL) {
                                add_text({ctx->tok_sli_img_start});
                            }
                            cur.entries.emplace_back(std::move(chunks[y * n_col + x]));
                            if (ctx->tok_sli_img_end != LLAMA_TOKEN_NULL) {
                                add_text({ctx->tok_sli_img_end});
                            }
                            if (!is_last_in_row && ctx->tok_sli_img_mid != LLAMA_TOKEN_NULL) {
                                add_text({ctx->tok_sli_img_mid});
                            }
                        }
                        if ((y != n_row - 1 || ctx->tok_row_end_trail) && ctx->tok_row_end != LLAMA_TOKEN_NULL) {
                            add_text({ctx->tok_row_end});
                        }
                    }
                    if (ctx->tok_slices_end != LLAMA_TOKEN_NULL) {
                        add_text({ctx->tok_slices_end});
                    }
                }

                // add overview image (last)
                if (!ctx->ov_img_first) {
                    if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
                        add_text({ctx->tok_ov_img_start});
                    }
                    cur.entries.emplace_back(std::move(ov_chunk));
                    if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
                        add_text({ctx->tok_ov_img_end});
                    }
                }

            } else {
                size_t n_tokens = 0;
                for (const auto & entry : batch_f32.entries) {
                    n_tokens += clip_n_output_tokens(ctx->ctx_v, entry.get());
                }

                mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
                if (ctx->use_mrope) {
                    // for Qwen2VL, we need this information for M-RoPE decoding positions
                    image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_v, batch_f32.entries[0].get());
                    image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_v, batch_f32.entries[0].get());
                    image_tokens->use_mrope_pos = true;
                } else {
                    // other models, we only need the total number of tokens
                    image_tokens->nx = n_tokens;
                    image_tokens->ny = 1;
                }
                image_tokens->batch_f32 = std::move(batch_f32);
                image_tokens->id = bitmap->id; // optional

                LOG_DBG("image_tokens->nx = %d\n", image_tokens->nx);
                LOG_DBG("image_tokens->ny = %d\n", image_tokens->ny);
                LOG_DBG("batch_f32 size = %d\n", (int)image_tokens->batch_f32.entries.size());

                mtmd_input_chunk chunk{
                    MTMD_INPUT_CHUNK_TYPE_IMAGE,
                    {}, // text tokens
                    std::move(image_tokens),
                    nullptr, // audio tokens
                };
                cur.entries.emplace_back(std::move(chunk));
            }

            if (!ctx->img_end.empty()) {
                add_text(ctx->img_end, true); // add image end token
            }

        } else {
            // handle audio

            if (!ctx->ctx_a) {
                LOG_ERR("%s: error: model does not support audio input\n", __func__);
                return 2;
            }

            if (bitmap->data.size() == 0) {
                LOG_ERR("%s: error: empty audio data\n", __func__);
                return 2;
            }

            if (!ctx->aud_beg.empty()) {
                add_text(ctx->aud_beg, true); // add audio begin token
            }

            // preprocess audio
            GGML_ASSERT(ctx->w_filters.n_mel); // make sure we have filter preloaded
            std::vector<whisper_preprocessor::whisper_mel> mel_spec_chunks;
            const float * samples = (const float *)bitmap->data.data();
            size_t n_samples = bitmap->data.size() / sizeof(float);
            bool ok = whisper_preprocessor::preprocess_audio(samples, n_samples, ctx->w_filters, mel_spec_chunks);
            if (!ok) {
                LOG_ERR("Unable to preprocess audio\n");
                return 2;
            }

            // consider each mel_spec as a separate audio chunk
            // TODO: maybe support batching, but this may come with memory cost
            for (auto & mel_spec : mel_spec_chunks) {
                clip_image_f32_ptr mel_f32(clip_image_f32_init());
                mel_f32->nx  = mel_spec.n_len;
                mel_f32->ny  = mel_spec.n_mel;
                mel_f32->buf = std::move(mel_spec.data);
                size_t n_tokens = clip_n_output_tokens(ctx->ctx_a, mel_f32.get());

                clip_image_f32_batch batch_f32;
                batch_f32.is_audio = true;
                batch_f32.entries.push_back(std::move(mel_f32));

                mtmd_audio_tokens_ptr audio_tokens(new mtmd_audio_tokens);
                audio_tokens->n_tokens = n_tokens;
                audio_tokens->batch_f32 = std::move(batch_f32);
                audio_tokens->id = bitmap->id; // optional

                LOG_DBG("audio_tokens->n_tokens = %d\n", audio_tokens->n_tokens);

                mtmd_input_chunk chunk{
                    MTMD_INPUT_CHUNK_TYPE_AUDIO,
                    {}, // text tokens
                    nullptr, // image tokens
                    std::move(audio_tokens),
                };
                cur.entries.emplace_back(std::move(chunk));
            }

            if (!ctx->aud_end.empty()) {
                add_text(ctx->aud_end, true); // add audio end token
            }
        }

        return 0;
    }

    std::vector<mtmd_input_chunk> split_batch_to_chunk(clip_image_f32_batch && batch_f32, const std::string & id) {
        std::vector<mtmd_input_chunk> chunks;

        for (auto & entry : batch_f32.entries) {
            mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
            image_tokens->nx = clip_n_output_tokens(ctx->ctx_v, entry.get());
            image_tokens->ny = 1;
            image_tokens->batch_f32.entries.push_back(std::move(entry));
            image_tokens->id = id;

            mtmd_input_chunk chunk{
                MTMD_INPUT_CHUNK_TYPE_IMAGE,
                {}, // text tokens
                std::move(image_tokens),
                nullptr, // audio tokens
            };
            chunks.emplace_back(std::move(chunk));
        }

        return chunks;
    }

    // for example: "a <__media__> b <__media__> c" --> "a", "<__media__>", "b", "<__media__>", "c"
    static std::vector<std::string> split_text(const std::string & input, const std::string & delimiter) {
        std::vector<std::string> result;
        if (input.empty()) {
            return result;
        }
        size_t start = 0;
        size_t pos = 0;
        while ((pos = input.find(delimiter, start)) != std::string::npos) {
            if (pos > start) {
                result.push_back(input.substr(start, pos - start));
            }
            result.push_back(delimiter);
            start = pos + delimiter.length();
        }
        if (start < input.length()) {
            result.push_back(input.substr(start));
        }
        return result;
    }

    // copied from common_tokenize
    static std::vector<llama_token> mtmd_tokenize_text_internal(
        const struct llama_vocab * vocab,
               const std::string & text,
                            bool   add_special,
                            bool   parse_special) {
        // upper limit for the number of tokens
        int n_tokens = text.length() + 2 * add_special;
        std::vector<llama_token> result(n_tokens);
        n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
        if (n_tokens < 0) {
            result.resize(-n_tokens);
            int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
            GGML_ASSERT(check == -n_tokens);
        } else {
            result.resize(n_tokens);
        }
        return result;
    }
};

int32_t mtmd_tokenize(mtmd_context * ctx,
            mtmd_input_chunks * output,
            const mtmd_input_text * text,
            const mtmd_bitmap ** bitmaps,
            size_t n_bitmaps) {
    mtmd_tokenizer tokenizer(ctx, text, bitmaps, n_bitmaps);
    return tokenizer.tokenize(output);
}

int32_t mtmd_encode_chunk(mtmd_context * ctx, const mtmd_input_chunk * chunk) {
    if (chunk->type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
        LOG_WRN("mtmd_encode_chunk has no effect for text chunks\n");
        return 0;
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
        if (!ctx->ctx_v) {
            LOG_ERR("%s: model does not support vision input\n", __func__);
            return 1;
        }
        return mtmd_encode(ctx, chunk->tokens_image.get());
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
        if (!ctx->ctx_a) {
            LOG_ERR("%s: model does not support audio input\n", __func__);
            return 1;
        }
        int n_mmproj_embd = ctx->n_embd_text;
        ctx->image_embd_v.resize(chunk->tokens_audio->n_tokens * n_mmproj_embd);
        bool ok = clip_image_batch_encode(
            ctx->ctx_a,
            ctx->n_threads,
            &chunk->tokens_audio->batch_f32,
            ctx->image_embd_v.data());
        return ok ? 0 : 1;
    }

    LOG_ERR("%s: unknown chunk type %d\n", __func__, (int)chunk->type);
    return 1;
}

int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) {
    clip_ctx * ctx_clip = ctx->ctx_v;
    if (!ctx_clip) {
        LOG_ERR("%s: this API does not support non-vision input, please use mtmd_encode_chunk instead\n", __func__);
        return 1;
    }
    int n_mmproj_embd = clip_n_mmproj_embd(ctx_clip);
    ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd);
    bool ok = false;

    if (clip_is_llava(ctx_clip) || clip_is_minicpmv(ctx_clip) || clip_is_glm(ctx_clip)) {
        // TODO @ngxson : llava does not support batched encoding ; this should be fixed inside clip_image_batch_encode()
        const auto & entries = image_tokens->batch_f32.entries;
        for (size_t i = 0; i < entries.size(); i++) {
            int n_tokens_per_image = clip_n_output_tokens(ctx_clip, entries[i].get());
            ok = clip_image_encode(
                ctx_clip,
                ctx->n_threads,
                entries[i].get(),
                ctx->image_embd_v.data() + i*n_mmproj_embd*n_tokens_per_image);
        }
    } else {
        ok = clip_image_batch_encode(
            ctx_clip,
            ctx->n_threads,
            &image_tokens->batch_f32,
            ctx->image_embd_v.data());
    }

    return ok ? 0 : 1;
}

float * mtmd_get_output_embd(mtmd_context * ctx) {
    return ctx->image_embd_v.data();
}

bool mtmd_decode_use_non_causal(mtmd_context * ctx) {
    if (ctx->ctx_v && clip_get_projector_type(ctx->ctx_v) == PROJECTOR_TYPE_GEMMA3) {
        return true;
    }
    return false;
}

bool mtmd_decode_use_mrope(mtmd_context * ctx) {
    return ctx->use_mrope;
}

bool mtmd_support_vision(mtmd_context * ctx) {
    return ctx->ctx_v != nullptr;
}

bool mtmd_support_audio(mtmd_context * ctx) {
    return ctx->ctx_a != nullptr;
}

int mtmd_get_audio_bitrate(mtmd_context * ctx) {
    if (!ctx->ctx_a) {
        return -1;
    }
    // for now, we assume that all audio models have the same bitrate
    return 16000; // 16kHz
}

//
// public API functions
//

// mtmd_bitmap

mtmd_bitmap * mtmd_bitmap_init(uint32_t nx,
                               uint32_t ny,
                               const unsigned char * data) {
    mtmd_bitmap * bitmap = new mtmd_bitmap;
    bitmap->nx = nx;
    bitmap->ny = ny;
    size_t data_size = (size_t)nx * ny * 3;
    bitmap->data.resize(data_size);
    std::memcpy(bitmap->data.data(), data, data_size);
    return bitmap;
}

mtmd_bitmap * mtmd_bitmap_init_from_audio(size_t n_samples,
                                          const float * data) {
    mtmd_bitmap * bitmap = new mtmd_bitmap;
    bitmap->nx = n_samples;
    bitmap->ny = 1;
    bitmap->is_audio = true;
    size_t data_size = n_samples * sizeof(float);
    bitmap->data.resize(data_size);
    std::memcpy(bitmap->data.data(), data, data_size);
    return bitmap;
}

uint32_t mtmd_bitmap_get_nx(const mtmd_bitmap * bitmap) {
    return bitmap->nx;
}

uint32_t mtmd_bitmap_get_ny(const mtmd_bitmap * bitmap) {
    return bitmap->ny;
}

const unsigned char * mtmd_bitmap_get_data(const mtmd_bitmap * bitmap) {
    return bitmap->data.data();
}

size_t mtmd_bitmap_get_n_bytes(const mtmd_bitmap * bitmap) {
    return bitmap->data.size();
}

bool mtmd_bitmap_is_audio(const mtmd_bitmap * bitmap) {
    return bitmap->is_audio;
}

const char * mtmd_bitmap_get_id(const mtmd_bitmap * bitmap) {
    return bitmap->id.c_str();
}

void mtmd_bitmap_set_id(mtmd_bitmap * bitmap, const char * id) {
    if (id) {
        bitmap->id = std::string(id);
    } else {
        bitmap->id.clear();
    }
}

void mtmd_bitmap_free(mtmd_bitmap * bitmap) {
    if (bitmap) {
        delete bitmap;
    }
}

// mtmd_input_chunks

mtmd_input_chunks * mtmd_input_chunks_init() {
    return new mtmd_input_chunks;
}

size_t mtmd_input_chunks_size(const mtmd_input_chunks * chunks) {
    return chunks->entries.size();
}

const mtmd_input_chunk * mtmd_input_chunks_get(const mtmd_input_chunks * chunks, size_t idx) {
    if (idx >= chunks->entries.size()) {
        return nullptr;
    }
    return &chunks->entries[idx];
}

void mtmd_input_chunks_free(mtmd_input_chunks * chunks) {
    if (chunks) {
        delete chunks;
    }
}

// mtmd_input_chunk

enum mtmd_input_chunk_type mtmd_input_chunk_get_type(const mtmd_input_chunk * chunk) {
    return chunk->type;
}

const llama_token * mtmd_input_chunk_get_tokens_text(const mtmd_input_chunk * chunk, size_t * n_tokens_output) {
    if (chunk->type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
        *n_tokens_output = chunk->tokens_text.size();
        return chunk->tokens_text.data();
    }
    *n_tokens_output = 0;
    return nullptr;
}

const mtmd_image_tokens * mtmd_input_chunk_get_tokens_image(const mtmd_input_chunk * chunk) {
    if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
        return chunk->tokens_image.get();
    }
    return nullptr;
}

size_t mtmd_input_chunk_get_n_tokens(const mtmd_input_chunk * chunk) {
    if (chunk->type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
        return chunk->tokens_text.size();
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
        return mtmd_image_tokens_get_n_tokens(chunk->tokens_image.get());
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
        return chunk->tokens_audio->n_tokens;
    } else {
        GGML_ABORT("invalid chunk type");
    }
}

llama_pos mtmd_input_chunk_get_n_pos(const mtmd_input_chunk * chunk) {
    if (chunk->type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
        return chunk->tokens_text.size();
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
        return mtmd_image_tokens_get_n_pos(chunk->tokens_image.get());
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
        return chunk->tokens_audio->n_tokens;
    } else {
        GGML_ABORT("invalid chunk type");
    }
}

const char * mtmd_input_chunk_get_id(const mtmd_input_chunk * chunk) {
    if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
        return chunk->tokens_image->id.c_str();
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
        return chunk->tokens_audio->id.c_str();
    }
    return nullptr;
}

mtmd_input_chunk * mtmd_input_chunk_copy(const mtmd_input_chunk * chunk) {
    mtmd_input_chunk * copy = new mtmd_input_chunk{
        chunk->type,
        chunk->tokens_text,
        nullptr,
        nullptr,
    };
    if (chunk->tokens_image) {
        // copy the image tokens
        copy->tokens_image = mtmd_image_tokens_ptr(new mtmd_image_tokens());
        *copy->tokens_image = chunk->tokens_image->clone();
    }
    if (chunk->tokens_audio) {
        // copy the audio tokens
        copy->tokens_audio = mtmd_audio_tokens_ptr(new mtmd_audio_tokens());
        *copy->tokens_audio = chunk->tokens_audio->clone();
    }
    return copy;
}

void mtmd_input_chunk_free(mtmd_input_chunk * chunk) {
    if (chunk) {
        delete chunk;
    }
}

// mtmd_image_tokens

size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens) {
    return image_tokens->n_tokens();
}

size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens) {
    return image_tokens->nx;
}

size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens) {
    return image_tokens->ny;
}

const char * mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens) {
    return image_tokens->id.c_str();
}

llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) {
    if (image_tokens->use_mrope_pos) {
        return 1; // for M-RoPE, the whole image is 1 in temporal dimension
    }
    return image_tokens->n_tokens();
}

// test function

mtmd_input_chunks * mtmd_test_create_input_chunks() {
    mtmd_input_chunks * chunks = mtmd_input_chunks_init();
    if (!chunks) {
        return nullptr;
    }

    // create a text chunk
    std::vector<llama_token> tokens_text = { 1, 2, 3, 4, 5 };
    mtmd_input_chunk chunk_text{
        MTMD_INPUT_CHUNK_TYPE_TEXT,
        std::move(tokens_text),
        nullptr, // image tokens
        nullptr, // audio tokens
    };
    chunks->entries.emplace_back(std::move(chunk_text));

    // create an image chunk
    mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
    image_tokens->nx = 4;
    image_tokens->ny = 4;
    image_tokens->batch_f32.entries.resize(16);
    image_tokens->id = "image_1";
    mtmd_input_chunk chunk_image{
        MTMD_INPUT_CHUNK_TYPE_IMAGE,
        {}, // text tokens
        std::move(image_tokens),
        nullptr, // audio tokens
    };
    chunks->entries.emplace_back(std::move(chunk_image));

    return chunks;
}