llama.cpp/tools/mtmd/mtmd-helper.cpp

#include "mtmd.h"
#include "llama.h"

#include <algorithm>
#include <cinttypes>
#include <vector>

#define LOG_INF(...) fprintf(stdout, __VA_ARGS__)
#define LOG_ERR(...) fprintf(stderr, __VA_ARGS__)

size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks) {
    size_t n_tokens = 0;
    for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) {
        auto chunk = mtmd_input_chunks_get(chunks, i);
        n_tokens += mtmd_input_chunk_get_n_tokens(chunk);
    }
    return n_tokens;
}

llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks) {
    llama_pos n_pos = 0;
    for (size_t i = 0; i < mtmd_input_chunks_size(chunks); i++) {
        auto chunk = mtmd_input_chunks_get(chunks, i);
        n_pos += mtmd_input_chunk_get_n_pos(chunk);
    }
    return n_pos;
}

// helper struct to make working with embd batch easier
// note: this will be removed after llama_batch_ext refactoring
struct decode_embd_batch {
    int n_pos_per_embd;
    int n_mmproj_embd;
    std::vector<llama_pos>      pos;
    std::vector<llama_pos>      pos_view; // used by mrope
    std::vector<int32_t>        n_seq_id;
    std::vector<llama_seq_id>   seq_id_0;
    std::vector<llama_seq_id *> seq_ids;
    std::vector<int8_t>         logits;
    llama_batch batch;
    decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
        pos     .resize(n_tokens * n_pos_per_embd);
        n_seq_id.resize(n_tokens);
        seq_ids .resize(n_tokens + 1);
        logits  .resize(n_tokens);
        seq_id_0.resize(1);
        seq_ids [n_tokens] = nullptr;
        batch = {
            /*n_tokens       =*/ n_tokens,
            /*tokens         =*/ nullptr,
            /*embd           =*/ embd,
            /*pos            =*/ pos.data(),
            /*n_seq_id       =*/ n_seq_id.data(),
            /*seq_id         =*/ seq_ids.data(),
            /*logits         =*/ logits.data(),
        };
    }

    void set_position_normal(llama_pos pos_0, llama_seq_id seq_id) {
        seq_id_0[0] = seq_id;
        for (int i = 0; i < batch.n_tokens; i++) {
            batch.pos     [i] = pos_0 + i;
            batch.n_seq_id[i] = 1;
            batch.seq_id  [i] = seq_id_0.data();
            batch.logits  [i] = false;
        }
    }

    void set_position_mrope(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) {
        GGML_ASSERT(n_pos_per_embd == 4);
        seq_id_0[0] = seq_id;
        for (int y = 0; y < ny; y++) {
            for (int x = 0; x < nx; x++) {
                int i = y * nx + x;
                pos[i                     ] = pos_0;
                pos[i + batch.n_tokens    ] = pos_0 + y;
                pos[i + batch.n_tokens * 2] = pos_0 + x;
                pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused
            }
        }
        for (int i = 0; i < batch.n_tokens; i++) {
            batch.n_seq_id[i] = 1;
            batch.seq_id  [i] = seq_id_0.data();
            batch.logits  [i] = false;
        }
    }

    llama_batch get_view(int offset, int n_tokens) {
        llama_pos * pos_ptr;
        pos_view.clear();
        pos_view.reserve(n_tokens * n_pos_per_embd);
        if (n_pos_per_embd > 1) {
            // mrope
            // for example, with layout of src: 1234...1234...1234...1234...
            //       offset 2 will give us dst: 34...34...34...34...
            for (int i = 0; i < n_pos_per_embd; i++) {
                // assume n_tokens is less than or equal to batch.n_tokens
                // batch.n_tokens is number of **total** tokens
                // n_tokens is number of viewed token
                size_t src_idx = i * batch.n_tokens + offset;
                pos_view.insert(pos_view.end(),
                    pos.data() + src_idx,
                    pos.data() + src_idx + n_tokens);
            }
            pos_ptr = pos_view.data();
        } else {
            // normal
            pos_ptr = pos.data() + offset;
        }
        return {
            /*n_tokens       =*/ n_tokens,
            /*tokens         =*/ nullptr,
            /*embd           =*/ batch.embd     + offset * n_mmproj_embd,
            /*pos            =*/ pos_ptr,
            /*n_seq_id       =*/ batch.n_seq_id + offset,
            /*seq_id         =*/ batch.seq_id   + offset,
            /*logits         =*/ batch.logits   + offset,
        };
    }
};

// Helper function for decoding an image whose embeddings have already been calculated
int32_t mtmd_helper_decode_image_chunk(
        mtmd_context * ctx,
        struct llama_context * lctx,
        const mtmd_input_chunk * chunk,
        float * encoded_embd,
        llama_pos n_past,
        llama_seq_id seq_id,
        int32_t n_batch,
        llama_pos * new_n_past) {
    auto chunk_type = mtmd_input_chunk_get_type(chunk);
    const char * name = chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE ? "image" : "audio";
    if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
        LOG_ERR("failed to decode chunk: input chunk not of image/audio type\n");
        return -1;
    }

    const llama_model * model = llama_get_model(lctx);
    int n_mmproj_embd = llama_model_n_embd(model);
    int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1;

    int32_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk);
    int32_t i_batch = 0;
    int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch;
    decode_embd_batch batch_embd(encoded_embd, n_tokens, n_pos_per_embd, n_mmproj_embd);

    if (mtmd_decode_use_mrope(ctx)) {
        const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk);
        if (chunk_type != MTMD_INPUT_CHUNK_TYPE_IMAGE) {
            LOG_ERR("failed to decode chunk: M-RoPE only accepts image chunk\n");
            return -1;
        }
        if (!image_tokens) {
            LOG_ERR("failed to decode chunk: image tokens are null\n");
            return -1;
        }
        const int nx = mtmd_image_tokens_get_nx(image_tokens);
        const int ny = mtmd_image_tokens_get_ny(image_tokens);
        batch_embd.set_position_mrope(n_past, nx, ny, seq_id);
    } else {
        batch_embd.set_position_normal(n_past, seq_id);
    }

    if (mtmd_decode_use_non_causal(ctx)) {
        llama_set_causal_attn(lctx, false);
        // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image
    }

    while (i_batch < n_img_batches) { // split into batches
        int pos_offset = i_batch*n_batch;
        int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset);
        llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch);

        LOG_INF("decoding %s batch %d/%d, n_tokens_batch = %d\n", name, i_batch+1, n_img_batches, n_tokens_batch);

        int64_t t1 = ggml_time_ms();
        int32_t ret = llama_decode(lctx, batch_embd_view);
        if (ret != 0) {
            LOG_ERR("failed to decode %s\n", name);
            llama_set_causal_attn(lctx, true); // restore causal attn
            return ret;
        }

        LOG_INF("%s decoded (batch %d/%d) in %" PRId64 " ms\n", name, i_batch+1, n_img_batches, ggml_time_ms() - t1);

        i_batch++;
    }

    n_past += mtmd_input_chunk_get_n_pos(chunk);
    *new_n_past = n_past;

    if (mtmd_decode_use_non_causal(ctx)) {
        llama_set_causal_attn(lctx, true);
    }
    return 0;
}

int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
        struct llama_context * lctx,
        const mtmd_input_chunk * chunk,
        llama_pos n_past,
        llama_seq_id seq_id,
        int32_t n_batch,
        bool logits_last,
        llama_pos * new_n_past) {
    int32_t ret;
    llama_batch text_batch = llama_batch_init(n_batch, 0, 1);
    auto chunk_type = mtmd_input_chunk_get_type(chunk);

    if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
        size_t n_tokens;
        const auto tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens);
        // LOG_INF("decoding text chunk, n_tokens = %zu\n", n_tokens);
        size_t i = 0;
        while (i < n_tokens) { // split into batches
            text_batch.n_tokens = 0; // clear the batch
            for (; i < n_tokens && text_batch.n_tokens < n_batch; i++) {
                int32_t j = text_batch.n_tokens;
                text_batch.token   [j]    = tokens[i];
                text_batch.pos     [j]    = n_past++;
                text_batch.n_seq_id[j]    = 1;
                text_batch.seq_id  [j][0] = seq_id;
                text_batch.logits  [j]    = false;

                text_batch.n_tokens++;
            }
            bool is_last_token = (i == n_tokens);
            if (logits_last && is_last_token) {
                text_batch.logits[text_batch.n_tokens - 1] = true;
            }
            ret = llama_decode(lctx, text_batch);
            if (ret != 0) {
                LOG_ERR("failed to decode text\n");
                llama_batch_free(text_batch);
                return ret;
            }
            *new_n_past += text_batch.n_tokens;
        }

    } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE || chunk_type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
        const char * name = chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE ? "image" : "audio";
        int64_t t0 = ggml_time_ms();

        LOG_INF("encoding %s slice...\n", name);

        ret = mtmd_encode_chunk(ctx, chunk);
        if (ret != 0) {
            LOG_ERR("failed to encode %s slice\n", name);
            llama_batch_free(text_batch);
            return ret;
        }

        LOG_INF("%s slice encoded in %" PRId64 " ms\n", name, ggml_time_ms() - t0);

        float * embd = mtmd_get_output_embd(ctx);
        ret = mtmd_helper_decode_image_chunk(ctx, lctx, chunk, embd, n_past, seq_id, n_batch, new_n_past);
        if (ret != 0) {
            LOG_ERR("failed to decode %s\n", name);
            llama_batch_free(text_batch);
            return ret;
        }
    } else {
        GGML_ABORT("chunk type not supported");
    }

    return 0;
}

int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
                                struct llama_context * lctx,
                                const mtmd_input_chunks * chunks,
                                llama_pos n_past,
                                llama_seq_id seq_id,
                                int32_t n_batch,
                                bool logits_last,
                                llama_pos * new_n_past) {
    size_t n_chunks = mtmd_input_chunks_size(chunks);
    if (n_chunks == 0) {
        LOG_ERR("no chunks to eval\n");
        return 0;
    }

    for (size_t i = 0; i < n_chunks; i++) {
        bool chunk_logits_last = (i == n_chunks - 1) && logits_last;
        auto chunk = mtmd_input_chunks_get(chunks, i);

        int32_t res = mtmd_helper_eval_chunk_single(ctx, lctx, chunk, n_past, seq_id, n_batch, chunk_logits_last, &n_past);
        if (res != 0) {
            LOG_ERR("failed to eval chunk %zu\n", i);
            return res;
        }
        *new_n_past = n_past;
    }

    return 0;
}