add mtmd_helper_init_opt

common/arg.cpp

@@ -2644,6 +2644,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.mtmd_batch_max_tokens = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MTMD_BATCH_MAX_TOKENS"));
+    add_opt(common_arg(
+        {"--video-fps"}, "N",
+        string_format("target video frame rate (default: %.1f)", params.video_fps),
+        [](common_params & params, const std::string & value) {
+            params.video_fps = std::stof(value);
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_FPS"));
+    add_opt(common_arg(
+        {"--video-timestamp-interval"}, "N",
+        string_format("interval in milliseconds between text timestamps (default: %" PRId64 ")", params.video_timestamp_interval_ms),
+        [](common_params & params, int value) {
+            params.video_timestamp_interval_ms = value;
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_TIMESTAMP_INTERVAL"));
+    add_opt(common_arg(
+        {"--video-ffmpeg-dir"}, "DIR",
+        "path to the directory containing ffmpeg and ffprobe (default: search in PATH)",
+        [](common_params & params, const std::string & value) {
+            params.video_ffmpeg_bin_dir = value;
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_FFMPEG_DIR"));
     if (params.is_gen_docs || llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",

common/common.h

@@ -589,6 +589,11 @@ struct common_params {
     int image_max_tokens = -1;
     int mtmd_batch_max_tokens = 1024;
 
+    // for video input
+    float       video_fps                   = 4.0f;
+    int64_t     video_timestamp_interval_ms = 5000;
+    std::string video_ffmpeg_bin_dir        = "";
+
     // finetune
     struct lr_opt lr;
     enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;

tools/cli/README.md

@@ -166,6 +166,9 @@
 | `--image, --audio, --video FILE` | path to an image, audio, or video file. use with multimodal models, use comma-separated values for multiple files |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
+| `--video-fps N` | target video frame rate (default: 4.0)<br/>(env: LLAMA_ARG_VIDEO_FPS) |
+| `--video-timestamp-interval N` | interval in milliseconds between text timestamps (default: 5000)<br/>(env: LLAMA_ARG_VIDEO_TIMESTAMP_INTERVAL) |
+| `--video-ffmpeg-dir DIR` | path to the directory containing ffmpeg and ffprobe (default: search in PATH)<br/>(env: LLAMA_ARG_VIDEO_FFMPEG_DIR) |
 | `-o, --output, --output-file FNAME` | output file (default: '') |
 | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) |
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |

tools/mtmd/mtmd-cli.cpp

@@ -87,6 +87,9 @@ struct mtmd_cli_context {
     mtmd::bitmaps bitmaps;
     std::vector<mtmd_helper::video_ptr> videos;
 
+    mtmd_helper_init_opt init_opt = mtmd_helper_init_opt_default();
+    std::string video_ffmpeg_bin_dir;
+
     mtmd::batch_ptr mbatch;
 
     // chat template
@@ -170,6 +173,12 @@ struct mtmd_cli_context {
             LOG_ERR("Failed to load vision model from %s\n", clip_path);
             exit(1);
         }
+
+        video_ffmpeg_bin_dir = params.video_ffmpeg_bin_dir;
+        init_opt.video_params.fps_target = params.video_fps;
+        init_opt.video_params.timestamp_interval_ms = params.video_timestamp_interval_ms;
+        init_opt.video_params.ffmpeg_bin_dir = video_ffmpeg_bin_dir.empty()
+                            ? nullptr : video_ffmpeg_bin_dir.c_str();
     }
 
     bool check_antiprompt(const llama_tokens & generated_tokens) {
@@ -184,7 +193,7 @@ struct mtmd_cli_context {
     }
 
     bool load_media(const std::string & fname) {
-        auto res = mtmd_helper_bitmap_init_from_file(ctx_vision.get(), fname.c_str(), false);
+        auto res = mtmd_helper_bitmap_init_from_file(ctx_vision.get(), fname.c_str(), false, init_opt);
         if (!res.bitmap) {
             return false;
         }

tools/mtmd/mtmd-helper.cpp

@@ -369,14 +369,18 @@ static bool is_webp_file(const unsigned char * buf, size_t len) {
 }
 
 #ifdef MTMD_VIDEO
-static mtmd_bitmap * decode_webp_with_ffmpeg(mtmd_context * mctx, const unsigned char * buf, size_t len, bool placeholder);
+static mtmd_bitmap * decode_webp_with_ffmpeg(mtmd_context * mctx, const unsigned char * buf, size_t len, bool placeholder,
+                                             const mtmd_helper_video_init_params & params);
 #endif
 
-mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder) {
+mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder,
+                                                            mtmd_helper_init_opt opt) {
     // calculate the hash if needed
     std::string id;
     mtmd_bitmap * result = nullptr;
 
+    GGML_UNUSED(opt); // only used by video code paths
+
     if (!placeholder) {
         // use sha256 to prevent cache poisoning
         id = hash_sha256_hex(buf, len);
@@ -414,7 +418,7 @@ mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx,
 #ifdef MTMD_VIDEO
     // stb_image does not support webp; decode it with ffmpeg as a single frame
     if (!result && is_webp_file(buf, len)) {
-        result = decode_webp_with_ffmpeg(ctx, buf, len, placeholder);
+        result = decode_webp_with_ffmpeg(ctx, buf, len, placeholder, opt.video_params);
         if (!result) {
             LOG_ERR("%s: failed to decode webp buffer\n", __func__);
             return {nullptr, nullptr};
@@ -427,8 +431,7 @@ mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx,
     // last try: load as video
 #ifdef MTMD_VIDEO
     if (!result) {
-        auto params = mtmd_helper_video_init_params_default();
-        auto video_ctx = mtmd_helper_video_init_from_buf(ctx, buf, len, params);
+        auto video_ctx = mtmd_helper_video_init_from_buf(ctx, buf, len, opt.video_params);
         if (!video_ctx) {
             LOG_ERR("%s: failed to decode buffer as either image/audio/video\n", __func__);
             return {nullptr, nullptr};
@@ -456,7 +459,8 @@ mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx,
     return {nullptr, nullptr};
 }
 
-mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder) {
+mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder,
+                                                             mtmd_helper_init_opt opt) {
 #ifdef _WIN32
     int wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, NULL, 0);
     if (!wlen) {
@@ -497,7 +501,7 @@ mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx,
         return {nullptr, nullptr};
     }
 
-    return mtmd_helper_bitmap_init_from_buf(ctx, buf.data(), buf.size(), placeholder);
+    return mtmd_helper_bitmap_init_from_buf(ctx, buf.data(), buf.size(), placeholder, opt);
 }
 
 bool mtmd_helper_support_video(mtmd_context * ctx) {
@@ -855,6 +859,12 @@ mtmd_helper_video_init_params mtmd_helper_video_init_params_default() {
     };
 }
 
+mtmd_helper_init_opt mtmd_helper_init_opt_default() {
+    return {
+        /* video_params */ mtmd_helper_video_init_params_default(),
+    };
+}
+
 static std::string video_resolve_bin(const char * bin_dir, const char * name) {
     if (!bin_dir || bin_dir[0] == '\0') {
         return name; // rely on PATH
@@ -876,8 +886,8 @@ static std::string video_resolve_bin(const char * bin_dir, const char * name) {
 }
 
 #ifdef MTMD_VIDEO
-static mtmd_bitmap * decode_webp_with_ffmpeg(mtmd_context * mctx, const unsigned char * buf, size_t len, bool placeholder) {
-    auto params = mtmd_helper_video_init_params_default();
+static mtmd_bitmap * decode_webp_with_ffmpeg(mtmd_context * mctx, const unsigned char * buf, size_t len, bool placeholder,
+                                             const mtmd_helper_video_init_params & params) {
     mtmd_helper_video vctx;
     vctx.mctx        = mctx;
     vctx.input_buf.assign(buf, buf + len);

tools/mtmd/mtmd-helper.h

@@ -23,6 +23,23 @@ extern "C" {
 struct mtmd_helper_video;
 typedef struct mtmd_helper_video mtmd_helper_video;
 
+struct mtmd_helper_video_init_params {
+    float fps_target;            // desired output fps; <= 0 means use the video's native fps, defaulted to 4.0f
+    const char * ffmpeg_bin_dir; // directory containing ffmpeg/ffprobe binaries; NULL means search PATH
+    int64_t timestamp_interval_ms; // interval for adding timestamp as text chunk (example: "[10m50.5s]"); <= 0 means no timestamp, defaulted to 5000ms
+    // TODO @ngxson : allow "placeholder" bitmap output for counting tokens
+};
+
+MTMD_API struct mtmd_helper_video_init_params mtmd_helper_video_init_params_default(void);
+
+// opt for mtmd_helper_bitmap_init_from_*()
+struct mtmd_helper_init_opt {
+    struct mtmd_helper_video_init_params video_params;
+};
+typedef struct mtmd_helper_init_opt mtmd_helper_init_opt;
+
+MTMD_API struct mtmd_helper_init_opt mtmd_helper_init_opt_default(void);
+
 // Set callback for all future logging events.
 // If this is not called, or NULL is supplied, everything is output on stderr.
 // Note: this also call mtmd_log_set() internally
@@ -40,7 +57,11 @@ struct mtmd_helper_bitmap_wrapper {
 // it calls mtmd_helper_bitmap_init_from_buf() internally
 // returns nullptr on failure
 // this function is thread-safe
-MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder);
+MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(
+                    mtmd_context * ctx,
+                    const char * fname,
+                    bool placeholder,
+                    struct mtmd_helper_init_opt opt);
 
 // helper function to construct a mtmd_bitmap from a buffer containing a file
 // supported formats:
@@ -53,7 +74,11 @@ MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtm
 //   - output bitmap will have SHA-256 hash (hex string) as the ID
 // returns nullptr on failure
 // this function is thread-safe
-MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder);
+MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(
+                    mtmd_context * ctx,
+                    const unsigned char * buf, size_t len,
+                    bool placeholder,
+                    struct mtmd_helper_init_opt opt);
 
 // helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache
 MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks);
@@ -124,14 +149,7 @@ struct mtmd_helper_video_info {
     int32_t  n_frames; // estimated total frames at effective fps (-1 if unknown)
 };
 
-struct mtmd_helper_video_init_params {
-    float fps_target;            // desired output fps; <= 0 means use the video's native fps, defaulted to 4.0f
-    const char * ffmpeg_bin_dir; // directory containing ffmpeg/ffprobe binaries; NULL means search PATH
-    int64_t timestamp_interval_ms; // interval for adding timestamp as text chunk (example: "[10m50.5s]"); <= 0 means no timestamp, defaulted to 5000ms
-    // TODO @ngxson : allow "placeholder" bitmap output for counting tokens
-};
-
-MTMD_API struct mtmd_helper_video_init_params mtmd_helper_video_init_params_default(void);
+// note: mtmd_helper_video_init_params is defined at the top, as it is part of mtmd_helper_init_opt
 
 // returns NULL on failure (ffprobe not found, file unreadable, etc.)
 MTMD_API mtmd_helper_video * mtmd_helper_video_init(

tools/server/README.md

@@ -182,6 +182,9 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
 | `--mtmd-batch-max-tokens N` | maximum number of image tokens per batch when encoding images (default: 1024)<br/>(env: LLAMA_ARG_MTMD_BATCH_MAX_TOKENS) |
+| `--video-fps N` | target video frame rate (default: 4.0)<br/>(env: LLAMA_ARG_VIDEO_FPS) |
+| `--video-timestamp-interval N` | interval in milliseconds between text timestamps (default: 5000)<br/>(env: LLAMA_ARG_VIDEO_TIMESTAMP_INTERVAL) |
+| `--video-ffmpeg-dir DIR` | path to the directory containing ffmpeg and ffprobe (default: search in PATH)<br/>(env: LLAMA_ARG_VIDEO_FFMPEG_DIR) |
 | `-a, --alias STRING` | set model name aliases, comma-separated (to be used by API)<br/>(env: LLAMA_ARG_ALIAS) |
 | `--tags STRING` | set model tags, comma-separated (informational, not used for routing)<br/>(env: LLAMA_ARG_TAGS) |
 | `--embd-normalize N` | normalisation for embeddings (default: 2) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) |

tools/server/server-common.cpp

@@ -910,12 +910,17 @@ size_t validate_utf8(const std::string& text) {
     return len;
 }
 
-server_tokens process_mtmd_prompt(mtmd_context * mctx, const std::string & prompt, const std::vector<raw_buffer> & files, bool is_placeholder) {
+server_tokens process_mtmd_prompt(
+        mtmd_context * mctx,
+        const std::string & prompt,
+        const std::vector<raw_buffer> & files,
+        const mtmd_helper_init_opt & init_opt,
+        bool is_placeholder) {
     // these will be freed upon going out of scope
     mtmd::bitmaps bitmaps;
     std::vector<mtmd_helper::video_ptr> videos;
     for (auto & file : files) {
-        auto out = mtmd_helper_bitmap_init_from_buf(mctx, file.data(), file.size(), is_placeholder);
+        auto out = mtmd_helper_bitmap_init_from_buf(mctx, file.data(), file.size(), is_placeholder, init_opt);
         if (!out.bitmap) {
             throw std::runtime_error("Failed to load image or audio file");
         }
@@ -956,7 +961,7 @@ server_tokens process_mtmd_prompt(mtmd_context * mctx, const std::string & promp
  * - "prompt": [12, 34, "string", 56, 78]
  * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] }
  */
-static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) {
+static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special, const mtmd_helper_init_opt & init_opt) {
     constexpr char JSON_STRING_PROMPT_KEY[] = "prompt_string";
     constexpr char JSON_MTMD_DATA_KEY[] = "multimodal_data";
     const bool has_mtmd = mctx != nullptr;
@@ -979,7 +984,7 @@ static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_co
             for (const auto & entry : json_prompt.at(JSON_MTMD_DATA_KEY)) {
                 files.push_back(base64_decode(entry));
             }
-            return process_mtmd_prompt(mctx, json_prompt.at(JSON_STRING_PROMPT_KEY), files);
+            return process_mtmd_prompt(mctx, json_prompt.at(JSON_STRING_PROMPT_KEY), files, init_opt);
         } else {
             // Not multimodal, but contains a subobject.
             llama_tokens tmp = tokenize_mixed(vocab, json_prompt.at(JSON_STRING_PROMPT_KEY), add_special, parse_special);
@@ -990,15 +995,15 @@ static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_co
    }
 }
 
-std::vector<server_tokens> tokenize_input_prompts(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) {
+std::vector<server_tokens> tokenize_input_prompts(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special, const mtmd_helper_init_opt & init_opt) {
     std::vector<server_tokens> result;
     if (json_prompt.is_array() && !json_is_array_and_contains_numbers(json_prompt)) {
         result.reserve(json_prompt.size());
         for (const auto & p : json_prompt) {
-            result.push_back(tokenize_input_subprompt(vocab, mctx, p,add_special, parse_special));
+            result.push_back(tokenize_input_subprompt(vocab, mctx, p, add_special, parse_special, init_opt));
         }
     } else {
-        result.push_back(tokenize_input_subprompt(vocab, mctx, json_prompt, add_special, parse_special));
+        result.push_back(tokenize_input_subprompt(vocab, mctx, json_prompt, add_special, parse_special, init_opt));
     }
     if (result.empty()) {
         throw std::runtime_error("\"prompt\" must not be empty");
@@ -1787,7 +1792,8 @@ server_tokens format_prompt_rerank(
         const struct llama_vocab * vocab,
         mtmd_context * mctx,
         const std::string & query,
-        const std::string & doc) {
+        const std::string & doc,
+        const mtmd_helper_init_opt & init_opt) {
     server_tokens result = {};
 
     const char * rerank_prompt = llama_model_chat_template(model, "rerank");
@@ -1796,12 +1802,12 @@ server_tokens format_prompt_rerank(
         std::string prompt = rerank_prompt;
         string_replace_all(prompt, "{query}"   , query);
         string_replace_all(prompt, "{document}", doc  );
-        server_tokens tokens = tokenize_input_subprompt(vocab, mctx, prompt, false, true);
+        server_tokens tokens = tokenize_input_subprompt(vocab, mctx, prompt, false, true, init_opt);
         result.push_back(tokens);
     } else {
         // Get EOS token - use SEP token as fallback if EOS is not available
-        server_tokens query_tokens = tokenize_input_subprompt(vocab, mctx, query, false, false);
-        server_tokens doc_tokens   = tokenize_input_subprompt(vocab, mctx, doc,   false, false);
+        server_tokens query_tokens = tokenize_input_subprompt(vocab, mctx, query, false, false, init_opt);
+        server_tokens doc_tokens   = tokenize_input_subprompt(vocab, mctx, doc,   false, false, init_opt);
         llama_token eos_token = llama_vocab_eos(vocab);
         if (eos_token == LLAMA_TOKEN_NULL) {
             eos_token = llama_vocab_sep(vocab);

tools/server/server-common.h

@@ -5,6 +5,7 @@
 #include "llama.h"
 #include "chat.h"
 #include "mtmd.h"
+#include "mtmd-helper.h"
 
 #include "json.h"
 
@@ -269,7 +270,12 @@ size_t validate_utf8(const std::string& text);
 
 // process mtmd prompt, return the server_tokens containing both text tokens and media chunks
 // if is_placeholder is true, the media chunk will be treated as placeholder for counting tokens; the output tokens are not usable for actual inference (e.g. for submitting a task to server_queue)
-server_tokens process_mtmd_prompt(mtmd_context * mctx, const std::string & prompt, const std::vector<raw_buffer> & files, bool is_placeholder = false);
+server_tokens process_mtmd_prompt(
+                                        mtmd_context * mctx,
+                                        const std::string & prompt,
+                                        const std::vector<raw_buffer> & files,
+                                        const mtmd_helper_init_opt & init_opt,
+                                        bool is_placeholder = false);
 
 /**
  * break the input "prompt" object into multiple prompt if needed, then tokenize them
@@ -289,7 +295,8 @@ std::vector<server_tokens> tokenize_input_prompts(
                                         mtmd_context * mctx,
                                         const json & json_prompt,
                                         bool add_special,
-                                        bool parse_special);
+                                        bool parse_special,
+                                        const mtmd_helper_init_opt & init_opt);
 
 //
 // OAI utils
@@ -538,7 +545,8 @@ server_tokens format_prompt_rerank(
         const struct llama_vocab * vocab,
         mtmd_context * mctx,
         const std::string & query,
-        const std::string & doc);
+        const std::string & doc,
+        const mtmd_helper_init_opt & init_opt);
 
 // simple implementation of a pipe
 // used for streaming data between threads

tools/server/server-context.cpp

@@ -794,6 +794,8 @@ public:
     llama_model * model_tgt = nullptr;
 
     mtmd_context * mctx = nullptr;
+    // note: video_params.ffmpeg_bin_dir points into params_base, which outlives this struct
+    mtmd_helper_init_opt init_opt = mtmd_helper_init_opt_default();
     const llama_vocab * vocab = nullptr;
 
     server_queue    queue_tasks;
@@ -1118,6 +1120,11 @@ private:
             }
             SRV_INF("loaded multimodal model, '%s'\n", mmproj_path.c_str());
 
+            init_opt.video_params.fps_target = params_base.video_fps;
+            init_opt.video_params.timestamp_interval_ms = params_base.video_timestamp_interval_ms;
+            init_opt.video_params.ffmpeg_bin_dir = params_base.video_ffmpeg_bin_dir.empty()
+                                ? nullptr : params_base.video_ffmpeg_bin_dir.c_str();
+
             if (params_base.ctx_shift) {
                 params_base.ctx_shift = false;
                 SRV_WRN("%s\n", "ctx_shift is not supported by multimodal, it will be disabled");
@@ -2134,9 +2141,9 @@ private:
         try {
             auto & prompt = task.cli_prompt;
             if (mctx != nullptr) {
-                task.tokens = process_mtmd_prompt(mctx, prompt, task.cli_files);
+                task.tokens = process_mtmd_prompt(mctx, prompt, task.cli_files, init_opt);
             } else {
-                task.tokens = std::move(tokenize_input_prompts(vocab, mctx, prompt, true, true)[0]);
+                task.tokens = std::move(tokenize_input_prompts(vocab, mctx, prompt, true, true, init_opt)[0]);
             }
             task.cli_prompt.clear();
             task.cli_files.clear();
@@ -4165,10 +4172,10 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
 
         if (res_type != TASK_RESPONSE_TYPE_NONE && ctx_server.mctx != nullptr) {
             // This is the case used by OAI compatible chat path with MTMD. TODO It can be moved to the path below.
-            inputs.push_back(process_mtmd_prompt(ctx_server.mctx, prompt.get<std::string>(), files));
+            inputs.push_back(process_mtmd_prompt(ctx_server.mctx, prompt.get<std::string>(), files, ctx_server.init_opt));
         } else {
             // Everything else, including multimodal completions.
-            inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
+            inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true, ctx_server.init_opt);
         }
 
         // tasks.reserve(inputs.size()); // TODO: this is inaccurate due to child tasks
@@ -4752,7 +4759,7 @@ void server_routes::init_routes() {
         data["input_extra"] = input_extra; // default to empty array if it's not exist
 
         std::string prompt = json_value(data, "prompt", std::string());
-        std::vector<server_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, false, true);
+        std::vector<server_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, false, true, ctx_server.init_opt);
         SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
         data["prompt"] = format_prompt_infill(
             ctx_server.vocab,
@@ -4816,7 +4823,7 @@ void server_routes::init_routes() {
     };
 
     this->post_chat_completions_tok = [this](const server_http_req & req) {
-        return handle_count_tokens(ctx_server.vocab, ctx_server.mctx, req, TASK_RESPONSE_TYPE_OAI_CHAT);
+        return handle_count_tokens(ctx_server.vocab, ctx_server.mctx, ctx_server.init_opt, req, TASK_RESPONSE_TYPE_OAI_CHAT);
     };
 
     this->post_control = [this](const server_http_req & req) {
@@ -4875,7 +4882,7 @@ void server_routes::init_routes() {
     };
 
     this->post_responses_tok_oai = [this](const server_http_req & req) {
-        return handle_count_tokens(ctx_server.vocab, ctx_server.mctx, req, TASK_RESPONSE_TYPE_OAI_RESP);
+        return handle_count_tokens(ctx_server.vocab, ctx_server.mctx, ctx_server.init_opt, req, TASK_RESPONSE_TYPE_OAI_RESP);
     };
 
     this->post_transcriptions_oai = [this](const server_http_req & req) {
@@ -4925,7 +4932,7 @@ void server_routes::init_routes() {
     };
 
     this->post_anthropic_count_tokens = [this](const server_http_req & req) {
-        return handle_count_tokens(ctx_server.vocab, ctx_server.mctx, req, TASK_RESPONSE_TYPE_ANTHROPIC);
+        return handle_count_tokens(ctx_server.vocab, ctx_server.mctx, ctx_server.init_opt, req, TASK_RESPONSE_TYPE_ANTHROPIC);
     };
 
     // same with handle_chat_completions, but without inference part
@@ -5058,7 +5065,7 @@ void server_routes::init_routes() {
             std::vector<server_task> tasks;
             tasks.reserve(documents.size());
             for (size_t i = 0; i < documents.size(); i++) {
-                auto tmp = format_prompt_rerank(ctx_server.model_tgt, ctx_server.vocab, ctx_server.mctx, query, documents[i]);
+                auto tmp = format_prompt_rerank(ctx_server.model_tgt, ctx_server.vocab, ctx_server.mctx, query, documents[i], ctx_server.init_opt);
                 server_task task = server_task(SERVER_TASK_TYPE_RERANK);
                 task.id     = rd.get_new_id();
                 task.tokens = std::move(tmp);
@@ -5296,7 +5303,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_embeddings_impl(cons
         }
     }
 
-    auto tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
+    auto tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true, ctx_server.init_opt);
     for (const auto & tokens : tokenized_prompts) {
         // this check is necessary for models that do not add BOS token to the input
         if (tokens.empty()) {
@@ -5357,7 +5364,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_embeddings_impl(cons
     return res;
 }
 
-std::unique_ptr<server_res_generator> server_routes::handle_count_tokens(const llama_vocab * vocab, mtmd_context * mctx, const server_http_req & req, task_response_type res_type) {
+std::unique_ptr<server_res_generator> server_routes::handle_count_tokens(const llama_vocab * vocab, mtmd_context * mctx, const mtmd_helper_init_opt & init_opt, const server_http_req & req, task_response_type res_type) {
     auto res = create_response();
     std::vector<raw_buffer> files;
     json body = json::parse(req.body);
@@ -5395,7 +5402,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_count_tokens(const l
         if (!prompt.is_string()) {
             throw std::runtime_error("for mtmd, input prompt must be a string.");
         }
-        n_tokens = process_mtmd_prompt(mctx, prompt.get<std::string>(), files, true).size();
+        n_tokens = process_mtmd_prompt(mctx, prompt.get<std::string>(), files, init_opt, true).size();
     } else {
         n_tokens = tokenize_mixed(vocab, prompt, true, true).size();
     }

tools/server/server-context.h

@@ -169,7 +169,7 @@ private:
     std::unique_ptr<server_res_generator> handle_slots_restore(const server_http_req & req, int id_slot);
     std::unique_ptr<server_res_generator> handle_slots_erase(const server_http_req &, int id_slot);
     std::unique_ptr<server_res_generator> handle_embeddings_impl(const server_http_req & req, task_response_type res_type);
-    std::unique_ptr<server_res_generator> handle_count_tokens(const llama_vocab * vocab, mtmd_context * mctx, const server_http_req & req, task_response_type res_type);
+    std::unique_ptr<server_res_generator> handle_count_tokens(const llama_vocab * vocab, mtmd_context * mctx, const mtmd_helper_init_opt & init_opt, const server_http_req & req, task_response_type res_type);
 
     // using unique_ptr to allow late initialization of const
     std::unique_ptr<const server_context_meta> meta;

tools/tts/tts.cpp

@@ -103,7 +103,7 @@ int main(int argc, char ** argv) {
 
     mtmd::bitmap_ptr speaker_bitmap;
     if (!params.tts_speaker_file.empty()) {
-        auto wrapper = mtmd_helper_bitmap_init_from_file(mctx.get(), params.tts_speaker_file.c_str(), false);
+        auto wrapper = mtmd_helper_bitmap_init_from_file(mctx.get(), params.tts_speaker_file.c_str(), false, mtmd_helper_init_opt_default());
         if (!wrapper.bitmap) {
             LOG_ERR("failed to load speaker file %s\n", params.tts_speaker_file.c_str());
             return 1;