Merge pull request #67 from menloresearch/update-dev-from-master-2025-04-25-00-08

Sync master with upstream release b5186

Author: jan-service-account

.clang-tidy

@@ -13,6 +13,7 @@ Checks: >
     -readability-magic-numbers,
     -readability-uppercase-literal-suffix,
     -readability-simplify-boolean-expr,
+    -readability-math-missing-parentheses,
     clang-analyzer-*,
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,

common/arg.cpp

@@ -38,6 +38,11 @@
 
 using json = nlohmann::ordered_json;
 
+std::initializer_list<enum llama_example> mmproj_examples = {
+    LLAMA_EXAMPLE_LLAVA,
+    // TODO: add LLAMA_EXAMPLE_SERVER when it's ready
+};
+
 common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
     this->examples = std::move(examples);
     return *this;
@@ -641,11 +646,16 @@ static struct common_hf_file_res common_get_hf_file(const std::string &, const s
 // utils
 //
 
-static void common_params_handle_model(
+struct handle_model_result {
+    bool found_mmproj = false;
+    common_params_model mmproj;
+};
+
+static handle_model_result common_params_handle_model(
         struct common_params_model & model,
         const std::string & bearer_token,
-        const std::string & model_path_default,
-        bool is_mmproj = false) { // TODO: move is_mmproj to an enum when we have more files?
+        const std::string & model_path_default) {
+    handle_model_result result;
     // handle pre-fill default model path and url based on hf_repo and hf_file
     {
         if (!model.hf_repo.empty()) {
@@ -657,7 +667,12 @@ static void common_params_handle_model(
                         exit(1); // built without CURL, error message already printed
                     }
                     model.hf_repo = auto_detected.repo;
-                    model.hf_file = is_mmproj ? auto_detected.mmprojFile : auto_detected.ggufFile;
+                    model.hf_file = auto_detected.ggufFile;
+                    if (!auto_detected.mmprojFile.empty()) {
+                        result.found_mmproj   = true;
+                        result.mmproj.hf_repo = model.hf_repo;
+                        result.mmproj.hf_file = auto_detected.mmprojFile;
+                    }
                 } else {
                     model.hf_file = model.path;
                 }
@@ -694,6 +709,8 @@ static void common_params_handle_model(
             exit(1);
         }
     }
+
+    return result;
 }
 
 const std::vector<ggml_type> kv_cache_types = {
@@ -827,16 +844,25 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    common_params_handle_model(params.model,             params.hf_token, DEFAULT_MODEL_PATH);
-    common_params_handle_model(params.speculative.model, params.hf_token, "");
-    common_params_handle_model(params.vocoder.model,     params.hf_token, "");
-
-    // allow --mmproj to be set from -hf
-    // assuming that mmproj is always in the same repo as text model
-    if (!params.model.hf_repo.empty() && ctx_arg.ex == LLAMA_EXAMPLE_LLAVA) {
-        params.mmproj.hf_repo = params.model.hf_repo;
+    // handle model and download
+    {
+        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH);
+        if (params.no_mmproj) {
+            params.mmproj = {};
+        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
+            // optionally, handle mmproj model when -hf is specified
+            params.mmproj = res.mmproj;
+        }
+        // only download mmproj if the current example is using it
+        for (auto & ex : mmproj_examples) {
+            if (ctx_arg.ex == ex) {
+                common_params_handle_model(params.mmproj,    params.hf_token, "");
+                break;
+            }
+        }
+        common_params_handle_model(params.speculative.model, params.hf_token, "");
+        common_params_handle_model(params.vocoder.model,     params.hf_token, "");
     }
-    common_params_handle_model(params.mmproj,            params.hf_token, "", true);
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -968,7 +994,6 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-embedding",
         "llama-eval-callback",
         "llama-export-lora",
-        "llama-gbnf-validator",
         "llama-gen-docs",
         "llama-gguf",
         "llama-gguf-hash",
@@ -988,7 +1013,6 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-perplexity",
         "llama-q8dot",
         "llama-quantize",
-        "llama-quantize-stats",
         "llama-qwen2vl-cli",
         "llama-retrieval",
         "llama-run",
@@ -2095,18 +2119,32 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
     add_opt(common_arg(
         {"--mmproj"}, "FILE",
-        "path to a multimodal projector file for LLaVA. see examples/llava/README.md",
+        "path to a multimodal projector file. see examples/llava/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+    ).set_examples(mmproj_examples));
     add_opt(common_arg(
         {"--mmproj-url"}, "URL",
-        "URL to a multimodal projector file for LLaVA. see examples/llava/README.md",
+        "URL to a multimodal projector file. see examples/llava/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.url = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+    ).set_examples(mmproj_examples));
+    add_opt(common_arg(
+        {"--no-mmproj"},
+        "explicitly disable multimodal projector, useful when using -hf",
+        [](common_params & params) {
+            params.no_mmproj = true;
+        }
+    ).set_examples(mmproj_examples));
+    add_opt(common_arg(
+        {"--no-mmproj-offload"},
+        "do not offload multimodal projector to GPU",
+        [](common_params & params) {
+            params.mmproj_use_gpu = false;
+        }
+    ).set_examples(mmproj_examples));
     add_opt(common_arg(
         {"--image"}, "FILE",
         "path to an image file. use with multimodal models. Specify multiple times for batching",
@@ -2381,6 +2419,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
         "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
+        "mmproj is also downloaded automatically if available. to disable, add --no-mmproj\n"
         "example: unsloth/phi-4-GGUF:q4_k_m\n"
         "(default: unused)",
         [](common_params & params, const std::string & value) {

common/common.h

@@ -342,6 +342,8 @@ struct common_params {
 
     // multimodal models (see examples/llava)
     struct common_params_model mmproj;
+    bool mmproj_use_gpu = true;     // use GPU for multimodal model
+    bool no_mmproj = false;         // explicitly disable multimodal model
     std::vector<std::string> image; // path to image file(s)
 
     // embedding

examples/CMakeLists.txt

@@ -21,11 +21,6 @@ else()
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
 
-    if (NOT WIN32)
-        # disabled on Windows because it uses internal functions not exported with LLAMA_API
-        add_subdirectory(gbnf-validator)
-    endif()
-
     add_subdirectory(gguf-hash)
     add_subdirectory(gguf-split)
     add_subdirectory(gguf)
@@ -58,10 +53,6 @@ else()
         add_subdirectory(convert-llama2c-to-ggml)
         add_subdirectory(cvector-generator)
         add_subdirectory(export-lora)
-        if (NOT WIN32)
-            # disabled on Windows because it uses internal functions not exported with LLAMA_API
-            add_subdirectory(quantize-stats)
-        endif()
         add_subdirectory(llava)
         if (GGML_RPC)
             add_subdirectory(rpc)

examples/embedding/embedding.cpp

@@ -89,6 +89,13 @@ int main(int argc, char ** argv) {
     common_init();
 
     params.embedding = true;
+
+    // utilize the full context
+    if (params.n_batch < params.n_ctx) {
+        LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx);
+        params.n_batch = params.n_ctx;
+    }
+
     // For non-causal models, batch size must be equal to ubatch size
     params.n_ubatch = params.n_batch;
 
@@ -134,7 +141,6 @@ int main(int argc, char ** argv) {
 
     // max batch size
     const uint64_t n_batch = params.n_batch;
-    GGML_ASSERT(params.n_batch >= params.n_ctx);
 
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;

examples/gbnf-validator/CMakeLists.txt

@@ -90,8 +90,6 @@
 #define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
 #define TN_GLM_ADAPTER_GATE     "adapter.linear.gate.%s"
 #define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
-#define TN_GLM_BOI_W            "adapter.boi"
-#define TN_GLM_EOI_W            "adapter.eoi"
 
 enum projector_type {
     PROJECTOR_TYPE_MLP,

examples/llava/clip-impl.h

@@ -244,8 +244,6 @@ struct clip_vision_model {
     //GLMV-Edge projection
     struct ggml_tensor * mm_model_adapter_conv_w = nullptr;
     struct ggml_tensor * mm_model_adapter_conv_b = nullptr;
-    struct ggml_tensor * boi_w = nullptr;
-    struct ggml_tensor * eoi_w = nullptr;
 
     // MobileVLM projection
     struct ggml_tensor * mm_model_mlp_1_w = nullptr;
@@ -1697,8 +1695,6 @@ struct clip_model_loader {
                     vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
                     vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE,"weight"));
                     vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
-                    vision_model.boi_w = get_tensor(TN_GLM_BOI_W);
-                    vision_model.eoi_w = get_tensor(TN_GLM_EOI_W);
                 } break;
             case PROJECTOR_TYPE_MERGER:
                 {
@@ -2593,8 +2589,7 @@ void clip_free(clip_ctx * ctx) {
 }
 
 size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
-    int extra_tokens = ctx->has_glm_projector ? 2 : 0;
-    return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float);
+    return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }
 
 size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
@@ -2790,9 +2785,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     }
     if (ctx->has_glm_projector) {
         GGML_ASSERT(batch_size == 1);
-        ggml_tensor * boi = ctx->vision_model.boi_w;
-        ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
-        vec = (float*)(vec+ggml_nelements(boi)); //offset for boi
     }
 
     // build the inference graph
@@ -3001,13 +2993,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     // copy the embeddings to the location passed by the user
     ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
 
-    if (ctx->has_glm_projector) {
-        //eoi
-        ggml_tensor * eoi = ctx->vision_model.eoi_w;
-        int offset = ggml_nelements(embeddings);
-        ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi));
-    }
-
     return true;
 }
 

examples/llava/clip.cpp

@@ -40,7 +40,8 @@ static void show_additional_info(int /*argc*/, char ** argv) {
         "Usage: %s [options] -m <model> --mmproj <mmproj> --image <image> -p <prompt>\n\n"
         "  -m and --mmproj are required\n"
         "  -hf user/repo can replace both -m and --mmproj in most cases\n"
-        "  --image and -p are optional, if NOT provided, the CLI will run in chat mode\n",
+        "  --image and -p are optional, if NOT provided, the CLI will run in chat mode\n"
+        "  to disable using GPU for mmproj model, add --no-mmproj-offload\n",
         argv[0]
     );
 }
@@ -112,10 +113,10 @@ struct mtmd_cli_context {
     void init_vision_context(common_params & params) {
         const char * clip_path = params.mmproj.path.c_str();
         ctx_vision.reset(mtmd_init_from_file(clip_path, model, mtmd_context_params{
-            /* use_gpu */   true,
+            /* use_gpu */   params.mmproj_use_gpu,
             /* timings */   true,
             /* n_threads */ params.cpuparams.n_threads,
-            /* verbosity */ GGML_LOG_LEVEL_INFO,
+            /* verbosity */ params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO,
         }));
         if (!ctx_vision.get()) {
             LOG_ERR("Failed to load vision model from %s\n", clip_path);
@@ -261,6 +262,7 @@ int main(int argc, char ** argv) {
 
     if (params.mmproj.path.empty()) {
         show_additional_info(argc, argv);
+        LOG_ERR("ERR: Missing --mmproj argument\n");
         return 1;
     }
 

examples/llava/mtmd-cli.cpp

@@ -186,6 +186,11 @@ int32_t mtmd_tokenize(mtmd_context * ctx,
         marker_modified = "<start_of_image>" + ctx->image_marker + "<end_of_image>";
         string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
 
+    } else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) {
+        // <|begin_of_image|> ... (image embeddings) ... <|end_of_image|>
+        marker_modified = "<|begin_of_image|>" + ctx->image_marker + "<|end_of_image|>";
+        string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
+
     } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) {
         // https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
         marker_modified = "<fake_token_around_image><global-img>" + ctx->image_marker + "<fake_token_around_image>";