feat: support building with upstream ggml

CMakeLists.txt

@@ -95,6 +95,8 @@ option(SD_MUSA                       "sd: musa backend" OFF)
 option(SD_BUILD_SHARED_LIBS          "sd: build shared libs" OFF)
 option(SD_BUILD_SHARED_GGML_LIB      "sd: build ggml as a separate shared lib" OFF)
 option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
+option(SD_USE_UPSTREAM_GGML          "sd: build with upstream GGML instead of the patched GGML extensions" OFF)
+set(SD_GGML_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/ggml" CACHE PATH "sd: ggml source directory (also supplies private headers for system ggml)")
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
 set(CMAKE_C_STANDARD 11)
@@ -325,18 +327,7 @@ if (NOT SD_USE_SYSTEM_GGML)
 endif()
 
 # deps
-# Only add ggml if it hasn't been added yet
-if (NOT TARGET ggml)
-    if (SD_USE_SYSTEM_GGML)
-        find_package(ggml REQUIRED)
-        if (NOT ggml_FOUND)
-            message(FATAL_ERROR "System-installed GGML library not found.")
-        endif()
-        add_library(ggml ALIAS ggml::ggml)
-    else()
-        add_subdirectory(ggml)
-    endif()
-endif()
+include(cmake/ggml.cmake)
 
 add_subdirectory(thirdparty)
 

cmake/ggml.cmake

@@ -0,0 +1,27 @@
+if(NOT TARGET ggml AND NOT TARGET ggml::ggml)
+    if(SD_USE_SYSTEM_GGML)
+        find_package(ggml REQUIRED)
+    else()
+        add_subdirectory("${SD_GGML_SOURCE_DIR}" "${CMAKE_CURRENT_BINARY_DIR}/ggml")
+    endif()
+endif()
+if(NOT TARGET ggml)
+    add_library(ggml ALIAS ggml::ggml)
+endif()
+
+get_target_property(sd_ggml_imported ggml IMPORTED)
+if(sd_ggml_imported)
+    set(sd_ggml_private_include "${SD_GGML_SOURCE_DIR}/src")
+else()
+    get_target_property(sd_ggml_private_include ggml SOURCE_DIR)
+endif()
+if(NOT EXISTS "${sd_ggml_private_include}/ggml-impl.h")
+    message(FATAL_ERROR "Set SD_GGML_SOURCE_DIR to the source tree matching the selected ggml library (ggml-impl.h is required).")
+endif()
+target_include_directories(${SD_LIB} PRIVATE "${sd_ggml_private_include}")
+set_property(TARGET ${SD_LIB} PROPERTY SD_GGML_PRIVATE_INCLUDE_DIR "${sd_ggml_private_include}")
+
+if(SD_USE_UPSTREAM_GGML)
+    target_compile_definitions(${SD_LIB} PUBLIC SD_USE_UPSTREAM_GGML)
+    message(WARNING "Using upstream GGML: FP8 and INT8 tensorwise/convrot are disabled. Some operators may be unsupported and performance may be lower than with patched GGML.")
+endif()

docs/build.md

@@ -16,6 +16,38 @@ git submodule init
 git submodule update
 ```
 
+## Selecting a GGML source tree
+
+By default, sd.cpp builds the patched GGML submodule in `ggml/`. To build with
+an upstream GGML checkout instead, enable `SD_USE_UPSTREAM_GGML` and set
+`SD_GGML_SOURCE_DIR`:
+
+```shell
+cmake -S . -B build-upstream -DSD_USE_UPSTREAM_GGML=ON -DSD_GGML_SOURCE_DIR=../ggml-upstream
+cmake --build build-upstream --config Release
+```
+
+The selected source tree supplies both the library and its private headers.
+Backend options such as `-DSD_CUDA=ON` apply to the selected tree as usual.
+
+`SD_USE_UPSTREAM_GGML` defaults to `OFF`, which enables the patched GGML
+extensions. Set it to `ON` when using upstream GGML; it selects the compatibility
+mode and does not download or replace the GGML source tree. Upstream mode
+disables FP8 and INT8 tensorwise/convrot and rejects their model files with an
+explicit error. FP8 weight type requests, tensor type rules and conversion
+outputs are also rejected; no automatic conversion is performed.
+
+Upstream GGML may lack some operators and performance optimizations provided by
+the patched version. A warning is emitted during CMake configuration and when
+creating an inference context. Ordinary floating-point and shared GGML
+quantization types remain available, subject to backend operator support.
+
+`SD_USE_SYSTEM_GGML=ON` instead links an installed GGML CMake package, located
+with `ggml_DIR` or `CMAKE_PREFIX_PATH`. In that mode, `SD_GGML_SOURCE_DIR` must
+point to the matching source tree for private headers. The installed library
+must use the same ABI settings as sd.cpp, including `GGML_MAX_NAME`.
+Set `SD_USE_UPSTREAM_GGML=ON` as well if the installed package is upstream GGML.
+
 ## WebP and WebM Support in Examples
 
 The example applications (`examples/cli` and `examples/server`) use `libwebp` to support WebP image I/O, and `examples/cli` can also use `libwebm` for `.webm` video output. Both are enabled by default. WebM output currently reuses `libwebp` to encode each frame as VP8 before muxing with `libwebm`.

docs/int8_convrot.md

@@ -2,6 +2,9 @@
 
 sd.cpp can load and execute ComfyUI `int8_tensorwise` safetensors with `convrot` metadata directly. The stored INT8 weights are not converted to another weight type at load time.
 
+This requires the INT8 tensorwise/convrot extensions in the patched GGML.
+Builds with `SD_USE_UPSTREAM_GGML=ON` reject these files during loading.
+
 ## Checkpoint format
 
 Each quantized linear module contains the following tensors:

src/convert.cpp

@@ -362,6 +362,9 @@ bool convert_with_components(const char* model_path,
                              const char* tensor_type_rules,
                              bool convert_name,
                              int n_threads) {
+    if (!validate_tensor_types(output_type, tensor_type_rules)) {
+        return false;
+    }
     ModelLoader model_loader;
     bool loaded_any = false;
 

src/core/compute_workspace.cpp

@@ -11,7 +11,7 @@
 #include "core/ggml_graph_cut.h"
 #include "core/util.h"
 #include "ggml-cpu.h"
-#include "ggml/src/ggml-impl.h"
+#include "ggml-impl.h"
 
 namespace sd {
     ComputeWorkspace::~ComputeWorkspace() {

src/core/ggml_extend.cpp

@@ -1,6 +1,7 @@
 #include "core/ggml_extend.h"
 
 #include <cmath>
+#include <stdexcept>
 #include <utility>
 
 #include "core/ggml_extend_backend.h"
@@ -247,6 +248,7 @@ ggml_tensor* ggml_ext_linear_i8_tensorwise(ggml_context* ctx,
                                            ggml_tensor* b,
                                            int convrot_group_size,
                                            float scale) {
+#ifndef SD_USE_UPSTREAM_GGML
     GGML_ASSERT(x->type == GGML_TYPE_F32 || (x->type == GGML_TYPE_I8 && scale == 1.f));
     if (scale != 1.f) {
         x = ggml_ext_scale(ctx, x, scale);
@@ -270,6 +272,16 @@ ggml_tensor* ggml_ext_linear_i8_tensorwise(ggml_context* ctx,
         }
     }
     return x;
+#else
+    GGML_UNUSED(ctx);
+    GGML_UNUSED(x);
+    GGML_UNUSED(w);
+    GGML_UNUSED(weight_scale);
+    GGML_UNUSED(b);
+    GGML_UNUSED(convrot_group_size);
+    GGML_UNUSED(scale);
+    throw std::runtime_error("INT8 tensorwise/convrot is not supported by this ggml build");
+#endif
 }
 
 ggml_tensor* ggml_ext_pad_ext(ggml_context* ctx,

src/core/ggml_extend_backend.cpp

@@ -13,7 +13,7 @@
 #endif
 
 #include "core/util.h"
-#include "ggml/src/ggml-impl.h"
+#include "ggml-impl.h"
 #include "stable-diffusion.h"
 
 static std::string trim_copy(const std::string& value) {

src/core/ggml_graph_cut.cpp

@@ -16,7 +16,7 @@
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
-#include "ggml/src/ggml-impl.h"
+#include "ggml-impl.h"
 
 namespace sd::ggml_graph_cut {
 

src/core/util.cpp

@@ -414,14 +414,43 @@ std::vector<std::string> split_string(const std::string& str, char delimiter) {
 }
 
 ggml_type sd_type_to_ggml_type(sd_type_t sdtype) {
+    if (sdtype == SD_TYPE_F8_E4M3 || sdtype == SD_TYPE_F8_E5M2) {
+#ifndef SD_USE_UPSTREAM_GGML
+        return sdtype == SD_TYPE_F8_E4M3 ? GGML_TYPE_F8_E4M3 : GGML_TYPE_F8_E5M2;
+#else
+        return GGML_TYPE_COUNT;
+#endif
+    }
     const int type_value = static_cast<int>(sdtype);
-    if (type_value < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)) {
+    if (type_value >= 0 && type_value < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)) {
         return static_cast<ggml_type>(type_value);
     } else {
         return GGML_TYPE_COUNT;
     }
 }
 
+bool validate_tensor_types(sd_type_t type, const char* tensor_type_rules) {
+    if (type != SD_TYPE_COUNT && sd_type_to_ggml_type(type) == GGML_TYPE_COUNT) {
+        LOG_ERROR("weight type %s is not supported by this ggml build", sd_type_name(type));
+        return false;
+    }
+#ifdef SD_USE_UPSTREAM_GGML
+    for (const auto& rule : split_string(SAFE_STR(tensor_type_rules), ',')) {
+        const auto pos = rule.find('=');
+        if (pos != std::string::npos) {
+            const auto name = rule.substr(pos + 1);
+            if (name == "f8_e4m3" || name == "f8_e5m2") {
+                LOG_ERROR("FP8 is not supported by this ggml build (tensor type rule '%s')", rule.c_str());
+                return false;
+            }
+        }
+    }
+#else
+    GGML_UNUSED(tensor_type_rules);
+#endif
+    return true;
+}
+
 KeyValueArgs parse_key_value_args(const char* args, const char* context) {
     KeyValueArgs pairs;
 

src/core/util.h

@@ -90,6 +90,7 @@ void log_printf(sd_log_level_t level, const char* file, int line, const char* fo
 void sd_ggml_log_callback(ggml_log_level level, const char* text, void*);
 
 ggml_type sd_type_to_ggml_type(sd_type_t sdtype);
+bool validate_tensor_types(sd_type_t type, const char* tensor_type_rules);
 
 std::string trim(const std::string& s);
 

src/model/common/ggml_block.hpp

@@ -208,6 +208,7 @@ class Linear : public UnaryBlock {
         ggml_tensor* w            = params["weight"];
         const float scale         = ctx->linear_scale > 0.f ? ctx->linear_scale : this->scale;
         ggml_tensor* weight_scale = has_weight_scale ? params["weight_scale"] : nullptr;
+#ifndef SD_USE_UPSTREAM_GGML
         if (w->type == GGML_TYPE_F8_E4M3 || w->type == GGML_TYPE_F8_E5M2) {
             bool supports_fp8_matmul = false;
             if (ctx->backend != nullptr) {
@@ -221,6 +222,7 @@ class Linear : public UnaryBlock {
                 w = ggml_cast(ctx->ggml_ctx, w, GGML_TYPE_BF16);
             }
         }
+#endif
         ggml_tensor* b = nullptr;
         if (bias) {
             b = params["bias"];
@@ -238,6 +240,7 @@ class Linear : public UnaryBlock {
             if (ctx->weight_adapter && b != nullptr) {
                 b = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, b, prefix + "bias");
             }
+#ifndef SD_USE_UPSTREAM_GGML
             if (int8_convrot && scale == 1.f) {
                 const auto cache_key = std::make_pair(x, int8_convrot_group_size);
                 auto cached          = ctx->int8_convrot_cache.find(cache_key);
@@ -248,6 +251,7 @@ class Linear : public UnaryBlock {
                     x = cached->second;
                 }
             }
+#endif
             out = ggml_ext_linear_i8_tensorwise(ctx->ggml_ctx,
                                                 x,
                                                 w,

src/model_io/gguf_io.cpp

@@ -57,6 +57,18 @@ bool read_gguf_file(const std::string& file_path,
 
         size_t data_offset = gguf_reader.data_offset();
         for (const auto& gguf_tensor_info : gguf_reader.tensors()) {
+#ifdef SD_USE_UPSTREAM_GGML
+            if (static_cast<int>(gguf_tensor_info.type) == SD_TYPE_F8_E4M3 ||
+                static_cast<int>(gguf_tensor_info.type) == SD_TYPE_F8_E5M2) {
+                set_error(error, "FP8 is not supported by this ggml build (tensor '" + gguf_tensor_info.name + "')");
+                return false;
+            }
+#endif
+            if (static_cast<unsigned>(gguf_tensor_info.type) >= GGML_TYPE_COUNT ||
+                ggml_get_type_traits(gguf_tensor_info.type)->type_size == 0) {
+                set_error(error, "unsupported GGUF tensor type (tensor '" + gguf_tensor_info.name + "')");
+                return false;
+            }
             TensorStorage tensor_storage(
                 gguf_tensor_info.name,
                 gguf_tensor_info.type,

src/model_io/safetensors_io.cpp

@@ -86,10 +86,12 @@ static ggml_type safetensors_dtype_to_ggml_type(const std::string& dtype) {
         ttype = GGML_TYPE_F32;
     } else if (dtype == "F64") {
         ttype = GGML_TYPE_F32;
+#ifndef SD_USE_UPSTREAM_GGML
     } else if (dtype == "F8_E4M3") {
         ttype = GGML_TYPE_F8_E4M3;
     } else if (dtype == "F8_E5M2") {
         ttype = GGML_TYPE_F8_E5M2;
+#endif
     } else if (dtype == "I32") {
         ttype = GGML_TYPE_I32;
     } else if (dtype == "I64") {
@@ -230,6 +232,12 @@ bool read_safetensors_file(const std::string& file_path,
         if (!read_comfy_quant_config(file, file_path, name, data_start + begin, end - begin, config, error)) {
             return false;
         }
+#ifdef SD_USE_UPSTREAM_GGML
+        if (config.format == "int8_tensorwise") {
+            set_error(error, "INT8 tensorwise/convrot is not supported by this ggml build (tensor '" + name + "')");
+            return false;
+        }
+#endif
         const std::string module_name = name.substr(0, name.size() - std::string(".comfy_quant").size());
         comfy_quant_configs.emplace(module_name, std::move(config));
     }
@@ -279,6 +287,12 @@ bool read_safetensors_file(const std::string& file_path,
             continue;
         }
 
+#ifdef SD_USE_UPSTREAM_GGML
+        if (dtype == "F8_E4M3" || dtype == "F8_E5M2") {
+            set_error(error, "FP8 is not supported by this ggml build (tensor '" + name + "')");
+            return false;
+        }
+#endif
         ggml_type type = safetensors_dtype_to_ggml_type(dtype);
         if (type == GGML_TYPE_COUNT) {
             set_error(error, "unsupported dtype '" + dtype + "' (tensor '" + name + "')");

src/pipeline/diffusion_engine.cpp

@@ -857,6 +857,14 @@ bool StableDiffusionGGML::init_model_loader(ModelLoader& model_loader, ModelConf
 }
 
 bool StableDiffusionGGML::init(const sd_ctx_params_t* sd_ctx_params) {
+#ifdef SD_USE_UPSTREAM_GGML
+    LOG_WARN(
+        "Using upstream GGML: FP8 and INT8 tensorwise/convrot are disabled. "
+        "Some operators may be unsupported and performance may be lower than with patched GGML.");
+#endif
+    if (!validate_tensor_types(sd_ctx_params->wtype, sd_ctx_params->tensor_type_rules)) {
+        return false;
+    }
     for (float scale : {sd_ctx_params->linear_scale, sd_ctx_params->attn_scale}) {
         if (!std::isfinite(scale) || scale < 0.f || (scale > 0.f && !std::isfinite(1.f / scale))) {
             LOG_ERROR("scale overrides must be finite positive values, or 0 to keep model defaults");

src/stable-diffusion.cpp

@@ -26,13 +26,26 @@ static float get_cache_reuse_threshold(const sd_cache_params_t& params) {
 }
 
 const char* sd_type_name(enum sd_type_t type) {
-    if ((int)type < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)) {
-        return ggml_type_name((ggml_type)type);
+    if (type == SD_TYPE_F8_E4M3) {
+        return "f8_e4m3";
+    }
+    if (type == SD_TYPE_F8_E5M2) {
+        return "f8_e5m2";
+    }
+    const auto ggml_type = sd_type_to_ggml_type(type);
+    if (ggml_type != GGML_TYPE_COUNT) {
+        return ggml_type_name(ggml_type);
     }
     return NONE_STR;
 }
 
 enum sd_type_t str_to_sd_type(const char* str) {
+    if (!strcmp(str, "f8_e4m3")) {
+        return SD_TYPE_F8_E4M3;
+    }
+    if (!strcmp(str, "f8_e5m2")) {
+        return SD_TYPE_F8_E5M2;
+    }
     for (int i = 0; i < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT); i++) {
         auto trait = ggml_get_type_traits((ggml_type)i);
         if (!strcmp(str, trait->type_name)) {