cont : fix comments

ggml-ci

.devops/intel.Dockerfile

@@ -49,23 +49,19 @@ COPY --from=build /app/full /app
 
 WORKDIR /app
 
-RUN apt-get update && \
-    apt-get install -y \
-        git \
-        python3 \
-        python3-pip \
-        python3-venv && \
-    python3 -m venv /opt/venv && \
-    . /opt/venv/bin/activate && \
-    pip install --upgrade pip setuptools wheel && \
-    pip install -r requirements.txt && \
-    apt autoremove -y && \
-    apt clean -y && \
-    rm -rf /tmp/* /var/tmp/* && \
-    find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \
-    find /var/cache -type f -delete
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    && pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
 
-ENV PATH="/opt/venv/bin:$PATH"
 
 ENTRYPOINT ["/app/tools.sh"]
 

CMakeLists.txt

@@ -89,14 +89,6 @@ option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
 
-if (NOT DEFINED LLAMA_BUILD_NUMBER)
-    set(LLAMA_BUILD_NUMBER        ${BUILD_NUMBER})
-endif()
-if (NOT DEFINED LLAMA_BUILD_COMMIT)
-    set(LLAMA_BUILD_COMMIT        ${BUILD_COMMIT})
-endif()
-set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER})
-
 # override ggml options
 set(GGML_ALL_WARNINGS   ${LLAMA_ALL_WARNINGS})
 set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS})
@@ -163,8 +155,6 @@ if (LLAMA_USE_SYSTEM_GGML)
 endif()
 
 if (NOT TARGET ggml AND NOT LLAMA_USE_SYSTEM_GGML)
-    set(GGML_BUILD_NUMBER ${LLAMA_BUILD_NUMBER})
-    set(GGML_BUILD_COMMIT ${LLAMA_BUILD_COMMIT})
     add_subdirectory(ggml)
     # ... otherwise assume ggml is added by a parent CMakeLists.txt
 endif()
@@ -214,6 +204,10 @@ endif()
 include(GNUInstallDirs)
 include(CMakePackageConfigHelpers)
 
+set(LLAMA_BUILD_NUMBER        ${BUILD_NUMBER})
+set(LLAMA_BUILD_COMMIT        ${BUILD_COMMIT})
+set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER})
+
 set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header  files")
 set(LLAMA_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
 set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")

README.md

@@ -6,7 +6,7 @@
 [![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp)](https://github.com/ggml-org/llama.cpp/releases)
 [![Server](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
 
-[Roadmap](https://github.com/users/ggerganov/projects/7) / [Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml)
+[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggml-org/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml)
 
 Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
@@ -18,6 +18,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 ## Hot topics
 
 - 🔥 Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
+- **GGML developer experience survey (organized and reviewed by NVIDIA):** [link](https://forms.gle/Gasw3cRgyhNEnrwK9)
 - A new binary `llama-mtmd-cli` is introduced to replace `llava-cli`, `minicpmv-cli`, `gemma3-cli` ([#13012](https://github.com/ggml-org/llama.cpp/pull/13012)) and `qwen2vl-cli` ([#13141](https://github.com/ggml-org/llama.cpp/pull/13141)), `libllava` will be deprecated
 - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
 - Universal [tool call support](./docs/function-calling.md) in `llama-server` https://github.com/ggml-org/llama.cpp/pull/9639

common/CMakeLists.txt

@@ -23,21 +23,31 @@ if(EXISTS "${PROJECT_SOURCE_DIR}/.git")
     endif()
 
     if(EXISTS "${GIT_DIR}/index")
-        # For build-info.cpp below
-        set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS "${GIT_DIR}/index")
+        set(GIT_INDEX "${GIT_DIR}/index")
     else()
         message(WARNING "Git index not found in git repository.")
+        set(GIT_INDEX "")
     endif()
 else()
     message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
+    set(GIT_INDEX "")
 endif()
 
-set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in")
-set(OUTPUT_FILE   "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp")
-configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-
+# Add a custom command to rebuild build-info.cpp when .git/index changes
+add_custom_command(
+    OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp"
+    COMMENT "Generating build details from Git"
+    COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
+            -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
+            -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME} -DCMAKE_SYSTEM_PROCESSOR=${CMAKE_SYSTEM_PROCESSOR}
+            -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
+    WORKING_DIRECTORY "${PROJECT_SOURCE_DIR}"
+    DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
+    VERBATIM
+)
 set(TARGET build_info)
-add_library(${TARGET} OBJECT ${OUTPUT_FILE})
+add_library(${TARGET} OBJECT build-info.cpp)
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()

common/build-info.cpp.in

@@ -1,4 +1,4 @@
-int LLAMA_BUILD_NUMBER = @LLAMA_BUILD_NUMBER@;
-char const *LLAMA_COMMIT = "@LLAMA_BUILD_COMMIT@";
+int LLAMA_BUILD_NUMBER = @BUILD_NUMBER@;
+char const *LLAMA_COMMIT = "@BUILD_COMMIT@";
 char const *LLAMA_COMPILER = "@BUILD_COMPILER@";
 char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@";

common/chat-parser.cpp

@@ -49,7 +49,6 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
 
     // LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str());
     result_.tool_calls.emplace_back(tool_call);
-
     return true;
 }
 bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
@@ -379,7 +378,3 @@ std::optional<common_chat_msg_parser::consume_json_result> common_chat_msg_parse
         /* .is_partial = */ found_healing_marker,
     };
 }
-
-void common_chat_msg_parser::clear_tools() {
-    result_.tool_calls.clear();
-}

common/chat-parser.h

@@ -115,6 +115,4 @@ class common_chat_msg_parser {
         const std::vector<std::vector<std::string>> & args_paths = {},
         const std::vector<std::vector<std::string>> & content_paths = {}
     );
-
-    void clear_tools();
 };

common/chat.cpp

@@ -1921,9 +1921,7 @@ common_chat_msg common_chat_parse(const std::string & input, bool is_partial, co
     } catch (const common_chat_msg_partial_exception & ex) {
         LOG_DBG("Partial parse: %s\n", ex.what());
         if (!is_partial) {
-            builder.clear_tools();
-            builder.move_to(0);
-            common_chat_parse_content_only(builder);
+            throw std::runtime_error(ex.what());
         }
     }
     auto msg = builder.result();

common/cmake/build-info-gen-cpp.cmake

@@ -0,0 +1,24 @@
+include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
+
+set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
+set(OUTPUT_FILE   "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
+
+# Only write the build info if it changed
+if(EXISTS ${OUTPUT_FILE})
+    file(READ ${OUTPUT_FILE} CONTENTS)
+    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_COMMIT ${CMAKE_MATCH_1})
+    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_COMPILER ${CMAKE_MATCH_1})
+    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_TARGET ${CMAKE_MATCH_1})
+    if (
+        NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
+        NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
+        NOT OLD_TARGET   STREQUAL BUILD_TARGET
+    )
+        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+    endif()
+else()
+    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+endif()

docs/function-calling.md

@@ -11,7 +11,7 @@ Function calling is supported for all models (see https://github.com/ggml-org/ll
   - Llama 3.1 / 3.3 (including builtin tools support - tool names for `wolfram_alpha`, `web_search` / `brave_search`, `code_interpreter`), Llama 3.2
   - Functionary v3.1 / v3.2
   - Hermes 2/3, Qwen 2.5
-  - Qwen 2.5 Coder
+  - Qwen 2.5 Coder (WIP: https://github.com/ggml-org/llama.cpp/pull/12034)
   - Mistral Nemo
   - Firefunction v2
   - Command R7B

docs/multimodal.md

@@ -107,7 +107,3 @@ NOTE: some models may require large context window, for example: `-c 8192`
 (tool_name) -hf ggml-org/Qwen2.5-Omni-3B-GGUF
 (tool_name) -hf ggml-org/Qwen2.5-Omni-7B-GGUF
 ```
-
-## Finding more models:
-
-GGUF models on Huggingface with vision capabilities can be found here: https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending&search=gguf

ggml/src/ggml-metal/CMakeLists.txt

@@ -44,22 +44,21 @@ if (GGML_METAL_EMBED_LIBRARY)
     set(METALLIB_SOURCE_EMBED_TMP "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal.tmp")
 
     add_custom_command(
-        OUTPUT "${METALLIB_EMBED_ASM}"
+        OUTPUT ${METALLIB_EMBED_ASM}
         COMMAND echo "Embedding Metal library"
-        COMMAND sed -e "/__embed_ggml-common.h__/r ${METALLIB_COMMON}"       -e "/__embed_ggml-common.h__/d"         < "${METALLIB_SOURCE}"           > "${METALLIB_SOURCE_EMBED_TMP}"
-        COMMAND sed -e "/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}" -e "/\#include \"ggml-metal-impl.h\"/d" < "${METALLIB_SOURCE_EMBED_TMP}" > "${METALLIB_SOURCE_EMBED}"
-        COMMAND echo ".section __DATA,__ggml_metallib"          >  "${METALLIB_EMBED_ASM}"
-        COMMAND echo ".globl _ggml_metallib_start"              >> "${METALLIB_EMBED_ASM}"
-        COMMAND echo "_ggml_metallib_start:"                    >> "${METALLIB_EMBED_ASM}"
-        COMMAND echo .incbin "\"${METALLIB_SOURCE_EMBED}\""     >> "${METALLIB_EMBED_ASM}"
-        COMMAND echo ".globl _ggml_metallib_end"                >> "${METALLIB_EMBED_ASM}"
-        COMMAND echo "_ggml_metallib_end:"                      >> "${METALLIB_EMBED_ASM}"
+        COMMAND sed -e '/__embed_ggml-common.h__/r         ${METALLIB_COMMON}' -e '/__embed_ggml-common.h__/d'         < ${METALLIB_SOURCE}           > ${METALLIB_SOURCE_EMBED_TMP}
+        COMMAND sed -e '/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}'   -e '/\#include \"ggml-metal-impl.h\"/d' < ${METALLIB_SOURCE_EMBED_TMP} > ${METALLIB_SOURCE_EMBED}
+        COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
+        COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
+        COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
+        COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
+        COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
+        COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
         DEPENDS ../ggml-common.h ggml-metal.metal ggml-metal-impl.h
         COMMENT "Generate assembly for embedded Metal library"
-        VERBATIM
     )
 
-    target_sources(ggml-metal PRIVATE "${METALLIB_EMBED_ASM}")
+    target_sources(ggml-metal PRIVATE ${METALLIB_EMBED_ASM})
 else()
     if (GGML_METAL_SHADER_DEBUG)
         # custom command to do the following:

ggml/src/ggml-sycl/CMakeLists.txt

@@ -142,7 +142,7 @@ else()
         FetchContent_Declare(
             ONEMATH
             GIT_REPOSITORY https://github.com/uxlfoundation/oneMath.git
-            GIT_TAG 8efe85f5aaebb37f1d8c503b7af66315feabf142
+            GIT_TAG c255b1b4c41e2ee3059455c1f96a965d6a62568a
         )
         FetchContent_MakeAvailable(ONEMATH)
         # Create alias to match with find_package targets name

ggml/src/ggml-sycl/common.hpp

@@ -513,9 +513,9 @@ constexpr size_t ceil_div(const size_t m, const size_t n) {
 
 bool gpu_has_xmx(sycl::device &dev);
 
-template <int N, class T> std::string debug_get_array_str(const std::string & prefix, const T array[N]) {
+template <int N, class T> void debug_print_array(const std::string & prefix, const T array[N]) {
     if (LIKELY(!g_ggml_sycl_debug)) {
-        return "";
+        return;
     }
     std::stringstream ss;
     ss << prefix << "=[";
@@ -526,26 +526,29 @@ template <int N, class T> std::string debug_get_array_str(const std::string & pr
         ss << array[N - 1];
     }
     ss << "]";
-    return ss.str();
+    GGML_SYCL_DEBUG("%s", ss.str().c_str());
 }
 
-inline std::string debug_get_tensor_str(const std::string &prefix,
-        const ggml_tensor *tensor, const std::string &suffix = "") {
-    std::stringstream ss;
-    if (LIKELY(!g_ggml_sycl_debug)) { return ss.str(); }
-    ss << prefix.c_str() << "=";
-    if (tensor) {
-        ss << "'" << tensor->name << "':type=" << ggml_type_name(tensor->type);
-        ss << debug_get_array_str<GGML_MAX_DIMS>(";ne", tensor->ne);
-        ss << debug_get_array_str<GGML_MAX_DIMS>(";nb", tensor->nb);
-
-        if (!ggml_is_contiguous(tensor)) { ss << ";strided"; }
-        if (ggml_is_permuted(tensor)) { ss << ";permuted"; }
-    } else {
-        ss << "nullptr";
+inline void debug_print_tensor(const std::string & prefix, const ggml_tensor * tensor,
+                               const std::string & suffix = "") {
+    if (LIKELY(!g_ggml_sycl_debug)) {
+        return;
     }
-    ss << suffix;
-    return ss.str();
+    GGML_SYCL_DEBUG("%s=", prefix.c_str());
+    if (tensor) {
+        GGML_SYCL_DEBUG("'%s':type=%s", tensor->name, ggml_type_name(tensor->type));
+        debug_print_array<GGML_MAX_DIMS>(";ne", tensor->ne);
+        debug_print_array<GGML_MAX_DIMS>(";nb", tensor->nb);
+        if (!ggml_is_contiguous(tensor)) {
+            GGML_SYCL_DEBUG(";strided");
+        }
+        if (ggml_is_permuted(tensor)) {
+            GGML_SYCL_DEBUG(";permuted");
+        }
+    } else {
+        GGML_SYCL_DEBUG("nullptr");
+    }
+    GGML_SYCL_DEBUG("%s", suffix.c_str());
 }
 
 // Use scope_op_debug_print to log operations coming from running a model
@@ -561,10 +564,10 @@ struct scope_op_debug_print {
             return;
         }
         GGML_SYCL_DEBUG("[SYCL][OP] call %s%s:", func.data(), func_suffix.data());
-        GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" dst", dst).c_str());
+        debug_print_tensor(" dst", dst);
         if (dst) {
             for (std::size_t i = 0; i < num_src; ++i) {
-                GGML_SYCL_DEBUG("%s", debug_get_tensor_str("\tsrc" + std::to_string(i), dst->src[i]).c_str());
+                debug_print_tensor("\tsrc" + std::to_string(i), dst->src[i]);
             }
         }
         GGML_SYCL_DEBUG("%s\n", suffix.data());

ggml/src/ggml-sycl/cpy.cpp

@@ -723,7 +723,8 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const
 
 void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1) try {
     // Unlike other operators ggml_sycl_cpy takes 2 distinct tensors instead of a dst ggml_tensor and rely on its src field
-    scope_op_debug_print scope_dbg_print(__func__, src1, /*num_src=*/0, debug_get_tensor_str("\tsrc0", src0));
+    scope_op_debug_print scope_dbg_print(__func__, src1, /*num_src=*/0,
+                                         std::string(" src0 type=") + ggml_type_name(src0->type));
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
 

ggml/src/ggml-sycl/gemm.hpp

@@ -65,9 +65,6 @@ public:
 
         dnnl::primitive_attr primitive_attr;
         primitive_attr.set_scratchpad_mode(dnnl::scratchpad_mode::user);
-#ifdef GGML_SYCL_F16
-        primitive_attr.set_fpmath_mode(dnnl::fpmath_mode::f16);
-#endif
 
         auto a_mem = dnnl::memory(a_in_md, eng, const_cast<void*>(a));
         auto b_mem = dnnl::memory(b_in_md, eng, const_cast<void*>(b));

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -347,7 +347,7 @@ static enum ggml_status
 ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
                                      ggml_tensor *tensor) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor, "\n").c_str());
+    debug_print_tensor(": tensor=", tensor, "\n");
     ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
 
     if (tensor->view_src != NULL) {
@@ -385,7 +385,7 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
                                                 const void *data, size_t offset,
                                                 size_t size) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
     ggml_sycl_set_device(ctx->device);
@@ -413,7 +413,7 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                                 void *data, size_t offset,
                                                 size_t size) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
 
@@ -444,8 +444,8 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
                                     ggml_tensor *dst) try {
     bool is_cpy_supported = ggml_backend_buffer_is_sycl(src->buffer);
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": dst", dst).c_str());
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" src", src).c_str());
+    debug_print_tensor(": dst=", dst);
+    debug_print_tensor(" src=", src);
     GGML_SYCL_DEBUG(" is_cpy_supported=%d\n", is_cpy_supported);
     if (is_cpy_supported) {
         ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context;
@@ -525,7 +525,7 @@ catch (sycl::exception const &exc) {
 static void ggml_backend_sycl_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value,
                                                    size_t offset, size_t size) {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu value=%u\n", size, offset, value);
     ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *) buffer->context;
     SYCL_CHECK(ggml_sycl_set_device(ctx->device));
@@ -805,7 +805,7 @@ static enum ggml_status
 ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
                                            ggml_tensor *tensor) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor, "\n").c_str());
+    debug_print_tensor(": tensor=", tensor, "\n");
     GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
 
     ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
@@ -891,7 +891,7 @@ ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer,
                                           ggml_tensor *tensor, const void *data,
                                           size_t offset, size_t size) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
     // split tensors must always be set in their entirety at once
     GGML_ASSERT(offset == 0);
@@ -947,7 +947,7 @@ ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                           const ggml_tensor *tensor, void *data,
                                           size_t offset, size_t size) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
     // split tensors must always be set in their entirety at once
     GGML_ASSERT(offset == 0);
@@ -2127,18 +2127,21 @@ inline void ggml_sycl_op_mul_mat_sycl(
         const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16
                 ? (const sycl::half *)src1->data + src1_padded_row_size
                                          : src1_as_f16.get();
+        ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool(), row_diff * src1_ncols);
 
 #if GGML_SYCL_DNNL
         if (!g_ggml_sycl_disable_dnn) {
             DnnlGemmWrapper::row_gemm(ctx, src1_ncols, row_diff, ne10, src1_ptr,
                                       DnnlGemmWrapper::to_dt<sycl::half>(), src0_ptr, DnnlGemmWrapper::to_dt<sycl::half>(),
-                                      dst_dd_i, DnnlGemmWrapper::to_dt<float>(), stream);
+                                      dst_f16.get(), DnnlGemmWrapper::to_dt<sycl::half>(), stream);
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
+                                                 " : converting dst to fp32");
+            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16, dst);
+            to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff* src1_ncols, stream);
         }
         else
 #endif
         {
-            ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool(), row_diff * src1_ncols);
-
             const sycl::half alpha_f16 = 1.0f;
             const sycl::half beta_f16  = 0.0f;
             SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
@@ -3863,7 +3866,7 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
                                                const void *data, size_t offset,
                                                size_t size) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
     ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
@@ -3884,7 +3887,7 @@ static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
                                                void *data, size_t offset,
                                                size_t size) try {
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
+    debug_print_tensor(": tensor=", tensor);
     GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
     ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
@@ -3907,8 +3910,8 @@ static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend,
     bool is_cpy_supported                = dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) &&
                             ggml_backend_buffer_is_sycl(src->buffer);
     GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": dst", dst).c_str());
-    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" src", src).c_str());
+    debug_print_tensor(": dst=", dst);
+    debug_print_tensor(" src=", src);
     GGML_SYCL_DEBUG(" is_cpy_supported=%d\n", is_cpy_supported);
     if (is_cpy_supported) {
         /*

include/llama.h

@@ -243,14 +243,14 @@ extern "C" {
 
     typedef bool (*llama_progress_callback)(float progress, void * user_data);
 
-    // Input data for llama_encode/llama_decode
+    // Input data for llama_decode
     // A llama_batch object can contain input about one or many sequences
     // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
     //
     // - token  : the token ids of the input (used when embd is NULL)
     // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
     // - pos    : the positions of the respective token in the sequence
-    //            (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode)
+    //            (if set to NULL, the token position will be tracked automatically by llama_decode)
     // - seq_id : the sequence to which the respective token belongs
     //            (if set to NULL, the sequence ID will be assumed to be 0)
     // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output

requirements/requirements-compare-llama-bench.txt

@@ -1,3 +1,2 @@
 tabulate~=0.9.0
 GitPython~=3.1.43
-matplotlib~=3.10.0

scripts/compare-llama-bench.py

@@ -19,7 +19,6 @@ except ImportError as e:
     print("the following Python libraries are required: GitPython, tabulate.") # noqa: NP100
     raise e
 
-
 logger = logging.getLogger("compare-llama-bench")
 
 # All llama-bench SQL fields
@@ -123,15 +122,11 @@ help_s = (
 parser.add_argument("--check", action="store_true", help="check if all required Python libraries are installed")
 parser.add_argument("-s", "--show", help=help_s)
 parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
-parser.add_argument("--plot", help="generate a performance comparison plot and save to specified file (e.g., plot.png)")
-parser.add_argument("--plot_x", help="parameter to use as x axis for plotting (default: n_depth)", default="n_depth")
-parser.add_argument("--plot_log_scale", action="store_true", help="use log scale for x axis in plots (off by default)")
 
 known_args, unknown_args = parser.parse_known_args()
 
 logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO)
 
-
 if known_args.check:
     # Check if all required Python libraries are installed. Would have failed earlier if not.
     sys.exit(0)
@@ -504,6 +499,7 @@ else:
 
 name_compare = bench_data.get_commit_name(hexsha8_compare)
 
+
 # If the user provided columns to group the results by, use them:
 if known_args.show is not None:
     show = known_args.show.split(",")
@@ -548,14 +544,6 @@ else:
             show.remove(prop)
         except ValueError:
             pass
-
-    # Add plot_x parameter to parameters to show if it's not already present:
-    if known_args.plot:
-        for k, v in PRETTY_NAMES.items():
-            if v == known_args.plot_x and k not in show:
-                show.append(k)
-                break
-
     rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare)
 
 if not rows_show:
@@ -612,161 +600,6 @@ if "gpu_info" in show:
 headers  = [PRETTY_NAMES[p] for p in show]
 headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"]
 
-if known_args.plot:
-    def create_performance_plot(table_data: list[list[str]], headers: list[str], baseline_name: str, compare_name: str, output_file: str, plot_x_param: str, log_scale: bool = False):
-        try:
-            import matplotlib.pyplot as plt
-            import matplotlib
-            matplotlib.use('Agg')
-        except ImportError as e:
-            logger.error("matplotlib is required for --plot.")
-            raise e
-
-        data_headers = headers[:-4] # Exclude the last 4 columns (Test, baseline t/s, compare t/s, Speedup)
-        plot_x_index = None
-        plot_x_label = plot_x_param
-
-        if plot_x_param not in ["n_prompt", "n_gen", "n_depth"]:
-            pretty_name = PRETTY_NAMES.get(plot_x_param, plot_x_param)
-            if pretty_name in data_headers:
-                plot_x_index = data_headers.index(pretty_name)
-                plot_x_label = pretty_name
-            elif plot_x_param in data_headers:
-                plot_x_index = data_headers.index(plot_x_param)
-                plot_x_label = plot_x_param
-            else:
-                logger.error(f"Parameter '{plot_x_param}' not found in current table columns. Available columns: {', '.join(data_headers)}")
-                return
-
-        grouped_data = {}
-
-        for i, row in enumerate(table_data):
-            group_key_parts = []
-            test_name = row[-4]
-
-            base_test = ""
-            x_value = None
-
-            if plot_x_param in ["n_prompt", "n_gen", "n_depth"]:
-                for j, val in enumerate(row[:-4]):
-                    header_name = data_headers[j]
-                    if val is not None and str(val).strip():
-                        group_key_parts.append(f"{header_name}={val}")
-
-                if plot_x_param == "n_prompt" and "pp" in test_name:
-                    base_test = test_name.split("@")[0]
-                    x_value = base_test
-                elif plot_x_param == "n_gen" and "tg" in test_name:
-                    x_value = test_name.split("@")[0]
-                elif plot_x_param == "n_depth" and "@d" in test_name:
-                    base_test = test_name.split("@d")[0]
-                    x_value = int(test_name.split("@d")[1])
-                else:
-                    base_test = test_name
-
-                if base_test.strip():
-                    group_key_parts.append(f"Test={base_test}")
-            else:
-                for j, val in enumerate(row[:-4]):
-                    if j != plot_x_index:
-                        header_name = data_headers[j]
-                        if val is not None and str(val).strip():
-                            group_key_parts.append(f"{header_name}={val}")
-                    else:
-                        x_value = val
-
-                group_key_parts.append(f"Test={test_name}")
-
-            group_key = tuple(group_key_parts)
-
-            if group_key not in grouped_data:
-                grouped_data[group_key] = []
-
-            grouped_data[group_key].append({
-                'x_value': x_value,
-                'baseline': float(row[-3]),
-                'compare': float(row[-2]),
-                'speedup': float(row[-1])
-            })
-
-        if not grouped_data:
-            logger.error("No data available for plotting")
-            return
-
-        def make_axes(num_groups, max_cols=2, base_size=(8, 4)):
-            from math import ceil
-            cols = 1 if num_groups == 1 else min(max_cols, num_groups)
-            rows = ceil(num_groups / cols)
-
-            # Scale figure size by grid dimensions
-            w, h = base_size
-            fig, ax_arr = plt.subplots(rows, cols,
-                                       figsize=(w * cols, h * rows),
-                                       squeeze=False)
-
-            axes = ax_arr.flatten()[:num_groups]
-            return fig, axes
-
-        num_groups = len(grouped_data)
-        fig, axes = make_axes(num_groups)
-
-        plot_idx = 0
-
-        for group_key, points in grouped_data.items():
-            if plot_idx >= len(axes):
-                break
-            ax = axes[plot_idx]
-
-            try:
-                points_sorted = sorted(points, key=lambda p: float(p['x_value']) if p['x_value'] is not None else 0)
-                x_values = [float(p['x_value']) if p['x_value'] is not None else 0 for p in points_sorted]
-            except ValueError:
-                points_sorted = sorted(points, key=lambda p: group_key)
-                x_values = [p['x_value'] for p in points_sorted]
-
-            baseline_vals = [p['baseline'] for p in points_sorted]
-            compare_vals = [p['compare'] for p in points_sorted]
-
-            ax.plot(x_values, baseline_vals, 'o-', color='skyblue',
-                    label=f'{baseline_name}', linewidth=2, markersize=6)
-            ax.plot(x_values, compare_vals, 's--', color='lightcoral', alpha=0.8,
-                    label=f'{compare_name}', linewidth=2, markersize=6)
-
-            if log_scale:
-                ax.set_xscale('log', base=2)
-                unique_x = sorted(set(x_values))
-                ax.set_xticks(unique_x)
-                ax.set_xticklabels([str(int(x)) for x in unique_x])
-
-            title_parts = []
-            for part in group_key:
-                if '=' in part:
-                    key, value = part.split('=', 1)
-                    title_parts.append(f"{key}: {value}")
-
-            title = ', '.join(title_parts) if title_parts else "Performance comparison"
-
-            ax.set_xlabel(plot_x_label, fontsize=12, fontweight='bold')
-            ax.set_ylabel('Tokens per second (t/s)', fontsize=12, fontweight='bold')
-            ax.set_title(title, fontsize=12, fontweight='bold')
-            ax.legend(loc='best', fontsize=10)
-            ax.grid(True, alpha=0.3)
-
-            plot_idx += 1
-
-        for i in range(plot_idx, len(axes)):
-            axes[i].set_visible(False)
-
-        fig.suptitle(f'Performance comparison: {compare_name} vs. {baseline_name}',
-                     fontsize=14, fontweight='bold')
-        fig.subplots_adjust(top=1)
-
-        plt.tight_layout()
-        plt.savefig(output_file, dpi=300, bbox_inches='tight')
-        plt.close()
-
-    create_performance_plot(table, headers, name_baseline, name_compare, known_args.plot, known_args.plot_x, known_args.plot_log_scale)
-
 print(tabulate( # noqa: NP100
     table,
     headers=headers,

src/llama-batch.cpp

@@ -1,14 +1,8 @@
 #include "llama-batch.h"
 
-#include "llama-impl.h"
-#include "llama-cparams.h"
-#include "llama-vocab.h"
-#include "llama-memory.h"
-
 #include <cassert>
 #include <cstring>
 #include <algorithm>
-#include <sstream>
 
 llama_ubatch llama_sbatch::reserve_ubatch(size_t n_ubatch, bool has_embd) {
     // clear empty sequences
@@ -285,55 +279,17 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
             );
 }
 
-llama_batch_allocr::llama_batch_allocr() {
-    const char * LLAMA_BATCH_DEBUG = getenv("LLAMA_BATCH_DEBUG");
-    debug = LLAMA_BATCH_DEBUG ? atoi(LLAMA_BATCH_DEBUG) : 0;
-
-    seq_pos.resize(LLAMA_MAX_PARALLEL_SEQUENCES);
-    seq_cpl.resize(LLAMA_MAX_PARALLEL_SEQUENCES);
-    for (auto & cur : seq_cpl) {
-        cur.resize(LLAMA_MAX_PARALLEL_SEQUENCES);
-    }
-}
-
-bool llama_batch_allocr::init(
-        const llama_batch & batch_inp,
-        const llama_vocab & vocab,
-        const llama_memory_i * memory) {
-    clear();
-
-    batch = batch_inp;
-
+llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0) {
+    batch = in_batch;
     GGML_ASSERT(batch.n_tokens > 0);
-
-    //
-    // validate input batch
-    //
-
-    if (batch.token) {
-        for (int32_t i = 0; i < batch.n_tokens; ++i) {
-            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= vocab.n_tokens()) {
-                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
-                return false;
-            }
+    if (!batch.pos) {
+        assert(p0 >= 0);
+        pos.resize(batch.n_tokens);
+        for (int32_t i = 0; i < batch.n_tokens; i++) {
+            pos[i] = p0 + i;
         }
+        batch.pos = pos.data();
     }
-
-    if (batch.seq_id) {
-        for (int32_t i = 0; i < batch.n_tokens; ++i) {
-            for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
-                if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
-                    LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], LLAMA_MAX_PARALLEL_SEQUENCES);
-                    return false;
-                }
-            }
-        }
-    }
-
-    //
-    // auto-generate missing fields
-    //
-
     if (!batch.n_seq_id) {
         n_seq_id.resize(batch.n_tokens);
         for (int32_t i = 0; i < batch.n_tokens; i++) {
@@ -341,7 +297,6 @@ bool llama_batch_allocr::init(
         }
         batch.n_seq_id = n_seq_id.data();
     }
-
     if (!batch.seq_id) {
         seq_id.resize(batch.n_tokens + 1);
         seq_id[batch.n_tokens] = NULL;
@@ -350,201 +305,12 @@ bool llama_batch_allocr::init(
         }
         batch.seq_id = seq_id.data();
     }
-
-    if (!batch.pos) {
-        pos.resize(batch.n_tokens);
-
-        // initialize the starting position for each sequence based on the positions in the memory
-        llama_pos p0[LLAMA_MAX_PARALLEL_SEQUENCES];
-        for (int32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
-            if (!memory) {
-                p0[s] = 0;
-            } else {
-                p0[s] = memory->seq_pos_max(s) + 1;
-            }
-        }
-
-        for (int32_t i = 0; i < batch.n_tokens; i++) {
-            const llama_seq_id seq_id = batch.seq_id[i][0];
-
-            pos[i] = p0[seq_id];
-
-            for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
-                p0[batch.seq_id[i][s]] = pos[i] + 1;
-            }
-        }
-
-        batch.pos = pos.data();
-    }
-
     if (!batch.logits) {
         // by default return the output only for the last token
         output.resize(batch.n_tokens);
         output[output.size() - 1] = true;
         batch.logits = output.data();
     }
-
-    //
-    // compute stats
-    //
-
-    for (int32_t i = 0; i < batch.n_tokens; ++i) {
-        n_outputs += batch.logits[i] != 0;
-    }
-
-    // determine coupled sequences
-    // these are pairs of sequences that have at least one token in the input batch that is assigned to both of them
-    for (int32_t i = 0; i < batch.n_tokens; ++i) {
-        for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
-            seq_pos[batch.seq_id[i][s]].insert(batch.pos[i]);
-
-            if (s > 0) {
-                const llama_seq_id s0 = batch.seq_id[i][0];
-                const llama_seq_id s1 = batch.seq_id[i][s];
-
-                // mark that sequence s1 is coupled to s0
-                seq_cpl[s1][s0] = true;
-
-                // note: the other way around is not necessary for now
-                //seq_cpl[s0][s1] = true;
-            }
-        }
-    }
-
-    if (debug > 0) {
-        LLAMA_LOG_DEBUG("%s: input batch info:\n", __func__);
-        LLAMA_LOG_DEBUG("%s:   n_tokens  = %d\n", __func__,          batch.n_tokens);
-        LLAMA_LOG_DEBUG("%s:   token     = %p\n", __func__, (void *) batch.token);
-        LLAMA_LOG_DEBUG("%s:   embd      = %p\n", __func__, (void *) batch.embd);
-        LLAMA_LOG_DEBUG("%s:   pos       = %p\n", __func__, (void *) batch.pos);
-        LLAMA_LOG_DEBUG("%s:   n_seq_id  = %p\n", __func__, (void *) batch.n_seq_id);
-        LLAMA_LOG_DEBUG("%s:   seq_id    = %p\n", __func__, (void *) batch.seq_id);
-        LLAMA_LOG_DEBUG("%s:   logits    = %p\n", __func__, (void *) batch.logits);
-        LLAMA_LOG_DEBUG("%s:   n_outputs = %d\n", __func__, n_outputs);
-
-        if (debug > 1) {
-            int seq_id_max = 0;
-            for (int32_t i = 0; i < batch.n_tokens; ++i) {
-                for (int s = 0; s < batch.n_seq_id[i]; ++s) {
-                    for (int s = 0; s < batch.n_seq_id[i]; ++s) {
-                        seq_id_max = std::max(seq_id_max, batch.seq_id[i][s]);
-                    }
-                }
-            }
-            ++seq_id_max;
-
-            LLAMA_LOG_DEBUG("%s:   token     = [\n", __func__);
-            for (int32_t i = 0; i < batch.n_tokens; ++i) {
-                std::vector<int8_t> seq_id(seq_id_max);
-
-                for (int s = 0; s < batch.n_seq_id[i]; ++s) {
-                    seq_id[batch.seq_id[i][s]] = 1;
-                }
-
-                std::stringstream ss;
-                for (int s = 0; s < seq_id_max; ++s) {
-                    if (seq_id[s]) {
-                        ss << s%10;
-                    } else {
-                        ss << ".";
-                    }
-                }
-
-                LLAMA_LOG_DEBUG("%s:  %4d: id = %6d (%16s), pos = %4d, n_seq_id = %2d, seq_id = [%s], output = %d\n",
-                        __func__, i, batch.token[i], vocab.token_to_piece(batch.token[i]).c_str(),
-                        batch.pos[i], batch.n_seq_id[i], ss.str().c_str(), batch.logits[i]);
-            }
-            LLAMA_LOG_DEBUG("%s:   ]\n", __func__);
-
-            LLAMA_LOG_DEBUG("%s:   seq       = [\n", __func__);
-            for (int s0 = 0; s0 < (int) seq_pos.size(); ++s0) {
-                if (seq_pos[s0].empty()) {
-                    continue;
-                }
-
-                std::stringstream ss;
-                for (int s1 = 0; s1 < (int) seq_cpl[s0].size(); ++s1) {
-                    if (seq_cpl[s0][s1]) {
-                        ss << s1 << " ";
-                    }
-                }
-
-                LLAMA_LOG_DEBUG("%s:  %4d: pos = [%4d, %4d], cpl = %s\n",
-                        __func__, s0, seq_pos_min(s0), seq_pos_max(s0), ss.str().empty() ? "-" : ss.str().c_str());
-            }
-            LLAMA_LOG_DEBUG("%s:   ]\n", __func__);
-        }
-    }
-
-    //
-    // consistency checks
-    //
-
-    for (int32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
-        if (seq_pos[s].empty()) {
-            continue;
-        }
-
-        if (memory && seq_pos_min(s) != memory->seq_pos_max(s) + 1) {
-            LLAMA_LOG_ERROR("%s: sequence %d does not start from the last position stored in the memory\n", __func__, s);
-            return false;
-        }
-
-        if (seq_pos_max(s) - seq_pos_min(s) + 1 > (int) seq_pos[s].size()) {
-            LLAMA_LOG_ERROR("%s: sequence %d positions are not continuous\n", __func__, s);
-            return false;
-        }
-    }
-
-    if (memory) {
-        for (int32_t s0 = 0; s0 < LLAMA_MAX_PARALLEL_SEQUENCES; ++s0) {
-            for (int32_t s1 = 0; s1 < LLAMA_MAX_PARALLEL_SEQUENCES; ++s1) {
-                if (seq_cpl[s0][s1]) {
-                    if (memory->seq_pos_min(s0) != memory->seq_pos_min(s1) ||
-                        memory->seq_pos_max(s0) != memory->seq_pos_max(s1)) {
-                        LLAMA_LOG_ERROR("%s: sequence %d is coupled to %d in the input batch, but have divereged\n", __func__, s0, s1);
-                        return false;
-                    }
-                }
-            }
-        }
-    }
-
-    return true;
-}
-
-const llama_batch & llama_batch_allocr::get_batch() const {
-    return batch;
-}
-
-uint32_t llama_batch_allocr::get_n_outputs() const {
-    return n_outputs;
-}
-
-llama_pos llama_batch_allocr::seq_pos_min(llama_seq_id seq_id) const {
-    return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].begin();
-}
-
-llama_pos llama_batch_allocr::seq_pos_max(llama_seq_id seq_id) const {
-    return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].rbegin();
-}
-
-void llama_batch_allocr::clear() {
-    n_outputs = 0;
-
-    batch = {};
-    pos.clear();
-    n_seq_id.clear();
-    seq_id.clear();
-    output.clear();
-
-    for (auto & cur : seq_pos) {
-        cur.clear();
-    }
-
-    for (auto & cur : seq_cpl) {
-        std::fill(cur.begin(), cur.end(), false);
-    }
 }
 
 //

src/llama-batch.h

@@ -4,7 +4,6 @@
 
 #include <array>
 #include <vector>
-#include <set>
 
 // very similar to llama_batch,
 // but has more metadata about sequences
@@ -19,8 +18,8 @@ struct llama_ubatch {
     llama_token  *  token;    // [n_tokens]
     float        *  embd;     // [n_embd, n_tokens]
     llama_pos    *  pos;      // [n_tokens]
-    int32_t      *  n_seq_id; // [n_seqs]
-    llama_seq_id ** seq_id;   // [n_seqs]
+    int32_t      *  n_seq_id; // [n_seqs] // TODO: remove, should belong to only 1 sequence
+    llama_seq_id ** seq_id;   // [n_seqs] // TODO: become llama_seq_id * seq_id;
     int8_t       *  output;   // [n_tokens]
 };
 
@@ -78,41 +77,16 @@ struct llama_sbatch {
     llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false);
 };
 
-// a helper for sanitizing and fulfilling a batch
-class llama_batch_allocr {
-public:
-    llama_batch_allocr();
-
-    // sanitize and auto-gen missing data in the input batch
-    // memory is optional. if provided will be used to check for sequence continuity and to determine the positions
-    bool init(
-            const llama_batch & batch_inp,
-            const llama_vocab & vocab,
-            const llama_memory_i * memory);
-
-    const llama_batch & get_batch() const;
-
-    uint32_t get_n_outputs() const;
-
-    llama_pos seq_pos_min(llama_seq_id seq_id) const;
-    llama_pos seq_pos_max(llama_seq_id seq_id) const;
-
-private:
-    void clear();
-
-    llama_batch batch;
-
-    uint32_t n_outputs;
+// temporary allocate memory for the input batch if needed
+struct llama_batch_allocr {
+    struct llama_batch batch;
 
     std::array<llama_seq_id, 1> seq_id_0 = { 0 }; // default sequence id
-
     std::vector<llama_pos>      pos;
     std::vector<int32_t>        n_seq_id;
     std::vector<llama_seq_id *> seq_id;
     std::vector<int8_t>         output;
 
-    std::vector<std::set<llama_pos>> seq_pos; // seq_pos[s]: the set of positions in sequence s
-    std::vector<std::vector<bool>>   seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1
-
-    int debug;
+    // optionally fulfill the batch returned by llama_batch_get_one
+    llama_batch_allocr(struct llama_batch in_batch, llama_pos p0);
 };

src/llama-context.cpp

@@ -1,7 +1,6 @@
 #include "llama-context.h"
 
 #include "llama-impl.h"
-#include "llama-batch.h"
 #include "llama-io.h"
 #include "llama-memory.h"
 #include "llama-mmap.h"
@@ -19,8 +18,7 @@
 llama_context::llama_context(
         const llama_model & model,
               llama_context_params params) :
-    model(model),
-    batch_allocr(std::make_unique<llama_batch_allocr>()) {
+    model(model) {
     LLAMA_LOG_INFO("%s: constructing llama_context\n", __func__);
 
     t_start_us = model.t_start_us;
@@ -496,7 +494,7 @@ float * llama_context::get_logits() {
 }
 
 float * llama_context::get_logits_ith(int32_t i) {
-    int64_t j = -1;
+    int32_t j = -1;
 
     try {
         if (logits == nullptr) {
@@ -519,7 +517,7 @@ float * llama_context::get_logits_ith(int32_t i) {
         }
         if (j >= n_outputs) {
             // This should not happen
-            throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
+            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, n_outputs));
         }
 
         return logits + j*model.vocab.n_tokens();
@@ -538,7 +536,7 @@ float * llama_context::get_embeddings() {
 }
 
 float * llama_context::get_embeddings_ith(int32_t i) {
-    int64_t j = -1;
+    int32_t j = -1;
 
     try {
         if (embd == nullptr) {
@@ -561,7 +559,7 @@ float * llama_context::get_embeddings_ith(int32_t i) {
         }
         if (j >= n_outputs) {
             // This should not happen
-            throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
+            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, n_outputs));
         }
 
         return embd + j*model.hparams.n_embd;
@@ -721,26 +719,40 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch,
     return res;
 }
 
-int llama_context::encode(const llama_batch & batch_inp) {
-    if (batch_inp.n_tokens == 0) {
+int llama_context::encode(llama_batch & inp_batch) {
+    if (inp_batch.n_tokens == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
         return -1;
     }
 
+    // temporary allocate memory for the input batch if needed
     // note: during encode, we always pass the full sequence starting from pos = 0
-    if (!batch_allocr->init(batch_inp, model.vocab, nullptr)) {
-        LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
-        return -1;
-    }
+    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : 0);
 
-    const llama_batch & batch = batch_allocr->get_batch();
+    const llama_batch & batch = batch_allocr.batch;
+    const int32_t n_tokens = batch.n_tokens;
 
-    const uint32_t n_tokens = batch.n_tokens;
+    const auto & hparams = model.hparams;
 
     GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
 
+    // TODO: move the validation to the llama_batch_allocr
+    if (batch.token) {
+        for (int32_t i = 0; i < n_tokens; ++i) {
+            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
+                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
+                return -1;
+            }
+
+            if (batch.seq_id && (batch.seq_id[i][0] < 0 || batch.seq_id[i][0] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
+                LLAMA_LOG_ERROR("%s: invalid seq_id[%d] = %d > %d\n", __func__, i, batch.seq_id[i][0], LLAMA_MAX_PARALLEL_SEQUENCES);
+                throw -1;
+            }
+        }
+    }
+
     // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
-    GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
+    GGML_ASSERT(cparams.n_ubatch >= (uint32_t) n_tokens && "encoder requires n_ubatch >= n_tokens");
 
     if (t_compute_start_us == 0) {
         t_compute_start_us = ggml_time_us();
@@ -751,8 +763,6 @@ int llama_context::encode(const llama_batch & batch_inp) {
 
     n_queued_tokens += n_tokens;
 
-    const auto & hparams = model.hparams;
-
     const int64_t n_embd = hparams.n_embd;
 
     llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true);
@@ -765,7 +775,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
         return -2;
     };
 
-    for (uint32_t i = 0; i < n_tokens; ++i) {
+    for (int32_t i = 0; i < n_tokens; ++i) {
         output_ids[i] = i;
     }
 
@@ -821,8 +831,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
 
                     GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits
 
-                    // TODO: fix indexing [UBATCH_IDX]
-                    for (uint32_t i = 0; i < n_tokens; i++) {
+                    for (int32_t i = 0; i < n_tokens; i++) {
                         const llama_seq_id seq_id = ubatch.seq_id[i][0];
                         if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
                             continue;
@@ -837,7 +846,6 @@ int llama_context::encode(const llama_batch & batch_inp) {
                     auto & embd_seq_out = embd_seq;
                     const uint32_t n_cls_out = hparams.n_cls_out;
 
-                    // TODO: fix indexing [UBATCH_IDX]
                     for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
                         const llama_seq_id seq_id = ubatch.seq_id[s][0];
                         if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
@@ -870,11 +878,13 @@ int llama_context::encode(const llama_batch & batch_inp) {
         memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd));
 
         // remember the sequence ids used during the encoding - needed for cross attention later
+        // TODO: the seuqence indexing here is likely not correct in the general case
+        //       probably works only for split_simple
         cross.seq_ids_enc.resize(n_tokens);
-        for (uint32_t i = 0; i < n_tokens; i++) {
+        for (int32_t i = 0; i < n_tokens; i++) {
             cross.seq_ids_enc[i].clear();
-            for (int s = 0; s < batch.n_seq_id[i]; s++) {
-                llama_seq_id seq_id = batch.seq_id[i][s];
+            for (int s = 0; s < ubatch.n_seq_id[i]; s++) {
+                llama_seq_id seq_id = ubatch.seq_id[i][s];
                 cross.seq_ids_enc[i].insert(seq_id);
             }
         }
@@ -883,43 +893,68 @@ int llama_context::encode(const llama_batch & batch_inp) {
     return 0;
 }
 
-int llama_context::decode(const llama_batch & batch_inp) {
+int llama_context::decode(llama_batch & inp_batch) {
     if (!memory) {
         LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
-        return encode(batch_inp);
+        return encode(inp_batch);
     }
 
-    if (batch_inp.n_tokens == 0) {
+    if (inp_batch.n_tokens == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
         return -1;
     }
 
-    if (!batch_allocr->init(batch_inp, model.vocab, memory.get())) {
-        LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
-        return -1;
+    if (!inp_batch.pos) {
+        if (inp_batch.seq_id) {
+            LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__);
+            return -1;
+        }
     }
 
-    const llama_batch & batch = batch_allocr->get_batch();
+    // temporary allocate memory for the input batch if needed
+    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : memory->seq_pos_max(0) + 1);
+
+    const llama_batch & batch = batch_allocr.batch;
 
     const auto & vocab   = model.vocab;
     const auto & hparams = model.hparams;
 
     const int32_t n_vocab = vocab.n_tokens();
-    const int64_t n_embd  = hparams.n_embd;
 
-    const uint32_t n_tokens_all = batch.n_tokens;
+    const int64_t n_tokens_all = batch.n_tokens;
+    const int64_t n_embd       = hparams.n_embd;
 
     GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
 
+    // TODO: move the validation to the llama_batch_allocr
+    if (batch.token) {
+        for (int64_t i = 0; i < n_tokens_all; ++i) {
+            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
+                LLAMA_LOG_ERROR("%s: invalid token[%" PRId64 "] = %d\n", __func__, i, batch.token[i]);
+                return -1;
+            }
+
+            if (batch.seq_id && (batch.seq_id[i][0] < 0 || batch.seq_id[i][0] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
+                LLAMA_LOG_ERROR("%s: invalid seq_id[%" PRId64 "] = %d >= %d\n", __func__, i, batch.seq_id[i][0], LLAMA_MAX_PARALLEL_SEQUENCES);
+                return -1;
+            }
+        }
+    }
+
     // this indicates we are doing pooled embedding
     const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
 
-    const uint32_t n_outputs_all = batch_allocr->get_n_outputs();
+    int64_t n_outputs_all = 0;
+
+    // count outputs
+    for (uint32_t i = 0; i < n_tokens_all; ++i) {
+        n_outputs_all += batch.logits[i] != 0;
+    }
 
     if (embd_pooled) {
         // require that all tokens are output
         if (n_outputs_all != n_tokens_all) {
-            LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n",
+            LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %" PRId64 ", n_tokens_all = %" PRId64 ")\n",
                     __func__, n_outputs_all, n_tokens_all);
             return -1;
         }
@@ -989,7 +1024,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     // reserve output buffer
     if (output_reserve(n_outputs_all) < n_outputs_all) {
-        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
         return -2;
     };
 
@@ -1028,7 +1063,6 @@ int llama_context::decode(const llama_batch & batch_inp) {
                 pos_min[s] = std::numeric_limits<llama_pos>::max();
             }
 
-            // TODO: fix sequence indexing
             for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
                 const auto & seq_id = ubatch.seq_id[i][0];
 
@@ -1142,14 +1176,14 @@ int llama_context::decode(const llama_batch & batch_inp) {
     n_outputs = n_outputs_all;
 
     // set output mappings
-    if (n_outputs > 0) {
+    {
         bool sorted_output = true;
 
         auto & out_ids = mstate->out_ids();
 
-        GGML_ASSERT(out_ids.size() == (size_t) n_outputs);
+        GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all);
 
-        for (int64_t i = 0; i < n_outputs; ++i) {
+        for (int64_t i = 0; i < n_outputs_all; ++i) {
             int64_t out_id = out_ids[i];
             output_ids[out_id] = i;
             if (out_id != i) {
@@ -1161,22 +1195,20 @@ int llama_context::decode(const llama_batch & batch_inp) {
         // note: this is mostly relevant for recurrent models atm
         if (!sorted_output) {
             const uint32_t n_vocab = model.vocab.n_tokens();
-            const uint64_t n_embd  = model.hparams.n_embd;
+            const uint32_t n_embd  = model.hparams.n_embd;
 
             GGML_ASSERT((size_t) n_outputs == out_ids.size());
 
             // TODO: is there something more efficient which also minimizes swaps?
             // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
-            for (uint32_t i = 0; i < n_outputs - 1; ++i) {
-                uint32_t j_min = i;
-                for (uint32_t j = i + 1; j < n_outputs; ++j) {
+            for (int32_t i = 0; i < n_outputs - 1; ++i) {
+                int32_t j_min = i;
+                for (int32_t j = i + 1; j < n_outputs; ++j) {
                     if (out_ids[j] < out_ids[j_min]) {
                         j_min = j;
                     }
                 }
-                if (j_min == i) {
-                    continue;
-                }
+                if (j_min == i) { continue; }
                 std::swap(out_ids[i], out_ids[j_min]);
                 if (logits_size > 0) {
                     for (uint32_t k = 0; k < n_vocab; k++) {
@@ -1189,10 +1221,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
                     }
                 }
             }
-
             std::fill(output_ids.begin(), output_ids.end(), -1);
-
-            for (uint32_t i = 0; i < n_outputs; ++i) {
+            for (int32_t i = 0; i < n_outputs; ++i) {
                 output_ids[out_ids[i]] = i;
             }
         }
@@ -1212,7 +1242,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
 // output
 //
 
-uint32_t llama_context::output_reserve(int32_t n_outputs) {
+int32_t llama_context::output_reserve(int32_t n_outputs) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
@@ -1278,7 +1308,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     // set all ids as invalid (negative)
     std::fill(output_ids.begin(), output_ids.end(), -1);
 
-    this->n_outputs = 0;
+    this->n_outputs     = 0;
+    this->n_outputs_max = n_outputs_max;
 
     return n_outputs_max;
 }
@@ -1769,12 +1800,14 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
 
         std::vector<int32_t> w_output_pos;
 
+        GGML_ASSERT(n_outputs <= n_outputs_max);
+
         w_output_pos.resize(n_outputs);
 
         // build a more compact representation of the output ids
         for (size_t i = 0; i < n_batch(); ++i) {
             // map an output id to a position in the batch
-            int64_t pos = output_ids[i];
+            int32_t pos = output_ids[i];
             if (pos >= 0) {
                 GGML_ASSERT(pos < n_outputs);
                 w_output_pos[pos] = i;
@@ -2049,7 +2082,7 @@ void llama_context::opt_epoch_iter(
 
         embd_seq.clear();
 
-        uint32_t n_outputs_all = n_tokens_all;
+        int64_t n_outputs_all = n_tokens_all;
 
         auto mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled);
         if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
@@ -2059,7 +2092,7 @@ void llama_context::opt_epoch_iter(
 
         // reserve output buffer
         if (output_reserve(n_outputs_all) < n_outputs_all) {
-            LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
+            LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
             GGML_ABORT("TODO: handle this error");
         };
 

src/llama-context.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include "llama.h"
+#include "llama-batch.h"
 #include "llama-cparams.h"
 #include "llama-graph.h"
 #include "llama-adapter.h"
@@ -12,7 +13,6 @@
 #include <vector>
 
 struct llama_model;
-class llama_batch_allocr;
 
 class llama_io_read_i;
 class llama_io_write_i;
@@ -102,8 +102,8 @@ struct llama_context {
             llama_memory_state_i * mstate,
                      ggml_status & ret);
 
-    int encode(const llama_batch & batch_inp);
-    int decode(const llama_batch & batch_inp);
+    int encode(llama_batch & inp_batch);
+    int decode(llama_batch & inp_batch);
 
     //
     // state save/load
@@ -181,7 +181,7 @@ private:
 
     // Make sure enough space is available for outputs.
     // Returns max number of outputs for which space was reserved.
-    uint32_t output_reserve(int32_t n_outputs);
+    int32_t output_reserve(int32_t n_outputs);
 
     //
     // graph
@@ -246,10 +246,8 @@ private:
     // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
     std::map<llama_seq_id, std::vector<float>> embd_seq;
 
-    // reuse the batch_allocr to avoid unnecessary memory allocations
-    std::unique_ptr<llama_batch_allocr> batch_allocr;
-
-    uint32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch
+    int32_t n_outputs     = 0; // number of actually-used outputs in the current ubatch or last logical batch
+    int32_t n_outputs_max = 0; // capacity (of tokens positions) for the output buffers
 
     std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
 

src/llama-cparams.h

@@ -4,7 +4,6 @@
 
 #include <cstdint>
 
-// TODO: rename to something shorter
 #define LLAMA_MAX_PARALLEL_SEQUENCES 64
 
 struct llama_cparams {

src/llama-graph.cpp

@@ -139,7 +139,6 @@ void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
 
         std::vector<uint64_t> sum(n_tokens, 0);
 
-        // TODO: fix indexing [UBATCH_IDX]
         for (int s = 0; s < n_seqs; ++s) {
             const llama_seq_id seq_id = ubatch->seq_id[s][0];
 
@@ -157,7 +156,6 @@ void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
             }
         }
 
-        // TODO: fix indexing [UBATCH_IDX]
         for (int s = 0; s < n_seqs; ++s) {
             const llama_seq_id seq_id = ubatch->seq_id[s][0];
 
@@ -182,7 +180,6 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
         uint32_t * data = (uint32_t *) cls->data;
         memset(cls->data, 0, n_tokens * ggml_element_size(cls));
 
-        // TODO: fix indexing [UBATCH_IDX]
         for (int s = 0; s < n_seqs; ++s) {
             const llama_seq_id seq_id = ubatch->seq_id[s][0];
 
@@ -213,7 +210,6 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
         std::vector<int> last_pos(n_tokens, -1);
         std::vector<int> last_row(n_tokens, -1);
 
-        // TODO: fix indexing [UBATCH_IDX]
         for (int s = 0; s < n_seqs; ++s) {
             const llama_seq_id seq_id = ubatch->seq_id[s][0];
 
@@ -287,7 +283,6 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                                 const int32_t ti = s0*n_seq_tokens + i;
                                 float f = -INFINITY;
 
-                                // TODO: fix indexing [UBATCH_IDX]
                                 for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
                                     if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
                                         if (hparams.use_alibi) {
@@ -327,7 +322,6 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                                 const int32_t ti = s0*n_seq_tokens + i;
                                 float f = -INFINITY;
 
-                                // TODO: fix indexing [UBATCH_IDX]
                                 for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
                                     if (ubatch->seq_id[s0][s] == seq_id) {
                                         if (hparams.use_alibi) {
@@ -383,7 +377,6 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
             for (int j = 0; j < n_tokens; ++j) {
                 for (int i = 0; i < n_enc; ++i) {
                     float f = -INFINITY;
-                    // TODO: fix indexing [UBATCH_IDX]
                     for (int s = 0; s < ubatch->n_seq_id[j]; ++s) {
                         const llama_seq_id seq_id = ubatch->seq_id[j][s];
                         if (cross->seq_ids_enc[i].find(seq_id) != cross->seq_ids_enc[i].end()) {
@@ -1563,30 +1556,23 @@ void llm_graph_context::build_pooling(
                 ggml_tensor * inp_cls = build_inp_cls();
                 inp = ggml_get_rows(ctx0, inp, inp_cls);
 
-                if (cls) {
+                if (cls != nullptr && cls_b != nullptr) {
                     // classification head
                     // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
-                    cur = ggml_mul_mat(ctx0, cls, inp);
-                    if (cls_b) {
-                        cur = ggml_add(ctx0, cur, cls_b);
-                    }
+                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
                     cur = ggml_tanh(ctx0, cur);
 
                     // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
                     // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
                     if (cls_out) {
-                        cur = ggml_mul_mat(ctx0, cls_out, cur);
-                        if (cls_out_b) {
-                            cur = ggml_add(ctx0, cur, cls_out_b);
-                        }
+                        GGML_ASSERT(cls_out_b != nullptr);
+                        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
                     }
                 } else if (cls_out) {
                     // Single layer classification head (direct projection)
                     // https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/bert/modeling_bert.py#L1476
-                    cur = ggml_mul_mat(ctx0, cls_out, inp);
-                    if (cls_out_b) {
-                        cur = ggml_add(ctx0, cur, cls_out_b);
-                    }
+                    GGML_ASSERT(cls_out_b != nullptr);
+                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, inp), cls_out_b);
                 } else {
                     GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
                 }

src/llama-graph.h

@@ -378,7 +378,7 @@ struct llm_graph_params {
     const llama_memory_state_i * mstate;
     const llama_cross          * cross;
 
-    uint32_t n_outputs;
+    int32_t n_outputs;
 
     const llm_graph_cb & cb;
 };
@@ -412,8 +412,8 @@ struct llm_graph_context {
     const float norm_eps;
     const float norm_rms_eps;
 
-    const int64_t n_tokens;
-    const int64_t n_outputs;
+    const int32_t n_tokens;
+    const int32_t n_outputs;
     const int32_t n_ctx_orig; // yarn
 
     const enum llama_pooling_type pooling_type;

src/llama-kv-cache-unified.cpp

@@ -674,7 +674,6 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
 
             cells.pos_set(head_cur + idx, ubatch.pos[idx]);
 
-            // TODO: fix indexing [UBATCH_IDX]
             for (int32_t i = 0; i < ubatch.n_seq_id[s]; i++) {
                 cells.seq_add(head_cur + idx, ubatch.seq_id[s][i]);
             }

src/llama-vocab.cpp

@@ -9,16 +9,16 @@
 
 #include <algorithm>
 #include <cassert>
-#include <cctype>
 #include <cfloat>
+#include <climits>
 #include <cstdarg>
 #include <cstring>
 #include <forward_list>
-#include <limits>
 #include <map>
 #include <queue>
 #include <set>
 #include <unordered_map>
+#include <cctype>
 
 //
 // helpers
@@ -2572,10 +2572,6 @@ int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t
     // copy piece chars to output text buffer
     // skip up to 'lstrip' leading spaces before copying
     auto _try_copy = [=] (const char * token, size_t size) -> int32_t {
-        if (size >= static_cast<size_t>(std::numeric_limits<int32_t>::max())) {
-            GGML_ABORT("invalid token size: %zu exceeds int32_t limit", size);
-        }
-
         for (int32_t i = 0; i < lstrip && size && *token == ' '; ++i) {
             token++;
             size--;
@@ -2772,26 +2768,26 @@ void llama_vocab::impl::print_info() const {
     LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (uint32_t) bpe_ranks.size());
 
     // special tokens
-    if (special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, special_bos_id,     id_to_token.at(special_bos_id).text.c_str() );  }
-    if (special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, special_eos_id,     id_to_token.at(special_eos_id).text.c_str() );  }
-    if (special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, special_eot_id,     id_to_token.at(special_eot_id).text.c_str() );  }
-    if (special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, special_eom_id,     id_to_token.at(special_eom_id).text.c_str() );  }
-    if (special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, special_unk_id,     id_to_token.at(special_unk_id).text.c_str() );  }
-    if (special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, special_sep_id,     id_to_token.at(special_sep_id).text.c_str() );  }
-    if (special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, special_pad_id,     id_to_token.at(special_pad_id).text.c_str() );  }
-    if (special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, special_mask_id,    id_to_token.at(special_mask_id).text.c_str() ); }
+    if (special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, special_bos_id,     id_to_token[special_bos_id].text.c_str() );  }
+    if (special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, special_eos_id,     id_to_token[special_eos_id].text.c_str() );  }
+    if (special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, special_eot_id,     id_to_token[special_eot_id].text.c_str() );  }
+    if (special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, special_eom_id,     id_to_token[special_eom_id].text.c_str() );  }
+    if (special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, special_unk_id,     id_to_token[special_unk_id].text.c_str() );  }
+    if (special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, special_sep_id,     id_to_token[special_sep_id].text.c_str() );  }
+    if (special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, special_pad_id,     id_to_token[special_pad_id].text.c_str() );  }
+    if (special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, special_mask_id,    id_to_token[special_mask_id].text.c_str() ); }
 
-    if (linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, linefeed_id,        id_to_token.at(linefeed_id).text.c_str() ); }
+    if (linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, linefeed_id,        id_to_token[linefeed_id].text.c_str() ); }
 
-    if (special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, special_fim_pre_id, id_to_token.at(special_fim_pre_id).text.c_str() ); }
-    if (special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, special_fim_suf_id, id_to_token.at(special_fim_suf_id).text.c_str() ); }
-    if (special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, special_fim_mid_id, id_to_token.at(special_fim_mid_id).text.c_str() ); }
-    if (special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, special_fim_pad_id, id_to_token.at(special_fim_pad_id).text.c_str() ); }
-    if (special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, special_fim_rep_id, id_to_token.at(special_fim_rep_id).text.c_str() ); }
-    if (special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, special_fim_sep_id, id_to_token.at(special_fim_sep_id).text.c_str() ); }
+    if (special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, special_fim_pre_id, id_to_token[special_fim_pre_id].text.c_str() ); }
+    if (special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, special_fim_suf_id, id_to_token[special_fim_suf_id].text.c_str() ); }
+    if (special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, special_fim_mid_id, id_to_token[special_fim_mid_id].text.c_str() ); }
+    if (special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, special_fim_pad_id, id_to_token[special_fim_pad_id].text.c_str() ); }
+    if (special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, special_fim_rep_id, id_to_token[special_fim_rep_id].text.c_str() ); }
+    if (special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, special_fim_sep_id, id_to_token[special_fim_sep_id].text.c_str() ); }
 
     for (const auto & id : special_eog_ids) {
-        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, id_to_token.at(id).text.c_str() );
+        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, id_to_token[id].text.c_str() );
     }
 
     LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, max_token_len);

tools/server/server.cpp

@@ -2017,6 +2017,11 @@ struct server_context {
                 params_base.n_cache_reuse = 0;
                 SRV_WRN("%s\n", "cache_reuse is not supported by this context, it will be disabled");
             }
+
+            if (!params_base.speculative.model.path.empty()) {
+                SRV_ERR("%s\n", "err: speculative decode is not supported by this context");
+                return false;
+            }
         }
 
         return true;
@@ -3217,7 +3222,7 @@ struct server_context {
                                 }
 
                                 const auto n_swa = llama_model_n_swa(model);
-                                if (pos_min > std::max(0, slot.n_past - n_swa)) {
+                                if (pos_min > slot.n_past - n_swa) {
                                     SLT_WRN(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.cache_tokens.size(), slot.id, pos_min, n_swa);
                                     SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA, see %s)\n",
                                             "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");