up.

* server : fix incorrect usage of llama_get_embeddings()

ggml-ci

* cont : fix the fix

ggml-ci

.devops/intel.Dockerfile

@@ -49,19 +49,23 @@ COPY --from=build /app/full /app
 
 WORKDIR /app
 
-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
+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
 
+ENV PATH="/opt/venv/bin:$PATH"
 
 ENTRYPOINT ["/app/tools.sh"]
 

.github/workflows/build.yml

@@ -693,7 +693,7 @@ jobs:
           - build: 'openblas-x64'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
           - build: 'vulkan-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON'
+            defines: '-DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON'
           - build: 'llvm-arm64'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
           - build: 'llvm-arm64-opencl-adreno'
@@ -778,6 +778,7 @@ jobs:
           cmake -S . -B build ${{ matrix.defines }} `
             -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include"
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
+          cp $env:CURL_PATH/bin/libcurl-*.dll build/bin/Release
 
       - name: Add libopenblas.dll
         id: add_libopenblas_dll

CMakeLists.txt

@@ -89,6 +89,14 @@ 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})
@@ -155,6 +163,8 @@ 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()
@@ -204,10 +214,6 @@ 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) / [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)
+[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)
 
 Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
@@ -18,7 +18,6 @@ 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

ci/run.sh

@@ -39,7 +39,7 @@ sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
-CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=OFF"
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON"

common/CMakeLists.txt

@@ -7,8 +7,8 @@ llama_add_compile_flags()
 # Build info header
 #
 
-if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
-    set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
+if(EXISTS "${PROJECT_SOURCE_DIR}/.git")
+    set(GIT_DIR "${PROJECT_SOURCE_DIR}/.git")
 
     # Is git submodule
     if(NOT IS_DIRECTORY "${GIT_DIR}")
@@ -18,36 +18,26 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
         if (SLASH_POS EQUAL 0)
             set(GIT_DIR "${REAL_GIT_DIR}")
         else()
-            set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
+            set(GIT_DIR "${PROJECT_SOURCE_DIR}/${REAL_GIT_DIR}")
         endif()
     endif()
 
     if(EXISTS "${GIT_DIR}/index")
-        set(GIT_INDEX "${GIT_DIR}/index")
+        # For build-info.cpp below
+        set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS "${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()
 
-# 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 "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
-    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
-    DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
-    VERBATIM
-)
+set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in")
+set(OUTPUT_FILE   "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp")
+configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+
 set(TARGET build_info)
-add_library(${TARGET} OBJECT build-info.cpp)
+add_library(${TARGET} OBJECT ${OUTPUT_FILE})
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()

common/arg.cpp

@@ -988,10 +988,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         params.tensor_buft_overrides.push_back({nullptr, nullptr});
     }
 
-    if (params.reranking && params.embedding) {
-        throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both");
-    }
-
     if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
         throw std::runtime_error(string_format(
             "error: the supplied chat template is not supported: %s%s\n",
@@ -2747,9 +2743,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
     add_opt(common_arg(
         {"--reranking", "--rerank"},
-        string_format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"),
+        string_format("enable reranking endpoint on server (default: %s)", "disabled"),
         [](common_params & params) {
-            params.reranking = true;
+            params.embedding = true;
+            params.pooling_type = LLAMA_POOLING_TYPE_RANK;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING"));
     add_opt(common_arg(

common/build-info.cpp.in

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

common/chat-parser.cpp

@@ -49,6 +49,7 @@ 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) {
@@ -378,3 +379,7 @@ 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

@@ -115,4 +115,6 @@ 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

@@ -1838,7 +1838,7 @@ static common_chat_params common_chat_templates_apply_legacy(
     if (res < 0) {
         // if the custom "tmpl" is not supported, we throw an error
         // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
-        throw std::runtime_error("this custom template is not supported");
+        throw std::runtime_error("this custom template is not supported, try using --jinja");
     }
 
     // if it turns out that our buffer is too small, we resize it
@@ -1921,7 +1921,9 @@ 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) {
-            throw std::runtime_error(ex.what());
+            builder.clear_tools();
+            builder.move_to(0);
+            common_chat_parse_content_only(builder);
         }
     }
     auto msg = builder.result();

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

@@ -1,24 +0,0 @@
-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()

common/common.cpp

@@ -466,7 +466,7 @@ size_t string_find_partial_stop(const std::string_view & str, const std::string_
 
 std::string regex_escape(const std::string & s) {
     static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
-    return std::regex_replace(s, special_chars, "\\$0");
+    return std::regex_replace(s, special_chars, "\\$&");
 }
 
 std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
@@ -767,6 +767,9 @@ bool fs_validate_filename(const std::string & filename) {
     return true;
 }
 
+#include <iostream>
+
+
 // returns true if successful, false otherwise
 bool fs_create_directory_with_parents(const std::string & path) {
 #ifdef _WIN32
@@ -784,9 +787,16 @@ bool fs_create_directory_with_parents(const std::string & path) {
     // process path from front to back, procedurally creating directories
     while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
         const std::wstring subpath = wpath.substr(0, pos_slash);
-        const wchar_t * test = subpath.c_str();
 
-        const bool success = CreateDirectoryW(test, NULL);
+        pos_slash += 1;
+
+        // skip the drive letter, in some systems it can return an access denied error
+        if (subpath.length() == 2 && subpath[1] == ':') {
+            continue;
+        }
+
+        const bool success = CreateDirectoryW(subpath.c_str(), NULL);
+
         if (!success) {
             const DWORD error = GetLastError();
 
@@ -800,8 +810,6 @@ bool fs_create_directory_with_parents(const std::string & path) {
                 return false;
             }
         }
-
-        pos_slash += 1;
     }
 
     return true;
@@ -897,34 +905,6 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
-    if (params.reranking) {
-        bool ok = true;
-
-        if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have a  BOS token, reranking will not work\n", __func__);
-            ok = false;
-        }
-
-        bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
-        bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
-
-        if (!has_eos && !has_sep) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
-            ok = false;
-        } else if (!has_eos) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
-        } else if (!has_sep) {
-            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
-            ok = false;
-        }
-
-        if (!ok) {
-            llama_model_free(model);
-
-            return iparams;
-        }
-    }
-
     auto cparams = common_context_params_to_llama(params);
 
     llama_context * lctx = llama_init_from_model(model, cparams);
@@ -966,6 +946,35 @@ struct common_init_result common_init_from_params(common_params & params) {
         }
     }
 
+    if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) {
+        bool ok = true;
+
+        if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: vocab does not have a  BOS token, reranking will not work\n", __func__);
+            ok = false;
+        }
+
+        bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
+        bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
+
+        if (!has_eos && !has_sep) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
+            ok = false;
+        } else if (!has_eos) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
+        } else if (!has_sep) {
+            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
+            ok = false;
+        }
+
+        if (!ok) {
+            llama_free(lctx);
+            llama_model_free(model);
+
+            return iparams;
+        }
+    }
+
     // load and optionally apply lora adapters
     for (auto & la : params.lora_adapters) {
         llama_adapter_lora_ptr lora;
@@ -1143,11 +1152,6 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.op_offload        = !params.no_op_offload;
     cparams.swa_full          = params.swa_full;
 
-    if (params.reranking) {
-        cparams.embeddings    = true;
-        cparams.pooling_type  = LLAMA_POOLING_TYPE_RANK;
-    }
-
     cparams.type_k = params.cache_type_k;
     cparams.type_v = params.cache_type_v;
 

common/common.h

@@ -355,7 +355,6 @@ struct common_params {
     int32_t embd_normalize = 2;     // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
     std::string embd_out   = "";    // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
     std::string embd_sep   = "\n";  // separator of embeddings
-    bool reranking         = false; // enable reranking support on server
 
     // server params
     int32_t port           = 8080;         // server listens on this network port

convert_hf_to_gguf.py

@@ -519,7 +519,7 @@ class TextModel(ModelBase):
     def set_gguf_parameters(self):
         self.gguf_writer.add_block_count(self.block_count)
 
-        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions"], optional=True)) is not None:
+        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None:
             self.gguf_writer.add_context_length(n_ctx)
             logger.info(f"gguf: context length = {n_ctx}")
 
@@ -556,8 +556,11 @@ class TextModel(ModelBase):
             logger.info(f"gguf: experts used count = {n_experts_used}")
 
         if (head_dim := self.hparams.get("head_dim")) is not None:
-            self.gguf_writer.add_key_length(head_dim)
-            self.gguf_writer.add_value_length(head_dim)
+            # Workaround for incorrect AutoConfig value for DeepSeekV3 (is set correctly in DeepSeekV2Model class)
+            # https://github.com/huggingface/transformers/blob/19224c3642705c5b6988c9f5f4251f83323d05ae/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py#L210
+            if self.hparams.get("model_type") != "deepseek_v3":
+                self.gguf_writer.add_key_length(head_dim)
+                self.gguf_writer.add_value_length(head_dim)
 
         self.gguf_writer.add_file_type(self.ftype)
         logger.info(f"gguf: file type = {self.ftype}")
@@ -2017,6 +2020,20 @@ class LlamaModel(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("ArceeForCausalLM")
+class ArceeModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.ARCEE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+
 @ModelBase.register(
     "LlavaForConditionalGeneration", # pixtral
     "Mistral3ForConditionalGeneration", # mistral small 3.1
@@ -4059,6 +4076,34 @@ class NomicBertModel(BertModel):
         raise ValueError(f"unknown tokenizer: {toktyp}")
 
 
+@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
+class NeoBert(BertModel):
+    model_arch = gguf.MODEL_ARCH.NEO_BERT
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # NeoBERT uses 2/3 of the intermediate size as feed forward length
+        self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3))
+        self.gguf_writer.add_rope_freq_base(10000.0)  # default value for NeoBERT
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+        f_rms_eps = self.hparams.get("norm_eps", 1e-6)  # default value for NeoBERT
+        self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
+        logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
+
+        self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use
+
+    def modify_tensors(self, data_torch, name, bid):
+        if name.startswith("decoder."):
+            return []
+
+        if name.startswith("model."):
+            name = name[6:]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
 class XLMRobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
@@ -5259,6 +5304,34 @@ class DeepseekV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("Dots1ForCausalLM")
+class Dots1Model(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.DOTS1
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.hparams["num_experts"] = self.hparams["n_routed_experts"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
+        self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+
+        if self.hparams["scoring_func"] == "noaux_tc":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        else:
+            raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+        if "shared_experts" in name:
+            return [(self.map_tensor_name(name), data_torch)]
+        return super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("PLMForCausalLM")
 class PLMModel(TextModel):
     model_arch = gguf.MODEL_ARCH.PLM
@@ -6225,6 +6298,17 @@ class UltravoxWhisperEncoderModel(WhisperEncoderModel):
         super().set_gguf_parameters()
         self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
 
+@ModelBase.register("SmolLM3ForCausalLM")
+class SmolLM3Model(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.SMOLLM3
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        no_rope_layer_interval = self.hparams.get("no_rope_layer_interval")
+        if no_rope_layer_interval is not None:
+            self.gguf_writer.add_uint32("no_rope_layer_interval", no_rope_layer_interval)
+
 ###### CONVERSION LOGIC ######
 
 

docs/development/HOWTO-add-model.md

@@ -83,20 +83,22 @@ NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the conv
 
 ### 2. Define the model architecture in `llama.cpp`
 
-The model params and tensors layout must be defined in `llama.cpp`:
-1. Define a new `llm_arch`
-2. Define the tensors layout in `LLM_TENSOR_NAMES`
-3. Add any non-standard metadata in `llm_load_hparams`
-4. Create the tensors for inference in `llm_load_tensors`
-5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
+The model params and tensors layout must be defined in `llama.cpp` source files:
+1. Define a new `llm_arch` enum value in `src/llama-arch.h`.
+2. In `src/llama-arch.cpp`:
+    - Add the architecture name to the `LLM_ARCH_NAMES` map.
+    - Add the tensor mappings to the `LLM_TENSOR_NAMES` map.
+3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`.
+4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`.
 
 NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
 
 ### 3. Build the GGML graph implementation
 
-This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
-
-Have a look at existing implementations like `build_llama`, `build_dbrx` or `build_bert`.
+This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `src/llama-model.cpp`.
+Create a new struct that inherits from `llm_graph_context` and implement the graph-building logic in its constructor.
+Have a look at existing implementations like `llm_build_llama`, `llm_build_dbrx` or `llm_build_bert`.
+Then, in the `llama_model::build_graph` method, add a case for your architecture to instantiate your new graph-building struct.
 
 Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR.
 

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 (WIP: https://github.com/ggml-org/llama.cpp/pull/12034)
+  - Qwen 2.5 Coder
   - Mistral Nemo
   - Firefunction v2
   - Command R7B

docs/multimodal.md

@@ -107,3 +107,7 @@ 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

examples/gritlm/gritlm.cpp

@@ -41,12 +41,11 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
 
         // add input to batch (this increments n_tokens)
         for (int32_t j = 0; j < n_toks; j++) {
-            common_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
+            common_batch_add(batch, inputs[j], j, { 0 }, true);
         }
 
         // clear previous kv_cache values (irrelevant for embeddings)
         llama_memory_clear(llama_get_memory(ctx), true);
-        llama_set_embeddings(ctx, true);
         llama_set_causal_attn(ctx, false);
 
         // run model
@@ -103,7 +102,6 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
     llama_token eos_token = llama_vocab_eos(vocab);
 
     llama_memory_clear(llama_get_memory(ctx), true);
-    llama_set_embeddings(ctx, false);
     llama_set_causal_attn(ctx, true);
 
     llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
@@ -166,6 +164,8 @@ int main(int argc, char * argv[]) {
     llama_model_params mparams = common_model_params_to_llama(params);
     llama_context_params cparams = common_context_params_to_llama(params);
 
+    cparams.embeddings = true;
+
     llama_backend_init();
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
@@ -213,6 +213,8 @@ int main(int argc, char * argv[]) {
         std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1);
     }
 
+    llama_set_embeddings(ctx, false);
+
     // ### Generation ###
     // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
     {

ggml/CMakeLists.txt

@@ -172,6 +172,7 @@ option(GGML_HIP                             "ggml: use HIP"
 option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
 option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
+option(GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12   "ggml: enable rocWMMA FlashAttention on GFX12"    OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
 option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"                      OFF)
 option(GGML_VULKAN_DEBUG                    "ggml: enable Vulkan debug output"                OFF)

ggml/cmake/common.cmake

@@ -36,8 +36,7 @@ function(ggml_get_system_arch)
             (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
             CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
         set(GGML_SYSTEM_ARCH "x86" PARENT_SCOPE)
-    elseif ("${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "ppc64le " OR
-            "${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "powerpc ")
+    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc|power")
         set(GGML_SYSTEM_ARCH "PowerPC" PARENT_SCOPE)
     elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
         set(GGML_SYSTEM_ARCH "loongarch64"  PARENT_SCOPE)

ggml/src/CMakeLists.txt

@@ -270,17 +270,23 @@ endfunction()
 function(ggml_add_cpu_backend_variant tag_name)
     set(GGML_CPU_TAG_NAME ${tag_name})
     # other: OPENMP LLAMAFILE CPU_HBM
-    foreach (feat NATIVE
-                  SSE42
-                  AVX AVX2 BMI2 AVX_VNNI FMA F16C
-                  AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16
-                  AMX_TILE AMX_INT8 AMX_BF16)
-        set(GGML_${feat} OFF)
-    endforeach()
+    if (GGML_SYSTEM_ARCH STREQUAL "x86")
+        foreach (feat NATIVE
+                      SSE42
+                      AVX AVX2 BMI2 AVX_VNNI FMA F16C
+                      AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16
+                      AMX_TILE AMX_INT8 AMX_BF16)
+            set(GGML_${feat} OFF)
+        endforeach()
 
-    foreach (feat ${ARGN})
-        set(GGML_${feat} ON)
-    endforeach()
+        foreach (feat ${ARGN})
+            set(GGML_${feat} ON)
+        endforeach()
+    elseif (GGML_SYSTEM_ARCH STREQUAL "ARM")
+        foreach (feat ${ARGN})
+            set(GGML_INTERNAL_${feat} ON)
+        endforeach()
+    endif()
 
     ggml_add_cpu_backend_variant_impl(${tag_name})
 endfunction()
@@ -290,6 +296,8 @@ ggml_add_backend(CPU)
 if (GGML_CPU_ALL_VARIANTS)
     if (NOT GGML_BACKEND_DL)
         message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
+    elseif (GGML_CPU_ARM_ARCH)
+        message(FATAL_ERROR "Cannot use both GGML_CPU_ARM_ARCH and GGML_CPU_ALL_VARIANTS")
     endif()
     if (GGML_SYSTEM_ARCH STREQUAL "x86")
         ggml_add_cpu_backend_variant(x64)
@@ -303,8 +311,30 @@ if (GGML_CPU_ALL_VARIANTS)
             # MSVC doesn't support AMX
             ggml_add_cpu_backend_variant(sapphirerapids SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
         endif()
+    elseif(GGML_SYSTEM_ARCH STREQUAL "ARM")
+        if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+            # Many of these features are optional so we build versions with popular
+            # combinations and name the backends based on the version they were
+            # first released with
+            ggml_add_cpu_backend_variant(armv8.0_1)
+            ggml_add_cpu_backend_variant(armv8.2_1    DOTPROD)
+            ggml_add_cpu_backend_variant(armv8.2_2    DOTPROD FP16_VECTOR_ARITHMETIC)
+            ggml_add_cpu_backend_variant(armv8.2_3    DOTPROD FP16_VECTOR_ARITHMETIC SVE)
+            ggml_add_cpu_backend_variant(armv8.6_1    DOTPROD FP16_VECTOR_ARITHMETIC SVE MATMUL_INT8)
+            ggml_add_cpu_backend_variant(armv8.6_2    DOTPROD FP16_VECTOR_ARITHMETIC SVE MATMUL_INT8 SVE2)
+            ggml_add_cpu_backend_variant(armv9.2_1    DOTPROD FP16_VECTOR_ARITHMETIC SVE MATMUL_INT8 SME)
+            ggml_add_cpu_backend_variant(armv9.2_2    DOTPROD FP16_VECTOR_ARITHMETIC SVE MATMUL_INT8 SVE2 SME)
+        elseif (CMAKE_SYSTEM_NAME MATCHES "Android")
+            # Android-specific backends with SoC-compatible feature sets
+            ggml_add_cpu_backend_variant(android_armv8.0_1)
+            ggml_add_cpu_backend_variant(android_armv8.2_1    DOTPROD)
+            ggml_add_cpu_backend_variant(android_armv8.2_2    DOTPROD FP16_VECTOR_ARITHMETIC)
+            ggml_add_cpu_backend_variant(android_armv8.6_1    DOTPROD FP16_VECTOR_ARITHMETIC MATMUL_INT8)
+        else()
+            message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}")
+        endif()
     else()
-        message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported on ${GGML_SYSTEM_ARCH}")
+        message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported with ${GGML_SYSTEM_ARCH} on ${CMAKE_SYSTEM_NAME}")
     endif()
 elseif (GGML_CPU)
     ggml_add_cpu_backend_variant_impl("")

ggml/src/ggml-cpu/CMakeLists.txt

@@ -1,3 +1,17 @@
+function(ggml_add_cpu_backend_features cpu_name arch)
+    # The feature detection code is compiled as a separate target so that
+    # it can be built without the architecture flags
+    # Since multiple variants of the CPU backend may be included in the same
+    # build, using set_source_files_properties() to set the arch flags is not possible
+    set(GGML_CPU_FEATS_NAME ${cpu_name}-feats)
+    add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/arch/${arch}/cpu-feats.cpp)
+    target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
+    target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARGN})
+    target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
+    set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    target_link_libraries(${cpu_name} PRIVATE ${GGML_CPU_FEATS_NAME})
+endfunction()
+
 function(ggml_add_cpu_backend_variant_impl tag_name)
     if (tag_name)
         set(GGML_CPU_NAME ggml-cpu-${tag_name})
@@ -143,6 +157,46 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             else()
                 if (GGML_CPU_ARM_ARCH)
                     list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})
+                elseif(GGML_CPU_ALL_VARIANTS)
+                    # Begin with the lowest baseline
+                    set(ARM_MCPU "armv8-a")
+                    set(ARCH_TAGS "")
+                    set(ARCH_DEFINITIONS "")
+
+                    # When a feature is selected, bump the MCPU to the first
+                    # version that supported it
+                    if (GGML_INTERNAL_DOTPROD)
+                        set(ARM_MCPU "armv8.2-a")
+                        set(ARCH_TAGS "${ARCH_TAGS}+dotprod")
+                        list(APPEND ARCH_DEFINITIONS GGML_USE_DOTPROD)
+                    endif()
+                    if (GGML_INTERNAL_FP16_VECTOR_ARITHMETIC)
+                        set(ARM_MCPU "armv8.2-a")
+                        set(ARCH_TAGS "${ARCH_TAGS}+fp16")
+                        list(APPEND ARCH_DEFINITIONS GGML_USE_FP16_VECTOR_ARITHMETIC)
+                    endif()
+                    if (GGML_INTERNAL_SVE)
+                        set(ARM_MCPU "armv8.2-a")
+                        set(ARCH_TAGS "${ARCH_TAGS}+sve")
+                        list(APPEND ARCH_DEFINITIONS GGML_USE_SVE)
+                    endif()
+                    if (GGML_INTERNAL_MATMUL_INT8)
+                        set(ARM_MCPU "armv8.6-a")
+                        set(ARCH_TAGS "${ARCH_TAGS}+i8mm")
+                        list(APPEND ARCH_DEFINITIONS GGML_USE_MATMUL_INT8)
+                    endif()
+                    if (GGML_INTERNAL_SVE2)
+                        set(ARM_MCPU "armv8.6-a")
+                        set(ARCH_TAGS "${ARCH_TAGS}+sve2")
+                        list(APPEND ARCH_DEFINITIONS GGML_USE_SVE2)
+                    endif()
+                    if (GGML_INTERNAL_SME)
+                        set(ARM_MCPU "armv9.2-a")
+                        set(ARCH_TAGS "${ARCH_TAGS}+sme")
+                        list(APPEND ARCH_DEFINITIONS GGML_USE_SME)
+                    endif()
+                    list(APPEND ARCH_FLAGS "-march=${ARM_MCPU}${ARCH_TAGS}")
+                    ggml_add_cpu_backend_features(${GGML_CPU_NAME} arm ${ARCH_DEFINITIONS})
                 endif()
             endif()
 
@@ -306,18 +360,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
                 message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
             endif()
-
-            # The feature detection code is compiled as a separate target so that
-            # it can be built without the architecture flags
-            # Since multiple variants of the CPU backend may be included in the same
-            # build, using set_source_files_properties() to set the arch flags is not possible
-            set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats)
-            add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/arch/x86/cpu-feats.cpp)
-            target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
-            target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS})
-            target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
-            set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-            target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME})
+            ggml_add_cpu_backend_features(${GGML_CPU_NAME} x86 ${ARCH_DEFINITIONS})
         endif()
     elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC")
         message(STATUS "PowerPC detected")

ggml/src/ggml-cpu/apple-fallback.h

@@ -0,0 +1,88 @@
+#pragma once
+
+// Solve alias issue for Apple targets (currently PowerPC, x86, and ARM64).
+// Mach-O has a weak alias equivalent but no practical compiler support can
+// be found, so we need to do it manually.
+// ref: https://stackoverflow.com/questions/42757744
+//
+// This file is a complement to native implementations in the `arch` folder.
+// A kernel in quants.c or repack.cpp is either:
+// - implemented in the `arch` folder, or
+// - defined in this file to remove the `_generic` suffix
+
+#if defined(GGML_CPU_GENERIC)
+// quants.c
+#define quantize_row_q8_0_generic quantize_row_q8_0
+#define quantize_row_q8_1_generic quantize_row_q8_1
+#define quantize_row_q8_K_generic quantize_row_q8_K
+#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0
+#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
+#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
+#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
+#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
+#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
+#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
+#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
+#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K
+#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K
+#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K
+#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K
+#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
+#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
+#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
+#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
+#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
+#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
+#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
+#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
+// repack.cpp
+#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
+#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
+#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
+#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
+#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
+#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
+#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#elif defined(__aarch64__) || defined(__arm__)
+// repack.cpp
+#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
+#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#elif defined(__x86_64__) || defined(__i386__)
+// repack.cpp
+#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
+#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
+#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
+#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
+#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
+#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#elif defined(__POWERPC__)
+// ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
+// quants.c
+#define quantize_row_q8_K_generic quantize_row_q8_K
+#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
+#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
+#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+// repack.cpp
+#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
+#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
+#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
+#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
+#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
+#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
+#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#endif

ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp

@@ -0,0 +1,94 @@
+#include "ggml-backend-impl.h"
+
+#if defined(__aarch64__)
+
+#if defined(__linux__)
+#include <sys/auxv.h>
+#elif defined(__APPLE__)
+#include <sys/sysctl.h>
+#endif
+
+#if !defined(HWCAP2_I8MM)
+#define HWCAP2_I8MM (1 << 13)
+#endif
+
+#if !defined(HWCAP2_SME)
+#define HWCAP2_SME (1 << 23)
+#endif
+
+struct aarch64_features {
+    // has_neon not needed, aarch64 has NEON guaranteed
+    bool has_dotprod     = false;
+    bool has_fp16_va     = false;
+    bool has_sve         = false;
+    bool has_sve2        = false;
+    bool has_i8mm        = false;
+    bool has_sme         = false;
+
+    aarch64_features() {
+#if defined(__linux__)
+        uint32_t hwcap = getauxval(AT_HWCAP);
+        uint32_t hwcap2 = getauxval(AT_HWCAP2);
+
+        has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
+        has_fp16_va = !!(hwcap & HWCAP_FPHP);
+        has_sve     = !!(hwcap & HWCAP_SVE);
+        has_sve2    = !!(hwcap2 & HWCAP2_SVE2);
+        has_i8mm    = !!(hwcap2 & HWCAP2_I8MM);
+        has_sme     = !!(hwcap2 & HWCAP2_SME);
+#elif defined(__APPLE__)
+        int oldp = 0;
+        size_t size = sizeof(oldp);
+
+        if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) == 0) {
+            has_dotprod = static_cast<bool>(oldp);
+        }
+
+        if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) == 0) {
+            has_i8mm = static_cast<bool>(oldp);
+        }
+
+        if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) == 0) {
+            has_sme = static_cast<bool>(oldp);
+        }
+
+        // Apple apparently does not implement SVE yet
+#endif
+    }
+};
+
+static int ggml_backend_cpu_aarch64_score() {
+    int score = 1;
+    aarch64_features af;
+
+#ifdef GGML_USE_DOTPROD
+    if (!af.has_dotprod) { return 0; }
+    score += 1<<1;
+#endif
+#ifdef GGML_USE_FP16_VECTOR_ARITHMETIC
+    if (!af.has_fp16_va) { return 0; }
+    score += 1<<2;
+#endif
+#ifdef GGML_USE_SVE
+    if (!af.has_sve) { return 0; }
+    score += 1<<3;
+#endif
+#ifdef GGML_USE_MATMUL_INT8
+    if (!af.has_i8mm) { return 0; }
+    score += 1<<4;
+#endif
+#ifdef GGML_USE_SVE2
+    if (!af.has_sve2) { return 0; }
+    score += 1<<5;
+#endif
+#ifdef GGML_USE_SME
+    if (!af.has_sme) { return 0; }
+    score += 1<<6;
+#endif
+
+    return score;
+}
+
+GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_aarch64_score)
+
+# endif // defined(__aarch64__)

ggml/src/ggml-cpu/ggml-cpu-impl.h

@@ -503,13 +503,16 @@ static __m256 __lasx_xvreplfr2vr_s(const float val) {
 // TODO: move to ggml-threading
 void ggml_barrier(struct ggml_threadpool * tp);
 
+void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value);
+int  ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value);
+
 #ifdef __cplusplus
 }
 #endif
 
 #define GGML_DO_PRAGMA_(x) _Pragma (#x)
 #define GGML_DO_PRAGMA(x) GGML_DO_PRAGMA_(x)
-#if defined(GGML_CPU_GENERIC) || defined(__HIPCC__)
+#if defined(GGML_CPU_GENERIC) || defined(__HIPCC__) || defined(__APPLE__)
 // Note for Apple targets:
 // - clang: aliases are not supported on darwin
 // - all native kernels need to be implemented in both x86 and arm files

ggml/src/ggml-cpu/ggml-cpu.c

@@ -559,6 +559,14 @@ void ggml_barrier(struct ggml_threadpool * tp) {
 #endif
 }
 
+void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value) {
+    atomic_store_explicit(&tp->current_chunk, value, memory_order_relaxed);
+}
+
+int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value) {
+    return atomic_fetch_add_explicit(&tp->current_chunk, value, memory_order_relaxed);
+}
+
 #if defined(__gnu_linux__)
 static cpu_set_t ggml_get_numa_affinity(void) {
     cpu_set_t cpuset;

ggml/src/ggml-cpu/llamafile/sgemm.cpp

@@ -53,7 +53,6 @@
 #include "ggml-cpu-impl.h"
 #include "ggml-quants.h"
 
-#include <atomic>
 #include <array>
 #include <type_traits>
 
@@ -394,8 +393,6 @@ class tinyBLAS {
 
     template <int RM, int RN, int BM>
     NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) {
-        static std::atomic<int64_t> current_chunk;
-
         GGML_ASSERT(m % (RM * BM) == 0);
         const int64_t ytiles = m / (RM * BM);
         const int64_t xtiles = (n + RN -1) / RN;
@@ -410,7 +407,7 @@ class tinyBLAS {
         if (params->ith == 0) {
             GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles);
             // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
-            std::atomic_store_explicit(&current_chunk, (int64_t)params->nth, std::memory_order_relaxed);
+            ggml_threadpool_chunk_set(params->threadpool, params->nth);
         }
 
         ggml_barrier(params->threadpool);
@@ -439,8 +436,7 @@ class tinyBLAS {
                 GGML_ASSERT(jj == jj2);
             }
 
-            // next step.
-            job = std::atomic_fetch_add_explicit(&current_chunk, (int64_t)1, std::memory_order_relaxed);
+            job = ggml_threadpool_chunk_add(params->threadpool, 1);
         }
 
         ggml_barrier(params->threadpool);

ggml/src/ggml-cpu/quants.c

@@ -5,6 +5,10 @@
 #include "ggml-quants.h"
 #include "quants.h"
 
+#if defined(__APPLE__)
+#include "apple-fallback.h"
+#endif
+
 #include <string.h>
 #include <assert.h>
 #include <float.h>

ggml/src/ggml-cpu/quants.h

@@ -84,33 +84,6 @@ void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
-#if defined(GGML_CPU_GENERIC)
-#define quantize_row_q8_0_generic quantize_row_q8_0
-#define quantize_row_q8_1_generic quantize_row_q8_1
-#define quantize_row_q8_K_generic quantize_row_q8_K
-#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0
-#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
-#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
-#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
-#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
-#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
-#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
-#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
-#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K
-#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K
-#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K
-#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K
-#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
-#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
-#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
-#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
-#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
-#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
-#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
-#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
-#endif
-
 #ifdef __cplusplus
 }
 #endif

ggml/src/ggml-cpu/repack.cpp

@@ -8,6 +8,10 @@
 #include "ggml-cpu-impl.h"
 #include "traits.h"
 
+#if defined(__APPLE__)
+#include "apple-fallback.h"
+#endif
+
 #include <cmath>
 #include <cstring>
 #include <cassert>

ggml/src/ggml-cpu/repack.h

@@ -67,7 +67,7 @@ extern "C" {
 // Workaround for clang:
 // clang++ complains: ``error: call to 'ggml_gemm_q4_0_4x4_q8_0' is ambiguous''
 // repro: https://godbolt.org/z/oKdeWKonM (ICE), https://godbolt.org/z/1szq6P36v (ambiguous call)
-#if defined(GGML_CPU_CLANG_WORKAROUND) || !(defined(__GNUC__) && defined(__clang__)) || defined(__HIPCC__)
+#if defined(GGML_CPU_CLANG_WORKAROUND) || defined(__APPLE__) || !(defined(__GNUC__) && defined(__clang__)) || defined(__HIPCC__)
 void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@@ -98,22 +98,6 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
-#if defined(GGML_CPU_GENERIC)
-#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
-#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
-#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
-#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
-#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
-#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
-#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
-#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
-#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
-#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
-#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
-#endif
-
 #if defined(__cplusplus)
 } // extern "C"
 #endif

ggml/src/ggml-cuda/common.cuh

@@ -207,9 +207,9 @@ typedef float2 dfloat2;
 #define FP16_MMA_AVAILABLE
 #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
 
-#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4))
+#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
 #define FP16_MMA_AVAILABLE
-#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4))
+#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
 
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 #define NEW_MMA_AVAILABLE
@@ -262,11 +262,11 @@ static bool cp_async_available(const int cc) {
 }
 
 static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
-#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
-    return __AMDGCN_WAVEFRONT_SIZE;
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
+    return 64;
 #else
     return 32;
-#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
 }
 
 [[noreturn]]

ggml/src/ggml-cuda/ssm-scan.cu

@@ -10,6 +10,8 @@ __global__ void __launch_bounds__(splitD, 2)
                  float * __restrict__ dst, const int64_t L) {
     GGML_UNUSED(src1_nb0);
     GGML_UNUSED(src2_nb0);
+
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
     const int bidx = blockIdx.x;  // split along B
     const int bidy = blockIdx.y;  // split along D
     const int tid  = threadIdx.x;
@@ -44,16 +46,16 @@ __global__ void __launch_bounds__(splitD, 2)
     if (N == 16) {
 #pragma unroll
         for (size_t i = 0; i < splitD / 4; i += 2) {
-            float value = A_block[(wid * warpSize + i) * stride_A + wtid];
+            float value = A_block[(wid * warp_size + i) * stride_A + wtid];
             // todo: bank conflict
             // I am always confused with how to use the swizzling method to solve
             // bank conflit. Hoping somebody can tell me.
-            smem_A[(wid * warpSize + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
+            smem_A[(wid * warp_size + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
         }
 #pragma unroll
         for (size_t i = 0; i < splitD / 4; i += 2) {
-            float value = s0_block[(wid * warpSize + i) * stride_s0 + wtid];
-            smem_s0[(wid * warpSize + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
+            float value = s0_block[(wid * warp_size + i) * stride_s0 + wtid];
+            smem_s0[(wid * warp_size + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
         }
     }
 

ggml/src/ggml-hip/CMakeLists.txt

@@ -113,6 +113,10 @@ if (GGML_HIP_ROCWMMA_FATTN)
     add_compile_definitions(GGML_HIP_ROCWMMA_FATTN)
 endif()
 
+if (GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12 OR ${hip_VERSION} VERSION_GREATER_EQUAL 7.0)
+    add_compile_definitions(GGML_HIP_ROCWMMA_FATTN_GFX12)
+endif()
+
 if (NOT GGML_CUDA_FA)
     add_compile_definitions(GGML_CUDA_NO_FA)
 endif()

ggml/src/ggml-metal/CMakeLists.txt

@@ -44,21 +44,22 @@ 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-opencl/CMakeLists.txt

@@ -80,6 +80,7 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q4_0_f32_1d_8x_flat
     mul_mv_q4_0_f32_1d_16x_flat
     mul_mv_q6_k
+    mul_mv_id_q4_0_f32_8x_flat
     mul
     norm
     relu

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -321,6 +321,7 @@ struct ggml_backend_opencl_context {
     cl_program program_upscale;
     cl_program program_concat;
     cl_program program_tsembd;
+    cl_program program_mul_mv_id_q4_0_f32_8x_flat;
 
     cl_kernel kernel_add, kernel_add_row;
     cl_kernel kernel_mul, kernel_mul_row;
@@ -366,6 +367,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_concat_f32_contiguous;
     cl_kernel kernel_concat_f32_non_contiguous;
     cl_kernel kernel_timestep_embedding;
+    cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     // Transpose kernels
@@ -1112,7 +1114,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
-        // repeat
+    // repeat
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src {
@@ -1256,6 +1258,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         }
     }
 
+    // mul_mv_id_q4_0_f32_8x_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_id_q4_0_f32_8x_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_id_q4_0_f32_8x_flat.cl");
+#endif
+        backend_ctx->program_mul_mv_id_q4_0_f32_8x_flat =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_id_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_id_q4_0_f32_8x_flat, "kernel_mul_mv_id_q4_0_f32_8x_flat", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // Adreno kernels
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     // transpose
@@ -2178,6 +2196,13 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
             }
             return false;
+        case GGML_OP_MUL_MAT_ID:
+            if (op->src[0]->type == GGML_TYPE_Q4_0) {
+                if (op->src[1]->type == GGML_TYPE_F32) {
+                    return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
+                }
+            }
+            return false;
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
@@ -5536,6 +5561,136 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     }
 }
 
+static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const ggml_tensor * src2 = dst->src[2];
+    GGML_ASSERT(src2);
+    GGML_ASSERT(src2->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offset2 = extra2->offset + src2->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+#ifdef GGML_OPENCL_SOA_Q
+    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+#endif
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb02 = src0->nb[2];
+
+    const int ne10 = src1->ne[0];
+    const int ne11 = src1->ne[1];
+    const int ne12 = src1->ne[2];
+    const int ne13 = src1->ne[3];
+
+    const cl_ulong nb11 = src1->nb[1];
+    const cl_ulong nb12 = src1->nb[2];
+
+    const int ne20 = src2->ne[0];
+    const int ne21 = src2->ne[1];
+
+    const cl_ulong nb21 = src2->nb[1];
+
+    const int ne0 = dst->ne[0];
+    const int ne1 = dst->ne[1];
+
+    const int r2 = ne12/ne02;
+    const int r3 = ne13/ne03;
+    const int dst_rows = ne20*ne21; // ne20 = n_used_experts, ne21 = n_rows
+
+    GGML_ASSERT(ne00 == ne10);
+
+    int sgs   = 32; // subgroup size
+    int nsg   = 1;  // number of subgroups
+    int nrows = 1;  // number of row in src1
+    int ndst  = 4;  // number of values produced by each subgroup
+
+    cl_kernel kernel;
+
+    // subgroup mat vec
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0: {
+            kernel = backend_ctx->kernel_mul_mv_id_q4_0_f32_8x_flat;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                sgs  = 16;
+                nsg  = 1;
+                ndst = 8;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                sgs  = 64;
+                nsg  = 1;
+                ndst = 8;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb00));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne20));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &ne21));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb21));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &r3));
+
+            break;
+        }
+        default:
+            GGML_ASSERT(false && "not implemented");;
+    }
+
+    int _ne1 = 1;
+    int ne123 = dst_rows;
+
+    size_t global_work_size[] = {(size_t)(ne01+ndst*nsg-1)/(ndst*nsg)*sgs, (size_t)(_ne1+nrows-1)/nrows*nsg, (size_t)ne123};
+    size_t local_work_size[] = {(size_t)sgs, (size_t)nsg, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
 static void ggml_cl_scale(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -6444,6 +6599,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
             }
             func = ggml_cl_mul_mat;
             break;
+        case GGML_OP_MUL_MAT_ID:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_mul_mat_id;
+            break;
         case GGML_OP_SCALE:
             if (!any_on_device) {
                 return false;

ggml/src/ggml-opencl/kernels/mul_mv_id_q4_0_f32_8x_flat.cl

@@ -0,0 +1,283 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+// This function requires the original shuffled weights.
+// As a reminder, the original weights are shuffled so that (q[0], q[16]) are
+// packed together in a byte, so are (q[1], q[17]) and so on.
+inline float block_q_4_0_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float           d   = *dh;
+    global ushort * qs  = ((global ushort *)x + il/2);
+    float           acc = 0.f;
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+//
+// This variant outputs 8 values.
+//
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 8 // each SIMD group works on 8 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_DST 8
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_8x_flat(
+        global char  * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = 0;
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float8 sumf = 0.f;
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 0*nb*QK4_0/2, d + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 1*nb*QK4_0/2, d + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 2*nb*QK4_0/2, d + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 3*nb*QK4_0/2, d + ib + 3*nb, sumy, yl, il);
+
+        sumf.s4 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 4*nb*QK4_0/2, d + ib + 4*nb, sumy, yl, il);
+        sumf.s5 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 5*nb*QK4_0/2, d + ib + 5*nb, sumy, yl, il);
+        sumf.s6 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 6*nb*QK4_0/2, d + ib + 6*nb, sumy, yl, il);
+        sumf.s7 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 7*nb*QK4_0/2, d + ib + 7*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float8 tot = (float8)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_id_q4_0_f32_8x_flat(
+        global char  *  src0_q,
+        global half  *  src0_d,
+        global float *  src1,
+        ulong           offset1,
+        global char  *  src2,
+        ulong           offset2,
+        global float *  dst,
+        ulong           offsetd,
+        int             ne00,
+        int             ne01,
+        int             ne02,
+        ulong           nb00,
+        ulong           nb02,
+        int             ne10,
+        int             ne11,
+        int             ne12,
+        ulong           nb11,
+        ulong           nb12,
+        int             ne20,
+        int             ne21,
+        ulong           nb21,
+        int             ne0,
+        int             ne1,
+        int             r2,
+        int             r3
+) {
+    src1 = (global float *)((global char *)src1 + offset1);
+    src2 = (global char  *)((global char *)src2 + offset2);
+    dst  = (global float *)((global char *)dst  + offsetd);
+
+    const int iid1 = get_group_id(2)/ne20;
+    const int idx  = get_group_id(2)%ne20;
+
+    const int i02 = ((global int *)(src2 + iid1*nb21))[idx];
+
+    const int i11 = idx%ne11;
+    const int i12 = iid1;
+
+    const int i1 = idx;
+    const int i2 = i12;
+
+    global char  * src0_q_cur = src0_q + (i02*nb02/nb00)*(QK4_0/2);
+    global half  * src0_d_cur = src0_d + (i02*nb02/nb00);
+    global float * src1_cur   = (global float *)((global char *) src1  + i11*nb11 + i12*nb12);
+    global float * dst_cur    = dst + i1*ne0 + i2*ne1*ne0;
+
+    mul_vec_q_n_f32_8x_flat(src0_q_cur, src0_d_cur, src1_cur, dst_cur, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-sycl/CMakeLists.txt

@@ -142,7 +142,7 @@ else()
         FetchContent_Declare(
             ONEMATH
             GIT_REPOSITORY https://github.com/uxlfoundation/oneMath.git
-            GIT_TAG c255b1b4c41e2ee3059455c1f96a965d6a62568a
+            GIT_TAG 8efe85f5aaebb37f1d8c503b7af66315feabf142
         )
         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> void debug_print_array(const std::string & prefix, const T array[N]) {
+template <int N, class T> std::string debug_get_array_str(const std::string & prefix, const T array[N]) {
     if (LIKELY(!g_ggml_sycl_debug)) {
-        return;
+        return "";
     }
     std::stringstream ss;
     ss << prefix << "=[";
@@ -526,29 +526,26 @@ template <int N, class T> void debug_print_array(const std::string & prefix, con
         ss << array[N - 1];
     }
     ss << "]";
-    GGML_SYCL_DEBUG("%s", ss.str().c_str());
+    return ss.str();
 }
 
-inline void debug_print_tensor(const std::string & prefix, const ggml_tensor * tensor,
-                               const std::string & suffix = "") {
-    if (LIKELY(!g_ggml_sycl_debug)) {
-        return;
-    }
-    GGML_SYCL_DEBUG("%s=", prefix.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) {
-        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");
-        }
+        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 {
-        GGML_SYCL_DEBUG("nullptr");
+        ss << "nullptr";
     }
-    GGML_SYCL_DEBUG("%s", suffix.c_str());
+    ss << suffix;
+    return ss.str();
 }
 
 // Use scope_op_debug_print to log operations coming from running a model
@@ -564,10 +561,10 @@ struct scope_op_debug_print {
             return;
         }
         GGML_SYCL_DEBUG("[SYCL][OP] call %s%s:", func.data(), func_suffix.data());
-        debug_print_tensor(" dst", dst);
+        GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" dst", dst).c_str());
         if (dst) {
             for (std::size_t i = 0; i < num_src; ++i) {
-                debug_print_tensor("\tsrc" + std::to_string(i), dst->src[i]);
+                GGML_SYCL_DEBUG("%s", debug_get_tensor_str("\tsrc" + std::to_string(i), dst->src[i]).c_str());
             }
         }
         GGML_SYCL_DEBUG("%s\n", suffix.data());

ggml/src/ggml-sycl/cpy.cpp

@@ -723,8 +723,7 @@ 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,
-                                         std::string(" src0 type=") + ggml_type_name(src0->type));
+    scope_op_debug_print scope_dbg_print(__func__, src1, /*num_src=*/0, debug_get_tensor_str("\tsrc0", src0));
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
 

ggml/src/ggml-sycl/gemm.hpp

@@ -65,6 +65,9 @@ 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__);
-    debug_print_tensor(": tensor=", tensor, "\n");
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor, "\n").c_str());
     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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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__);
-    debug_print_tensor(": dst=", dst);
-    debug_print_tensor(" src=", src);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": dst", dst).c_str());
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" src", src).c_str());
     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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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__);
-    debug_print_tensor(": tensor=", tensor, "\n");
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor, "\n").c_str());
     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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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);
@@ -2127,21 +2127,18 @@ 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_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);
+                                      dst_dd_i, DnnlGemmWrapper::to_dt<float>(), 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(
@@ -3866,7 +3863,7 @@ 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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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;
@@ -3887,7 +3884,7 @@ 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__);
-    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": tensor", tensor).c_str());
     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;
@@ -3910,8 +3907,8 @@ 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__);
-    debug_print_tensor(": dst=", dst);
-    debug_print_tensor(" src=", src);
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(": dst", dst).c_str());
+    GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" src", src).c_str());
     GGML_SYCL_DEBUG(" is_cpy_supported=%d\n", is_cpy_supported);
     if (is_cpy_supported) {
         /*

ggml/src/ggml-vulkan/CMakeLists.txt

@@ -49,15 +49,7 @@ if (Vulkan_FOUND)
                              ../../include/ggml-vulkan.h
                             )
 
-    set(VULKAN_SHADER_GEN_CMAKE_ARGS
-        -DCMAKE_INSTALL_PREFIX=${CMAKE_BINARY_DIR}
-        -DCMAKE_RUNTIME_OUTPUT_DIRECTORY=${CMAKE_RUNTIME_OUTPUT_DIRECTORY}
-    )
-
-    set(VULKAN_SHADER_GEN_CMAKE_BUILD_ARGS "")
-    if (CMAKE_BUILD_TYPE AND CMAKE_BUILD_TYPE MATCHES "Debug|Release|MinSizeRel|RelWithDebInfo")
-        list(APPEND VULKAN_SHADER_GEN_CMAKE_BUILD_ARGS --config=${CMAKE_BUILD_TYPE})
-    endif()
+    set(VULKAN_SHADER_GEN_CMAKE_ARGS "")
 
     # Test all shader extensions
     test_shader_extension_support(
@@ -136,42 +128,39 @@ if (Vulkan_FOUND)
         set(HOST_CMAKE_TOOLCHAIN_FILE "")
     endif()
 
-    # Always use ExternalProject_Add approach
     include(ExternalProject)
 
-    # Add toolchain file if cross-compiling
     if (CMAKE_CROSSCOMPILING)
         list(APPEND VULKAN_SHADER_GEN_CMAKE_ARGS -DCMAKE_TOOLCHAIN_FILE=${HOST_CMAKE_TOOLCHAIN_FILE})
         message(STATUS "vulkan-shaders-gen toolchain file: ${HOST_CMAKE_TOOLCHAIN_FILE}")
     endif()
 
-    # Native build through ExternalProject_Add
     ExternalProject_Add(
         vulkan-shaders-gen
         SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders
-        CMAKE_ARGS ${VULKAN_SHADER_GEN_CMAKE_ARGS}
-        BUILD_COMMAND ${CMAKE_COMMAND} --build . ${VULKAN_SHADER_GEN_CMAKE_BUILD_ARGS}
-        INSTALL_COMMAND ${CMAKE_COMMAND} --install .
-        INSTALL_DIR ${CMAKE_BINARY_DIR}
+        CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${CMAKE_BINARY_DIR}/$<CONFIG>
+                   -DCMAKE_INSTALL_BINDIR=.
+                   -DCMAKE_BUILD_TYPE=$<CONFIG>
+                   ${VULKAN_SHADER_GEN_CMAKE_ARGS}
+
+        BUILD_COMMAND   ${CMAKE_COMMAND} --build   . --config $<CONFIG>
+        INSTALL_COMMAND ${CMAKE_COMMAND} --install . --config $<CONFIG>
     )
     ExternalProject_Add_StepTargets(vulkan-shaders-gen build install)
 
     set (_ggml_vk_host_suffix $<IF:$<STREQUAL:${CMAKE_HOST_SYSTEM_NAME},Windows>,.exe,>)
-    set (_ggml_vk_genshaders_cmd ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/vulkan-shaders-gen${_ggml_vk_host_suffix})
-    set (_ggml_vk_header     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp)
-    set (_ggml_vk_source     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp)
-    set (_ggml_vk_input_dir  ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders)
-    set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv)
+    set (_ggml_vk_genshaders_dir "${CMAKE_BINARY_DIR}/$<CONFIG>")
+    set (_ggml_vk_genshaders_cmd "${_ggml_vk_genshaders_dir}/vulkan-shaders-gen${_ggml_vk_host_suffix}")
+    set (_ggml_vk_header     "${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp")
+    set (_ggml_vk_source     "${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp")
+    set (_ggml_vk_input_dir  "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders")
+    set (_ggml_vk_output_dir "${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv")
 
-    file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
-    set (_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen)
-
-    # Add build and install dependencies for all builds
-    set(_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen-build vulkan-shaders-gen-install)
+    file(GLOB _ggml_vk_shader_files CONFIGURE_DEPENDS "${_ggml_vk_input_dir}/*.comp")
 
     add_custom_command(
         OUTPUT ${_ggml_vk_header}
-                ${_ggml_vk_source}
+               ${_ggml_vk_source}
 
         COMMAND ${_ggml_vk_genshaders_cmd}
             --glslc      ${Vulkan_GLSLC_EXECUTABLE}
@@ -181,7 +170,11 @@ if (Vulkan_FOUND)
             --target-cpp ${_ggml_vk_source}
             --no-clean
 
-        DEPENDS ${_ggml_vk_shader_deps}
+        DEPENDS ${_ggml_vk_shader_files}
+                vulkan-shaders-gen
+                vulkan-shaders-gen-build
+                vulkan-shaders-gen-install
+
         COMMENT "Generate vulkan shaders"
     )
 

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -78,7 +78,7 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 #define VK_VENDOR_ID_INTEL 0x8086
 #define VK_VENDOR_ID_NVIDIA 0x10de
 
-#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 32
+#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 256
 
 #define GGML_VK_MAX_NODES 8192
 
@@ -102,25 +102,11 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 
 struct ggml_backend_vk_context;
 
-struct vk_queue {
-    uint32_t queue_family_index;
-    vk::Queue queue;
-    vk::CommandPool pool;
-    uint32_t cmd_buffer_idx;
-    std::vector<vk::CommandBuffer> cmd_buffers;
-
-    vk::PipelineStageFlags stage_flags;
-
-    bool transfer_only;
-};
+#define MAX_PARAMETER_COUNT 8
 
 struct vk_pipeline_struct {
     std::string name;
     vk::ShaderModule shader_module;
-    vk::DescriptorSetLayout dsl;
-    std::vector<vk::DescriptorPool> descriptor_pools;
-    std::vector<vk::DescriptorSet> descriptor_sets;
-    uint32_t descriptor_set_idx;
     vk::PipelineLayout layout;
     vk::Pipeline pipeline;
     uint32_t push_constant_size;
@@ -167,6 +153,45 @@ struct ggml_backend_vk_buffer_type_context {
     vk_device device;
 };
 
+struct vk_queue;
+
+// Stores command pool/buffers. There's an instance of this
+// for each (context,queue) pair and for each (device,queue) pair.
+struct vk_command_pool {
+    void init(vk_device& device, vk_queue *q_);
+    void destroy(vk::Device& device);
+
+    vk::CommandPool pool;
+    uint32_t cmd_buffer_idx;
+    std::vector<vk::CommandBuffer> cmd_buffers;
+
+    vk_queue *q;
+};
+
+// Prevent simultaneous submissions to the same queue.
+// This could be per vk_queue if we stopped having two vk_queue structures
+// sharing the same vk::Queue.
+static std::mutex queue_mutex;
+
+struct vk_queue {
+    uint32_t queue_family_index;
+    vk::Queue queue;
+
+    vk_command_pool cmd_pool;
+
+    vk::PipelineStageFlags stage_flags;
+
+    bool transfer_only;
+
+    // copy everything except the cmd_pool
+    void copyFrom(vk_queue &other) {
+        queue_family_index = other.queue_family_index;
+        queue = other.queue;
+        stage_flags = other.stage_flags;
+        transfer_only = other.transfer_only;
+    }
+};
+
 static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft);
 static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
 static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft);
@@ -341,6 +366,8 @@ struct vk_device_struct {
     // set to true to indicate that some shaders need to be compiled after the dryrun
     bool need_compiles {};
 
+    vk::DescriptorSetLayout dsl;
+
     vk_matmul_pipeline pipeline_matmul_f32 {};
     vk_matmul_pipeline pipeline_matmul_f32_f16 {};
     vk_matmul_pipeline pipeline_matmul_bf16 {};
@@ -458,7 +485,6 @@ struct vk_device_struct {
     vk_pipeline pipeline_flash_attn_split_k_reduce;
 
     std::unordered_map<std::string, vk_pipeline_ref> pipelines;
-    std::unordered_map<std::string, uint64_t> pipeline_descriptor_set_requirements;
 
     std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
 
@@ -483,10 +509,8 @@ struct vk_device_struct {
 
         ggml_vk_destroy_buffer(sync_staging);
 
-        device.destroyCommandPool(compute_queue.pool);
-        if (!single_queue) {
-            device.destroyCommandPool(transfer_queue.pool);
-        }
+        compute_queue.cmd_pool.destroy(device);
+        transfer_queue.cmd_pool.destroy(device);
 
         for (auto& pipeline : pipelines) {
             if (pipeline.second.expired()) {
@@ -498,10 +522,26 @@ struct vk_device_struct {
         }
         pipelines.clear();
 
+        device.destroyDescriptorSetLayout(dsl);
+
         device.destroy();
     }
 };
 
+void vk_command_pool::init(vk_device& device, vk_queue *q_) {
+    cmd_buffer_idx = 0;
+    q = q_;
+
+    vk::CommandPoolCreateInfo command_pool_create_info(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), q->queue_family_index);
+    pool = device->device.createCommandPool(command_pool_create_info);
+}
+
+void vk_command_pool::destroy(vk::Device& device) {
+    device.destroyCommandPool(pool);
+    pool = nullptr;
+    cmd_buffers.clear();
+}
+
 struct vk_buffer_struct {
     vk::Buffer buffer = VK_NULL_HANDLE;
     vk::DeviceMemory device_memory = VK_NULL_HANDLE;
@@ -819,7 +859,7 @@ struct vk_context_struct {
     std::vector<vk_staging_memcpy> in_memcpys;
     std::vector<vk_staging_memcpy> out_memcpys;
 
-    vk_queue * q;
+    vk_command_pool * p {};
 };
 typedef std::shared_ptr<vk_context_struct> vk_context;
 typedef std::weak_ptr<vk_context_struct> vk_context_ref;
@@ -930,6 +970,14 @@ struct ggml_backend_vk_context {
     vk_context_ref transfer_ctx;
 
     std::vector<vk_context_ref> tensor_ctxs;
+
+    std::vector<vk::DescriptorPool> descriptor_pools;
+    std::vector<vk::DescriptorSet> descriptor_sets;
+    uint32_t descriptor_set_idx {};
+    uint32_t pipeline_descriptor_set_requirements {};
+
+    vk_command_pool compute_cmd_pool;
+    vk_command_pool transfer_cmd_pool;
 };
 
 static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000;  // NOLINT
@@ -1060,39 +1108,19 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
                  ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " <<
                  disable_robustness << ", " << require_full_subgroups << ", " << required_subgroup_size << ")");
     GGML_ASSERT(parameter_count > 0);
+    GGML_ASSERT(parameter_count <= MAX_PARAMETER_COUNT);
     GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
 
     vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
     pipeline->shader_module = device->device.createShaderModule(shader_module_create_info);
 
-    std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
-    std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
-    for (uint32_t i = 0; i < parameter_count; i++) {
-        dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
-        dsl_binding_flags.push_back({});
-    }
-
-    vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };
-
     vk::PushConstantRange pcr(
         vk::ShaderStageFlagBits::eCompute,
         0,
         pipeline->push_constant_size
     );
 
-    vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
-        {},
-        dsl_binding);
-    descriptor_set_layout_create_info.setPNext(&dslbfci);
-    pipeline->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
-
-    vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
-    vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
-    pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
-
-    pipeline->descriptor_set_idx = 0;
-
-    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr);
+    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), device->dsl, pcr);
     pipeline->layout = device->device.createPipelineLayout(pipeline_layout_create_info);
 
     std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());
@@ -1167,15 +1195,6 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
 
 static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
     VK_LOG_DEBUG("ggml_pipeline_destroy_pipeline(" << pipeline->name << ")");
-    for (auto& pool : pipeline->descriptor_pools) {
-        device.destroyDescriptorPool(pool);
-    }
-    pipeline->descriptor_pools.clear();
-    pipeline->descriptor_sets.clear();
-    pipeline->descriptor_set_idx = 0;
-
-    device.destroyDescriptorSetLayout(pipeline->dsl);
-
     device.destroyPipelineLayout(pipeline->layout);
 
     device.destroyShaderModule(pipeline->shader_module);
@@ -1183,97 +1202,77 @@ static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline)
     device.destroyPipeline(pipeline->pipeline);
 }
 
-static void ggml_pipeline_request_descriptor_sets(vk_device& device, vk_pipeline& pipeline, uint32_t n) {
+static void ggml_pipeline_request_descriptor_sets(ggml_backend_vk_context *ctx, vk_pipeline& pipeline, uint32_t n) {
     VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")");
-    device->pipeline_descriptor_set_requirements[pipeline->name] += n;
+    ctx->pipeline_descriptor_set_requirements += n;
     if (!pipeline->compiled) {
         pipeline->needed = true;
-        device->need_compiles = true;
+        ctx->device->need_compiles = true;
     }
 }
 
-static void ggml_pipeline_allocate_descriptor_sets(vk_device& device) {
-    std::lock_guard<std::mutex> guard(device->mutex);
+static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx) {
 
-    for (auto& pair : device->pipeline_descriptor_set_requirements) {
-        vk_pipeline pipeline = device->pipelines.at(pair.first).lock();
-        const uint64_t n = pair.second;
+    if (ctx->descriptor_sets.size() >= ctx->pipeline_descriptor_set_requirements) {
+        // Enough descriptors are available
+        return;
+    }
 
-        VK_LOG_DEBUG("ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")");
+    vk_device& device = ctx->device;
 
-        if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) {
-            // Enough descriptors are available
-            continue;
+    uint32_t to_alloc = ctx->pipeline_descriptor_set_requirements - ctx->descriptor_sets.size();
+    uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - ctx->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE;
+    uint32_t pool_idx = ctx->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE;
+
+    while (to_alloc > 0) {
+        const uint32_t alloc_count = std::min(pool_remaining, to_alloc);
+        to_alloc -= alloc_count;
+        pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE;
+
+        if (pool_idx >= ctx->descriptor_pools.size()) {
+            vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, MAX_PARAMETER_COUNT * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
+            vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
+            ctx->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
         }
 
-        uint32_t to_alloc = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size();
-        uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - pipeline->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE;
-        uint32_t pool_idx = pipeline->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE;
-
-        while (to_alloc > 0) {
-            const uint32_t alloc_count = std::min(pool_remaining, to_alloc);
-            to_alloc -= alloc_count;
-            pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE;
-
-            if (pool_idx >= pipeline->descriptor_pools.size()) {
-                vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
-                vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
-                pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
-            }
-
-            std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
-            for (uint32_t i = 0; i < alloc_count; i++) {
-                layouts[i] = pipeline->dsl;
-            }
-            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[pool_idx], alloc_count, layouts.data());
-            std::vector<vk::DescriptorSet> sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info);
-            pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end());
-
-            pool_idx++;
+        std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
+        for (uint32_t i = 0; i < alloc_count; i++) {
+            layouts[i] = device->dsl;
         }
+        vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(ctx->descriptor_pools[pool_idx], alloc_count, layouts.data());
+        std::vector<vk::DescriptorSet> sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+        ctx->descriptor_sets.insert(ctx->descriptor_sets.end(), sets.begin(), sets.end());
+
+        pool_idx++;
     }
 }
 
-static void ggml_pipeline_cleanup(vk_pipeline& pipeline) {
-    VK_LOG_DEBUG("ggml_pipeline_cleanup(" << pipeline->name << ")");
-    pipeline->descriptor_set_idx = 0;
-}
-
-static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_queue& q) {
+static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_command_pool& p) {
     VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()");
-    std::lock_guard<std::mutex> guard(device->mutex);
 
-    if (q.cmd_buffers.size() > q.cmd_buffer_idx) {
+    if (p.cmd_buffers.size() > p.cmd_buffer_idx) {
         // Reuse command buffer
-        return q.cmd_buffers[q.cmd_buffer_idx++];
+        return p.cmd_buffers[p.cmd_buffer_idx++];
     }
 
     vk::CommandBufferAllocateInfo command_buffer_alloc_info(
-        q.pool,
+        p.pool,
         vk::CommandBufferLevel::ePrimary,
         1);
     const std::vector<vk::CommandBuffer> cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info);
     auto buf = cmd_buffers.front();
 
-    q.cmd_buffers.push_back(buf);
-    q.cmd_buffer_idx++;
+    p.cmd_buffers.push_back(buf);
+    p.cmd_buffer_idx++;
 
     return buf;
 }
 
-static vk_submission ggml_vk_create_submission(vk_device& device, vk_queue& q, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
-    VK_LOG_DEBUG("ggml_vk_create_submission()");
-    vk_submission s;
-    s.buffer = ggml_vk_create_cmd_buffer(device, q);
-    s.wait_semaphores = std::move(wait_semaphores);
-    s.signal_semaphores = std::move(signal_semaphores);
-    return s;
-}
-
 static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
     if (ctx->seqs.empty()) {
         if (fence) {
-            ctx->q->queue.submit({}, fence);
+            std::lock_guard<std::mutex> guard(queue_mutex);
+            ctx->p->q->queue.submit({}, fence);
         }
         return;
     }
@@ -1312,7 +1311,7 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
             tl_signal_vals.push_back({});
             tl_signal_semaphores.push_back({});
             for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
-                stage_flags[idx].push_back(ctx->q->stage_flags);
+                stage_flags[idx].push_back(ctx->p->q->stage_flags);
                 tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
                 tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
             }
@@ -1342,7 +1341,8 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
         }
     }
 
-    ctx->q->queue.submit(submit_infos, fence);
+    std::lock_guard<std::mutex> guard(queue_mutex);
+    ctx->p->q->queue.submit(submit_infos, fence);
 
     ctx->seqs.clear();
 }
@@ -1400,28 +1400,25 @@ static void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_
     q.queue_family_index = queue_family_index;
     q.transfer_only = transfer_only;
 
-    vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index);
-    q.pool = device->device.createCommandPool(command_pool_create_info_compute);
-
-    q.cmd_buffer_idx = 0;
+    q.cmd_pool.init(device, &q);
 
     q.queue = device->device.getQueue(queue_family_index, queue_index);
 
     q.stage_flags = stage_flags;
 }
 
-static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) {
+static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p) {
     vk_context result = std::make_shared<vk_context_struct>();
     VK_LOG_DEBUG("ggml_vk_create_context(" << result << ")");
     ctx->gc.contexts.emplace_back(result);
-    result->q = &q;
+    result->p = &p;
     return result;
 }
 
-static vk_context ggml_vk_create_temporary_context(vk_queue& q) {
+static vk_context ggml_vk_create_temporary_context(vk_command_pool& p) {
     vk_context result = std::make_shared<vk_context_struct>();
     VK_LOG_DEBUG("ggml_vk_create_temporary_context(" << result << ")");
-    result->q = &q;
+    result->p = &p;
     return result;
 }
 
@@ -1454,15 +1451,29 @@ static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
     return ctx->gc.events[ctx->event_idx++];
 }
 
-static void ggml_vk_queue_cleanup(vk_device& device, vk_queue& q) {
-    VK_LOG_DEBUG("ggml_vk_queue_cleanup()");
-    std::lock_guard<std::mutex> guard(device->mutex);
+static void ggml_vk_command_pool_cleanup(vk_device& device, vk_command_pool& p) {
+    VK_LOG_DEBUG("ggml_vk_command_pool_cleanup()");
 
     // Requires command buffers to be done
-    device->device.resetCommandPool(q.pool);
-    q.cmd_buffer_idx = 0;
+    device->device.resetCommandPool(p.pool);
+    p.cmd_buffer_idx = 0;
 }
 
+static void ggml_vk_queue_command_pools_cleanup(vk_device& device) {
+    VK_LOG_DEBUG("ggml_vk_queue_command_pools_cleanup()");
+
+    // Arbitrary frequency to cleanup/reuse command buffers
+    static constexpr uint32_t cleanup_frequency = 10;
+
+    if (device->compute_queue.cmd_pool.cmd_buffer_idx >= cleanup_frequency) {
+        ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool);
+    }
+    if (device->transfer_queue.cmd_pool.cmd_buffer_idx >= cleanup_frequency) {
+        ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool);
+    }
+}
+
+
 static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
     for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
         vk::MemoryType memory_type = mem_props->memoryTypes[i];
@@ -1481,8 +1492,6 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor
         throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device memory allocation limit");
     }
 
-    std::lock_guard<std::mutex> guard(device->mutex);
-
     vk_buffer buf = std::make_shared<vk_buffer_struct>();
 
     if (size == 0) {
@@ -1611,11 +1620,11 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) {
 static void ggml_vk_sync_buffers(vk_context& ctx) {
     VK_LOG_DEBUG("ggml_vk_sync_buffers()");
 
-    const bool transfer_queue = ctx->q->transfer_only;
+    const bool transfer_queue = ctx->p->q->transfer_only;
 
     ctx->s->buffer.pipelineBarrier(
-        ctx->q->stage_flags,
-        ctx->q->stage_flags,
+        ctx->p->q->stage_flags,
+        ctx->p->q->stage_flags,
         {},
         { {
           { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) },
@@ -1634,8 +1643,8 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events
 
     ctx->s->buffer.waitEvents(
         events,
-        ctx->q->stage_flags,
-        ctx->q->stage_flags,
+        ctx->p->q->stage_flags,
+        ctx->p->q->stage_flags,
         {},
         {},
         {}
@@ -3369,6 +3378,22 @@ static vk_device ggml_vk_get_device(size_t idx) {
             }
         }
 
+
+        std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
+        std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
+        for (uint32_t i = 0; i < MAX_PARAMETER_COUNT; i++) {
+            dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
+            dsl_binding_flags.push_back({});
+        }
+
+        vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };
+
+        vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
+            {},
+            dsl_binding);
+        descriptor_set_layout_create_info.setPNext(&dslbfci);
+        device->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
+
         ggml_vk_load_shaders(device);
 
         if (!device->single_queue) {
@@ -3376,7 +3401,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
             ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true);
         } else {
             // TODO: Use pointer or reference to avoid copy
-            device->transfer_queue = device->compute_queue;
+            device->transfer_queue.copyFrom(device->compute_queue);
+            device->transfer_queue.cmd_pool.init(device, &device->transfer_queue);
         }
 
         device->buffer_type = {
@@ -3742,6 +3768,9 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
     ctx->fence = ctx->device->device.createFence({});
     ctx->almost_ready_fence = ctx->device->device.createFence({});
 
+    ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue);
+    ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue);
+
 #ifdef GGML_VULKAN_CHECK_RESULTS
     const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
     vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
@@ -4107,9 +4136,9 @@ static void ggml_vk_host_get(vk_device& device, const void * ptr, vk_buffer& buf
     }
 }
 
-static vk_submission ggml_vk_begin_submission(vk_device& device, vk_queue& q, bool one_time = true) {
+static vk_submission ggml_vk_begin_submission(vk_device& device, vk_command_pool& p, bool one_time = true) {
     vk_submission s;
-    s.buffer = ggml_vk_create_cmd_buffer(device, q);
+    s.buffer = ggml_vk_create_cmd_buffer(device, p);
     if (one_time) {
         s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
     } else {
@@ -4154,10 +4183,10 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context&
         std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.range << "), ";
     }
     std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
-    GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());
-    GGML_ASSERT(descriptor_buffer_infos.size() == pipeline->parameter_count);
+    GGML_ASSERT(ctx->descriptor_set_idx < ctx->descriptor_sets.size());
+    GGML_ASSERT(descriptor_buffer_infos.size() <= MAX_PARAMETER_COUNT);
 
-    vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++];
+    vk::DescriptorSet& descriptor_set = ctx->descriptor_sets[ctx->descriptor_set_idx++];
     vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() };
     ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {});
 
@@ -4194,7 +4223,7 @@ static void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) {
         ggml_vk_ctx_end(subctx);
     }
 
-    subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->q) });
+    subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->p) });
     subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
 }
 
@@ -4395,7 +4424,9 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
             memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
         }
     } else {
-        vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue);
+        std::lock_guard<std::mutex> guard(dst->device->mutex);
+
+        vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
         ggml_vk_ctx_begin(dst->device, subctx);
         ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, width, height, true);
         ggml_vk_ctx_end(subctx);
@@ -4407,6 +4438,7 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
         ggml_vk_submit(subctx, dst->device->fence);
         VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
         dst->device->device.resetFences({ dst->device->fence });
+        ggml_vk_queue_command_pools_cleanup(dst->device);
     }
 }
 
@@ -4483,7 +4515,9 @@ static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_
 
         memcpy(dst, (uint8_t *) src->ptr + offset, size);
     } else {
-        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue);
+        std::lock_guard<std::mutex> guard(src->device->mutex);
+
+        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
         ggml_vk_ctx_begin(src->device, subctx);
         ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true);
         ggml_vk_ctx_end(subctx);
@@ -4491,6 +4525,7 @@ static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_
         ggml_vk_submit(subctx, src->device->fence);
         VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
         src->device->device.resetFences({ src->device->fence });
+        ggml_vk_queue_command_pools_cleanup(src->device);
 
         for (auto& cpy : subctx->out_memcpys) {
             memcpy(cpy.dst, cpy.src, cpy.n);
@@ -4510,15 +4545,17 @@ static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t ds
 
 static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
     if (src->device == dst->device) {
+        std::lock_guard<std::mutex> guard(src->device->mutex);
         VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")");
         // Copy within the device
-        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue);
+        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
         ggml_vk_ctx_begin(src->device, subctx);
         ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
         ggml_vk_ctx_end(subctx);
         ggml_vk_submit(subctx, src->device->fence);
         VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
         src->device->device.resetFences({ src->device->fence });
+        ggml_vk_queue_command_pools_cleanup(src->device);
     } else {
         VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")");
         // Copy device to device
@@ -4543,7 +4580,8 @@ static void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t
 static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
     VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")");
 
-    vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue);
+    std::lock_guard<std::mutex> guard(dst->device->mutex);
+    vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
     ggml_vk_ctx_begin(dst->device, subctx);
     subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
     ggml_vk_ctx_end(subctx);
@@ -4551,6 +4589,7 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz
     ggml_vk_submit(subctx, dst->device->fence);
     VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences");
     dst->device->device.resetFences({ dst->device->fence });
+    ggml_vk_queue_command_pools_cleanup(dst->device);
 }
 
 static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, int m, int n, int k, const vk_pipeline& pipeline) {
@@ -4964,18 +5003,18 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         }
 
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
         if (qx_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
         }
         if (qy_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
         }
         if (quantize_y) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_q8_1, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
         }
         if (split_k > 1) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
         }
         return;
     }
@@ -5157,12 +5196,12 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
 
         // Request descriptor sets
         if (qx_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
         }
         if (qy_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
         }
-        ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, dmmv, 1);
         return;
     }
 
@@ -5295,7 +5334,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
 
     if (dryrun) {
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1], 1);
+        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1], 1);
         return;
     }
 
@@ -5384,7 +5423,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
 
     if (dryrun) {
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
         return;
     }
 
@@ -5571,12 +5610,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
         }
 
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
         if (qx_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
         }
         if (qy_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
         }
         return;
     }
@@ -5765,12 +5804,12 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
 
         // Request descriptor sets
         if (qx_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
         }
         if (qy_needs_dequant) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
         }
-        ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, dmmv, 1);
         return;
     }
 
@@ -6090,9 +6129,9 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     if (dryrun) {
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
         if (split_k > 1) {
-            ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
+            ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
         }
         return;
     }
@@ -6655,7 +6694,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     }
 
     if (dryrun) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
         return;
     }
 
@@ -7036,7 +7075,7 @@ static void ggml_vk_op_f32_wkv(ggml_backend_vk_context * ctx, vk_context& subctx
     GGML_ASSERT(pipeline != nullptr);
 
     if (dryrun) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
         return;
     }
 
@@ -7175,7 +7214,7 @@ static void ggml_vk_op_f32_opt_step_adamw(ggml_backend_vk_context * ctx, vk_cont
     GGML_ASSERT(pipeline != nullptr);
 
     if (dryrun) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
         return;
     }
 
@@ -7853,9 +7892,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
         }
     }
 
-    ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it);
+    ggml_pipeline_request_descriptor_sets(ctx, p, num_it);
     if (split_k > 1) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
 
         if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
             // Resize buffer
@@ -7870,7 +7909,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
         ggml_vk_load_shaders(ctx->device);
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
+    ggml_pipeline_allocate_descriptor_sets(ctx);
 
     vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
     vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
@@ -7912,7 +7951,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     ggml_vk_buffer_write(d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
     ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
 
-    vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
     ggml_vk_ctx_begin(ctx->device, subctx);
     for (size_t i = 0; i < num_it; i++) {
         ggml_vk_matmul(
@@ -7928,6 +7967,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     ggml_vk_submit(subctx, ctx->fence);
     VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
     ctx->device->device.resetFences({ ctx->fence });
+    ggml_vk_queue_command_pools_cleanup(ctx->device);
 
     auto end = std::chrono::high_resolution_clock::now();
     double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
@@ -8029,16 +8069,13 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
     free(d_chk);
 
-    ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue);
-    ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue);
+    ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool);
+    ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool);
 
     ggml_vk_destroy_buffer(d_X);
     ggml_vk_destroy_buffer(d_Y);
     ggml_vk_destroy_buffer(d_D);
 
-    ggml_pipeline_cleanup(p);
-    ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce);
-
     free(x);
     free(y);
     free(d);
@@ -8116,17 +8153,17 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     ggml_vk_quantize_data(x, qx, ne, quant);
     ggml_vk_dequantize_data(qx, x_ref, ne, quant);
 
-    ggml_pipeline_request_descriptor_sets(ctx->device, p, 1);
+    ggml_pipeline_request_descriptor_sets(ctx, p, 1);
 
     if (ctx->device->need_compiles) {
         ggml_vk_load_shaders(ctx->device);
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
+    ggml_pipeline_allocate_descriptor_sets(ctx);
 
     ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
 
-    vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
     ggml_vk_ctx_begin(ctx->device, subctx);
     const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
     ggml_vk_dispatch_pipeline(ctx, subctx, p, { vk_subbuffer{ qx_buf, 0, qx_sz }, vk_subbuffer{ x_buf, 0, x_sz_f16 } }, pc, { (uint32_t)ne, 1, 1});
@@ -8137,6 +8174,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     ggml_vk_submit(subctx, ctx->fence);
     VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
     ctx->device->device.resetFences({ ctx->fence });
+    ggml_vk_queue_command_pools_cleanup(ctx->device);
 
     auto end = std::chrono::high_resolution_clock::now();
 
@@ -8216,17 +8254,17 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
 //
 //     vk_pipeline p = ggml_vk_get_quantize_pipeline(ctx, quant);
 //
-//     ggml_pipeline_request_descriptor_sets(ctx->device, p, 1);
+//     ggml_pipeline_request_descriptor_sets(ctx, p, 1);
 //
 //     if (ctx->device->need_compiles) {
 //         ggml_vk_load_shaders(ctx->device);
 //     }
 //
-//     ggml_pipeline_allocate_descriptor_sets(ctx->device);
+//     ggml_pipeline_allocate_descriptor_sets(ctx);
 //
 //     ggml_vk_buffer_write(x_buf, 0, x, x_sz);
 //
-//     vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+//     vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
 //     ggml_vk_ctx_begin(ctx->device, subctx);
 //     ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(x_buf), ggml_vk_subbuffer(qx_buf), ne);
 //     ggml_vk_ctx_end(subctx);
@@ -8236,6 +8274,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
 //     ggml_vk_submit(subctx, ctx->fence);
 //     VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_quantize waitForFences");
 //     ctx->device->device.resetFences({ ctx->fence });
+//     ggml_vk_queue_command_pools_cleanup(ctx->device);
 //
 //     auto end = std::chrono::high_resolution_clock::now();
 //
@@ -8375,9 +8414,9 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
         // y[i] = i % k;
     }
 
-    ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it);
+    ggml_pipeline_request_descriptor_sets(ctx, p, num_it);
     if (split_k > 1) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
 
         if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
             // Resize buffer
@@ -8388,19 +8427,19 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
         }
     }
     if (mmq) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_quantize_q8_1, num_it);
+        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_quantize_q8_1, num_it);
     }
 
     if (ctx->device->need_compiles) {
         ggml_vk_load_shaders(ctx->device);
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
+    ggml_pipeline_allocate_descriptor_sets(ctx);
 
     ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
     ggml_vk_buffer_write(y_buf, 0, y, y_sz);
 
-    vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
     ggml_vk_ctx_begin(ctx->device, subctx);
     if (mmq) {
         for (size_t i = 0; i < num_it; i++) {
@@ -8429,6 +8468,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
     ggml_vk_submit(subctx, ctx->fence);
     VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
     ctx->device->device.resetFences({ ctx->fence });
+    ggml_vk_queue_command_pools_cleanup(ctx->device);
 
     auto end = std::chrono::high_resolution_clock::now();
 
@@ -8743,7 +8783,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
 
     if (!dryrun) {
         if (ctx->compute_ctx.expired()) {
-            compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+            compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
             ctx->compute_ctx = compute_ctx;
             ggml_vk_ctx_begin(ctx->device, compute_ctx);
         } else {
@@ -8797,7 +8837,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
                 // These operations all go through ggml_vk_op_f32, so short-circuit and
                 // do the only thing needed for the dryrun.
                 vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, node, node->op);
-                ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+                ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
                 return false;
             }
         default:
@@ -9189,19 +9229,8 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
     }
     ctx->gc.temp_buffers.clear();
 
-    for (auto& dsr : ctx->device->pipeline_descriptor_set_requirements) {
-        vk_pipeline_ref plr = ctx->device->pipelines[dsr.first];
-
-        if (plr.expired()) {
-            continue;
-        }
-
-        vk_pipeline pl = plr.lock();
-        ggml_pipeline_cleanup(pl);
-    }
-
-    ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue);
-    ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue);
+    ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool);
+    ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool);
 
     for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
         ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
@@ -9222,7 +9251,8 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
 
     ctx->tensor_ctxs.clear();
     ctx->gc.contexts.clear();
-    ctx->device->pipeline_descriptor_set_requirements.clear();
+    ctx->pipeline_descriptor_set_requirements = 0;
+    ctx->descriptor_set_idx = 0;
 }
 
 // Clean up on backend free
@@ -9249,6 +9279,15 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
 
     ctx->device->device.destroyFence(ctx->fence);
     ctx->device->device.destroyFence(ctx->almost_ready_fence);
+
+    for (auto& pool : ctx->descriptor_pools) {
+        ctx->device->device.destroyDescriptorPool(pool);
+    }
+    ctx->descriptor_pools.clear();
+    ctx->descriptor_sets.clear();
+
+    ctx->compute_cmd_pool.destroy(ctx->device->device);
+    ctx->transfer_cmd_pool.destroy(ctx->device->device);
 }
 
 static int ggml_vk_get_device_count() {
@@ -9515,7 +9554,7 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor
 
     if (ctx->transfer_ctx.expired()) {
         // Initialize new transfer context
-        transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        transfer_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
         ctx->transfer_ctx = transfer_ctx;
         ggml_vk_ctx_begin(ctx->device, transfer_ctx);
     } else {
@@ -9538,7 +9577,7 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_
 
     if (ctx->transfer_ctx.expired()) {
         // Initialize new transfer context
-        transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        transfer_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
         ctx->transfer_ctx = transfer_ctx;
         ggml_vk_ctx_begin(ctx->device, transfer_ctx);
     } else {
@@ -9561,7 +9600,7 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_
 
         if (ctx->transfer_ctx.expired()) {
             // Initialize new transfer context
-            transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+            transfer_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
             ctx->transfer_ctx = transfer_ctx;
             ggml_vk_ctx_begin(ctx->device, transfer_ctx);
         } else {
@@ -9622,7 +9661,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         ggml_vk_load_shaders(ctx->device);
     }
     ggml_vk_preallocate_buffers(ctx);
-    ggml_pipeline_allocate_descriptor_sets(ctx->device);
+    ggml_pipeline_allocate_descriptor_sets(ctx);
 
     int last_node = cgraph->n_nodes - 1;
 
@@ -9654,7 +9693,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         ctx->device->device.resetQueryPool(ctx->device->query_pool, 0, cgraph->n_nodes+1);
 
         GGML_ASSERT(ctx->compute_ctx.expired());
-        compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+        compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
         ctx->compute_ctx = compute_ctx;
         ggml_vk_ctx_begin(ctx->device, compute_ctx);
         compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->device->query_pool, 0);
@@ -9689,7 +9728,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
         if (vk_perf_logger_enabled) {
             if (ctx->compute_ctx.expired()) {
-                compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+                compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
                 ctx->compute_ctx = compute_ctx;
                 ggml_vk_ctx_begin(ctx->device, compute_ctx);
             } else {

ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt

@@ -25,15 +25,3 @@ add_executable(${TARGET} vulkan-shaders-gen.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads)
-
-# Configure output directories for MSVC builds
-if(MSVC)
-    # Get the main project's runtime output directory if possible
-    if(DEFINED CMAKE_RUNTIME_OUTPUT_DIRECTORY)
-        foreach(CONFIG ${CMAKE_CONFIGURATION_TYPES})
-            string(TOUPPER ${CONFIG} CONFIG)
-            set_target_properties(${TARGET} PROPERTIES
-                RUNTIME_OUTPUT_DIRECTORY_${CONFIG} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
-        endforeach()
-    endif()
-endif()

gguf-py/gguf/constants.py

@@ -291,6 +291,7 @@ class MODEL_ARCH(IntEnum):
     BERT             = auto()
     NOMIC_BERT       = auto()
     NOMIC_BERT_MOE   = auto()
+    NEO_BERT         = auto()
     JINA_BERT_V2     = auto()
     BLOOM            = auto()
     STABLELM         = auto()
@@ -343,6 +344,9 @@ class MODEL_ARCH(IntEnum):
     WAVTOKENIZER_DEC = auto()
     PLM              = auto()
     BAILINGMOE       = auto()
+    DOTS1            = auto()
+    ARCEE            = auto()
+    SMOLLM3          = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -571,6 +575,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.BERT:             "bert",
     MODEL_ARCH.NOMIC_BERT:       "nomic-bert",
     MODEL_ARCH.NOMIC_BERT_MOE:   "nomic-bert-moe",
+    MODEL_ARCH.NEO_BERT:         "neo-bert",
     MODEL_ARCH.JINA_BERT_V2:     "jina-bert-v2",
     MODEL_ARCH.BLOOM:            "bloom",
     MODEL_ARCH.STABLELM:         "stablelm",
@@ -623,6 +628,9 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
     MODEL_ARCH.PLM:              "plm",
     MODEL_ARCH.BAILINGMOE:       "bailingmoe",
+    MODEL_ARCH.DOTS1:            "dots1",
+    MODEL_ARCH.ARCEE:            "arcee",
+    MODEL_ARCH.SMOLLM3:          "smollm3",
 }
 
 VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@@ -1077,6 +1085,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.LAYER_OUT_NORM,
     ],
+    MODEL_ARCH.NEO_BERT: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ENC_OUTPUT_NORM,
+        MODEL_TENSOR.CLS,
+        MODEL_TENSOR.CLS_OUT,
+    ],
     MODEL_ARCH.JINA_BERT_V2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.TOKEN_EMBD_NORM,
@@ -2044,6 +2064,61 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
     ],
+    MODEL_ARCH.DOTS1: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
+    MODEL_ARCH.ARCEE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.SMOLLM3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     # TODO
 }
 

gguf-py/gguf/gguf_writer.py

@@ -271,7 +271,7 @@ class GGUFWriter:
 
     def add_key_value(self, key: str, val: Any, vtype: GGUFValueType, sub_type: GGUFValueType | None = None) -> None:
         if any(key in kv_data for kv_data in self.kv_data):
-            raise ValueError(f'Duplicated key name {key!r}')
+            logger.warning(f'Duplicated key name {key!r}, overwriting it with new value {val!r} of type {vtype.name}')
 
         self.kv_data[0][key] = GGUFValue(value=val, type=vtype, sub_type=sub_type)
 

gguf-py/gguf/tensor_mapping.py

@@ -31,6 +31,7 @@ class TensorNameMap:
             "model.embeddings",                          # rwkv7
             "model.word_embeddings",                     # bailingmoe
             "language_model.model.embed_tokens",         # llama4
+            "encoder",                                   # neobert
         ),
 
         # Token type embeddings
@@ -134,6 +135,7 @@ class TensorNameMap:
             "rwkv.blocks.{bid}.ln1",                                # rwkv6
             "model.layers.{bid}.ln1",                               # rwkv7
             "model.layers.{bid}.input_layernorm",                   # llama4
+            "transformer_encoder.{bid}.attention_norm",             # neobert
         ),
 
         # Attention norm 2
@@ -161,6 +163,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.qkv_proj",                               # phi3
             "encoder.layers.{bid}.self_attention.query_key_value",                 # chatglm
             "transformer.layers.{bid}.attn.qkv_proj",                              # openelm
+            "transformer_encoder.{bid}.qkv",                                       # neobert
         ),
 
         # Attention query
@@ -236,6 +239,7 @@ class TensorNameMap:
             "transformer.layers.{bid}.attn.out_proj",                       # openelm
             "transformer.h.{bid}.attn.attention.out_proj",                  # exaone
             "model.layers.{bid}.self_attn.o_proj",                          # llama4
+            "transformer_encoder.{bid}.wo",                                 # neobert
         ),
 
         # Attention output norm
@@ -276,6 +280,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.post_attention_layernorm",                 # chatglm
             "transformer.layers.{bid}.ffn_norm",                             # openelm
             "model.layers.{bid}.post_attention_layernorm",                   # llama4
+            "transformer_encoder.{bid}.ffn_norm",                            # neobert
         ),
 
         # Post feed-forward norm
@@ -305,7 +310,7 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_EXP_PROBS_B: (
-            "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3
+            "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1
         ),
 
         # Feed-forward up
@@ -340,6 +345,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.dense_h_to_4h",                 # chatglm
             "transformer.h.{bid}.mlp.c_fc_1",                         # exaone
             "model.layers.{bid}.feed_forward.up_proj",                # llama4
+            "transformer_encoder.{bid}.ffn.w12",                      # neobert
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -422,6 +428,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.dense_4h_to_h",                 # chatglm
             "model.layers.h.{bid}.mlp.c_proj",                        # exaone
             "model.layers.{bid}.feed_forward.down_proj",              # llama4
+            "transformer_encoder.{bid}.ffn.w3",                       # neobert
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
@@ -832,12 +839,14 @@ class TensorNameMap:
         # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
+            "layer_norm",               # neobert
         ),
 
         MODEL_TENSOR.CLS: (
             "classifier",       # jina
             "classifier.dense", # roberta
             "pre_classifier",   # distillbert
+            "dense",            # neobert
         ),
 
         MODEL_TENSOR.CLS_OUT: (

include/llama.h

@@ -243,18 +243,21 @@ extern "C" {
 
     typedef bool (*llama_progress_callback)(float progress, void * user_data);
 
-    // Input data for llama_decode
+    // Input data for llama_encode/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_decode)
+    //            (if set to NULL, the token position will be tracked automatically by llama_encode/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
-    //            (if set to NULL, only the logits for last token will be returned)
+    //            (if set to NULL:
+    //               - if embeddings: all tokens are output
+    //               - if not:        only the last token is output
+    //            )
     //
     typedef struct llama_batch {
         int32_t n_tokens;
@@ -262,8 +265,8 @@ extern "C" {
         llama_token  *  token;
         float        *  embd;
         llama_pos    *  pos;
-        int32_t      *  n_seq_id; // TODO: remove, should belong to only 1 sequence
-        llama_seq_id ** seq_id;   // TODO: become llama_seq_id * seq_id;
+        int32_t      *  n_seq_id;
+        llama_seq_id ** seq_id;
         int8_t       *  logits;   // TODO: rename this to "output"
     } llama_batch;
 
@@ -961,8 +964,8 @@ extern "C" {
     // Get the number of threads used for prompt and batch processing (multiple token).
     LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx);
 
-    // Set whether the model is in embeddings mode or not
-    // If true, embeddings will be returned but logits will not
+    // Set whether the context outputs embeddings or not
+    // TODO: rename to avoid confusion with llama_get_embeddings()
     LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
 
     // Set whether to use causal attention or not

requirements/requirements-compare-llama-bench.txt

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

scripts/compare-llama-bench.py

@@ -19,6 +19,7 @@ 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
@@ -122,11 +123,15 @@ 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)
@@ -499,7 +504,6 @@ 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(",")
@@ -544,6 +548,14 @@ 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:
@@ -600,6 +612,161 @@ 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-arch.cpp

@@ -20,6 +20,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_BERT,             "bert"             },
     { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
     { LLM_ARCH_NOMIC_BERT_MOE,   "nomic-bert-moe"   },
+    { LLM_ARCH_NEO_BERT,         "neo-bert"         },
     { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
     { LLM_ARCH_BLOOM,            "bloom"            },
     { LLM_ARCH_STABLELM,         "stablelm"         },
@@ -72,6 +73,9 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
     { LLM_ARCH_PLM,              "plm"              },
     { LLM_ARCH_BAILINGMOE,       "bailingmoe"       },
+    { LLM_ARCH_DOTS1,            "dots1"            },
+    { LLM_ARCH_ARCEE,            "arcee"            },
+    { LLM_ARCH_SMOLLM3,          "smollm3"          },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -243,6 +247,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
+    {
+        LLM_ARCH_ARCEE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_LLAMA4,
         {
@@ -494,6 +516,21 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
+    {
+        LLM_ARCH_NEO_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
+            { LLM_TENSOR_CLS,             "cls" },
+            { LLM_TENSOR_CLS_OUT,         "cls.output" },
+        },
+    },
     {
         LLM_ARCH_JINA_BERT_V2,
         {
@@ -1555,12 +1592,58 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
         },
     },
+    {
+        LLM_ARCH_DOTS1,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
+        }
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
         },
     },
+    {
+        LLM_ARCH_SMOLLM3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,     "token_embd.weight"            },
+            { LLM_TENSOR_OUTPUT_NORM,    "output_norm.weight"           },
+            { LLM_TENSOR_OUTPUT,         "output.weight"                },
+            { LLM_TENSOR_ROPE_FREQS,     "rope_freqs"                   },
+            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm.weight"      },
+            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q.weight"         },
+            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k.weight"         },
+            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v.weight"         },
+            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output.weight"    },
+            { LLM_TENSOR_ATTN_ROT_EMBD,  "blk.%d.attn_rot_embd"         },
+            { LLM_TENSOR_FFN_GATE,       "blk.%d.ffn_gate.weight"       },
+            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down.weight"       },
+            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up.weight"         },
+        },
+    },
 };
 
 static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {

src/llama-arch.h

@@ -24,6 +24,7 @@ enum llm_arch {
     LLM_ARCH_BERT,
     LLM_ARCH_NOMIC_BERT,
     LLM_ARCH_NOMIC_BERT_MOE,
+    LLM_ARCH_NEO_BERT,
     LLM_ARCH_JINA_BERT_V2,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
@@ -76,6 +77,9 @@ enum llm_arch {
     LLM_ARCH_WAVTOKENIZER_DEC,
     LLM_ARCH_PLM,
     LLM_ARCH_BAILINGMOE,
+    LLM_ARCH_DOTS1,
+    LLM_ARCH_ARCEE,
+    LLM_ARCH_SMOLLM3,
     LLM_ARCH_UNKNOWN,
 };
 

src/llama-batch.cpp

@@ -1,8 +1,14 @@
 #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
@@ -105,12 +111,7 @@ void llama_sbatch::add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & s
             ubatch.seq_id = batch->seq_id + seq.offset;
         }
     }
-    if (logits_all) {
-        for (size_t i = 0; i < length; ++i) {
-            ubatch.output[ubatch.n_tokens + i] = 1;
-            out_ids.push_back(ids[seq.offset + i]);
-        }
-    } else if (batch->logits) {
+    if (batch->logits) {
         if (ubatch.equal_seqs) {
             for (size_t i = 0; i < length; ++i) {
                 size_t id = ids[seq.offset + i];
@@ -197,11 +198,10 @@ llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) {
     return ubatch;
 }
 
-llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) {
+llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split) {
     GGML_ASSERT(batch.n_tokens >= 0);
     this->batch = &batch;
     this->n_embd = n_embd;
-    this->logits_all = logits_all;
 
     n_tokens = batch.n_tokens;
     ids.resize(n_tokens);
@@ -285,17 +285,56 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
             );
 }
 
-llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0) {
-    batch = in_batch;
-    GGML_ASSERT(batch.n_tokens > 0);
-    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();
+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_SEQ);
+    seq_cpl.resize(LLAMA_MAX_SEQ);
+    for (auto & cur : seq_cpl) {
+        cur.resize(LLAMA_MAX_SEQ);
     }
+}
+
+bool llama_batch_allocr::init(
+        const llama_batch & batch_inp,
+        const llama_vocab & vocab,
+        const llama_memory_i * memory,
+        bool embd_all) {
+    clear();
+
+    batch = batch_inp;
+
+    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.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_SEQ)) {
+                    LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], LLAMA_MAX_SEQ);
+                    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++) {
@@ -303,6 +342,7 @@ llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0
         }
         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;
@@ -311,10 +351,221 @@ llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0
         }
         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_SEQ];
+        for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++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) {
-        logits.resize(batch.n_tokens);
-        logits[logits.size() - 1] = true;
-        batch.logits = logits.data();
+        if (embd_all) {
+            // return the output for all tokens
+            output.resize(batch.n_tokens, true);
+        } else {
+            // return the output only for the last token
+            output.resize(batch.n_tokens, false);
+            output[output.size() - 1] = true;
+        }
+
+        batch.logits = output.data();
+    } else if (embd_all) {
+        bool warn = false;
+
+        for (int32_t i = 0; i < batch.n_tokens; ++i) {
+            if (batch.logits[i] == 0) {
+                warn = true;
+            }
+        }
+
+        if (warn) {
+            LLAMA_LOG_WARN("%s: embeddings required but some input tokens were not marked as outputs -> overriding\n", __func__);
+
+            output.resize(batch.n_tokens, 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_SEQ; ++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_SEQ; ++s0) {
+            for (int32_t s1 = 0; s1 < LLAMA_MAX_SEQ; ++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

@@ -4,6 +4,7 @@
 
 #include <array>
 #include <vector>
+#include <set>
 
 // very similar to llama_batch,
 // but has more metadata about sequences
@@ -18,8 +19,8 @@ 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] // TODO: remove, should belong to only 1 sequence
-    llama_seq_id ** seq_id;   // [n_seqs] // TODO: become llama_seq_id * seq_id;
+    int32_t      *  n_seq_id; // [n_seqs]
+    llama_seq_id ** seq_id;   // [n_seqs]
     int8_t       *  output;   // [n_tokens]
 };
 
@@ -39,8 +40,6 @@ struct llama_sbatch {
 
     size_t n_embd;
 
-    bool logits_all; // TODO: remove once lctx.logits_all is removed too
-
     // sorted indices into the batch
     std::vector<int64_t> ids;
     // batch indices of the output
@@ -76,19 +75,45 @@ struct llama_sbatch {
     llama_ubatch split_seq(size_t n_ubatch);
 
     llama_sbatch() = default;
-    llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false);
+    llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false);
 };
 
-// temporary allocate memory for the input batch if needed
-struct llama_batch_allocr {
-    struct llama_batch batch;
+// 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,
+            bool embd_all);
+
+    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;
 
     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>         logits;
+    std::vector<int8_t>         output;
 
-    // optionally fulfill the batch returned by llama_batch_get_one
-    llama_batch_allocr(struct llama_batch in_batch, llama_pos p0);
+    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;
 };

src/llama-chat.cpp

@@ -183,6 +183,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_BAILING;
     } else if (tmpl_contains("<|header_start|>") && tmpl_contains("<|header_end|>")) {
         return LLM_CHAT_TEMPLATE_LLAMA4;
+    } else if (tmpl_contains("<|endofuserprompt|>")) {
+        return LLM_CHAT_TEMPLATE_DOTS1;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -643,6 +645,21 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "Assistant:";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_DOTS1) {
+        // dots.llm1.inst (DOTS1)
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|system|>" << message->content << "<|endofsystem|>";
+            } else if (role == "user") {
+                ss << "<|userprompt|>" << message->content << "<|endofuserprompt|>";
+            } else {
+                ss << "<|response|>" << message->content << "<|endofresponse|>";
+            }
+        }
+        if (add_ass) {
+            ss << "<|response|>";
+        }
     } else {
         // template not supported
         return -1;

src/llama-chat.h

@@ -43,6 +43,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_BAILING,
     LLM_CHAT_TEMPLATE_LLAMA4,
     LLM_CHAT_TEMPLATE_SMOLVLM,
+    LLM_CHAT_TEMPLATE_DOTS1,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 

src/llama-context.cpp

@@ -1,6 +1,7 @@
 #include "llama-context.h"
 
 #include "llama-impl.h"
+#include "llama-batch.h"
 #include "llama-io.h"
 #include "llama-memory.h"
 #include "llama-mmap.h"
@@ -18,7 +19,8 @@
 llama_context::llama_context(
         const llama_model & model,
               llama_context_params params) :
-    model(model) {
+    model(model),
+    batch_allocr(std::make_unique<llama_batch_allocr>()) {
     LLAMA_LOG_INFO("%s: constructing llama_context\n", __func__);
 
     t_start_us = model.t_start_us;
@@ -27,8 +29,8 @@ llama_context::llama_context(
     const auto & hparams = model.hparams;
 
     cparams.n_seq_max = std::max(1u, params.n_seq_max);
-    if (cparams.n_seq_max > LLAMA_MAX_PARALLEL_SEQUENCES) {
-        throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_PARALLEL_SEQUENCES));
+    if (cparams.n_seq_max > LLAMA_MAX_SEQ) {
+        throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_SEQ));
     }
 
     cparams.n_threads        = params.n_threads;
@@ -494,7 +496,7 @@ float * llama_context::get_logits() {
 }
 
 float * llama_context::get_logits_ith(int32_t i) {
-    int32_t j = -1;
+    int64_t j = -1;
 
     try {
         if (logits == nullptr) {
@@ -517,7 +519,7 @@ 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=%d, n_outputs=%d)", j, n_outputs));
+            throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
         }
 
         return logits + j*model.vocab.n_tokens();
@@ -536,7 +538,7 @@ float * llama_context::get_embeddings() {
 }
 
 float * llama_context::get_embeddings_ith(int32_t i) {
-    int32_t j = -1;
+    int64_t j = -1;
 
     try {
         if (embd == nullptr) {
@@ -559,7 +561,7 @@ 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=%d, n_outputs=%d)", j, n_outputs));
+            throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
         }
 
         return embd + j*model.hparams.n_embd;
@@ -719,52 +721,41 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch,
     return res;
 }
 
-int llama_context::encode(llama_batch & inp_batch) {
-    if (inp_batch.n_tokens == 0) {
+int llama_context::encode(const llama_batch & batch_inp) {
+    if (batch_inp.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
-    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : 0);
+    if (!batch_allocr->init(batch_inp, model.vocab, nullptr, true)) {
+        LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
+        return -1;
+    }
 
-    const llama_batch & batch = batch_allocr.batch;
-    const int32_t n_tokens = batch.n_tokens;
+    const llama_batch & batch = batch_allocr->get_batch();
 
-    const auto & hparams = model.hparams;
+    const uint32_t n_tokens = batch.n_tokens;
 
     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 >= (uint32_t) n_tokens && "encoder requires n_ubatch >= n_tokens");
+    GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
 
     if (t_compute_start_us == 0) {
         t_compute_start_us = ggml_time_us();
     }
 
+    // TODO: this clear of the buffer can easily be forgotten - need something better
     embd_seq.clear();
 
     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, /* logits_all */ true);
+    llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true);
 
     const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
 
@@ -774,7 +765,7 @@ int llama_context::encode(llama_batch & inp_batch) {
         return -2;
     };
 
-    for (int32_t i = 0; i < n_tokens; ++i) {
+    for (uint32_t i = 0; i < n_tokens; ++i) {
         output_ids[i] = i;
     }
 
@@ -830,7 +821,8 @@ int llama_context::encode(llama_batch & inp_batch) {
 
                     GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits
 
-                    for (int32_t i = 0; i < n_tokens; i++) {
+                    // TODO: fix indexing [UBATCH_IDX]
+                    for (uint32_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;
@@ -845,6 +837,7 @@ int llama_context::encode(llama_batch & inp_batch) {
                     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()) {
@@ -878,10 +871,10 @@ int llama_context::encode(llama_batch & inp_batch) {
 
         // remember the sequence ids used during the encoding - needed for cross attention later
         cross.seq_ids_enc.resize(n_tokens);
-        for (int32_t i = 0; i < n_tokens; i++) {
+        for (uint32_t i = 0; i < n_tokens; i++) {
             cross.seq_ids_enc[i].clear();
-            for (int s = 0; s < ubatch.n_seq_id[i]; s++) {
-                llama_seq_id seq_id = ubatch.seq_id[i][s];
+            for (int s = 0; s < batch.n_seq_id[i]; s++) {
+                llama_seq_id seq_id = batch.seq_id[i][s];
                 cross.seq_ids_enc[i].insert(seq_id);
             }
         }
@@ -890,51 +883,45 @@ int llama_context::encode(llama_batch & inp_batch) {
     return 0;
 }
 
-int llama_context::decode(llama_batch & inp_batch) {
+int llama_context::decode(const llama_batch & batch_inp) {
     if (!memory) {
         LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
-        return encode(inp_batch);
+        return encode(batch_inp);
     }
 
-    if (inp_batch.n_tokens == 0) {
+    if (batch_inp.n_tokens == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0\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;
-        }
+    // when computing embeddings, all tokens are output
+    const bool embd_all = cparams.embeddings;
+
+    if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) {
+        LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
+        return -1;
     }
 
-    // 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 llama_batch & batch = batch_allocr->get_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 int64_t n_tokens_all = batch.n_tokens;
-    const int64_t n_embd       = hparams.n_embd;
+    const uint32_t n_tokens_all = batch.n_tokens;
 
     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;
-            }
+    const uint32_t n_outputs_all = batch_allocr->get_n_outputs();
 
-            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;
-            }
+    if (embd_all) {
+        // 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",
+                    __func__, n_outputs_all, n_tokens_all);
+            return -1;
         }
     }
 
@@ -947,25 +934,9 @@ int llama_context::decode(llama_batch & inp_batch) {
     }
     n_queued_tokens += n_tokens_all;
 
-    // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
-    const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
-
+    // TODO: this clear of the buffer can easily be forgotten - need something better
     embd_seq.clear();
 
-    int64_t n_outputs_all = 0;
-
-    // count outputs
-    if (batch.logits && !embd_pooled) {
-        for (uint32_t i = 0; i < n_tokens_all; ++i) {
-            n_outputs_all += batch.logits[i] != 0;
-        }
-    } else if (embd_pooled) {
-        n_outputs_all = n_tokens_all;
-    } else {
-        // keep last output only
-        n_outputs_all = 1;
-    }
-
     bool did_optimize = false;
 
     // handle any pending defrags/shifts
@@ -974,7 +945,7 @@ int llama_context::decode(llama_batch & inp_batch) {
     llama_memory_state_ptr mstate;
 
     while (true) {
-        mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled, /* logits_all */ n_outputs_all == n_tokens_all);
+        mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all);
         if (!mstate) {
             return -2;
         }
@@ -1018,7 +989,7 @@ int llama_context::decode(llama_batch & inp_batch) {
 
     // reserve output buffer
     if (output_reserve(n_outputs_all) < n_outputs_all) {
-        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
         return -2;
     };
 
@@ -1027,7 +998,7 @@ int llama_context::decode(llama_batch & inp_batch) {
     do {
         const auto & ubatch = mstate->get_ubatch();
 
-        // count the outputs in this u_batch
+        // count the outputs in this ubatch
         {
             int32_t n_outputs_new = 0;
 
@@ -1052,18 +1023,19 @@ int llama_context::decode(llama_batch & inp_batch) {
 
         if (!res) {
             // the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
-            llama_pos pos_min[LLAMA_MAX_PARALLEL_SEQUENCES];
-            for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+            llama_pos pos_min[LLAMA_MAX_SEQ];
+            for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
                 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];
 
                 pos_min[seq_id] = std::min(pos_min[seq_id], ubatch.pos[i]);
             }
 
-            for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+            for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
                 if (pos_min[s] == std::numeric_limits<llama_pos>::max()) {
                     continue;
                 }
@@ -1086,7 +1058,7 @@ int llama_context::decode(llama_batch & inp_batch) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        auto * t_logits = cparams.embeddings ? nullptr         : res->get_logits();
+        auto * t_logits = res->get_logits();
         auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
 
         if (t_embd && res->get_embd_pooled()) {
@@ -1170,14 +1142,14 @@ int llama_context::decode(llama_batch & inp_batch) {
     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_all);
+        GGML_ASSERT(out_ids.size() == (size_t) n_outputs);
 
-        for (int64_t i = 0; i < n_outputs_all; ++i) {
+        for (int64_t i = 0; i < n_outputs; ++i) {
             int64_t out_id = out_ids[i];
             output_ids[out_id] = i;
             if (out_id != i) {
@@ -1189,20 +1161,22 @@ int llama_context::decode(llama_batch & inp_batch) {
         // note: this is mostly relevant for recurrent models atm
         if (!sorted_output) {
             const uint32_t n_vocab = model.vocab.n_tokens();
-            const uint32_t n_embd  = model.hparams.n_embd;
+            const uint64_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 (int32_t i = 0; i < n_outputs - 1; ++i) {
-                int32_t j_min = i;
-                for (int32_t j = i + 1; j < n_outputs; ++j) {
+            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) {
                     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++) {
@@ -1215,8 +1189,10 @@ int llama_context::decode(llama_batch & inp_batch) {
                     }
                 }
             }
+
             std::fill(output_ids.begin(), output_ids.end(), -1);
-            for (int32_t i = 0; i < n_outputs; ++i) {
+
+            for (uint32_t i = 0; i < n_outputs; ++i) {
                 output_ids[out_ids[i]] = i;
             }
         }
@@ -1236,7 +1212,7 @@ int llama_context::decode(llama_batch & inp_batch) {
 // output
 //
 
-int32_t llama_context::output_reserve(int32_t n_outputs) {
+uint32_t llama_context::output_reserve(int32_t n_outputs) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
@@ -1246,9 +1222,8 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
     const auto n_vocab = vocab.n_tokens();
     const auto n_embd  = hparams.n_embd;
 
-    // TODO: use a per-batch flag for logits presence instead
-    bool has_logits = !cparams.embeddings;
-    bool has_embd   =  cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+    bool has_logits = true;
+    bool has_embd   = cparams.embeddings;
 
     // TODO: hacky enc-dec support
     if (model.arch == LLM_ARCH_T5) {
@@ -1302,8 +1277,7 @@ int32_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_max = n_outputs_max;
+    this->n_outputs = 0;
 
     return n_outputs_max;
 }
@@ -1332,7 +1306,7 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
     LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
 
     if (n_tokens % n_seqs != 0) {
-        n_tokens = (n_tokens / n_seqs) * n_seqs;
+        n_tokens = ((n_tokens + (n_seqs - 1)) / n_seqs) * n_seqs; // round to next multiple of n_seqs
         n_outputs = std::min(n_outputs, n_tokens);
 
         LLAMA_LOG_DEBUG("%s: making n_tokens a multiple of n_seqs - n_tokens = %u, n_seqs = %u, n_outputs = %u\n", __func__, n_tokens, n_seqs, n_outputs);
@@ -1794,14 +1768,12 @@ 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
-            int32_t pos = output_ids[i];
+            int64_t pos = output_ids[i];
             if (pos >= 0) {
                 GGML_ASSERT(pos < n_outputs);
                 w_output_pos[pos] = i;
@@ -2071,14 +2043,11 @@ void llama_context::opt_epoch_iter(
 
         n_queued_tokens += n_tokens_all;
 
-        // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
-        const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
-
         embd_seq.clear();
 
-        int64_t n_outputs_all = n_tokens_all;
+        uint32_t n_outputs_all = n_tokens_all;
 
-        auto mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled, /* logits_all */ true);
+        auto mstate = memory->init_batch(batch, cparams.n_ubatch, true);
         if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
             LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
             break;
@@ -2086,7 +2055,7 @@ 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 %" PRId64 " outputs\n", __func__, n_outputs_all);
+            LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
             GGML_ABORT("TODO: handle this error");
         };
 

src/llama-context.h

@@ -1,7 +1,6 @@
 #pragma once
 
 #include "llama.h"
-#include "llama-batch.h"
 #include "llama-cparams.h"
 #include "llama-graph.h"
 #include "llama-adapter.h"
@@ -13,6 +12,7 @@
 #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(llama_batch & inp_batch);
-    int decode(llama_batch & inp_batch);
+    int encode(const llama_batch & batch_inp);
+    int decode(const llama_batch & batch_inp);
 
     //
     // 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.
-    int32_t output_reserve(int32_t n_outputs);
+    uint32_t output_reserve(int32_t n_outputs);
 
     //
     // graph
@@ -246,8 +246,10 @@ private:
     // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
     std::map<llama_seq_id, std::vector<float>> embd_seq;
 
-    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
+    // 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
 
     std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
 

src/llama-cparams.cpp

@@ -1,5 +1,5 @@
 #include "llama-cparams.h"
 
 size_t llama_max_parallel_sequences(void) {
-    return LLAMA_MAX_PARALLEL_SEQUENCES;
+    return LLAMA_MAX_SEQ;
 }

src/llama-cparams.h

@@ -4,7 +4,7 @@
 
 #include <cstdint>
 
-#define LLAMA_MAX_PARALLEL_SEQUENCES 64
+#define LLAMA_MAX_SEQ 64
 
 struct llama_cparams {
     uint32_t n_ctx;           // context size used during inference

src/llama-graph.cpp

@@ -139,6 +139,7 @@ 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];
 
@@ -156,6 +157,7 @@ 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];
 
@@ -180,6 +182,7 @@ 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];
 
@@ -210,6 +213,7 @@ 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];
 
@@ -250,22 +254,6 @@ void llm_graph_input_s_copy::set_input(const llama_ubatch * ubatch) {
     }
 }
 
-void llm_graph_input_s_mask::set_input(const llama_ubatch * ubatch) {
-    GGML_UNUSED(ubatch);
-
-    const int64_t n_kv = kv_state->get_n_kv();
-
-    if (s_mask) {
-        GGML_ASSERT(ggml_backend_buffer_is_host(s_mask->buffer));
-        float * data = (float *) s_mask->data;
-
-        // clear unused states
-        for (int i = 0; i < n_kv; ++i) {
-            data[i] = kv_state->s_mask(i);
-        }
-    }
-}
-
 void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
     GGML_UNUSED(ubatch);
 
@@ -299,6 +287,7 @@ 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) {
@@ -338,6 +327,7 @@ 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) {
@@ -393,6 +383,7 @@ 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()) {
@@ -987,23 +978,6 @@ ggml_tensor * llm_graph_context::build_inp_s_copy() const {
     return cur;
 }
 
-ggml_tensor * llm_graph_context::build_inp_s_mask() const {
-    const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
-
-    auto inp = std::make_unique<llm_graph_input_s_mask>(kv_state);
-
-    const auto n_kv = kv_state->get_n_kv();
-
-    auto & cur = inp->s_mask;
-
-    cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
-    ggml_set_input(cur);
-
-    res->add_input(std::move(inp));
-
-    return cur;
-}
-
 ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
     auto inp = std::make_unique<llm_graph_input_cross_embd>(cross);
 
@@ -1456,43 +1430,53 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
-ggml_tensor * llm_graph_context::build_copy_mask_state(
+ggml_tensor * llm_graph_context::build_recurrent_state(
          ggml_cgraph * gf,
          ggml_tensor * s,
          ggml_tensor * state_copy,
-         ggml_tensor * state_mask,
-             int32_t   n_state,
-             int32_t   n_seqs) const {
+             int32_t   state_size,
+             int32_t   n_seqs,
+                bool   avoid_copies) const {
     const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
 
     const auto n_kv    = kv_state->get_n_kv();
     const auto kv_head = kv_state->get_head();
+    const auto rs_zero = kv_state->get_rs_z();
 
-    ggml_tensor * states = ggml_reshape_2d(ctx0, s, n_state, kv_state->get_size());
+    ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_state->get_size());
 
-    // copy states
-    // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
-    // this shrinks the tensors's ne[1] to n_kv
-    states = ggml_get_rows(ctx0, states, state_copy);
+    // Clear a single state which will then be copied to the other cleared states.
+    // Note that this is a no-op when the view is zero-sized.
+    ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
+    ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
 
-    // clear states of sequences which are starting at the beginning of this batch
-    // FIXME: zero-out NANs?
-    states = ggml_mul(ctx0, states, state_mask);
+    ggml_tensor * output_states;
 
-    // copy states which won't be changed further (between n_seqs and n_kv)
+    if (!avoid_copies) {
+        // copy states
+        // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
+        // {state_size, kv_size} -> {state_size, n_seqs}
+        output_states = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
+        ggml_build_forward_expand(gf, output_states);
+    } else {
+        // FIXME: make the gathering operation happen before the copy below
+        //        (maybe with an optional lambda function passed as a parameter instead of `avoid_copies`?)
+        output_states = states;
+    }
+
+    // copy extra states which won't be changed further (between n_seqs and n_kv)
+    ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_kv - n_seqs, n_seqs*state_copy->nb[0]));
     ggml_build_forward_expand(gf,
         ggml_cpy(ctx0,
-            ggml_view_1d(ctx0, states, n_state*(n_kv - n_seqs), (n_seqs          )*n_state*ggml_element_size(states)),
-            ggml_view_1d(ctx0, s,      n_state*(n_kv - n_seqs), (kv_head + n_seqs)*n_state*ggml_element_size(s))));
+            states_extra,
+            ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
 
-    // the part of the states that will be used and modified
-    return ggml_view_2d(ctx0, states, n_state, n_seqs, states->nb[1], 0);
+    return output_states;
 }
 
 ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
          ggml_cgraph * gf,
          ggml_tensor * state_copy,
-         ggml_tensor * state_mask,
   const llama_ubatch & ubatch,
                  int   il) const {
     const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@@ -1503,8 +1487,8 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
 
     ggml_tensor * token_shift_all = kv_state->get_k_l(il);
 
-    ggml_tensor * token_shift = build_copy_mask_state(
-            gf, token_shift_all, state_copy, state_mask,
+    ggml_tensor * token_shift = build_recurrent_state(
+            gf, token_shift_all, state_copy,
             hparams.n_embd_k_s(), n_seqs);
 
     token_shift = ggml_reshape_3d(ctx0, token_shift, hparams.n_embd, token_shift_count, n_seqs);
@@ -1579,23 +1563,30 @@ void llm_graph_context::build_pooling(
                 ggml_tensor * inp_cls = build_inp_cls();
                 inp = ggml_get_rows(ctx0, inp, inp_cls);
 
-                if (cls != nullptr && cls_b != nullptr) {
+                if (cls) {
                     // classification head
                     // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
-                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
+                    cur = ggml_mul_mat(ctx0, cls, inp);
+                    if (cls_b) {
+                        cur = ggml_add(ctx0, cur, 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) {
-                        GGML_ASSERT(cls_out_b != nullptr);
-                        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
+                        cur = ggml_mul_mat(ctx0, cls_out, cur);
+                        if (cls_out_b) {
+                            cur = ggml_add(ctx0, 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
-                    GGML_ASSERT(cls_out_b != nullptr);
-                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, inp), cls_out_b);
+                    cur = ggml_mul_mat(ctx0, cls_out, inp);
+                    if (cls_out_b) {
+                        cur = ggml_add(ctx0, cur, cls_out_b);
+                    }
                 } else {
                     GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
                 }

src/llama-graph.h

@@ -200,18 +200,6 @@ public:
     const llama_kv_cache_recurrent_state * kv_state;
 };
 
-class llm_graph_input_s_mask : public llm_graph_input_i {
-public:
-    llm_graph_input_s_mask(const llama_kv_cache_recurrent_state * kv_state) : kv_state(kv_state) {}
-    virtual ~llm_graph_input_s_mask() = default;
-
-    void set_input(const llama_ubatch * ubatch) override;
-
-    ggml_tensor * s_mask; // F32 [1, n_kv]
-
-    const llama_kv_cache_recurrent_state * kv_state;
-};
-
 class llm_graph_input_cross_embd : public llm_graph_input_i {
 public:
     llm_graph_input_cross_embd(
@@ -390,7 +378,7 @@ struct llm_graph_params {
     const llama_memory_state_i * mstate;
     const llama_cross          * cross;
 
-    int32_t n_outputs;
+    uint32_t n_outputs;
 
     const llm_graph_cb & cb;
 };
@@ -424,8 +412,8 @@ struct llm_graph_context {
     const float norm_eps;
     const float norm_rms_eps;
 
-    const int32_t n_tokens;
-    const int32_t n_outputs;
+    const int64_t n_tokens;
+    const int64_t n_outputs;
     const int32_t n_ctx_orig; // yarn
 
     const enum llama_pooling_type pooling_type;
@@ -521,7 +509,6 @@ struct llm_graph_context {
     ggml_tensor * build_inp_mean() const;
     ggml_tensor * build_inp_cls() const;
     ggml_tensor * build_inp_s_copy() const;
-    ggml_tensor * build_inp_s_mask() const;
 
     ggml_tensor * build_inp_cross_embd() const;
     ggml_tensor * build_inp_pos_bucket_enc() const;
@@ -606,18 +593,17 @@ struct llm_graph_context {
     // recurrent
     //
 
-    ggml_tensor * build_copy_mask_state(
+    ggml_tensor * build_recurrent_state(
              ggml_cgraph * gf,
              ggml_tensor * s,
              ggml_tensor * state_copy,
-             ggml_tensor * state_mask,
-                 int32_t   n_state,
-                 int32_t   n_seqs) const;
+                 int32_t   state_size,
+                 int32_t   n_seqs,
+                    bool   avoid_copies = false) const;
 
     ggml_tensor * build_rwkv_token_shift_load(
              ggml_cgraph * gf,
              ggml_tensor * state_copy,
-             ggml_tensor * state_mask,
       const llama_ubatch & ubatch,
                      int   il) const;
 

src/llama-hparams.h

@@ -186,6 +186,9 @@ struct llama_hparams {
     // dimension of the recurrent state embeddings
     uint32_t n_embd_v_s() const;
 
+    // for NoPE interval
+    uint32_t no_rope_layer_interval = 0;
+
     bool is_swa(uint32_t il) const;
 };
 

src/llama-kv-cache-recurrent.cpp

@@ -359,18 +359,16 @@ llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const {
     return result;
 }
 
-llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
-    GGML_UNUSED(embd_pooled);
-
-    auto sbatch = llama_sbatch(batch, hparams.n_embd, false, logits_all);
+llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) {
+    auto sbatch = llama_sbatch(batch, hparams.n_embd, false);
 
     std::vector<llama_ubatch> ubatches;
 
     while (sbatch.n_tokens > 0) {
         llama_ubatch ubatch;
 
-        if (embd_pooled) {
-            // Pooled embeddings cannot be split across ubatches (yet)
+        if (embd_all) {
+            // if all tokens are output, split by sequence
             ubatch = sbatch.split_seq(n_ubatch);
         } else {
             ubatch = sbatch.split_equal(n_ubatch);
@@ -406,21 +404,12 @@ bool llama_kv_cache_recurrent::prepare(const std::vector<llama_ubatch> & ubatche
 
     bool success = true;
 
-    // TODO: here we have to verify that all ubatches can fit in the cells
-    //       however, the current implementation is broken because it relies on s_copy() and s_mask() to update the cells
-    //         during the compute of each ubatch. to reproduce, uncomment the following loop and run:
-    //
-    //           $ llama-parallel -m ./mamba-130m/ggml-model-f16.gguf -np 5 -ns 8
-    //
-    //       recovery from failures when the batch does not fit in the KV cache will not work correctly until this is fixed
-    //
-    GGML_UNUSED(ubatches);
-    //for (const auto & ubatch : ubatches) {
-    //    if (!find_slot(ubatch)) {
-    //        success = false;
-    //        break;
-    //    }
-    //}
+    for (const auto & ubatch : ubatches) {
+        if (!find_slot(ubatch)) {
+            success = false;
+            break;
+        }
+    }
 
     // restore the original state
     cells = std::move(org_cells);
@@ -431,14 +420,13 @@ bool llama_kv_cache_recurrent::prepare(const std::vector<llama_ubatch> & ubatche
 }
 
 bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
-    const uint32_t n_tokens = ubatch.n_tokens;
-    const uint32_t n_seqs   = ubatch.n_seqs;
+    const uint32_t n_seqs = ubatch.n_seqs;
 
     const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
 
     // if we have enough unused cells before the current head ->
     //   better to start searching from the beginning of the cache, hoping to fill it
-    if (head > used + 2*n_tokens) {
+    if (head > used + 2*n_seqs) {
         head = 0;
     }
 
@@ -534,16 +522,16 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
                 empty_cell.src = orig_cell.src;
                 orig_cell.seq_id.erase(seq_id);
                 empty_cell.seq_id.insert(seq_id); // will be overwritten
+                GGML_ASSERT(!orig_cell.is_empty()); // has at least one remaining seq_id
             }
             seq_meta.tail = next_empty_cell;
             // find next empty cell
             if (s + 1 < n_seqs) {
-                next_empty_cell += 1;
                 for (uint32_t i = 0; i < size; ++i) {
+                    next_empty_cell += 1;
                     if (next_empty_cell >= size) { next_empty_cell -= size; }
                     kv_cell & cell = cells[next_empty_cell];
                     if (cell.is_empty()) { break; }
-                    next_empty_cell += 1;
                 }
             }
         }
@@ -553,8 +541,8 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
 
     // gather and re-order
     for (uint32_t s = 0; s < n_seqs; ++s) {
-        int32_t dst_id = s + min;
-        int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
+        const int32_t dst_id = s + min;
+        const int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
         if (dst_id != src_id) {
             kv_cell & dst_cell = cells[dst_id];
             kv_cell & src_cell = cells[src_id];
@@ -563,12 +551,14 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
             std::swap(dst_cell.src, src_cell.src);
             std::swap(dst_cell.seq_id, src_cell.seq_id);
 
-            // swap tails (assuming they NEVER overlap)
-            for (const llama_seq_id seq_id : src_cell.seq_id) {
-                cells[seq_id].tail = src_id;
-            }
-            for (const llama_seq_id seq_id : dst_cell.seq_id) {
-                cells[seq_id].tail = dst_id;
+            // swap tails
+            for (uint32_t i = 0; i < size; ++i) {
+                int32_t & tail = cells[i].tail;
+                if (tail == src_id) {
+                    tail = dst_id;
+                } else if (tail == dst_id) {
+                    tail = src_id;
+                }
             }
         }
     }
@@ -576,7 +566,7 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
     // update the pos of the used seqs
     for (uint32_t s = 0; s < n_seqs; ++s) {
         const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
-        int32_t cell_id = s + min;
+        const int32_t cell_id = s + min;
         kv_cell & cell = cells[cell_id];
 
         if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
@@ -594,6 +584,38 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
         }
     }
 
+    // Find first cell without src refs, to use as the zero-ed state
+    {
+        // TODO: bake-in src refcounts in the cell metadata
+        std::vector<int32_t> refcounts(size, 0);
+        for (size_t i = 0; i < size; ++i) {
+            const int32_t src = cells[i].src;
+            if (src >= 0) {
+                refcounts[src] += 1;
+            }
+        }
+
+        rs_z = -1;
+        for (int i = min; i <= max; ++i) {
+            if (refcounts[i] == 0) {
+                rs_z = i;
+                break;
+            }
+        }
+
+        for (int i = min; i <= max; ++i) {
+            if (cells[i].src < 0) {
+                GGML_ASSERT(rs_z >= 0);
+                cells[i].src0 = rs_z;
+            } else {
+                // Stage the source ids for all used cells to allow correct seq_* behavior
+                // and still make these values available when setting the inputs
+                cells[i].src0 = cells[i].src;
+            }
+            cells[i].src = i; // avoid moving or clearing twice
+        }
+    }
+
     // allow getting the range of used cells, from head to head + n
     head = min;
     n    = max - min + 1;
@@ -605,47 +627,8 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
 }
 
 bool llama_kv_cache_recurrent::get_can_shift() const {
-    return false;
-}
-
-int32_t llama_kv_cache_recurrent::s_copy(int i) const {
-    const uint32_t cell_id = i + head;
-
-    //////////////////////////////////////////////
-    // TODO: this should not mutate the KV cache !
-    kv_cell & cell = const_cast<kv_cell &>(cells[cell_id]);
-
-    // prevent out-of-bound sources
-    if (cell.src < 0 || (uint32_t) cell.src >= size) {
-        cell.src = cell_id;
-    }
-
-    int32_t res = cell.src;
-
-    // TODO: do not mutate the KV cache
-    // ensure copy only happens once
-    if (cell.src != (int32_t) cell_id) {
-        cell.src = cell_id;
-    }
-
-    return res;
-}
-
-float llama_kv_cache_recurrent::s_mask(int i) const {
-    const uint32_t cell_id = i + head;
-
-    //////////////////////////////////////////////
-    // TODO: this should not mutate the KV cache !
-    kv_cell & cell = const_cast<kv_cell &>(cells[cell_id]);
-
-    float res = (float) (cell.src >= 0);
-
-    // only clear once
-    if (cell.src < 0) {
-        cell.src = cell_id;
-    }
-
-    return res;
+    // shifting the pos is trivial for recurrent models
+    return true;
 }
 
 size_t llama_kv_cache_recurrent::total_size() const {
@@ -1111,6 +1094,10 @@ uint32_t llama_kv_cache_recurrent_state::get_head() const {
     return is_full ? 0 : kv->head;
 }
 
+int32_t llama_kv_cache_recurrent_state::get_rs_z() const {
+    return is_full ? 0 : kv->rs_z;
+}
+
 uint32_t llama_kv_cache_recurrent_state::get_size() const {
     return kv->size;
 }
@@ -1124,9 +1111,5 @@ ggml_tensor * llama_kv_cache_recurrent_state::get_v_l(int32_t il) const {
 }
 
 int32_t llama_kv_cache_recurrent_state::s_copy(int i) const {
-    return kv->s_copy(i);
-}
-
-float llama_kv_cache_recurrent_state::s_mask(int i) const {
-    return kv->s_mask(i);
+    return  kv->cells[i + kv->head].src0;
 }

src/llama-kv-cache-recurrent.h

@@ -32,8 +32,7 @@ public:
     llama_memory_state_ptr init_batch(
             const llama_batch & batch,
             uint32_t n_ubatch,
-            bool embd_pooled,
-            bool logits_all) override;
+            bool embd_all) override;
 
     llama_memory_state_ptr init_full() override;
 
@@ -57,10 +56,6 @@ public:
 
     bool get_can_shift() const override;
 
-    // TODO: temporary methods - they are not really const as they do const_cast<>, fix this
-    int32_t s_copy(int i) const;
-    float   s_mask(int i) const;
-
     // state write/load
 
     void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
@@ -73,10 +68,14 @@ public:
     // computed before each graph build
     uint32_t n = 0;
 
+    // first zero-ed state
+    int32_t rs_z = -1;
+
     // TODO: optimize for recurrent state needs
     struct kv_cell {
         llama_pos pos  = -1;
-        int32_t   src  = -1; // used to copy states
+        int32_t   src  = -1; // used to know where states should be copied from
+        int32_t   src0 = -1; // like src, but only used when setting the inputs (allowing to copy once)
         int32_t   tail = -1;
 
         std::set<llama_seq_id> seq_id;
@@ -157,13 +156,13 @@ public:
 
     uint32_t get_n_kv() const;
     uint32_t get_head() const;
+    int32_t  get_rs_z() const;
     uint32_t get_size() const;
 
     ggml_tensor * get_k_l(int32_t il) const;
     ggml_tensor * get_v_l(int32_t il) const;
 
     int32_t s_copy(int i) const;
-    float   s_mask(int i) const;
 
 private:
     const llama_memory_status status;

src/llama-kv-cache-unified-iswa.cpp

@@ -95,36 +95,69 @@ llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
     return kv_swa->seq_pos_max(seq_id);
 }
 
-llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
-    GGML_UNUSED(embd_pooled);
+llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) {
+    GGML_UNUSED(embd_all);
 
-    // TODO: if we fail with split_simple, we should attempt different splitting strategies
+    // first try simple split
+    do {
+        auto sbatch = llama_sbatch(batch, hparams.n_embd, true);
+
+        std::vector<llama_ubatch> ubatches;
+
+        while (sbatch.n_tokens > 0) {
+            auto ubatch = sbatch.split_simple(n_ubatch);
+
+            ubatches.push_back(ubatch);
+        }
+
+        auto heads_base = kv_base->prepare(ubatches);
+        if (heads_base.empty()) {
+            break;
+        }
+
+        auto heads_swa = kv_swa->prepare(ubatches);
+        if (heads_swa.empty()) {
+            break;
+        }
+
+        assert(heads_base.size() == heads_swa.size());
+
+        return std::make_unique<llama_kv_cache_unified_iswa_state>(
+                this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
+    } while (false);
+
+    // if it fails, try equal split
+    do {
+        auto sbatch = llama_sbatch(batch, hparams.n_embd, false);
+
+        std::vector<llama_ubatch> ubatches;
+
+        while (sbatch.n_tokens > 0) {
+            auto ubatch = sbatch.split_equal(n_ubatch);
+
+            ubatches.push_back(ubatch);
+        }
+
+        auto heads_base = kv_base->prepare(ubatches);
+        if (heads_base.empty()) {
+            break;
+        }
+
+        auto heads_swa = kv_swa->prepare(ubatches);
+        if (heads_swa.empty()) {
+            break;
+        }
+
+        assert(heads_base.size() == heads_swa.size());
+
+        return std::make_unique<llama_kv_cache_unified_iswa_state>(
+                this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
+    } while (false);
+
+    // TODO: if we fail again, we should attempt different splitting strategies
     //       but to do that properly, we first have to refactor the batches to be more flexible
 
-    auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
-
-    std::vector<llama_ubatch> ubatches;
-
-    while (sbatch.n_tokens > 0) {
-        auto ubatch = sbatch.split_simple(n_ubatch);
-
-        ubatches.push_back(ubatch);
-    }
-
-    auto heads_base = kv_base->prepare(ubatches);
-    if (heads_base.empty()) {
-        return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
-    }
-
-    auto heads_swa = kv_swa->prepare(ubatches);
-    if (heads_swa.empty()) {
-        return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
-    }
-
-    assert(heads_base.size() == heads_swa.size());
-
-    return std::make_unique<llama_kv_cache_unified_iswa_state>(
-            this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
+    return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
 }
 
 llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {

src/llama-kv-cache-unified-iswa.h

@@ -34,8 +34,7 @@ public:
     llama_memory_state_ptr init_batch(
             const llama_batch & batch,
             uint32_t n_ubatch,
-            bool embd_pooled,
-            bool logits_all) override;
+            bool embd_all) override;
 
     llama_memory_state_ptr init_full() override;
 

src/llama-kv-cache-unified.cpp

@@ -127,6 +127,9 @@ llama_kv_cache_unified::llama_kv_cache_unified(
                 ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
                 ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
     }
+
+    const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
+    debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;
 }
 
 void llama_kv_cache_unified::clear(bool data) {
@@ -307,24 +310,27 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
 llama_memory_state_ptr llama_kv_cache_unified::init_batch(
             const llama_batch & batch,
             uint32_t n_ubatch,
-            bool embd_pooled,
-            bool logits_all) {
-    GGML_UNUSED(embd_pooled);
+            bool embd_all) {
+    GGML_UNUSED(embd_all);
 
-    auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
+    do {
+        auto sbatch = llama_sbatch(batch, hparams.n_embd, true);
 
-    std::vector<llama_ubatch> ubatches;
-    while (sbatch.n_tokens > 0) {
-        ubatches.push_back(sbatch.split_simple(n_ubatch));
-    }
+        std::vector<llama_ubatch> ubatches;
+        while (sbatch.n_tokens > 0) {
+            ubatches.push_back(sbatch.split_simple(n_ubatch));
+        }
 
-    auto heads = prepare(ubatches);
-    if (heads.empty()) {
-        return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
-    }
+        auto heads = prepare(ubatches);
+        if (heads.empty()) {
+            break;
+        }
 
-    return std::make_unique<llama_kv_cache_unified_state>(
-            this, std::move(sbatch), std::move(heads), std::move(ubatches));
+        return std::make_unique<llama_kv_cache_unified_state>(
+                this, std::move(sbatch), std::move(heads), std::move(ubatches));
+    } while (false);
+
+    return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
 }
 
 llama_memory_state_ptr llama_kv_cache_unified::init_full() {
@@ -512,43 +518,68 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
         head_cur = 0;
     }
 
-    // otherwise, one cell per token.
-
     if (n_tokens > cells.size()) {
         LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
         return -1;
     }
 
-//#define FIND_SLOT_DEBUG 1
-#if FIND_SLOT_DEBUG
-    LLAMA_LOG_WARN("begin: n = %5d, used = %5d, head = %5d, n_swa = %5d\n", cells.used_max_p1(), cells.get_used(), head, n_swa);
+    if (debug > 0) {
+        LLAMA_LOG_DEBUG("%s: n = %5d, used = %5d, head = %5d, size = %5d, n_swa = %5d\n", __func__, cells.used_max_p1(), cells.get_used(), head, get_size(), n_swa);
 
-    // for debugging
-    {
-        std::string ss;
-        if (n_swa > 0) {
+        if ((debug == 2 && n_swa > 0) || debug > 2) {
+            std::string ss;
             for (uint32_t i = 0; i < cells.size(); ++i) {
                 if (cells.is_empty(i)) {
                     ss += '.';
                 } else {
-                    ss += std::to_string(cells.seq_get(i));
+                    assert(cells.seq_count(i) >= 1);
+
+                    if (cells.seq_count(i) == 1) {
+                        ss += std::to_string(cells.seq_get(i));
+                    } else {
+                        ss += 'M';
+                    }
                 }
                 if (i%256 == 255) {
+                    ss += " *";
                     ss += '\n';
                 }
             }
-        }
-        LLAMA_LOG_WARN("\n%s\n", ss.c_str());
-    }
-
-    for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
-        if (cells.seq_pos_min(s) < 0) {
-            continue;
+            LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
         }
 
-        LLAMA_LOG_WARN("kv_cells: n_swa = %4d, min[%d] = %5d, max[%d] = %5d\n", n_swa, s, cells.seq_pos_min(s), s, cells.seq_pos_max(s));
+        if ((debug == 2 && n_swa > 0) || debug > 2) {
+            std::string ss;
+            for (uint32_t i = 0; i < cells.size(); ++i) {
+                std::string cur;
+                if (cells.is_empty(i)) {
+                    cur = '.';
+                } else {
+                    cur = std::to_string(cells.pos_get(i));
+                }
+                const int n = cur.size();
+                for (int j = 0; j < 5 - n; ++j) {
+                    cur += ' ';
+                }
+                ss += cur;
+                if (i%256 == 255) {
+                    ss += " *";
+                }
+                if (i%64 == 63) {
+                    ss += '\n';
+                }
+            }
+            LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
+        }
+
+        for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
+            if (cells.seq_pos_min(s) < 0) {
+                continue;
+            }
+
+            LLAMA_LOG_DEBUG("%s: min[%d] = %5d, max[%d] = %5d\n", __func__, s, cells.seq_pos_min(s), s, cells.seq_pos_max(s));
+        }
     }
-#endif
 
     uint32_t n_tested = 0;
 
@@ -559,21 +590,15 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
             continue;
         }
 
-        // keep track of what the minimum sequence positions would be if we accept the ubatch
-        llama_seq_id seq_pos_min[LLAMA_MAX_PARALLEL_SEQUENCES];
-        for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
-            seq_pos_min[s] = cells.seq_pos_min(s);
-        }
-
         bool found = true;
         for (uint32_t i = 0; i < n_tokens; i++) {
-            const llama_pos    pos    = ubatch.pos[i];
-            const llama_seq_id seq_id = ubatch.seq_id[i][0];
+            //const llama_pos    pos    = ubatch.pos[i];
+            //const llama_seq_id seq_id = ubatch.seq_id[i][0];
 
             // can we use this cell? either:
             //  - the cell is empty
             //  - the cell is occupied only by one sequence:
-            //    - mask causally, if the sequence is the same as the one we are inserting
+            //    - (disabled) mask causally, if the sequence is the same as the one we are inserting
             //    - mask SWA, using current max pos for that sequence in the cache
             //                always insert in the cell with minimum pos
             bool can_use = cells.is_empty(head_cur + i);
@@ -581,21 +606,17 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
             if (!can_use && cells.seq_count(head_cur + i) == 1) {
                 const llama_pos pos_cell = cells.pos_get(head_cur + i);
 
-                // causal mask
-                if (cells.seq_has(head_cur + i, seq_id)) {
-                    can_use = pos_cell >= pos;
-                }
+                // (disabled) causal mask
+                // note: it's better to purge any "future" tokens beforehand
+                //if (cells.seq_has(head_cur + i, seq_id)) {
+                //    can_use = pos_cell >= pos;
+                //}
 
                 if (!can_use) {
                     const llama_seq_id seq_id_cell = cells.seq_get(head_cur + i);
 
                     // SWA mask
-                    // note: we insert only in the cell with minimum pos in order to preserve the invariant that
-                    //       all positions between [pos_min, pos_max] for each sequence will be present in the cache
-                    //       ref: https://github.com/ggml-org/llama.cpp/pull/13746#issuecomment-2916057092
-                    if (pos_cell == seq_pos_min[seq_id_cell] &&
-                        is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
-                        seq_pos_min[seq_id_cell]++;
+                    if (is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
                         can_use = true;
                     }
                 }
@@ -623,18 +644,58 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
 }
 
 void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) {
-    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
-        if (!cells.is_empty(head_cur + i)) {
-            cells.rm(head_cur + i);
-        }
+    if (debug > 0) {
+        LLAMA_LOG_DEBUG("%s: ubatch info:\n", __func__);
+        LLAMA_LOG_DEBUG("%s:   n_tokens = %d, equal_seqs = %d\n", __func__, ubatch.n_tokens, ubatch.equal_seqs);
+        LLAMA_LOG_DEBUG("%s:   n_seq_tokens = %d, n_seqs = %d\n", __func__, ubatch.n_seq_tokens, ubatch.n_seqs);
+    }
 
-        cells.pos_set(head_cur + i, ubatch.pos[i]);
+    // keep track of the max sequence position that we would overwrite with this ubatch
+    // for non-SWA cache, this would be always empty
+    llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
+    for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
+        seq_pos_max_rm[s] = -1;
+    }
 
-        for (int32_t j = 0; j < ubatch.n_seq_id[i]; j++) {
-            cells.seq_add(head_cur + i, ubatch.seq_id[i][j]);
+    for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
+        for (uint32_t j = 0; j < ubatch.n_seq_tokens; ++j) {
+            const uint32_t idx = s*ubatch.n_seq_tokens + j;
+
+            if (!cells.is_empty(head_cur + idx)) {
+                assert(cells.seq_count(head_cur + idx) == 1);
+
+                const llama_seq_id seq_id = cells.seq_get(head_cur + idx);
+                const llama_pos    pos    = cells.pos_get(head_cur + idx);
+
+                seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
+
+                cells.rm(head_cur + idx);
+            }
+
+            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]);
+            }
         }
     }
 
+    // note: we want to preserve the invariant that all positions between [pos_min, pos_max] for each sequence
+    //       will be present in the cache. so we have to purge any position which is less than those we would overwrite
+    //       ref: https://github.com/ggml-org/llama.cpp/pull/13746#issuecomment-2916057092
+    for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
+        if (seq_pos_max_rm[s] == -1) {
+            continue;
+        }
+
+        if (cells.seq_pos_min(s) <= seq_pos_max_rm[s]) {
+            LLAMA_LOG_DEBUG("%s: purging positions [%d, %d] of sequence %d from KV cache\n",
+                    __func__, cells.seq_pos_min(s), seq_pos_max_rm[s], s);
+
+            seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1);
+        }
+    }
     // move the head at the end of the slot
     head = head_cur + ubatch.n_tokens;
 }
@@ -731,14 +792,14 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
 }
 
 void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
-    const int64_t n_tokens     = ubatch->n_tokens;
-    const int64_t n_seq_tokens = ubatch->n_seq_tokens;
-    const int64_t n_seqs       = ubatch->n_seqs;
+    const uint32_t n_tokens     = ubatch->n_tokens;
+    const uint32_t n_seq_tokens = ubatch->n_seq_tokens;
+    const uint32_t n_seqs       = ubatch->n_seqs;
 
     GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
     float * data = (float *) dst->data;
 
-    const auto n_kv = dst->ne[0];
+    const int64_t n_kv = dst->ne[0];
 
     // Use only the previous KV cells of the correct sequence for each token of the ubatch.
     // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
@@ -752,12 +813,14 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
     //      xxxxx-----
     //      xxxxx-----
     // To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
-    for (int h = 0; h < 1; ++h) {
-        for (int s = 0; s < n_seqs; ++s) {
+    for (uint32_t h = 0; h < 1; ++h) {
+        for (uint32_t s = 0; s < n_seqs; ++s) {
             const llama_seq_id seq_id = ubatch->seq_id[s][0];
 
-            for (int j = 0; j < n_seq_tokens; ++j) {
-                const llama_pos p1 = ubatch->pos[s*n_seq_tokens + j];
+            for (uint32_t j = 0; j < n_seq_tokens; ++j) {
+                const uint32_t idx = s*n_seq_tokens + j;
+
+                const llama_pos p1 = ubatch->pos[idx];
 
                 for (uint32_t i = 0; i < n_kv; ++i) {
                     float f = 0.0f;
@@ -787,16 +850,16 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
                         f = -INFINITY;
                     }
 
-                    data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+                    data[h*(n_kv*n_tokens) + idx*n_kv + i] = f;
                 }
             }
         }
 
         // mask padded tokens
         if (data) {
-            for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                for (uint32_t j = 0; j < n_kv; ++j) {
-                    data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+            for (uint32_t j = n_tokens; j < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++j) {
+                for (uint32_t i = 0; i < n_kv; ++i) {
+                    data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
                 }
             }
         }
@@ -1447,9 +1510,11 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
         seq_rm(dest_seq_id, -1, -1);
 
         llama_sbatch sbatch;
-        llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
+        llama_ubatch ubatch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
 
-        batch.n_tokens = cell_count;
+        ubatch.n_tokens = cell_count;
+        ubatch.n_seq_tokens = cell_count;
+        ubatch.n_seqs = 1;
 
         for (uint32_t i = 0; i < cell_count; ++i) {
             llama_pos pos;
@@ -1469,18 +1534,18 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
                 io.read_to(&seq_id, sizeof(seq_id));
             }
 
-            batch.pos[i]      = pos;
-            batch.n_seq_id[i] = n_seq_id;
-            batch.seq_id[i]   = &dest_seq_id;
+            ubatch.pos[i]      = pos;
+            ubatch.n_seq_id[i] = n_seq_id;
+            ubatch.seq_id[i]   = &dest_seq_id;
         }
 
-        const auto head_cur = find_slot(batch);
+        const auto head_cur = find_slot(ubatch);
         if (head_cur < 0) {
             LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
             return false;
         }
 
-        apply_ubatch(head_cur, batch);
+        apply_ubatch(head_cur, ubatch);
 
         // keep the head at the old position because we will read the KV data into it in state_read_data()
         head = head_cur;
@@ -1488,8 +1553,8 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
         // DEBUG CHECK: head_cur should be our first cell, head_cur + cell_count - 1 should be our last cell (verify seq_id and pos values)
         // Assume that this is one contiguous block of cells
         GGML_ASSERT(head_cur + cell_count <= cells.size());
-        GGML_ASSERT(cells.pos_get(head_cur)                  == batch.pos[0]);
-        GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == batch.pos[cell_count - 1]);
+        GGML_ASSERT(cells.pos_get(head_cur)                  == ubatch.pos[0]);
+        GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == ubatch.pos[cell_count - 1]);
         GGML_ASSERT(cells.seq_has(head_cur,                  dest_seq_id));
         GGML_ASSERT(cells.seq_has(head_cur + cell_count - 1, dest_seq_id));
     } else {
@@ -1674,7 +1739,7 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state(
         llama_context * lctx,
         bool do_shift,
         defrag_info dinfo) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), lctx(lctx), do_shift(do_shift), dinfo(std::move(dinfo)) {
-    if (!do_shift && dinfo.empty()) {
+    if (!do_shift && this->dinfo.empty()) {
         status = LLAMA_MEMORY_STATUS_NO_UPDATE;
     }
 }

src/llama-kv-cache-unified.h

@@ -59,8 +59,7 @@ public:
     llama_memory_state_ptr init_batch(
             const llama_batch & batch,
             uint32_t n_ubatch,
-            bool embd_pooled,
-            bool logits_all) override;
+            bool embd_all) override;
 
     llama_memory_state_ptr init_full() override;
 
@@ -158,6 +157,8 @@ private:
     // SWA
     const uint32_t n_swa = 0;
 
+    int debug = 0;
+
     const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
 
     std::vector<ggml_context_ptr>        ctxs;

src/llama-kv-cells.h

@@ -23,7 +23,7 @@ public:
 
         used.clear();
 
-        for (uint32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+        for (uint32_t s = 0; s < LLAMA_MAX_SEQ; ++s) {
             seq_pos[s].clear();
         }
     }
@@ -240,7 +240,7 @@ public:
     llama_seq_id seq_get(uint32_t i) const {
         assert(seq[i].count() == 1);
 
-        for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+        for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
             if (seq[i].test(s)) {
                 return s;
             }
@@ -253,7 +253,7 @@ public:
     // return -1 if the sequence is not present
     llama_pos seq_pos_min(llama_seq_id seq_id) const {
         assert(seq_id >= 0);
-        assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES);
+        assert(seq_id < LLAMA_MAX_SEQ);
 
         if (seq_pos[seq_id].empty()) {
             return -1;
@@ -266,7 +266,7 @@ public:
     // return -1 if the sequence is not present
     llama_pos seq_pos_max(llama_seq_id seq_id) const {
         assert(seq_id >= 0);
-        assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES);
+        assert(seq_id < LLAMA_MAX_SEQ);
 
         if (seq_pos[seq_id].empty()) {
             return -1;
@@ -384,20 +384,20 @@ private:
     //
     std::vector<llama_pos> shift;
 
-    using bits_t = std::bitset<LLAMA_MAX_PARALLEL_SEQUENCES>;
+    using bits_t = std::bitset<LLAMA_MAX_SEQ>;
 
     // the bitset seq[i] tells us which sequences are currently occupying the i-th cell
     std::vector<bits_t> seq;
 
     // the set seq_pos[s] tells us which positions are currently present for sequence s
     // this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache
-    std::set<llama_pos> seq_pos[LLAMA_MAX_PARALLEL_SEQUENCES];
+    std::set<llama_pos> seq_pos[LLAMA_MAX_SEQ];
 
     // helper functions for updating `seq_pos`, once cell at a time:
 
     // remove cell i
     void seq_pos_rm(uint32_t i) {
-        for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+        for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
             if (seq[i].test(s)) {
                 seq_pos[s].erase(pos[i]);
             }
@@ -406,7 +406,7 @@ private:
 
     // add cell i
     void seq_pos_add(uint32_t i) {
-        for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+        for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
             if (seq[i].test(s)) {
                 seq_pos[s].insert(pos[i]);
             }

src/llama-memory.h

@@ -73,8 +73,7 @@ struct llama_memory_i {
     virtual llama_memory_state_ptr init_batch(
             const llama_batch & batch,
             uint32_t n_ubatch,
-            bool embd_pooled,
-            bool logits_all) = 0;
+            bool embd_all) = 0;
 
     // simulate full cache, used for allocating worst-case compute buffers
     virtual llama_memory_state_ptr init_full() = 0;

src/llama-model.cpp

@@ -80,6 +80,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_40B:           return "40B";
         case LLM_TYPE_65B:           return "65B";
         case LLM_TYPE_70B:           return "70B";
+        case LLM_TYPE_142B:          return "142B";
         case LLM_TYPE_236B:          return "236B";
         case LLM_TYPE_290B:          return "290B";
         case LLM_TYPE_314B:          return "314B";
@@ -442,6 +443,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         return;
     }
 
+    if (arch == LLM_ARCH_SMOLLM3) {
+        ml.get_key("no_rope_layer_interval", hparams.no_rope_layer_interval);
+    }
+
     ml.get_key(LLM_KV_CONTEXT_LENGTH,    hparams.n_ctx_train);
     ml.get_key(LLM_KV_EMBEDDING_LENGTH,  hparams.n_embd);
     ml.get_key(LLM_KV_BLOCK_COUNT,       hparams.n_layer);
@@ -598,6 +603,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     hparams.use_kq_norm = false;
                 }
             } break;
+        case LLM_ARCH_ARCEE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                // Arcee uses the same structure as Llama
+                switch (hparams.n_layer) {
+                    case 36: type = LLM_TYPE_4B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_DECI:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -738,6 +753,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     }
                 }
             } break;
+        case LLM_ARCH_NEO_BERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,            hparams.causal_attn);
+                ml.get_key(LLM_KV_POOLING_TYPE,                hparams.pooling_type);
+
+                if (hparams.n_layer == 28) {
+                    type = LLM_TYPE_250M;
+                }
+            } break;
         case LLM_ARCH_BLOOM:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -1444,6 +1469,20 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_DOTS1:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+                switch (hparams.n_layer) {
+                    case 62: type = LLM_TYPE_142B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -2187,6 +2226,32 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
                     }
                 } break;
+            case LLM_ARCH_NEO_BERT:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+
+                    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+                    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+                    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+                    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+
+                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff*2}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                    }
+                } break;
             case LLM_ARCH_JINA_BERT_V2:
                 {
                     tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
@@ -4123,6 +4188,89 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
                     }
                 } break;
+            case LLM_ARCH_DOTS1:
+                {
+                    const int64_t n_ff_exp        = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        } else {
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+                            if (n_expert == 0) {
+                                throw std::runtime_error("n_expert must be > 0");
+                            }
+                            if (n_expert_used == 0) {
+                                throw std::runtime_error("n_expert_used must be > 0");
+                            }
+
+                            // MoE branch
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_ARCEE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -6074,6 +6222,117 @@ struct llm_build_bert : public llm_graph_context {
     }
 };
 
+struct llm_build_neo_bert : public llm_graph_context {
+    llm_build_neo_bert(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        // construct input embeddings (token, type, position)
+        inpL = build_inp_embd(model.tok_embd);
+        cb(inpL, "inp_embd", -1);
+
+        auto * inp_attn = build_attn_inp_no_cache();
+
+        // iterate layers
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * cur = inpL;
+
+            ggml_tensor * Qcur;
+            ggml_tensor * Kcur;
+            ggml_tensor * Vcur;
+
+            // pre-norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+
+            // self-attention
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            // RoPE
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn, gf,
+                    model.layers[il].wo, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            cb(cur, "kqv_out", il);
+
+            if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // re-add the layer input
+            cur = ggml_add(ctx0, cur, inpL);
+
+            ggml_tensor * ffn_inp = cur;
+            cb(ffn_inp, "ffn_inp", il);
+
+            // pre-norm
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,
+                    NULL, NULL, NULL, NULL, NULL,
+                    model.layers[il].ffn_down,
+                    NULL, NULL, NULL,
+                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+
+            // attentions bypass the intermediate layer
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm_enc, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_embd", -1);
+        res->t_embd = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 struct llm_build_bloom : public llm_graph_context {
     llm_build_bloom(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
         const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -8857,7 +9116,6 @@ struct llm_build_mamba : public llm_graph_context {
         inpL = build_inp_embd(model.tok_embd);
 
         ggml_tensor * state_copy = build_inp_s_copy();
-        ggml_tensor * state_mask = build_inp_s_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             // norm
@@ -8866,8 +9124,7 @@ struct llm_build_mamba : public llm_graph_context {
                     LLM_NORM_RMS, il);
             cb(cur, "attn_norm", il);
 
-            //cur = build_mamba_layer(gf, cur, state_copy, state_mask, il);
-            cur = build_mamba_layer(gf, cur, state_copy, state_mask, ubatch, il);
+            cur = build_mamba_layer(gf, cur, state_copy, ubatch, il);
 
             if (il == n_layer - 1) {
                 // skip computing output for unused tokens
@@ -8908,7 +9165,6 @@ struct llm_build_mamba : public llm_graph_context {
              ggml_cgraph * gf,
              ggml_tensor * cur,
              ggml_tensor * state_copy,
-             ggml_tensor * state_mask,
       const llama_ubatch & ubatch,
                      int   il) const {
         const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@@ -8935,12 +9191,12 @@ struct llm_build_mamba : public llm_graph_context {
         ggml_tensor * ssm_states_all  = kv_state->get_v_l(il);
 
         // (ab)using the KV cache to store the states
-        ggml_tensor * conv = build_copy_mask_state(
-                gf, conv_states_all, state_copy, state_mask,
+        ggml_tensor * conv = build_recurrent_state(
+                gf, conv_states_all, state_copy,
                 hparams.n_embd_k_s(), n_seqs);
         conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs);
-        ggml_tensor * ssm = build_copy_mask_state(
-                gf, ssm_states_all, state_copy, state_mask,
+        ggml_tensor * ssm = build_recurrent_state(
+                gf, ssm_states_all, state_copy,
                 hparams.n_embd_v_s(), n_seqs);
         ssm = ggml_reshape_3d(ctx0, ssm, d_state, d_inner, n_seqs);
 
@@ -11656,7 +11912,6 @@ struct llm_build_rwkv6_base : public llm_graph_context {
             ggml_tensor * cur,
             ggml_tensor * x_prev,
             ggml_tensor * state_copy,
-            ggml_tensor * state_mask,
             const llama_ubatch & ubatch,
             int   il) const {
         const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@@ -11780,8 +12035,8 @@ struct llm_build_rwkv6_base : public llm_graph_context {
             k = ggml_sub(ctx0, k, ggml_mul(ctx0, k, w));
         }
 
-        ggml_tensor * wkv_state = build_copy_mask_state(
-                gf, kv_state->get_v_l(il), state_copy, state_mask,
+        ggml_tensor * wkv_state = build_recurrent_state(
+                gf, kv_state->get_v_l(il), state_copy,
                 hparams.n_embd_v_s(), n_seqs);
 
         ggml_tensor * wkv_output;
@@ -11837,7 +12092,6 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base {
         inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
 
         ggml_tensor * state_copy = build_inp_s_copy();
-        ggml_tensor * state_mask = build_inp_s_mask();
 
         const auto n_embd = hparams.n_embd;
         const auto n_seq_tokens = ubatch.n_seq_tokens;
@@ -11848,7 +12102,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base {
             inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
 
             ggml_tensor * token_shift = build_rwkv_token_shift_load(
-                    gf, state_copy, state_mask, ubatch, il
+                    gf, state_copy, ubatch, il
                     );
 
             ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
@@ -11864,7 +12118,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base {
                     1
                     );
 
-            cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, state_mask, ubatch, il);
+            cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, ubatch, il);
 
             ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
             cb(ffn_inp, "ffn_inp", il);
@@ -11935,7 +12189,6 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
         inpL = build_inp_embd(model.tok_embd);
 
         ggml_tensor * state_copy = build_inp_s_copy();
-        ggml_tensor * state_mask = build_inp_s_mask();
 
         const auto n_embd = hparams.n_embd;
         const auto n_seq_tokens = ubatch.n_seq_tokens;
@@ -11946,7 +12199,7 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
             inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
 
             ggml_tensor * token_shift = build_rwkv_token_shift_load(
-                    gf, state_copy, state_mask, ubatch, il
+                    gf, state_copy, ubatch, il
                     );
 
             ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
@@ -11959,7 +12212,7 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
                     1
                     );
 
-            cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, state_mask, ubatch, il);
+            cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, ubatch, il);
 
             token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
             ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
@@ -12051,7 +12304,6 @@ struct llm_build_rwkv7_base : public llm_graph_context {
             ggml_tensor * cur,
             ggml_tensor * x_prev,
             ggml_tensor * state_copy,
-            ggml_tensor * state_mask,
             ggml_tensor *& first_layer_value,
             const llama_ubatch & ubatch,
             int   il) const {
@@ -12134,8 +12386,8 @@ struct llm_build_rwkv7_base : public llm_graph_context {
         v = ggml_reshape_3d(ctx0, v, head_size, head_count, n_tokens);
         a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
 
-        ggml_tensor * wkv_state = build_copy_mask_state(
-                gf, kv_state->get_v_l(il), state_copy, state_mask,
+        ggml_tensor * wkv_state = build_recurrent_state(
+                gf, kv_state->get_v_l(il), state_copy,
                 hparams.n_embd_v_s(), n_seqs);
 
         ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
@@ -12193,7 +12445,6 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base {
         inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
 
         ggml_tensor * state_copy = build_inp_s_copy();
-        ggml_tensor * state_mask = build_inp_s_mask();
 
         const auto n_embd = hparams.n_embd;
         const auto n_seq_tokens = ubatch.n_seq_tokens;
@@ -12204,7 +12455,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base {
             inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
 
             ggml_tensor * token_shift = build_rwkv_token_shift_load(
-                    gf, state_copy, state_mask, ubatch, il
+                    gf, state_copy, ubatch, il
                     );
 
             ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
@@ -12220,7 +12471,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base {
                     1
                     );
 
-            cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, state_mask, v_first, ubatch, il);
+            cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, v_first, ubatch, il);
 
             ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
             cb(ffn_inp, "ffn_inp", il);
@@ -12287,7 +12538,6 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
         inpL = build_inp_embd(model.tok_embd);
 
         ggml_tensor * state_copy = build_inp_s_copy();
-        ggml_tensor * state_mask = build_inp_s_mask();
 
         const auto n_embd = hparams.n_embd;
         const auto n_seq_tokens = ubatch.n_seq_tokens;
@@ -12298,7 +12548,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
             inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
 
             ggml_tensor * token_shift = build_rwkv_token_shift_load(
-                    gf, state_copy, state_mask, ubatch, il
+                    gf, state_copy, ubatch, il
                     );
 
             ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
@@ -12311,7 +12561,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
                     1
                     );
 
-            cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, state_mask, v_first, ubatch, il);
+            cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, v_first, ubatch, il);
 
             token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
             ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
@@ -13203,6 +13453,432 @@ struct llm_build_bailingmoe : public llm_graph_context {
     }
 };
 
+struct llm_build_dots1 : public llm_graph_context {
+    llm_build_dots1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv_unified();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            if ((uint32_t) il < hparams.n_layer_dense_lead) {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                ggml_tensor * moe_out =
+                    build_moe_ffn(cur,
+                            model.layers[il].ffn_gate_inp,
+                            model.layers[il].ffn_up_exps,
+                            model.layers[il].ffn_gate_exps,
+                            model.layers[il].ffn_down_exps,
+                            model.layers[il].ffn_exp_probs_b,
+                            n_expert, n_expert_used,
+                            LLM_FFN_SILU, hparams.expert_weights_norm,
+                            true, hparams.expert_weights_scale,
+                            (llama_expert_gating_func_type) hparams.expert_gating_func,
+                            il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                {
+                    ggml_tensor * ffn_shexp = build_ffn(cur,
+                            model.layers[il].ffn_up_shexp,   NULL, NULL,
+                            model.layers[il].ffn_gate_shexp, NULL, NULL,
+                            model.layers[il].ffn_down_shexp, NULL, NULL,
+                            NULL,
+                            LLM_FFN_SILU, LLM_FFN_PAR, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
+
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                }
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
+struct llm_build_arcee : public llm_graph_context {
+    llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv_unified();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            // ARCEE uses relu^2 instead of silu
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    NULL,                      NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
+struct llm_build_smollm3 : public llm_graph_context {
+    llm_build_smollm3(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        const uint32_t interval = hparams.no_rope_layer_interval;
+
+        // token embeddings
+        ggml_tensor * inpL = build_inp_embd(model.tok_embd);
+
+        // positional ids
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        // attention helper (unified KV cache)
+        auto * inp_attn = build_attn_inp_kv_unified();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        ggml_tensor * cur = nullptr;
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // attention norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // ---- self-attention ----
+            {
+                // fused QKV projection
+                ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(qkv, "wqkv", il);
+                if (model.layers[il].bqkv) {
+                    qkv = ggml_add(ctx0, qkv, model.layers[il].bqkv);
+                    cb(qkv, "bqkv", il);
+                }
+
+                const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+
+                ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd,     n_tokens, qkv->nb[1], 0));
+                ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_gqa, n_tokens, qkv->nb[1], sizeof(float)*(n_embd)));
+                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_gqa, n_tokens, qkv->nb[1], sizeof(float)*(n_embd + n_embd_gqa)));
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                if (interval == 0 || il % interval != 0) {
+                    ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+                    Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+                    Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+                }
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            // skip padded tokens for final layer
+            if (il == n_layer - 1) {
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // ---- feed-forward ----
+            if (hparams.use_par_res) {
+                // parallel residual
+                ggml_tensor * ffn_cur = build_norm(inpL,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(ffn_cur, "ffn_norm", il);
+
+            ffn_cur = build_ffn(
+                ffn_cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   nullptr,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
+                nullptr,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, ffn_cur);
+                cb(cur, "par_res", il);
+            } else {
+                // sequential residual
+                ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+                cb(ffn_inp, "ffn_inp", il);
+
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(
+                cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   nullptr,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
+                nullptr,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+                            cb(cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, ffn_inp);
+                cb(cur, "ffn_out", il);
+            }
+
+            // post-processing
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            inpL = cur;
+        }
+
+        // final RMSNorm
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
     llama_memory_i * res;
 
@@ -13211,6 +13887,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
         case LLM_ARCH_JINA_BERT_V2:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
+        case LLM_ARCH_NEO_BERT:
         case LLM_ARCH_WAVTOKENIZER_DEC:
             {
                 res = nullptr;
@@ -13319,6 +13996,10 @@ llm_graph_result_ptr llama_model::build_graph(
             {
                 llm = std::make_unique<llm_build_bert>(*this, params, gf);
             } break;
+        case LLM_ARCH_NEO_BERT:
+            {
+                llm = std::make_unique<llm_build_neo_bert>(*this, params, gf);
+            } break;
         case LLM_ARCH_BLOOM:
             {
                 llm = std::make_unique<llm_build_bloom>(*this, params, gf);
@@ -13541,6 +14222,18 @@ llm_graph_result_ptr llama_model::build_graph(
             {
                 llm = std::make_unique<llm_build_bailingmoe>(*this, params, gf);
             } break;
+        case LLM_ARCH_DOTS1:
+            {
+                llm = std::make_unique<llm_build_dots1>(*this, params, gf);
+            } break;
+        case LLM_ARCH_ARCEE:
+            {
+                llm = std::make_unique<llm_build_arcee>(*this, params, gf);
+            } break;
+        case LLM_ARCH_SMOLLM3:
+            {
+                llm = std::make_unique<llm_build_smollm3>(*this, params, gf);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -13690,8 +14383,12 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_GRANITE_MOE:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_BAILINGMOE:
+        case LLM_ARCH_NEO_BERT:
+        case LLM_ARCH_SMOLLM3:
+        case LLM_ARCH_ARCEE:
             return LLAMA_ROPE_TYPE_NORM;
 
+
         // the pairs of head values are offset by n_rot/2
         case LLM_ARCH_FALCON:
         case LLM_ARCH_GROK:
@@ -13723,6 +14420,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_NEMOTRON:
         case LLM_ARCH_EXAONE:
         case LLM_ARCH_MINICPM3:
+        case LLM_ARCH_DOTS1:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:

src/llama-model.h

@@ -73,6 +73,7 @@ enum llm_type {
     LLM_TYPE_40B,
     LLM_TYPE_65B,
     LLM_TYPE_70B,
+    LLM_TYPE_142B,
     LLM_TYPE_236B,
     LLM_TYPE_290B,
     LLM_TYPE_314B,

src/llama-quant.cpp

@@ -585,7 +585,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
                 gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64);
             } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
-                gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64);
+                // Setting type to UINT32. See https://github.com/ggml-org/llama.cpp/pull/14182 for context
+                gguf_set_val_u32(ctx_out.get(), o.key, (uint32_t)abs(o.val_i64));
             } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
                 gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool);
             } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {

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
@@ -1987,6 +1987,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     || t.first == "<|eom_id|>"
                     || t.first == "<EOT>"
                     || t.first == "_<EOT>"
+                    || t.first == "<|end_of_text|>"
                ) {
                 special_eog_ids.insert(t.second);
                 if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2572,6 +2573,10 @@ 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--;
@@ -2768,26 +2773,26 @@ 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[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 (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 (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 (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 (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() ); }
+    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() ); }
 
     for (const auto & id : special_eog_ids) {
-        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, id_to_token[id].text.c_str() );
+        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, id_to_token.at(id).text.c_str() );
     }
 
     LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, max_token_len);

src/llama.cpp

@@ -198,14 +198,18 @@ static struct llama_model * llama_model_load_from_file_impl(
 
     // if using single GPU mode, remove all except the main GPU
     if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
-        if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) {
-            LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size());
-            llama_model_free(model);
-            return nullptr;
+        if (params.main_gpu < 0) {
+            model->devices.clear();
+        } else {
+            if (params.main_gpu >= (int)model->devices.size()) {
+                LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %zu)\n", __func__, params.main_gpu, model->devices.size());
+                llama_model_free(model);
+                return nullptr;
+            }
+            ggml_backend_dev_t main_gpu = model->devices[params.main_gpu];
+            model->devices.clear();
+            model->devices.push_back(main_gpu);
         }
-        ggml_backend_dev_t main_gpu = model->devices[params.main_gpu];
-        model->devices.clear();
-        model->devices.push_back(main_gpu);
     }
 
     for (auto * dev : model->devices) {

tests/CMakeLists.txt

@@ -42,6 +42,34 @@ function(llama_test target)
     set_property(TEST ${TEST_NAME} PROPERTY LABELS ${LLAMA_TEST_LABEL})
 endfunction()
 
+function(llama_test_cmd target)
+    include(CMakeParseArguments)
+    set(options)
+    set(oneValueArgs NAME LABEL WORKING_DIRECTORY)
+    set(multiValueArgs ARGS)
+    cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    if (NOT DEFINED LLAMA_TEST_LABEL)
+        set(LLAMA_TEST_LABEL "main")
+    endif()
+    if (NOT DEFINED LLAMA_TEST_WORKING_DIRECTORY)
+        set(LLAMA_TEST_WORKING_DIRECTORY .)
+    endif()
+    if (DEFINED LLAMA_TEST_NAME)
+        set(TEST_NAME ${LLAMA_TEST_NAME})
+    else()
+        set(TEST_NAME ${target})
+    endif()
+
+    add_test(
+        NAME ${TEST_NAME}
+        WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY}
+        COMMAND ${target}
+        ${LLAMA_TEST_ARGS})
+
+    set_property(TEST ${TEST_NAME} PROPERTY LABELS ${LLAMA_TEST_LABEL})
+endfunction()
+
 # Builds and runs a test source file.
 # Optional args:
 # - NAME: name of the executable & test target (defaults to the source file name without extension)
@@ -83,25 +111,31 @@ endfunction()
 # build test-tokenizer-0 target once and add many tests
 llama_build(test-tokenizer-0.cpp)
 
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge          ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge          ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-bert-bge.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r         ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-command-r.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-deepseek-coder.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-deepseek-llm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-falcon.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-gpt-2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-spm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-mpt.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-phi-3.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-qwen2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-refact.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-starcoder.gguf)
 
-# TODO: missing HF tokenizer for this model in convert_hf_to_gguf_update.py, see https://github.com/ggml-org/llama.cpp/pull/13847
-# llama_test(test-tokenizer-0 NAME test-tokenizer-0-nomic-bert-moe    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-nomic-bert-moe.gguf)
+if (NOT WIN32)
+    llama_test_cmd(
+        ${CMAKE_CURRENT_SOURCE_DIR}/test-tokenizers-repo.sh
+        NAME test-tokenizers-ggml-vocabs
+        WORKING_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}
+        ARGS https://huggingface.co/ggml-org/vocabs ${PROJECT_SOURCE_DIR}/models/ggml-vocabs
+    )
+endif()
 
 if (LLAMA_LLGUIDANCE)
-    llama_build_and_test(test-grammar-llguidance.cpp ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
+    llama_build_and_test(test-grammar-llguidance.cpp ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf)
 endif ()
 
 if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
@@ -113,8 +147,8 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
     llama_build_and_test(test-chat.cpp)
     # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
     if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
-        llama_build_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
-        target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../tools/server)
+        llama_build_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
+        target_include_directories(test-json-schema-to-grammar PRIVATE ${PROJECT_SOURCE_DIR}/tools/server)
     endif()
 
     if (NOT GGML_BACKEND_DL)
@@ -127,20 +161,20 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
     llama_build(test-tokenizer-1-bpe.cpp)
 
     # TODO: disabled due to slowness
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-2     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
-    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-aquila.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-falcon.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-2     ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-gpt-2.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-gpt-neox.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf --ignore-merges)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-mpt.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-refact.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-starcoder.gguf)
 
     # build test-tokenizer-1-spm target once and add many tests
     llama_build(test-tokenizer-1-spm.cpp)
 
-    llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
-    #llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-baichuan  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
+    llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-llama-spm ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-spm.gguf)
+    #llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-baichuan  ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-baichuan.gguf)
 
     # llama_build_and_test(test-double-float.cpp) # SLOW
 endif()
@@ -151,6 +185,8 @@ llama_build_and_test(test-json-partial.cpp)
 llama_build_and_test(test-log.cpp)
 llama_build_and_test(test-regex-partial.cpp)
 
+llama_build_and_test(test-thread-safety.cpp ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf -ngl 99 -p "The meaning of life is" -n 128 -c 256 -ub 32 -np 4)
+
 # this fails on windows (github hosted runner) due to curl DLL not found (exit code 0xc0000135)
 if (NOT WIN32)
     llama_build_and_test(test-arg-parser.cpp)

tests/test-thread-safety.cpp

@@ -0,0 +1,152 @@
+// thread safety test
+// - Loads a copy of the same model on each GPU, plus a copy on the CPU
+// - Creates n_parallel (--parallel) contexts per model
+// - Runs inference in parallel on each context
+
+#include <thread>
+#include <vector>
+#include <atomic>
+#include "llama.h"
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "sampling.h"
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
+        return 1;
+    }
+
+    common_init();
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+
+    //llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
+    //    if (level == GGML_LOG_LEVEL_ERROR) {
+    //        common_log_add(common_log_main(), level, "%s", text);
+    //    }
+    //}, NULL);
+
+    auto cparams = common_context_params_to_llama(params);
+
+    int dev_count = ggml_backend_dev_count();
+    int gpu_dev_count = 0;
+    for (int i = 0; i < dev_count; ++i) {
+        auto * dev = ggml_backend_dev_get(i);
+        if (dev && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
+            gpu_dev_count++;
+        }
+    }
+    const int num_models = gpu_dev_count + 1 + 1; // GPUs + 1 CPU model + 1 layer split
+    //const int num_models = std::max(1, gpu_dev_count);
+    const int num_contexts = std::max(1, params.n_parallel);
+
+    std::vector<llama_model_ptr> models;
+    std::vector<std::thread> threads;
+    std::atomic<bool> failed = false;
+
+    for (int m = 0; m < num_models; ++m) {
+        auto mparams = common_model_params_to_llama(params);
+
+        if (m < gpu_dev_count) {
+            mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
+            mparams.main_gpu = m;
+        } else if (m == gpu_dev_count) {
+            mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
+            mparams.main_gpu = -1; // CPU model
+        } else {
+            mparams.split_mode = LLAMA_SPLIT_MODE_LAYER;;
+        }
+
+        llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
+        if (model == NULL) {
+            LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
+            return 1;
+        }
+
+        models.emplace_back(model);
+    }
+
+    for  (int m = 0; m < num_models; ++m) {
+        auto * model = models[m].get();
+        for (int c = 0; c < num_contexts; ++c) {
+            threads.emplace_back([&, m, c, model]() {
+                LOG_INF("Creating context %d/%d for model %d/%d\n", c + 1, num_contexts, m + 1, num_models);
+
+                llama_context_ptr ctx { llama_init_from_model(model, cparams) };
+                if (ctx == NULL) {
+                    LOG_ERR("failed to create context\n");
+                    failed.store(true);
+                    return;
+                }
+
+                std::unique_ptr<common_sampler, decltype(&common_sampler_free)> sampler { common_sampler_init(model, params.sampling), common_sampler_free };
+                if (sampler == NULL) {
+                    LOG_ERR("failed to create sampler\n");
+                    failed.store(true);
+                    return;
+                }
+
+                llama_batch batch = {};
+                {
+                    auto prompt = common_tokenize(ctx.get(), params.prompt, true);
+                    if (prompt.empty()) {
+                        LOG_ERR("failed to tokenize prompt\n");
+                        failed.store(true);
+                        return;
+                    }
+                    batch = llama_batch_get_one(prompt.data(), prompt.size());
+                    if (llama_decode(ctx.get(), batch)) {
+                        LOG_ERR("failed to decode prompt\n");
+                        failed.store(true);
+                        return;
+                    }
+                }
+
+                const auto * vocab = llama_model_get_vocab(model);
+                std::string result = params.prompt;
+
+                for (int i = 0; i < params.n_predict; i++) {
+                    llama_token token;
+                    if (batch.n_tokens > 0) {
+                        token = common_sampler_sample(sampler.get(), ctx.get(), batch.n_tokens - 1);
+                    } else {
+                        token = llama_vocab_bos(vocab);
+                    }
+
+                    result += common_token_to_piece(ctx.get(), token);
+
+                    if (llama_vocab_is_eog(vocab, token)) {
+                        break;
+                    }
+
+                    batch = llama_batch_get_one(&token, 1);
+                    if (llama_decode(ctx.get(), batch)) {
+                        LOG_ERR("Model %d/%d, Context %d/%d: failed to decode\n", m + 1, num_models, c + 1, num_contexts);
+                        failed.store(true);
+                        return;
+                    }
+                }
+
+                LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str());
+            });
+        }
+    }
+
+    for (auto & thread : threads) {
+        thread.join();
+    }
+
+    if (failed) {
+        LOG_ERR("One or more threads failed.\n");
+        return 1;
+    }
+
+    LOG_INF("All threads finished without errors.\n");
+    return 0;
+}

tests/test-tokenizers-repo.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+if [ $# -lt 2 ]; then
+    printf "Usage: $0 <git-repo> <target-folder> [<test-exe>]\n"
+    exit 1
+fi
+
+if [ $# -eq 3 ]; then
+    toktest=$3
+else
+    toktest="./test-tokenizer-0"
+fi
+
+if [ ! -x $toktest ]; then
+    printf "Test executable \"$toktest\" not found!\n"
+    exit 1
+fi
+
+repo=$1
+folder=$2
+
+if [ -d $folder ] && [ -d $folder/.git ]; then
+    (cd $folder; git pull)
+else
+    git clone $repo $folder
+fi
+
+shopt -s globstar
+for gguf in $folder/**/*.gguf; do
+    if [ -f $gguf.inp ] && [ -f $gguf.out ]; then
+        $toktest $gguf
+    else
+        printf "Found \"$gguf\" without matching inp/out files, ignoring...\n"
+    fi
+done
+

tools/server/server.cpp

@@ -88,6 +88,26 @@ enum error_type {
     ERROR_TYPE_NOT_SUPPORTED, // custom error
 };
 
+static bool server_task_type_need_embd(server_task_type task_type) {
+    switch (task_type) {
+        case SERVER_TASK_TYPE_EMBEDDING:
+        case SERVER_TASK_TYPE_RERANK:
+            return true;
+        default:
+            return false;
+    }
+}
+
+static bool server_task_type_need_logits(server_task_type task_type) {
+    switch (task_type) {
+        case SERVER_TASK_TYPE_COMPLETION:
+        case SERVER_TASK_TYPE_INFILL:
+            return true;
+        default:
+            return false;
+    }
+}
+
 struct slot_params {
     bool stream        = true;
     bool cache_prompt  = true; // remember the prompt to avoid reprocessing all prompt
@@ -233,6 +253,7 @@ struct server_task {
         slot_params defaults;
         defaults.sampling    = params_base.sampling;
         defaults.speculative = params_base.speculative;
+        defaults.n_keep      = params_base.n_keep;
 
         // enabling this will output extra debug information in the HTTP responses from the server
         params.verbose           = params_base.verbosity > 9;
@@ -1329,13 +1350,24 @@ struct server_slot {
         n_draft_accepted = 0;
     }
 
-    bool is_non_causal() const {
-        return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK;
+    bool need_embd() const {
+        return server_task_type_need_embd(task_type);
+    }
+
+    bool need_logits() const {
+        return server_task_type_need_logits(task_type);
+    }
+
+    // if the context does not have a memory module then all embeddings have to be computed within a single ubatch
+    // also we cannot split if the pooling would require any past tokens
+    bool can_split() const {
+        return
+            !need_embd() ||
+            (llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST);
     }
 
     bool can_batch_with(server_slot & other_slot) const {
-        return is_non_causal() == other_slot.is_non_causal()
-            && are_lora_equal(lora, other_slot.lora);
+        return task_type == other_slot.task_type && are_lora_equal(lora, other_slot.lora);
     }
 
     bool has_budget(const common_params & global_params) {
@@ -1479,7 +1511,6 @@ struct server_slot {
             {"n_ctx",         n_ctx},
             {"speculative",   can_speculate()},
             {"is_processing", is_processing()},
-            {"non_causal",    is_non_causal()},
             {"params",        params.to_json()},
             {"prompt",        prompt_tokens.detokenize(ctx, true)},
             {"next_token",
@@ -2016,11 +2047,6 @@ 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;
@@ -2060,6 +2086,7 @@ struct server_context {
             SLT_INF(slot, "new slot n_ctx_slot = %d\n", slot.n_ctx);
 
             slot.params.sampling = params_base.sampling;
+            slot.params.n_keep = params_base.n_keep;
 
             slot.callback_on_release = [this](int) {
                 queue_tasks.pop_deferred_task();
@@ -2733,6 +2760,7 @@ struct server_context {
                         queue_tasks.defer(std::move(task));
                         break;
                     }
+
                     if (slot->is_processing()) {
                         // if requested slot is unavailable, we defer this task for processing later
                         SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id);
@@ -3095,7 +3123,14 @@ struct server_context {
                             continue;
                         }
 
-                        if (slot.is_non_causal()) {
+                        // TODO: support memory-less logits computation
+                        if (slot.need_logits() && !llama_get_memory(ctx)) {
+                            slot.release();
+                            send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER);
+                            continue;
+                        }
+
+                        if (!slot.can_split()) {
                             if (slot.n_prompt_tokens > n_ubatch) {
                                 slot.release();
                                 send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
@@ -3220,7 +3255,7 @@ struct server_context {
                                 }
 
                                 const auto n_swa = llama_model_n_swa(model);
-                                if (pos_min > slot.n_past - n_swa) {
+                                if (pos_min > std::max(0, 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");
@@ -3230,8 +3265,7 @@ struct server_context {
                         }
 
                         if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
-                            // we have to evaluate at least 1 token to generate logits.
-                            SLT_WRN(slot, "need to evaluate at least 1 token to generate logits, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens);
+                            SLT_WRN(slot, "need to evaluate at least 1 token for each active slot, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens);
 
                             slot.n_past--;
                         }
@@ -3239,8 +3273,7 @@ struct server_context {
                         slot.n_prompt_tokens_processed = 0;
                     }
 
-                    // non-causal tasks require to fit the entire prompt in the physical batch
-                    if (slot.is_non_causal()) {
+                    if (!slot.can_split()) {
                         // cannot fit the prompt in the current batch - will try next iter
                         if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
                             continue;
@@ -3262,8 +3295,7 @@ struct server_context {
                     slot.cache_tokens.keep_first(slot.n_past);
 
                     // check if we should process the image
-                    if (slot.n_past < slot.n_prompt_tokens
-                            && slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) {
+                    if (slot.n_past < slot.n_prompt_tokens && slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) {
                         // process the image
                         int32_t new_n_past;
                         int32_t res = slot.prompt_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past);
@@ -3294,8 +3326,8 @@ struct server_context {
                             break; // end of text chunk
                         }
 
-                        // without pooling, we want to output the embeddings for all the tokens in the batch
-                        const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE;
+                        // embedding requires all tokens in the batch to be output
+                        const bool need_embd = server_task_type_need_embd(slot.task_type);
 
                         common_batch_add(batch, cur_tok, slot.n_past, { slot.id }, need_embd);
                         slot.cache_tokens.push_back(cur_tok);
@@ -3349,17 +3381,15 @@ struct server_context {
         SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
 
         if (slot_batched) {
-            // make sure we're in the right embedding mode
-            llama_set_embeddings(ctx, slot_batched->is_non_causal());
             // apply lora, only need to do it once per batch
             common_set_adapter_lora(ctx, slot_batched->lora);
-        }
 
-        const bool do_encode = (params_base.embedding || params_base.reranking);
+            llama_set_embeddings(ctx, slot_batched->need_embd());
+        }
 
         // pad the batch so that batch.n_tokens >= n_slots
         // TODO: temporary workaround for https://github.com/ggml-org/llama.cpp/issues/13689
-        if (do_encode) {
+        if (slot_batched->need_embd()) {
             const int n_slots = slots.size();
 
             if (batch.n_tokens < n_slots) {
@@ -3381,8 +3411,11 @@ struct server_context {
                 SRV_WRN("adding %d dummy tokens to the batch, seq_id = %d\n", n_add, seq_id);
 
                 for (int j = 0; j < n_add; ++j) {
-                    common_batch_add(batch, 0, j, { seq_id }, false);
+                    common_batch_add(batch, 0, j, { seq_id }, true);
                 }
+
+                slots[seq_id].cache_tokens.clear();
+                llama_memory_seq_rm(llama_get_memory(ctx), seq_id, -1, -1);
             }
         }
 
@@ -4177,11 +4210,6 @@ int main(int argc, char ** argv) {
             oaicompat_type oaicompat) -> void {
         GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
 
-        if (ctx_server.params_base.embedding) {
-            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
-            return;
-        }
-
         auto completion_id = gen_chatcmplid();
         std::unordered_set<int> task_ids;
         try {
@@ -4436,12 +4464,8 @@ int main(int argc, char ** argv) {
             OAICOMPAT_TYPE_NONE); // infill is not OAI compatible
     };
 
-    const auto handle_chat_completions = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_chat_completions = [&ctx_server, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
         LOG_DBG("request: %s\n", req.body.c_str());
-        if (ctx_server.params_base.embedding) {
-            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
-            return;
-        }
 
         auto body = json::parse(req.body);
         std::vector<raw_buffer> files;
@@ -4569,13 +4593,18 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) {
-        const json body = json::parse(req.body);
+        if (!ctx_server.params_base.embedding) {
+            res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
 
         if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
             res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
+        const json body = json::parse(req.body);
+
         // for the shape of input/content, see tokenize_input_prompts()
         json prompt;
         if (body.count("input") != 0) {
@@ -4665,8 +4694,8 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
-        if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) {
-            res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED));
+        if (!ctx_server.params_base.embedding || ctx_server.params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) {
+            res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -4881,7 +4910,9 @@ int main(int argc, char ** argv) {
     };
 
     bool was_bound = false;
+    bool is_sock = false;
     if (string_ends_with(std::string(params.hostname), ".sock")) {
+        is_sock = true;
         LOG_INF("%s: setting address family to AF_UNIX\n", __func__);
         svr->set_address_family(AF_UNIX);
         // bind_to_port requires a second arg, any value other than 0 should
@@ -4959,7 +4990,9 @@ int main(int argc, char ** argv) {
     SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
 
-    LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port);
+    LOG_INF("%s: server is listening on %s - starting the main loop\n", __func__,
+            is_sock ? string_format("unix://%s", params.hostname.c_str()).c_str() :
+                      string_format("http://%s:%d", params.hostname.c_str(), params.port).c_str());
 
     // this call blocks the main thread until queue_tasks.terminate() is called
     ctx_server.queue_tasks.start_loop();

tools/server/webui/src/index.scss

@@ -41,6 +41,10 @@ html {
   max-width: 900px;
 }
 
+.chat-bubble {
+  @apply break-words;
+}
+
 .chat-bubble-base-300 {
   --tw-bg-opacity: 1;
   --tw-text-opacity: 1;