cli : move to HTTP-based implementation (#24948)

* cli: move to HTTP-based implementation

* wip

* working

* remote server ok

* cli support router mode

Co-authored-by: Piotr Wilkin <ilintar@gmail.com>

* case: router with only one model

* Apply suggestions from code review

Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>

* remove outdated comment

* use destructor instead

* add ftype

* cli-view --> cli-ui

* pimpl

* no more json in header

* nits fixes

* also show model aliases

---------

Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>

.github/workflows/hip-quality-check.yml

@@ -9,6 +9,8 @@ on:
       '.github/workflows/hip-quality-check.yml',
       '**/*.cu',
       '**/*.cuh',
+      'ggml/src/ggml-hip/CMakeLists.txt',
+      'ggml/src/ggml-cuda/vendors/hip.h',
       'scripts/hip/gcn-cdna-vgpr-check.py'
     ]
 
@@ -18,6 +20,8 @@ on:
       '.github/workflows/hip-quality-check.yml',
       '**/*.cu',
       '**/*.cuh',
+      'ggml/src/ggml-hip/CMakeLists.txt',
+      'ggml/src/ggml-cuda/vendors/hip.h',
       'scripts/hip/gcn-cdna-vgpr-check.py'
     ]
 

common/arg.cpp

@@ -27,6 +27,7 @@
 #include <cinttypes>
 #include <climits>
 #include <cstdarg>
+#include <filesystem>
 #include <fstream>
 #include <list>
 #include <regex>
@@ -718,9 +719,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
         // model is required (except for server)
         // TODO @ngxson : maybe show a list of available models in CLI in this case
-        if (params.model.path.empty()
-                && !params.usage
-                && !params.completion) {
+        bool can_skip_model = params.usage || params.completion || !params.server_base.empty();
+        if (!can_skip_model && params.model.path.empty()) {
             throw std::invalid_argument("error: --model is required\n");
         }
     }
@@ -1240,6 +1240,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.completion = true;
         }
     ));
+    add_opt(common_arg(
+        {"--server-base"}, "URL",
+        string_format("connect to this server instead of starting a new one, example: 'http://localhost:8080' (default: none)"),
+        [](common_params & params, const std::string & value) {
+            params.server_base = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--verbose-prompt"},
         string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
@@ -3451,9 +3458,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_LOG_FILE"));
     add_opt(common_arg(
         {"--log-prompts-dir"}, "PATH",
-        "Log prompts to directory (only used for debugging, default: disabled)",
+        "Log prompts to directory (auto-created if not present; only used for debugging, default: disabled)",
         [](common_params & params, const std::string & value) {
             params.path_prompts_log_dir = value;
+            std::error_code ec;
+            std::filesystem::create_directories(value, ec);
+            if (ec) {
+                fprintf(stderr, "warning: failed to create prompts-log-dir '%s': %s\n", value.c_str(), ec.message().c_str());
+            }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(

common/common.cpp

@@ -55,6 +55,10 @@
 #include <pwd.h>
 #endif
 
+#if defined(_AIX)
+#include <sys/systemcfg.h>
+#endif
+
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
@@ -72,7 +76,16 @@ common_time_meas::~common_time_meas() {
 //
 
 int32_t common_cpu_get_num_physical_cores() {
-#ifdef __linux__
+#if defined(_AIX)
+    int32_t logical_cpus = _system_configuration.ncpus;
+    int32_t smt_threads = _system_configuration.smt_threads;
+    if (smt_threads > 0) {
+        return static_cast<int32_t>(logical_cpus / smt_threads);
+    }
+    if (logical_cpus > 0) {
+        return static_cast<int32_t>(logical_cpus);
+    }
+#elif defined(__linux__)
     // enumerate the set of thread siblings, num entries is num cores
     std::unordered_set<std::string> siblings;
     for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
@@ -202,6 +215,14 @@ int32_t common_cpu_get_num_math() {
             }
         }
     }
+#elif defined(__powerpc64__) || defined(__powerpc__)
+    int32_t smt_factor = 1;
+    int phy_cpus = common_cpu_get_num_physical_cores();
+    int logical_cpus = sysconf(_SC_NPROCESSORS_ONLN);
+    if (phy_cpus > 0 && logical_cpus > phy_cpus) {
+        smt_factor = logical_cpus / phy_cpus;
+    }
+    return phy_cpus * std::min(smt_factor, 2);
 #endif
     return common_cpu_get_num_physical_cores();
 }

common/common.h

@@ -14,6 +14,7 @@
 #include <vector>
 #include <map>
 #include <algorithm>
+#include <fstream>
 
 #if defined(_WIN32) && !defined(_WIN32_WINNT)
 #define _WIN32_WINNT 0x0A00
@@ -643,6 +644,9 @@ struct common_params {
 
     std::map<std::string, std::string> default_template_kwargs;
 
+    // CLI params
+    std::string server_base; // if set, connect to this server instead of starting a new one
+
     // UI configs
     bool ui = true;
     bool ui_mcp_proxy = false;

common/http.h

@@ -2,6 +2,16 @@
 
 #include <cpp-httplib/httplib.h>
 
+#ifdef _WIN32
+#include <winsock2.h>
+#include <windows.h>
+#else
+#include <sys/socket.h>
+#include <netinet/in.h>
+#include <arpa/inet.h>
+#include <unistd.h>
+#endif
+
 struct common_http_url {
     std::string scheme;
     std::string user;
@@ -119,3 +129,63 @@ static std::pair<httplib::Client, common_http_url> common_http_client(const std:
 static std::string common_http_show_masked_url(const common_http_url & parts) {
     return parts.scheme + "://" + (parts.user.empty() ? "" : "****:****@") + common_http_format_host(parts.host) + parts.path;
 }
+
+static int common_http_get_free_port() {
+#ifdef _WIN32
+    WSADATA wsaData;
+    if (WSAStartup(MAKEWORD(2, 2), &wsaData) != 0) {
+        return -1;
+    }
+    typedef SOCKET native_socket_t;
+#define INVALID_SOCKET_VAL INVALID_SOCKET
+#define CLOSE_SOCKET(s) closesocket(s)
+#else
+    typedef int native_socket_t;
+#define INVALID_SOCKET_VAL -1
+#define CLOSE_SOCKET(s) close(s)
+#endif
+
+    native_socket_t sock = socket(AF_INET, SOCK_STREAM, 0);
+    if (sock == INVALID_SOCKET_VAL) {
+#ifdef _WIN32
+        WSACleanup();
+#endif
+        return -1;
+    }
+
+    struct sockaddr_in serv_addr;
+    std::memset(&serv_addr, 0, sizeof(serv_addr));
+    serv_addr.sin_family = AF_INET;
+    serv_addr.sin_addr.s_addr = htonl(INADDR_ANY);
+    serv_addr.sin_port = htons(0);
+
+    if (bind(sock, (struct sockaddr*)&serv_addr, sizeof(serv_addr)) != 0) {
+        CLOSE_SOCKET(sock);
+#ifdef _WIN32
+        WSACleanup();
+#endif
+        return -1;
+    }
+
+#ifdef _WIN32
+    int namelen = sizeof(serv_addr);
+#else
+    socklen_t namelen = sizeof(serv_addr);
+#endif
+    if (getsockname(sock, (struct sockaddr*)&serv_addr, &namelen) != 0) {
+        CLOSE_SOCKET(sock);
+#ifdef _WIN32
+        WSACleanup();
+#endif
+        return -1;
+    }
+
+    int port = ntohs(serv_addr.sin_port);
+
+    CLOSE_SOCKET(sock);
+#ifdef _WIN32
+    WSACleanup();
+#endif
+
+    return port;
+}

common/ngram-map.cpp

@@ -125,6 +125,16 @@ void common_ngram_map_begin(
     LOG_DBG("%s: begin, idx_last_draft=%zu, new begin=%zu, #keys=%zu\n", __func__,
             map.idx_last_check, size_begin, map.keys.size());
 
+    size_t idx_begin_cleanup = map.size_last_begin;
+    if (idx_begin_cleanup > size_begin) {
+        if (size_begin > (size_t) map.size_key + map.size_value) {
+            idx_begin_cleanup = size_begin - map.size_key - map.size_value;
+        } else {
+            idx_begin_cleanup = 0;
+        }
+        LOG_INF("%s: shrink cleanup begin: %zu -> %zu\n", __func__, map.size_last_begin, idx_begin_cleanup);
+    }
+
     size_t count_map_entries_upd = 0;
     if (!map.key_map.empty() && size_begin < map.idx_last_check) {
         if (map.show_key_map_stats) {
@@ -150,27 +160,23 @@ void common_ngram_map_begin(
         // Update the map from hash to key index (clear outdated entries).
         for (size_t i = 0; i < map.key_map.size(); ++i) {
             uint32_t key_idx = map.key_map[i];
-            if (key_idx >= map.size_last_begin) {
+            if (key_idx != 0 && key_idx >= idx_begin_cleanup) {
                 map.key_map[i] = 0;
                 count_map_entries_upd++;
             }
         }
-        map.key_map_last_idx = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0;
+        map.key_map_last_idx = (idx_begin_cleanup > 0) ? (uint32_t) (idx_begin_cleanup - 1) : 0;
     }
 
     if (size_begin < map.idx_last_check && !map.keys.empty()) {
-        // The next token generation will start at index size_begin.
-        // The tokens between map.size_last_begin and size_begin are no longer valid.
-        //
-        // Refresh map: Remove all entries with index >= map.size_last_begin.
         size_t count_keys = map.keys.size();
         size_t count_keys_del = 0;
         size_t count_values_del = 0;
         for (int32_t i = map.keys.size() - 1; i >= 0; --i) {
             common_ngram_map_key & key = map.keys[i];
-            if (key.key_idx >= map.size_last_begin) {
+            if (key.key_idx >= idx_begin_cleanup) {
                 // Delete the key.
-                LOG_DBG("%s: delete key %d at index %zu (>= size_last_begin=%zu)\n", __func__, i, key.key_idx, map.size_last_begin);
+                LOG_DBG("%s: delete key %d at index %zu (>= idx_begin_cleanup=%zu)\n", __func__, i, key.key_idx, idx_begin_cleanup);
                 map.keys.erase(map.keys.begin() + i);
                 count_keys_del++;
                 continue;
@@ -182,7 +188,7 @@ void common_ngram_map_begin(
             // Check the indices of the values.
             for (int16_t j = COMMON_NGRAM_MAX_VALUES - 1; j >= 0; --j) {
                 common_ngram_map_value & value = key.values[j];
-                if (value.value_idx >= map.size_last_begin) {
+                if (value.value_idx != 0 && value.value_idx >= idx_begin_cleanup) {
                     // Delete the value.
                     count_values_del++;
 

common/speculative.cpp

@@ -2221,6 +2221,112 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) {
     return n_max;
 }
 
+common_params common_base_params_to_speculative(const common_params & params) {
+    const bool has_draft = params.speculative.has_dft();
+
+    const auto & params_spec = params.speculative.draft;
+    common_params result = params;
+
+    if (has_draft) {
+        result.devices               = params_spec.devices;
+        result.model                 = params_spec.mparams;
+        result.n_gpu_layers          = params_spec.n_gpu_layers;
+        result.tensor_buft_overrides = params_spec.tensor_buft_overrides;
+
+        if (params_spec.cpuparams.n_threads > 0) {
+            result.cpuparams.n_threads       = params_spec.cpuparams.n_threads;
+            result.cpuparams_batch.n_threads = params_spec.cpuparams_batch.n_threads;
+        }
+    }
+
+    result.cache_type_k  = params_spec.cache_type_k;
+    result.cache_type_v  = params_spec.cache_type_v;
+    result.n_outputs_max = params.n_parallel;
+
+    return result;
+}
+
+struct common_speculative_init_result::impl {
+    impl() = default;
+    ~impl() = default;
+
+    // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top
+    llama_model_ptr   model;
+    llama_context_ptr context;
+};
+
+common_speculative_init_result::common_speculative_init_result(
+    common_params & params,
+      llama_model * model_tgt,
+    llama_context * ctx_tgt) :
+    pimpl(new impl{}) {
+    const bool has_draft = params.speculative.has_dft();
+    const bool spec_mtp = std::find(params.speculative.types.begin(),
+                                    params.speculative.types.end(),
+                                    COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+    GGML_ASSERT(has_draft || spec_mtp);
+
+    auto mparams = common_model_params_to_llama(params);
+    auto cparams = common_context_params_to_llama(params);
+
+    if (spec_mtp) {
+        cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
+    }
+
+    // note: for small models maybe we can set this to the maximum possible draft from all speculative types
+    //       the extra memory for small models is likely negligible?
+    cparams.n_rs_seq  = 0;
+    cparams.ctx_other = ctx_tgt;
+
+    std::string model_path;
+    if (has_draft) {
+        model_path = params.speculative.draft.mparams.path;
+        LOG_TRC("%s: loading draft model '%s'\n", __func__, model_path.c_str());
+
+        llama_model * model_dft = llama_model_load_from_file(params.model.path.c_str(), mparams);
+        if (model_dft == NULL) {
+            LOG_ERR("%s: failed to load draft model, '%s'\n", __func__, model_path.c_str());
+            return;
+        }
+
+        pimpl->model.reset(model_dft);
+
+        llama_context * ctx_dft = llama_init_from_model(model_dft, cparams);
+        if (ctx_dft == nullptr) {
+            LOG_ERR("%s: failed to create MTP context\n", __func__);
+            return;
+        }
+
+        pimpl->context.reset(ctx_dft);
+    } else if (spec_mtp) {
+        model_path = params.model.path;
+
+        LOG_TRC("%s: creating MTP draft context against the target model '%s'\n", __func__, model_path.c_str());
+
+        llama_context * ctx_dft = llama_init_from_model(model_tgt, cparams);
+        if (ctx_dft == nullptr) {
+            LOG_ERR("%s: failed to create MTP context\n", __func__);
+            return;
+        }
+
+        pimpl->context.reset(ctx_dft);
+    }
+}
+
+common_speculative_init_result::~common_speculative_init_result() = default;
+
+llama_model * common_speculative_init_result::model() {
+    return pimpl->model.get();
+}
+
+llama_context * common_speculative_init_result::context() {
+    return pimpl->context.get();
+}
+
+common_speculative_init_result_ptr common_speculative_init_from_params(common_params & params, llama_model * model_tgt, llama_context * ctx_tgt) {
+    return std::make_unique<common_speculative_init_result>(params, model_tgt, ctx_tgt);
+}
+
 // initialization of the speculative decoding system
 //
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq) {

common/speculative.h

@@ -23,6 +23,8 @@ std::string common_speculative_type_to_str(enum common_speculative_type type);
 // return the max number of draft tokens based on the speculative parameters
 int32_t common_speculative_n_max(const common_params_speculative * spec);
 
+common_params common_base_params_to_speculative(const common_params & params);
+
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq);
 
 void common_speculative_free(common_speculative * spec);
@@ -80,3 +82,19 @@ struct common_speculative_deleter {
 };
 
 typedef std::unique_ptr<common_speculative, common_speculative_deleter> common_speculative_ptr;
+
+struct common_speculative_init_result {
+    common_speculative_init_result(common_params & params, llama_model * model_tgt, llama_context * ctx_tgt);
+    ~common_speculative_init_result();
+
+    llama_model   * model();
+    llama_context * context();
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
+};
+
+using common_speculative_init_result_ptr = std::unique_ptr<common_speculative_init_result>;
+
+common_speculative_init_result_ptr common_speculative_init_from_params(common_params & params, llama_model * model_tgt, llama_context * ctx_tgt);

docs/backend/SYCL.md

@@ -790,10 +790,10 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
 | GGML_SYCL_DEV2DEV_MEMCPY | 0 (default) or 1 | Choose the SYCL or L0 API in dev2dev memory copy.<br>Value: <br>*  0: SYCL API (default)<br>* 1: L0 API -- L0 API is found to lead to abnormal crash in some case. This debug flag is used to check the issue.|
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
-| GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for Intel devices older than Gen 10) |
-| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
+| GGML_SYCL_ENABLE_OPT | 0 or 1 (default)| Enable optimize features for Intel GPUs. (Recommended to 0 for Intel devices older than Gen 10) |
+| GGML_SYCL_ENABLE_GRAPH | 0 (default) or 1 | Enable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
 | GGML_SYCL_USE_LEVEL_ZERO_API | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
-| GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
+| GGML_SYCL_ENABLE_DNN | 0 or 1 (default)| Enable running computations through oneDNN and always use oneMKL. |
 | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1. |
@@ -807,7 +807,7 @@ Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spo
 |-----------------|----------------------------------------------------------------------------------|
 | DEBUG_SYCL_POOL | Enable device memory pool logging on teardown. Useful for profiling allocations. |
 | DEBUG_SYCL_MALLOC | Enable verbose per-call logging of device pool alloc/free operations. |
-
+| GGML_SYCL_SUPPORT_VMM | Support to building with VMM code. Default is Yes. |
 
 ## Design Rule
 

docs/ops.md

@@ -21,12 +21,12 @@ Legend:
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
@@ -35,8 +35,8 @@ Legend:
 |                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             DIAG | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -70,7 +70,7 @@ Legend:
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
 |                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |

examples/llama-eval/llama-eval.py

@@ -362,7 +362,7 @@ class EvalState:
             case = cases.get(task_id, {})
             status = case.get("status", "pending")
             expected = case.get("expected", "")
-            answer = case.get("answer", "") if status == "ok" else ""
+            answer = case.get("answer") or "" if status == "ok" else ""
             is_correct = case.get("correct", False) if status == "ok" else False
             response = case.get("response", "") or ""
             prompt = case.get("prompt", "") or ""
@@ -647,7 +647,7 @@ class EvalState:
             question, prompt, expected = self.get_case(i)
             case = cases.get(task_id, {})
             status = case.get("status", "pending")
-            answer = case.get("answer", "N/A") if status == "ok" else "N/A"
+            answer = case.get("answer") or "N/A" if status == "ok" else "N/A"
             tokens = case.get("tokens")
             tokens_str = str(tokens) if tokens is not None else "N/A"
             tps_gen = case.get("tps_gen")

ggml/include/ggml.h

@@ -429,7 +429,8 @@ extern "C" {
         GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
         GGML_TYPE_NVFP4   = 40, // NVFP4 (4 blocks, E4M3 scale)
         GGML_TYPE_Q1_0    = 41,
-        GGML_TYPE_COUNT   = 42,
+        GGML_TYPE_Q2_0    = 42,
+        GGML_TYPE_COUNT   = 43,
     };
 
     // precision
@@ -473,6 +474,7 @@ extern "C" {
         GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
         GGML_FTYPE_MOSTLY_NVFP4   = 26, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q1_0    = 27, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_0    = 28, // except 1d tensors
     };
 
     // available tensor operations:

ggml/src/ggml-common.h

@@ -96,6 +96,9 @@ typedef sycl::half2 ggml_half2;
 #define QI1_0 (QK1_0 / 32)
 #define QR1_0 1
 
+#define QI2_0 (QK2_0 / 32)
+#define QR2_0 1
+
 
 #define QI4_0 (QK4_0 / (4 * QR4_0))
 #define QR4_0 2
@@ -181,6 +184,13 @@ typedef struct {
 } block_q1_0;
 static_assert(sizeof(block_q1_0) == sizeof(ggml_half) + QK1_0 / 8, "wrong q1_0 block size/padding");
 
+#define QK2_0 64
+typedef struct {
+    ggml_half d;              // delta (scale)
+    uint8_t qs[QK2_0 / 4];   // 2 bits per element
+} block_q2_0;
+static_assert(sizeof(block_q2_0) == sizeof(ggml_half) + QK2_0 / 4, "wrong q2_0 block size/padding");
+
 #define QK4_0 32
 typedef struct {
     ggml_half d;           // delta

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

@@ -17,6 +17,7 @@
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_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
@@ -82,6 +83,7 @@
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // quants.c
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_0_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
@@ -113,6 +115,7 @@
 #define quantize_row_q8_K_generic quantize_row_q8_K
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_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_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
@@ -162,6 +165,7 @@
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_0_q8_0
 // 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
@@ -202,6 +206,7 @@
 #elif defined(__riscv)
 // quants.c
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_0_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
@@ -243,6 +248,7 @@
 #define quantize_row_q8_K_generic quantize_row_q8_K
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_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
@@ -306,6 +312,7 @@
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
+#define ggml_vec_dot_q2_0_q8_0_generic ggml_vec_dot_q2_0_q8_0
 // 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

ggml/src/ggml-cpu/arch/arm/quants.c

@@ -219,6 +219,80 @@ void ggml_vec_dot_q1_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }
 
+void ggml_vec_dot_q2_0_q8_0(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) {
+    const int qk = QK2_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_q2_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    float sumf = 0.0f;
+
+#if defined(__ARM_NEON)
+    // Replicate pattern: each byte repeated 4 times
+    static const uint8_t tbl_idx_lo[16] = {0,0,0,0, 1,1,1,1, 2,2,2,2, 3,3,3,3};
+    static const uint8_t tbl_idx_hi[16] = {4,4,4,4, 5,5,5,5, 6,6,6,6, 7,7,7,7};
+    // Right-shift amounts: 0,2,4,6 repeated for each group of 4
+    static const int8_t shift_vals[16] = {0,-2,-4,-6, 0,-2,-4,-6, 0,-2,-4,-6, 0,-2,-4,-6};
+
+    const uint8x16_t idx_lo  = vld1q_u8(tbl_idx_lo);
+    const uint8x16_t idx_hi  = vld1q_u8(tbl_idx_hi);
+    const int8x16_t  shifts  = vld1q_s8(shift_vals);
+    const uint8x16_t mask2   = vdupq_n_u8(0x03);
+    const int8x16_t  one     = vdupq_n_s8(1);
+
+    float32x4_t sumv = vdupq_n_f32(0.0f);
+
+    for (int i = 0; i < nb; i++) {
+        const float d0 = GGML_CPU_FP16_TO_FP32(x[i].d);
+
+        // group 64: one Q2_0 block (64 weights) maps to two Q8_0 blocks (2 * 32 = 64)
+        for (int k = 0; k < 2; k++) {
+            const block_q8_0 * GGML_RESTRICT yb = &y[i * 2 + k];
+            const float d1 = GGML_CPU_FP16_TO_FP32(yb->d);
+
+            // Load 8 bytes of packed 2-bit values
+            const uint8x8_t raw = vld1_u8(&x[i].qs[k * 8]);
+            const uint8x16_t raw16 = vcombine_u8(raw, raw);
+
+            // First 16 elements: replicate bytes 0-3, shift, mask, subtract 1
+            uint8x16_t bytes0 = vqtbl1q_u8(raw16, idx_lo);
+            int8x16_t qv0 = vsubq_s8(
+                vreinterpretq_s8_u8(vandq_u8(vshlq_u8(bytes0, shifts), mask2)),
+                one);
+
+            // Second 16 elements: replicate bytes 4-7, shift, mask, subtract 1
+            uint8x16_t bytes1 = vqtbl1q_u8(raw16, idx_hi);
+            int8x16_t qv1 = vsubq_s8(
+                vreinterpretq_s8_u8(vandq_u8(vshlq_u8(bytes1, shifts), mask2)),
+                one);
+
+            // Load Q8_0 values and dot product
+            const int8x16_t y0 = vld1q_s8(yb->qs);
+            const int8x16_t y1 = vld1q_s8(yb->qs + 16);
+
+            int32x4_t p0 = ggml_vdotq_s32(vdupq_n_s32(0), qv0, y0);
+            int32x4_t p1 = ggml_vdotq_s32(p0, qv1, y1);
+
+            sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(p1), d0 * d1);
+        }
+    }
+
+    sumf = vaddvq_f32(sumv);
+#else
+    ggml_vec_dot_q2_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+    return;
+#endif
+
+    *s = sumf;
+}
 
 void ggml_vec_dot_q4_0_q8_0(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) {
     const int qk = QK8_0;

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

@@ -230,6 +230,12 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
     },
+    [GGML_TYPE_Q2_0] = {
+        .from_float               = quantize_row_q2_0,
+        .vec_dot                  = ggml_vec_dot_q2_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_Q4_0] = {
         .from_float               = quantize_row_q4_0,
         .vec_dot                  = ggml_vec_dot_q4_0_q8_0,

ggml/src/ggml-cpu/ops.cpp

@@ -665,6 +665,7 @@ void ggml_compute_forward_add(
                 ggml_compute_forward_add_non_quantized(params, dst);
             } break;
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -1115,6 +1116,7 @@ void ggml_compute_forward_add1(
                 }
             } break;
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -1245,6 +1247,7 @@ void ggml_compute_forward_acc(
         case GGML_TYPE_F16:
         case GGML_TYPE_BF16:
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -4454,6 +4457,7 @@ void ggml_compute_forward_out_prod(
 
     switch (src0->type) {
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -4730,6 +4734,7 @@ void ggml_compute_forward_set(
         case GGML_TYPE_F16:
         case GGML_TYPE_BF16:
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -4954,6 +4959,7 @@ void ggml_compute_forward_get_rows(
 
     switch (src0->type) {
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -5019,8 +5025,8 @@ void ggml_compute_forward_get_rows(
     //}
 }
 
-template<typename idx_t>
-static void ggml_compute_forward_set_rows_f32(
+template<typename src_t, typename idx_t>
+static void ggml_compute_forward_set_rows_impl(
         const ggml_compute_params * params,
               ggml_tensor * dst) {
 
@@ -5035,7 +5041,7 @@ static void ggml_compute_forward_set_rows_f32(
     assert(ne0  == nc);
     assert(ne2  == ne02);
     assert(ne3  == ne03);
-    assert(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16));
     assert(ne02 % ne11 == 0);
     assert(ne03 % ne12 == 0);
 
@@ -5049,6 +5055,8 @@ static void ggml_compute_forward_set_rows_f32(
     const int64_t ir0 = dr*ith;
     const int64_t ir1 = std::min(ir0 + dr, nr);
 
+    const size_t rs = ggml_row_size(src0->type, nc);
+
     ggml_from_float_t const from_float = ggml_get_type_traits_cpu(dst->type)->from_float;
 
     for (int64_t i03 = 0; i03 < ne03; ++i03) {
@@ -5062,9 +5070,18 @@ static void ggml_compute_forward_set_rows_f32(
 
                 GGML_ASSERT(i1 >= 0 && i1 < ne1);
 
-                from_float(
-                        (const float *) ((char *) src0->data +  i*nb01 + i02*nb02 + i03*nb03),
-                                        ((char *)  dst->data + i1*nb1  + i02*nb2  + i03*nb3), nc);
+                if constexpr (std::is_same_v<src_t, float>) {
+                    from_float(
+                            (const float *) ((char *) src0->data +  i*nb01 + i02*nb02 + i03*nb03),
+                                            ((char *)  dst->data + i1*nb1  + i02*nb2  + i03*nb3), nc);
+                } else if constexpr (std::is_same_v<src_t, ggml_fp16_t>) {
+                    memcpy(
+                                            ((char *)  dst->data + i1*nb1  + i02*nb2  + i03*nb3),
+                                            ((char *) src0->data +  i*nb01 + i02*nb02 + i03*nb03),
+                                            rs);
+                } else {
+                    GGML_ABORT("src0->type = %d (%s) not supported", src0->type, ggml_type_name(src0->type));
+                }
             }
         }
     }
@@ -5081,13 +5098,27 @@ void ggml_compute_forward_set_rows(
         case GGML_TYPE_F32:
             {
                 if (src1->type == GGML_TYPE_I64) {
-                    ggml_compute_forward_set_rows_f32<int64_t>(params, dst);
+                    ggml_compute_forward_set_rows_impl<float, int64_t>(params, dst);
                 } else if (src1->type == GGML_TYPE_I32) {
-                    ggml_compute_forward_set_rows_f32<int32_t>(params, dst);
+                    ggml_compute_forward_set_rows_impl<float, int32_t>(params, dst);
                 } else {
                     GGML_ABORT("src1->type = %d (%s) not supported", src1->type, ggml_type_name(src1->type));
                 }
             } break;
+        case GGML_TYPE_F16:
+            {
+                if (dst->type == GGML_TYPE_F16) {
+                    if (src1->type == GGML_TYPE_I64) {
+                        ggml_compute_forward_set_rows_impl<ggml_fp16_t, int64_t>(params, dst);
+                    } else if (src1->type == GGML_TYPE_I32) {
+                        ggml_compute_forward_set_rows_impl<ggml_fp16_t, int32_t>(params, dst);
+                    } else {
+                        GGML_ABORT("src1->type = %d (%s) not supported", src1->type, ggml_type_name(src1->type));
+                    }
+                } else {
+                    GGML_ABORT("dst->type = %d (%s) not supported with src0->type = %d (%s)", dst->type, ggml_type_name(dst->type), src0->type, ggml_type_name(src0->type));
+                }
+            } break;
         default:
             {
                 GGML_ABORT("src0->type = %d (%s) not supported", src0->type, ggml_type_name(src0->type));
@@ -5680,6 +5711,7 @@ void ggml_compute_forward_clamp(
             } break;
         case GGML_TYPE_BF16:
         case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:

ggml/src/ggml-cpu/quants.c

@@ -26,6 +26,10 @@ void quantize_row_q1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
     quantize_row_q1_0_ref(x, y, k);
 }
 
+void quantize_row_q2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
+    quantize_row_q2_0_ref(x, y, k);
+}
+
 void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
     quantize_row_q4_0_ref(x, y, k);
 }
@@ -170,6 +174,53 @@ void ggml_vec_dot_q1_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
     *s = sumf;
 }
 
+void ggml_vec_dot_q2_0_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) {
+    const int qk = QK2_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_q2_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    float sumf = 0.0f;
+
+    for (int i = 0; i < nb; i++) {
+        const float d0 = GGML_CPU_FP16_TO_FP32(x[i].d);
+
+        float sumi = 0.0f;
+
+        // group 64: one Q2_0 block (64 weights) maps to two Q8_0 blocks (2 * 32 = 64)
+        for (int k = 0; k < 2; k++) {
+            const block_q8_0 * GGML_RESTRICT yb = &y[i * 2 + k];
+            const float d1 = GGML_CPU_FP16_TO_FP32(yb->d);
+            int sumi_block = 0;
+
+            const uint8_t * GGML_RESTRICT qs = &x[i].qs[k * 8];
+            const int8_t  * GGML_RESTRICT qy = yb->qs;
+
+            for (int b = 0; b < 8; ++b) {
+                const uint8_t byte = qs[b];
+                // Extract 4 two-bit values, map {0,1,2,3} -> {-1,0,1,2}
+                sumi_block += ((int)((byte >> 0) & 3) - 1) * qy[b*4 + 0];
+                sumi_block += ((int)((byte >> 2) & 3) - 1) * qy[b*4 + 1];
+                sumi_block += ((int)((byte >> 4) & 3) - 1) * qy[b*4 + 2];
+                sumi_block += ((int)((byte >> 6) & 3) - 1) * qy[b*4 + 3];
+            }
+
+            sumi += d1 * sumi_block;
+        }
+
+        sumf += d0 * sumi;
+    }
+
+    *s = sumf;
+}
 
 void ggml_vec_dot_q4_0_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) {
     const int qk = QK8_0;

ggml/src/ggml-cpu/quants.h

@@ -13,6 +13,7 @@ extern "C" {
 
 // Quantization
 void quantize_row_q1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -38,6 +39,7 @@ void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y,
 
 // Dot product
 void ggml_vec_dot_q1_0_q8_0(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_q2_0_q8_0(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_q4_0_q8_0(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_q4_1_q8_1(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_q5_0_q8_0(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);
@@ -71,6 +73,7 @@ void quantize_row_q8_0_generic(const float * GGML_RESTRICT x, void * GGML_RESTRI
 void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void quantize_row_q8_K_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void ggml_vec_dot_q1_0_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_q2_0_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_q4_0_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_q4_1_q8_1_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_q5_0_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);

ggml/src/ggml-cpu/simd-gemm.h

@@ -78,7 +78,7 @@ static void simd_gemm(
             for (int64_t i = 0; i < GEMM_RM; i++) {
                 float a = C[i * N + jj];
                 for (int64_t kk = 0; kk < K; kk++) {
-                    a += A[i + kk] * B[kk * N + jj];
+                    a += A[i * K + kk] * B[kk * N + jj];
                 }
                 C[i * N + jj] = a;
             }

ggml/src/ggml-cuda/common.cuh

@@ -1505,12 +1505,16 @@ struct ggml_cuda_mm_fusion_args_host {
     const ggml_tensor * x_bias = nullptr;
     const ggml_tensor * gate = nullptr;
     const ggml_tensor * gate_bias = nullptr;
+    const ggml_tensor * x_scale = nullptr;
+    const ggml_tensor * gate_scale = nullptr;
     ggml_glu_op glu_op;
 };
 struct ggml_cuda_mm_fusion_args_device {
     const void * x_bias = nullptr;
     const void * gate = nullptr;
     const void * gate_bias = nullptr;
+    const void * x_scale = nullptr;
+    const void * gate_scale = nullptr;
     ggml_glu_op glu_op;
 };
 

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -1582,12 +1582,18 @@ static bool ggml_cuda_should_fuse_mul_mat(const ggml_tensor * ffn_up,
                                           const ggml_tensor * ffn_gate,
                                           const ggml_tensor * glu,
                                           const ggml_tensor * ffn_up_bias = nullptr,
-                                          const ggml_tensor * ffn_gate_bias = nullptr) {
+                                          const ggml_tensor * ffn_gate_bias = nullptr,
+                                          const ggml_tensor * ffn_up_scale = nullptr,
+                                          const ggml_tensor * ffn_gate_scale = nullptr) {
     const bool has_bias = ffn_up_bias != nullptr || ffn_gate_bias != nullptr;
+    const bool has_scale = ffn_up_scale != nullptr || ffn_gate_scale != nullptr;
 
     if (has_bias && (!ffn_up_bias || !ffn_gate_bias)) {
         return false;
     }
+    if (has_scale && (!ffn_up_scale || !ffn_gate_scale)) {
+        return false;
+    }
 
     const bool is_mul_mat     = ffn_up->op == GGML_OP_MUL_MAT     && ffn_gate->op == GGML_OP_MUL_MAT     && glu->op == GGML_OP_GLU;
     const bool is_mul_mat_id  = ffn_up->op == GGML_OP_MUL_MAT_ID  && ffn_gate->op == GGML_OP_MUL_MAT_ID  && glu->op == GGML_OP_GLU;
@@ -1599,34 +1605,45 @@ static bool ggml_cuda_should_fuse_mul_mat(const ggml_tensor * ffn_up,
     }
 
     const ggml_op expected_bias_op = is_mul_mat ? GGML_OP_ADD : GGML_OP_ADD_ID;
+    const ggml_tensor * ffn_up_bias_src   = has_scale ? ffn_up_scale   : ffn_up;
+    const ggml_tensor * ffn_gate_bias_src = has_scale ? ffn_gate_scale : ffn_gate;
+    const ggml_tensor * ffn_up_out        = has_bias ? ffn_up_bias     : ffn_up_bias_src;
+    const ggml_tensor * ffn_gate_out      = has_bias ? ffn_gate_bias   : ffn_gate_bias_src;
+
+    if (glu->src[0] != ffn_gate_out || glu->src[1] != ffn_up_out) {
+        return false;
+    }
+
+    if (has_scale) {
+        if (ffn_up_scale->op != GGML_OP_MUL || ffn_gate_scale->op != GGML_OP_MUL) {
+            return false;
+        }
+        const bool up_has_mm   = ffn_up_scale->src[0] == ffn_up || ffn_up_scale->src[1] == ffn_up;
+        const bool gate_has_mm = ffn_gate_scale->src[0] == ffn_gate || ffn_gate_scale->src[1] == ffn_gate;
+        if (!up_has_mm || !gate_has_mm) {
+            return false;
+        }
+    }
 
     if (has_bias) {
         if (ffn_up_bias->op != expected_bias_op || ffn_gate_bias->op != expected_bias_op) {
             return false;
         }
 
-        if (glu->src[0] != ffn_gate_bias || glu->src[1] != ffn_up_bias) {
-            return false;
-        }
-
         if (expected_bias_op == GGML_OP_ADD) {
-            const bool up_has_mul   = ffn_up_bias->src[0] == ffn_up || ffn_up_bias->src[1] == ffn_up;
-            const bool gate_has_mul = ffn_gate_bias->src[0] == ffn_gate || ffn_gate_bias->src[1] == ffn_gate;
+            const bool up_has_mul   = ffn_up_bias->src[0] == ffn_up_bias_src || ffn_up_bias->src[1] == ffn_up_bias_src;
+            const bool gate_has_mul = ffn_gate_bias->src[0] == ffn_gate_bias_src || ffn_gate_bias->src[1] == ffn_gate_bias_src;
             if (!up_has_mul || !gate_has_mul) {
                 return false;
             }
         } else { // GGML_OP_ADD_ID
-            if (ffn_up_bias->src[0] != ffn_up || ffn_gate_bias->src[0] != ffn_gate) {
+            if (ffn_up_bias->src[0] != ffn_up_bias_src || ffn_gate_bias->src[0] != ffn_gate_bias_src) {
                 return false;
             }
             if (ffn_up_bias->src[2] != ffn_up->src[2] || ffn_gate_bias->src[2] != ffn_gate->src[2]) {
                 return false;
             }
         }
-    } else {
-        if (glu->src[0] != ffn_gate && glu->src[1] != ffn_up) {
-            return false;
-        }
     }
 
     if (ffn_up->src[0]->type != ffn_gate->src[0]->type || !ggml_are_same_shape(ffn_up->src[0], ffn_gate->src[0]) ||
@@ -1638,7 +1655,7 @@ static bool ggml_cuda_should_fuse_mul_mat(const ggml_tensor * ffn_up,
         return false;
     }
 
-    if (ffn_up->src[2] && (ffn_up->src[2] != ffn_gate->src[2])) {
+    if (is_mul_mat_id && ffn_up->src[2] != ffn_gate->src[2]) {
         return false;
     }
 
@@ -3204,10 +3221,240 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
     bool fused_mul_mat_vec = false;
     int  fused_node_count  = 0;
 
-    // gate + glu + up
+    auto get_mul_mat_scale = [](const ggml_tensor * scale_node, const ggml_tensor * mm_node) -> const ggml_tensor * {
+        const bool scale_lhs_mm = scale_node->src[0] == mm_node;
+        const bool scale_rhs_mm = scale_node->src[1] == mm_node;
+        if (!scale_lhs_mm && !scale_rhs_mm) {
+            return nullptr;
+        }
+
+        const ggml_tensor * scale = scale_lhs_mm ? scale_node->src[1] : scale_node->src[0];
+        if (mm_node->src[0]->type != GGML_TYPE_NVFP4 || scale_node->type != GGML_TYPE_F32 ||
+                scale->type != GGML_TYPE_F32 || !ggml_is_contiguous(scale) || ggml_nelements(scale) != 1 ||
+                !ggml_are_same_shape(scale_node, mm_node)) {
+            return nullptr;
+        }
+
+        return scale;
+    };
+
+    auto get_mul_mat_id_scale = [](const ggml_tensor * reshape, const ggml_tensor * repeat, const ggml_tensor * getrows,
+            const ggml_tensor * scale_node, const ggml_tensor * mm_node) -> const ggml_tensor * {
+        if (repeat->src[0] != reshape || getrows->src[0] != repeat || getrows->src[1] != mm_node->src[2]) {
+            return nullptr;
+        }
+        if (!((scale_node->src[0] == mm_node && scale_node->src[1] == getrows) ||
+                (scale_node->src[0] == getrows && scale_node->src[1] == mm_node))) {
+            return nullptr;
+        }
+
+        const ggml_tensor * scale = reshape->src[0];
+        if (mm_node->src[0]->type != GGML_TYPE_NVFP4 || scale_node->type != GGML_TYPE_F32 ||
+                scale->type != GGML_TYPE_F32 || !ggml_is_contiguous(scale) || ggml_nelements(scale) != mm_node->src[0]->ne[2] ||
+                !ggml_are_same_shape(scale_node, mm_node)) {
+            return nullptr;
+        }
+
+        return scale;
+    };
+
+    auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) -> const ggml_tensor * {
+        if (op_bias == GGML_OP_ADD) {
+            if (bias_node->src[0] == mul_node) {
+                return bias_node->src[1];
+            }
+            if (bias_node->src[1] == mul_node) {
+                return bias_node->src[0];
+            }
+            return nullptr;
+        }
+        GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
+        GGML_ASSERT(bias_node->src[0] == mul_node);
+        return bias_node->src[1];
+    };
+
+    // gate + glu + up, with optional scale/bias on both lanes.
     for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
         const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
 
+        if (op == GGML_OP_MUL_MAT) {
+            for (const bool with_bias : { false, true }) {
+                const int gate_idx       = i;
+                const int gate_scale_idx = i + 1;
+                const int gate_bias_idx  = with_bias ? i + 2 : -1;
+                const int up_idx         = with_bias ? i + 3 : i + 2;
+                const int up_scale_idx   = up_idx + 1;
+                const int up_bias_idx    = with_bias ? up_idx + 2 : -1;
+                const int glu_idx        = with_bias ? up_idx + 3 : up_idx + 2;
+
+                const int out_nodes[] = { glu_idx };
+                ggml_op ops[7];
+                if (with_bias) {
+                    ops[0] = op;
+                    ops[1] = GGML_OP_MUL;
+                    ops[2] = bias_op;
+                    ops[3] = op;
+                    ops[4] = GGML_OP_MUL;
+                    ops[5] = bias_op;
+                    ops[6] = GGML_OP_GLU;
+                } else {
+                    ops[0] = op;
+                    ops[1] = GGML_OP_MUL;
+                    ops[2] = op;
+                    ops[3] = GGML_OP_MUL;
+                    ops[4] = GGML_OP_GLU;
+                }
+                const int n_ops = with_bias ? 7 : 5;
+
+                if (!ggml_can_fuse_subgraph(cgraph, i, n_ops, ops, out_nodes, 1) ||
+                        !ggml_cuda_check_fusion_memory_ranges(cgraph, i, n_ops, out_nodes, 1)) {
+                    continue;
+                }
+
+                ggml_tensor * gate_n       = cgraph->nodes[gate_idx];
+                ggml_tensor * gate_scale_n = cgraph->nodes[gate_scale_idx];
+                ggml_tensor * gate_out_n   = with_bias ? cgraph->nodes[gate_bias_idx] : gate_scale_n;
+                ggml_tensor * up_n         = cgraph->nodes[up_idx];
+                ggml_tensor * up_scale_n   = cgraph->nodes[up_scale_idx];
+                ggml_tensor * up_out_n     = with_bias ? cgraph->nodes[up_bias_idx] : up_scale_n;
+                const ggml_tensor * glu = cgraph->nodes[glu_idx];
+
+                if (!ggml_cuda_should_fuse_mul_mat(up_n, gate_n, glu,
+                        with_bias ? up_out_n : nullptr, with_bias ? gate_out_n : nullptr, up_scale_n, gate_scale_n)) {
+                    continue;
+                }
+
+                const ggml_tensor * gate_scale = get_mul_mat_scale(gate_scale_n, gate_n);
+                const ggml_tensor * up_scale   = get_mul_mat_scale(up_scale_n, up_n);
+                if (!gate_scale || !up_scale) {
+                    continue;
+                }
+
+                const ggml_tensor * up_bias   = with_bias ? get_bias_tensor(up_out_n, up_scale_n, bias_op) : nullptr;
+                const ggml_tensor * gate_bias = with_bias ? get_bias_tensor(gate_out_n, gate_scale_n, bias_op) : nullptr;
+                if (with_bias && (!ggml_are_same_shape(gate_out_n->src[0], gate_out_n->src[1]) ||
+                        !ggml_are_same_shape(up_out_n->src[0], up_out_n->src[1]))) {
+                    continue;
+                }
+
+                const ggml_tensor * src0 = up_n->src[0];
+                const ggml_tensor * src1 = up_n->src[1];
+                const ggml_tensor * ids  = up_n->src[2];
+
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate       = gate_n->src[0];
+                fusion_data.x_bias     = up_bias;
+                fusion_data.gate_bias  = gate_bias;
+                fusion_data.x_scale    = up_scale;
+                fusion_data.gate_scale = gate_scale;
+                fusion_data.glu_op     = ggml_get_glu_op(glu);
+
+                if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
+                    ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, cgraph->nodes[glu_idx], &fusion_data);
+                    fused_mul_mat_vec = true;
+                    fused_node_count  = n_ops;
+                    break;
+                }
+            }
+
+            if (fused_mul_mat_vec) {
+                break;
+            }
+        } else {
+            for (const bool with_bias : { false, true }) {
+                const int gate_idx       = i;
+                const int gate_scale_idx = i + 4;
+                const int gate_bias_idx  = with_bias ? i + 5 : -1;
+                const int up_idx         = with_bias ? i + 6 : i + 5;
+                const int up_scale_idx   = up_idx + 4;
+                const int up_bias_idx    = with_bias ? up_idx + 5 : -1;
+                const int glu_idx        = with_bias ? up_idx + 6 : up_idx + 5;
+
+                const int out_nodes[] = { glu_idx };
+                ggml_op ops[13];
+                if (with_bias) {
+                    ops[0]  = op;
+                    ops[1]  = GGML_OP_RESHAPE;
+                    ops[2]  = GGML_OP_REPEAT;
+                    ops[3]  = GGML_OP_GET_ROWS;
+                    ops[4]  = GGML_OP_MUL;
+                    ops[5]  = bias_op;
+                    ops[6]  = op;
+                    ops[7]  = GGML_OP_RESHAPE;
+                    ops[8]  = GGML_OP_REPEAT;
+                    ops[9]  = GGML_OP_GET_ROWS;
+                    ops[10] = GGML_OP_MUL;
+                    ops[11] = bias_op;
+                    ops[12] = GGML_OP_GLU;
+                } else {
+                    ops[0]  = op;
+                    ops[1]  = GGML_OP_RESHAPE;
+                    ops[2]  = GGML_OP_REPEAT;
+                    ops[3]  = GGML_OP_GET_ROWS;
+                    ops[4]  = GGML_OP_MUL;
+                    ops[5]  = op;
+                    ops[6]  = GGML_OP_RESHAPE;
+                    ops[7]  = GGML_OP_REPEAT;
+                    ops[8]  = GGML_OP_GET_ROWS;
+                    ops[9]  = GGML_OP_MUL;
+                    ops[10] = GGML_OP_GLU;
+                }
+                const int n_ops = with_bias ? 13 : 11;
+
+                if (!ggml_can_fuse_subgraph(cgraph, i, n_ops, ops, out_nodes, 1) ||
+                        !ggml_cuda_check_fusion_memory_ranges(cgraph, i, n_ops, out_nodes, 1)) {
+                    continue;
+                }
+
+                ggml_tensor * gate_n       = cgraph->nodes[gate_idx];
+                ggml_tensor * gate_scale_n = cgraph->nodes[gate_scale_idx];
+                ggml_tensor * gate_out_n   = with_bias ? cgraph->nodes[gate_bias_idx] : gate_scale_n;
+                ggml_tensor * up_n         = cgraph->nodes[up_idx];
+                ggml_tensor * up_scale_n   = cgraph->nodes[up_scale_idx];
+                ggml_tensor * up_out_n     = with_bias ? cgraph->nodes[up_bias_idx] : up_scale_n;
+                const ggml_tensor * glu = cgraph->nodes[glu_idx];
+
+                if (!ggml_cuda_should_fuse_mul_mat(up_n, gate_n, glu,
+                        with_bias ? up_out_n : nullptr, with_bias ? gate_out_n : nullptr, up_scale_n, gate_scale_n)) {
+                    continue;
+                }
+
+                const ggml_tensor * gate_scale = get_mul_mat_id_scale(cgraph->nodes[gate_idx + 1], cgraph->nodes[gate_idx + 2],
+                        cgraph->nodes[gate_idx + 3], gate_scale_n, gate_n);
+                const ggml_tensor * up_scale = get_mul_mat_id_scale(cgraph->nodes[up_idx + 1], cgraph->nodes[up_idx + 2],
+                        cgraph->nodes[up_idx + 3], up_scale_n, up_n);
+                if (!gate_scale || !up_scale) {
+                    continue;
+                }
+
+                const ggml_tensor * up_bias   = with_bias ? get_bias_tensor(up_out_n, up_scale_n, bias_op) : nullptr;
+                const ggml_tensor * gate_bias = with_bias ? get_bias_tensor(gate_out_n, gate_scale_n, bias_op) : nullptr;
+
+                const ggml_tensor * src0 = up_n->src[0];
+                const ggml_tensor * src1 = up_n->src[1];
+                const ggml_tensor * ids  = up_n->src[2];
+
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate       = gate_n->src[0];
+                fusion_data.x_bias     = up_bias;
+                fusion_data.gate_bias  = gate_bias;
+                fusion_data.x_scale    = up_scale;
+                fusion_data.gate_scale = gate_scale;
+                fusion_data.glu_op     = ggml_get_glu_op(glu);
+
+                if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
+                    ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, cgraph->nodes[glu_idx], &fusion_data);
+                    fused_mul_mat_vec = true;
+                    fused_node_count  = n_ops;
+                    break;
+                }
+            }
+
+            if (fused_mul_mat_vec) {
+                break;
+            }
+        }
+
         if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
             ggml_tensor * glu         = cgraph->nodes[i + 4];
             ggml_tensor * gate_bias_n = glu->src[0];
@@ -3227,23 +3474,8 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
                 continue;
             }
 
-            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
-                if (op_bias == GGML_OP_ADD) {
-                    if (bias_node->src[0] == mul_node) {
-                        return bias_node->src[1];
-                    }
-                    if (bias_node->src[1] == mul_node) {
-                        return bias_node->src[0];
-                    }
-                    return (ggml_tensor *) nullptr;
-                }
-                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
-                GGML_ASSERT(bias_node->src[0] == mul_node);
-                return bias_node->src[1];
-            };
-
-            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
-            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
+            const ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
+            const ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
 
             if (!up_bias_tensor || !gate_bias_tensor) {
                 continue;
@@ -3331,7 +3563,95 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
     fused_mul_mat_vec = false;
     fused_node_count  = 0;
 
-    // gate + add + glu + up + add
+    // mul_mat + scale + optional bias
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        for (const bool with_bias : { false, true }) {
+            const int n_ops = op == GGML_OP_MUL_MAT ? (with_bias ? 3 : 2) : (with_bias ? 6 : 5);
+            const int out_nodes[] = { i + n_ops - 1 };
+            ggml_op ops[6];
+            if (op == GGML_OP_MUL_MAT) {
+                if (with_bias) {
+                    ops[0] = op;
+                    ops[1] = GGML_OP_MUL;
+                    ops[2] = bias_op;
+                } else {
+                    ops[0] = op;
+                    ops[1] = GGML_OP_MUL;
+                }
+            } else {
+                if (with_bias) {
+                    ops[0] = op;
+                    ops[1] = GGML_OP_RESHAPE;
+                    ops[2] = GGML_OP_REPEAT;
+                    ops[3] = GGML_OP_GET_ROWS;
+                    ops[4] = GGML_OP_MUL;
+                    ops[5] = bias_op;
+                } else {
+                    ops[0] = op;
+                    ops[1] = GGML_OP_RESHAPE;
+                    ops[2] = GGML_OP_REPEAT;
+                    ops[3] = GGML_OP_GET_ROWS;
+                    ops[4] = GGML_OP_MUL;
+                }
+            }
+
+            if (!ggml_can_fuse_subgraph(cgraph, i, n_ops, ops, out_nodes, 1) ||
+                    !ggml_cuda_check_fusion_memory_ranges(cgraph, i, n_ops, out_nodes, 1)) {
+                continue;
+            }
+
+            ggml_tensor * mm_node    = cgraph->nodes[i];
+            ggml_tensor * scale_node = op == GGML_OP_MUL_MAT ? cgraph->nodes[i + 1] : cgraph->nodes[i + 4];
+            ggml_tensor * out_node   = with_bias ? cgraph->nodes[i + n_ops - 1] : scale_node;
+
+            const ggml_tensor * scale = nullptr;
+            if (op == GGML_OP_MUL_MAT) {
+                scale = get_mul_mat_scale(scale_node, mm_node);
+            } else {
+                scale = get_mul_mat_id_scale(cgraph->nodes[i + 1], cgraph->nodes[i + 2], cgraph->nodes[i + 3], scale_node, mm_node);
+            }
+            if (!scale) {
+                continue;
+            }
+
+            const ggml_tensor * bias = with_bias ? get_bias_tensor(out_node, scale_node, bias_op) : nullptr;
+            if (with_bias && !bias) {
+                continue;
+            }
+            if (with_bias && bias_op == GGML_OP_ADD && !ggml_are_same_shape(out_node->src[0], out_node->src[1])) {
+                continue;
+            }
+            if (with_bias && bias_op == GGML_OP_ADD_ID && out_node->src[2] != mm_node->src[2]) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = mm_node->src[0];
+            const ggml_tensor * src1 = mm_node->src[1];
+            const ggml_tensor * ids  = mm_node->src[2];
+
+            ggml_cuda_mm_fusion_args_host fusion_data{};
+            fusion_data.x_bias  = bias;
+            fusion_data.x_scale = scale;
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, out_node, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = n_ops;
+                break;
+            }
+        }
+        if (fused_mul_mat_vec) {
+            break;
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    // mul_mat + add
     for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
         const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
 
@@ -3562,12 +3882,6 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                     }
                 }
 
-#ifdef GGML_CUDA_DEBUG
-                const int nodes_fused = i - prev_i - 1;
-                if (nodes_fused > 0) {
-                    GGML_LOG_INFO("nodes_fused: %d\n", nodes_fused);
-                }
-#endif
                 prev_i = i;
 
                 if (ggml_cuda_is_view_or_noop(node)) {
@@ -3581,6 +3895,12 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                 int nodes_to_skip = ggml_cuda_try_fuse(cuda_ctx, cgraph, i);
 
                 if (nodes_to_skip != 0) {
+#ifdef GGML_CUDA_DEBUG
+                    const int last_fused = i + nodes_to_skip;
+                    GGML_LOG_INFO("nodes_fused: %d, first: %s (%s), last: %s (%s)\n",
+                            nodes_to_skip + 1, ggml_op_name(node->op), node->name,
+                            ggml_op_name(cgraph->nodes[last_fused]->op), cgraph->nodes[last_fused]->name);
+#endif
                     i += nodes_to_skip;
                     continue;
                 }
@@ -4389,10 +4709,16 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             } break;
         case GGML_OP_SET_ROWS:
             {
-                return (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 ||
-                       op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q5_0 ||
-                       op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_IQ4_NL) &&
-                       op->src[0]->type == GGML_TYPE_F32 &&
+                return (
+                           (
+                               (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 ||
+                               op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q5_0 ||
+                               op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_IQ4_NL) &&
+                               op->src[0]->type == GGML_TYPE_F32
+                           ) || (
+                               op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F16
+                           )
+                       ) &&
                        (op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32);
             } break;
         case GGML_OP_SET:

ggml/src/ggml-cuda/mmvq.cu

@@ -521,9 +521,13 @@ static __global__ void mul_mat_vec_q(
     bool use_gate = false;
     bool use_bias = false;
     bool use_gate_bias = false;
+    bool use_scale = false;
+    bool use_gate_scale = false;
     [[maybe_unused]] const void * vgate = nullptr;
     const float * x_bias = nullptr;
     const float * gate_bias = nullptr;
+    const float * x_scale = nullptr;
+    const float * gate_scale = nullptr;
     ggml_glu_op active_glu;
 
     if constexpr (has_fusion) {
@@ -534,34 +538,47 @@ static __global__ void mul_mat_vec_q(
         x_bias        = (const float *) fusion.x_bias;
         gate_bias     = (const float *) fusion.gate_bias;
         active_glu    = fusion.glu_op;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            use_scale      = fusion.x_scale    != nullptr;
+            use_gate_scale = fusion.gate_scale != nullptr && use_gate;
+            x_scale        = (const float *) fusion.x_scale;
+            gate_scale     = (const float *) fusion.gate_scale;
+        }
     }
 
 
     [[maybe_unused]] float x_biases[ncols_dst]    = { 0.0f };
     [[maybe_unused]] float gate_biases[ncols_dst] = { 0.0f };
+    [[maybe_unused]] float x_scales;
+    [[maybe_unused]] float gate_scales;
     if constexpr (has_fusion) {
+        // 1. Hide latency by prefetching bias, gates and scales here
+        // 2. load only on threads that won't die after partial sum calculation
         const uint32_t channel_bias = ids ? channel_x : channel_dst;
-        if (use_bias) {
-            x_bias = x_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
-            // 1. Hide latency by prefetching bias and gate here
-            // 2. load only on threads that won't die after partial sum calculation
-            if (threadIdx.x < rows_per_cuda_block && threadIdx.y == 0 &&
-                (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
+        if (threadIdx.x < rows_per_cuda_block && threadIdx.y == 0 &&
+            (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
+            if (use_bias) {
+                x_bias = x_bias + sample_dst * stride_sample_dst + channel_bias * stride_channel_dst + row0;
 #pragma unroll
                 for (int j = 0; j < ncols_dst; ++j) {
                     x_biases[j] = x_bias[j * stride_col_dst + threadIdx.x];
                 }
             }
-        }
-        if (use_gate_bias) {
-            gate_bias = gate_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
-            if (threadIdx.x < rows_per_cuda_block && threadIdx.y == 0 &&
-                (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
+            if (use_gate_bias) {
+                gate_bias = gate_bias + sample_dst * stride_sample_dst + channel_bias * stride_channel_dst + row0;
 #pragma unroll
                 for (int j = 0; j < ncols_dst; ++j) {
                     gate_biases[j] = gate_bias[j * stride_col_dst + threadIdx.x];
                 }
             }
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                if (use_scale) {
+                    x_scales = x_scale[ids ? channel_x : 0];
+                }
+                if (use_gate_scale) {
+                    gate_scales = gate_scale[ids ? channel_x : 0];
+                }
+            }
         }
     }
 
@@ -643,11 +660,21 @@ static __global__ void mul_mat_vec_q(
         if (threadIdx.x < rows_per_cuda_block && (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
             float result = tmp[j][threadIdx.x];
             if constexpr (has_fusion) {
+                if constexpr (type == GGML_TYPE_NVFP4) {
+                    if (use_scale) {
+                        result *= x_scales;
+                    }
+                }
                 if (use_bias) {
                     result += x_biases[j];
                 }
                 if (use_gate) {
                     float gate_value = tmp_gate[j][threadIdx.x];
+                    if constexpr (type == GGML_TYPE_NVFP4) {
+                        if (use_gate_scale) {
+                            gate_value *= gate_scales;
+                        }
+                    }
                     if (use_gate_bias) {
                         gate_value += gate_biases[j];
                     }
@@ -673,7 +700,10 @@ static __global__ void mul_mat_vec_q(
     }
 
     if constexpr (!has_fusion) {
-        GGML_UNUSED_VARS(use_gate, use_bias, use_gate_bias, active_glu, gate_bias, x_bias, tmp_gate);
+        GGML_UNUSED_VARS(use_gate, use_bias, use_gate_bias, use_scale, use_gate_scale, active_glu, gate_bias, x_bias, x_scale, gate_scale, tmp_gate);
+    }
+    if constexpr (type != GGML_TYPE_NVFP4) {
+        GGML_UNUSED_VARS(use_scale, use_gate_scale, x_scale, gate_scale, x_scales, gate_scales);
     }
 }
 
@@ -769,7 +799,8 @@ static void mul_mat_vec_q_switch_fusion(
         const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared,
         const uint32_t ids_stride, cudaStream_t stream) {
 
-    const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
+    const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr ||
+                            fusion.x_scale != nullptr || fusion.gate_scale != nullptr;
     if constexpr (c_ncols_dst == 1) {
         if (has_fusion) {
             const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, nbytes_shared, stream);
@@ -834,7 +865,6 @@ static void mul_mat_vec_q_switch_ncols_dst(
     const int warp_size = ggml_cuda_info().devices[device].warp_size;
     const mmvq_parameter_table_id table_id  = get_device_table_id(cc);
 
-    const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
     const bool has_ids = ids != nullptr;
 
     const auto should_use_small_k = [&](int c_ncols_dst) {
@@ -973,8 +1003,6 @@ static void mul_mat_vec_q_switch_ncols_dst(
             GGML_ABORT("fatal error");
             break;
     }
-
-    GGML_UNUSED(has_fusion);
 }
 static void mul_mat_vec_q_switch_type(
         const void * vx, const ggml_type type_x, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
@@ -1154,6 +1182,9 @@ void ggml_cuda_mul_mat_vec_q(
     if (fusion) {
         GGML_ASSERT( !ids || dst->ne[2] == 1);
         GGML_ASSERT(  ids || dst->ne[1] == 1);
+        // Scale fusion is only allowed for NVFP4 currently as the cost of checking this at run-time in the prologue is
+        // non-negligible for some models such as gpt-oss-20b
+        GGML_ASSERT((fusion->x_scale == nullptr && fusion->gate_scale == nullptr) || src0->type == GGML_TYPE_NVFP4);
 
         if (fusion->x_bias) {
             GGML_ASSERT(fusion->x_bias->type == GGML_TYPE_F32);
@@ -1171,6 +1202,18 @@ void ggml_cuda_mul_mat_vec_q(
             GGML_ASSERT(!ids || fusion->gate_bias->ne[1] == src0->ne[2]);
             fusion_local.gate_bias = fusion->gate_bias->data;
         }
+        if (fusion->x_scale) {
+            GGML_ASSERT(fusion->x_scale->type == GGML_TYPE_F32);
+            GGML_ASSERT(ggml_is_contiguous(fusion->x_scale));
+            GGML_ASSERT(ggml_nelements(fusion->x_scale) == (ids ? src0->ne[2] : 1));
+            fusion_local.x_scale = fusion->x_scale->data;
+        }
+        if (fusion->gate_scale) {
+            GGML_ASSERT(fusion->gate_scale->type == GGML_TYPE_F32);
+            GGML_ASSERT(ggml_is_contiguous(fusion->gate_scale));
+            GGML_ASSERT(ggml_nelements(fusion->gate_scale) == (ids ? src0->ne[2] : 1));
+            fusion_local.gate_scale = fusion->gate_scale->data;
+        }
         fusion_local.glu_op = fusion->glu_op;
     }
 

ggml/src/ggml-cuda/set-rows.cu

@@ -322,17 +322,77 @@ static void set_rows_cuda(ggml_backend_cuda_context & ctx, const ggml_tensor * s
     }
 }
 
+template<>
+void set_rows_cuda<half, int32_t>(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const half    * src0_d = (const half *)src0->data;
+    const int32_t * src1_d = (const int32_t *)src1->data;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    cudaStream_t stream = ctx.stream();
+
+
+    if (dst->type == GGML_TYPE_F16) {
+        set_rows_cuda(
+            src0_d, src1_d, (half*)dst->data,
+            ne00, ne01, ne02, ne03,
+            ne10, ne11, ne12, ne13,
+            nb01, nb02, nb03,
+            nb10, nb11, nb12,
+            nb1, nb2, nb3,
+            stream
+        );
+    } else {
+        GGML_ABORT("unsupported type %s", ggml_type_name(dst->type));
+    }
+}
+
+template<>
+void set_rows_cuda<half, int64_t>(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const half    * src0_d = (const half *)src0->data;
+    const int64_t * src1_d = (const int64_t *)src1->data;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    cudaStream_t stream = ctx.stream();
+
+
+    if (dst->type == GGML_TYPE_F16) {
+        set_rows_cuda(
+            src0_d, src1_d, (half*)dst->data,
+            ne00, ne01, ne02, ne03,
+            ne10, ne11, ne12, ne13,
+            nb01, nb02, nb03,
+            nb10, nb11, nb12,
+            nb1, nb2, nb3,
+            stream
+        );
+    } else {
+        GGML_ABORT("unsupported type %s", ggml_type_name(dst->type));
+    }
+}
+
 
 void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16));
     GGML_ASSERT(src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32);
 
-    if (src1->type == GGML_TYPE_I64) {
-        set_rows_cuda<float, int64_t>(ctx, src0, src1, dst);
+    if (src0->type == GGML_TYPE_F32) {
+        if (src1->type == GGML_TYPE_I64) {
+            set_rows_cuda<float, int64_t>(ctx, src0, src1, dst);
+        } else {
+            set_rows_cuda<float, int32_t>(ctx, src0, src1, dst);
+        }
+    } else if (src0->type == GGML_TYPE_F16) {
+        if (src1->type == GGML_TYPE_I64) {
+            set_rows_cuda<half, int64_t>(ctx, src0, src1, dst);
+        } else {
+            set_rows_cuda<half, int32_t>(ctx, src0, src1, dst);
+        }
     } else {
-        set_rows_cuda<float, int32_t>(ctx, src0, src1, dst);
+        GGML_ABORT("unsupported type %s", ggml_type_name(src0->type));
     }
 }

ggml/src/ggml-hip/CMakeLists.txt

@@ -156,4 +156,4 @@ endif()
 
 target_link_libraries(ggml-hip PRIVATE ggml-base hip::host roc::rocblas roc::hipblas)
 
-target_compile_options(ggml-hip PRIVATE "$<$<COMPILE_LANGUAGE:HIP>:-ffast-math>")
+target_compile_options(ggml-hip PRIVATE "$<$<COMPILE_LANGUAGE:HIP>:-ffast-math;-fno-finite-math-only>")

ggml/src/ggml-metal/ggml-metal-device.cpp

@@ -160,11 +160,15 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows(ggml_me
     return res;
 }
 
-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows(ggml_metal_library_t lib, ggml_type tidx, ggml_type tdst) {
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows(ggml_metal_library_t lib, const ggml_tensor * op) {
     char base[256];
     char name[256];
 
-    snprintf(base, 256, "kernel_set_rows_%s_%s", ggml_type_name(tdst), ggml_type_name(tidx));
+    const auto tsrc = op->src[0]->type;
+    const auto tidx = op->src[1]->type;
+    const auto tdst = op->type;
+
+    snprintf(base, 256, "kernel_set_rows_%s_%s_%s", ggml_type_name(tsrc), ggml_type_name(tidx), ggml_type_name(tdst));
     snprintf(name, 256, "%s", base);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);

ggml/src/ggml-metal/ggml-metal-device.h

@@ -112,7 +112,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cpy
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_1d           (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_2d           (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows          (ggml_metal_library_t lib, enum ggml_type tsrc);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows          (ggml_metal_library_t lib, enum ggml_type tidx, enum ggml_type tdst);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows          (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag              (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat            (ggml_metal_library_t lib, enum ggml_type tsrc);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_concat            (ggml_metal_library_t lib, enum ggml_type tsrc);

ggml/src/ggml-metal/ggml-metal-device.m

@@ -1334,7 +1334,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return op->src[0]->type != GGML_TYPE_NVFP4;
         case GGML_OP_SET_ROWS:
             {
-                if (op->src[0]->type != GGML_TYPE_F32) {
+                if (op->src[0]->type != GGML_TYPE_F32 && op->src[0]->type != GGML_TYPE_F16) {
                     return false;
                 }
 

ggml/src/ggml-metal/ggml-metal-ops.cpp

@@ -1202,7 +1202,7 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
-    auto pipeline = ggml_metal_library_get_pipeline_set_rows(lib, op->src[1]->type, op->type);
+    auto pipeline = ggml_metal_library_get_pipeline_set_rows(lib, op);
 
     const int32_t nk0 = ne0/ggml_blck_size(op->type);
 

ggml/src/ggml-metal/ggml-metal.metal

@@ -42,6 +42,8 @@ typedef matrix<bfloat, 4, 4> bfloat4x4;
 typedef matrix<bfloat, 2, 4> bfloat2x4;
 #endif
 
+#define QK_NL 16
+
 constexpr constant static float kvalues_iq4nl_f[16] = {
     -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
 };
@@ -9386,7 +9388,40 @@ kernel void kernel_get_rows_f(
     }
 }
 
-template<typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
+typedef decltype(kernel_get_rows_f<float, float>) get_rows_f_t;
+
+template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_f_t kernel_get_rows_f<float, float>;
+template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_f_t kernel_get_rows_f<half,  float>;
+template [[host_name("kernel_get_rows_i32")]]  kernel get_rows_f_t kernel_get_rows_f<int32_t, int32_t>;
+#if defined(GGML_METAL_HAS_BF16)
+template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f<bfloat, float>;
+#endif
+
+typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
+
+template [[host_name("kernel_get_rows_q1_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q1_0,    8, dequantize_q1_0>;
+template [[host_name("kernel_get_rows_q4_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_0,    2, dequantize_q4_0>;
+template [[host_name("kernel_get_rows_q4_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_1,    2, dequantize_q4_1>;
+template [[host_name("kernel_get_rows_q5_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_0,    2, dequantize_q5_0>;
+template [[host_name("kernel_get_rows_q5_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_1,    2, dequantize_q5_1>;
+template [[host_name("kernel_get_rows_q8_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q8_0,    2, dequantize_q8_0>;
+template [[host_name("kernel_get_rows_mxfp4")]]   kernel get_rows_q_t kernel_get_rows_q<block_mxfp4,   2, dequantize_mxfp4>;
+template [[host_name("kernel_get_rows_q2_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_get_rows_q3_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_get_rows_q4_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_get_rows_q5_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_get_rows_q6_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q6_K,    QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl,  2,     dequantize_iq4_nl>;
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+
+template<typename TS, typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
 kernel void kernel_set_rows_q32(
         constant ggml_metal_kargs_set_rows & args,
         device const  void * src0,
@@ -9410,14 +9445,14 @@ kernel void kernel_set_rows_q32(
     const TI      i1  = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
 
           device block_q * dst_row = (      device block_q *) ((      device char *) dst  +  i1*args.nb1  + i02*args.nb2  + i03*args.nb3);
-    const device float   * src_row = (const device float   *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
+    const device TS      * src_row = (const device TS      *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
 
     for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
         quantize_func(src_row + 32*ind, dst_row[ind]);
     }
 }
 
-template<typename T, typename TI>
+template<typename TS, typename TI, typename TD>
 kernel void kernel_set_rows_f(
         constant ggml_metal_kargs_set_rows & args,
         device const  void * src0,
@@ -9440,14 +9475,47 @@ kernel void kernel_set_rows_f(
     const int32_t i10 = i01;
     const TI      i1  = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
 
-          device T     * dst_row = (      device T     *) ((      device char *) dst  +  i1*args.nb1  + i02*args.nb2  + i03*args.nb3);
-    const device float * src_row = (const device float *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
+          device TD * dst_row = (      device TD *) ((      device char *) dst  +  i1*args.nb1  + i02*args.nb2  + i03*args.nb3);
+    const device TS * src_row = (const device TS *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
 
     for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
-        dst_row[ind] = (T) src_row[ind];
+        dst_row[ind] = (TD) src_row[ind];
     }
 }
 
+typedef decltype(kernel_set_rows_f<float, int64_t, float>) set_rows_f_t;
+
+template [[host_name("kernel_set_rows_f32_i64_f32")]]   kernel set_rows_f_t kernel_set_rows_f<float, int64_t, float>;
+template [[host_name("kernel_set_rows_f32_i32_f32")]]   kernel set_rows_f_t kernel_set_rows_f<float, int32_t, float>;
+template [[host_name("kernel_set_rows_f32_i64_f16")]]   kernel set_rows_f_t kernel_set_rows_f<float, int64_t, half>;
+template [[host_name("kernel_set_rows_f32_i32_f16")]]   kernel set_rows_f_t kernel_set_rows_f<float, int32_t, half>;
+#if defined(GGML_METAL_HAS_BF16)
+template [[host_name("kernel_set_rows_f32_i64_bf16")]]  kernel set_rows_f_t kernel_set_rows_f<float, int64_t, bfloat>;
+template [[host_name("kernel_set_rows_f32_i32_bf16")]]  kernel set_rows_f_t kernel_set_rows_f<float, int32_t, bfloat>;
+#endif
+
+template [[host_name("kernel_set_rows_f16_i64_f16")]]   kernel set_rows_f_t kernel_set_rows_f<half, int64_t, half>;
+template [[host_name("kernel_set_rows_f16_i32_f16")]]   kernel set_rows_f_t kernel_set_rows_f<half, int32_t, half>;
+#if defined(GGML_METAL_HAS_BF16)
+template [[host_name("kernel_set_rows_bf16_i64_bf16")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int64_t, bfloat>;
+template [[host_name("kernel_set_rows_bf16_i32_bf16")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int32_t, bfloat>;
+#endif
+
+typedef decltype(kernel_set_rows_q32<float, int64_t, block_q8_0, quantize_q8_0>) set_rows_q32_t;
+
+template [[host_name("kernel_set_rows_f32_i64_q8_0")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q8_0,   quantize_q8_0>;
+template [[host_name("kernel_set_rows_f32_i32_q8_0")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q8_0,   quantize_q8_0>;
+template [[host_name("kernel_set_rows_f32_i64_q4_0")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q4_0,   quantize_q4_0>;
+template [[host_name("kernel_set_rows_f32_i32_q4_0")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q4_0,   quantize_q4_0>;
+template [[host_name("kernel_set_rows_f32_i64_q4_1")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q4_1,   quantize_q4_1>;
+template [[host_name("kernel_set_rows_f32_i32_q4_1")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q4_1,   quantize_q4_1>;
+template [[host_name("kernel_set_rows_f32_i64_q5_0")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q5_0,   quantize_q5_0>;
+template [[host_name("kernel_set_rows_f32_i32_q5_0")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q5_0,   quantize_q5_0>;
+template [[host_name("kernel_set_rows_f32_i64_q5_1")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q5_1,   quantize_q5_1>;
+template [[host_name("kernel_set_rows_f32_i32_q5_1")]]   kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q5_1,   quantize_q5_1>;
+template [[host_name("kernel_set_rows_f32_i64_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_iq4_nl, quantize_iq4_nl>;
+template [[host_name("kernel_set_rows_f32_i32_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_iq4_nl, quantize_iq4_nl>;
+
 kernel void kernel_diag_f32(
         constant ggml_metal_kargs_diag & args,
         device   const char * src0,
@@ -10190,75 +10258,6 @@ kernel void kernel_mul_mm_id(
     }
 }
 
-#define QK_NL 16
-
-//
-// get rows
-//
-
-typedef decltype(kernel_get_rows_f<float, float>) get_rows_f_t;
-
-template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_f_t kernel_get_rows_f<float, float>;
-template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_f_t kernel_get_rows_f<half,  float>;
-template [[host_name("kernel_get_rows_i32")]]  kernel get_rows_f_t kernel_get_rows_f<int32_t, int32_t>;
-#if defined(GGML_METAL_HAS_BF16)
-template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f<bfloat, float>;
-#endif
-
-typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
-
-template [[host_name("kernel_get_rows_q1_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q1_0,    8, dequantize_q1_0>;
-template [[host_name("kernel_get_rows_q4_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_0,    2, dequantize_q4_0>;
-template [[host_name("kernel_get_rows_q4_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_1,    2, dequantize_q4_1>;
-template [[host_name("kernel_get_rows_q5_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_0,    2, dequantize_q5_0>;
-template [[host_name("kernel_get_rows_q5_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_1,    2, dequantize_q5_1>;
-template [[host_name("kernel_get_rows_q8_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q8_0,    2, dequantize_q8_0>;
-template [[host_name("kernel_get_rows_mxfp4")]]   kernel get_rows_q_t kernel_get_rows_q<block_mxfp4,   2, dequantize_mxfp4>;
-template [[host_name("kernel_get_rows_q2_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q2_K,    QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_get_rows_q3_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q3_K,    QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_get_rows_q4_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_K,    QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_get_rows_q5_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_K,    QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_get_rows_q6_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q6_K,    QK_NL, dequantize_q6_K>;
-template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
-template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
-template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
-template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq3_s,   QK_NL, dequantize_iq3_s>;
-template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq2_s,   QK_NL, dequantize_iq2_s>;
-template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_s,   QK_NL, dequantize_iq1_s>;
-template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_m,   QK_NL, dequantize_iq1_m>;
-template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl,  2,     dequantize_iq4_nl>;
-template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
-
-//
-// set rows
-//
-
-typedef decltype(kernel_set_rows_f<float, int64_t>) set_rows_f_t;
-
-template [[host_name("kernel_set_rows_f32_i64")]]  kernel set_rows_f_t kernel_set_rows_f<float, int64_t>;
-template [[host_name("kernel_set_rows_f32_i32")]]  kernel set_rows_f_t kernel_set_rows_f<float, int32_t>;
-template [[host_name("kernel_set_rows_f16_i64")]]  kernel set_rows_f_t kernel_set_rows_f<half, int64_t>;
-template [[host_name("kernel_set_rows_f16_i32")]]  kernel set_rows_f_t kernel_set_rows_f<half, int32_t>;
-#if defined(GGML_METAL_HAS_BF16)
-template [[host_name("kernel_set_rows_bf16_i64")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int64_t>;
-template [[host_name("kernel_set_rows_bf16_i32")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int32_t>;
-#endif
-
-typedef decltype(kernel_set_rows_q32<int64_t, block_q8_0, quantize_q8_0>) set_rows_q32_t;
-
-template [[host_name("kernel_set_rows_q8_0_i64")]]   kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q8_0,   quantize_q8_0>;
-template [[host_name("kernel_set_rows_q8_0_i32")]]   kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q8_0,   quantize_q8_0>;
-template [[host_name("kernel_set_rows_q4_0_i64")]]   kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q4_0,   quantize_q4_0>;
-template [[host_name("kernel_set_rows_q4_0_i32")]]   kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q4_0,   quantize_q4_0>;
-template [[host_name("kernel_set_rows_q4_1_i64")]]   kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q4_1,   quantize_q4_1>;
-template [[host_name("kernel_set_rows_q4_1_i32")]]   kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q4_1,   quantize_q4_1>;
-template [[host_name("kernel_set_rows_q5_0_i64")]]   kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q5_0,   quantize_q5_0>;
-template [[host_name("kernel_set_rows_q5_0_i32")]]   kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q5_0,   quantize_q5_0>;
-template [[host_name("kernel_set_rows_q5_1_i64")]]   kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q5_1,   quantize_q5_1>;
-template [[host_name("kernel_set_rows_q5_1_i32")]]   kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q5_1,   quantize_q5_1>;
-template [[host_name("kernel_set_rows_iq4_nl_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_iq4_nl, quantize_iq4_nl>;
-template [[host_name("kernel_set_rows_iq4_nl_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_iq4_nl, quantize_iq4_nl>;
-
 //
 // matrix-matrix multiplication
 //

ggml/src/ggml-opencl/fa_tune.h

@@ -20,6 +20,7 @@ static const ggml_opencl_fa_dim g_fa_dims_adreno_default[] = {
     {192, 128, 16, 16, 1, 0},
     {192, 192, 16, 16, 1, 0},
     {256, 256, 16, 16, 16, 0},
+    {512, 512,  8, 16, 64, 0},
 };
 
 struct ggml_opencl_fa_dim_table {

ggml/src/ggml-opencl/kernels/flash_attn_f16.cl

@@ -10,7 +10,12 @@
 #define DK_VEC (DK/4)
 #define DV_VEC (DV/4)
 #define WG_SIZE (BLOCK_M)
-#define Q1_WG_SIZE 64
+// q1 reduces over a Q1_WG_SIZE-wide WG via work-group barriers; the launch WG
+// must match. Defaults to the Adreno sg (64); host passes -D FA_SG=32 on Intel.
+#ifndef FA_SG
+#define FA_SG 64
+#endif
+#define Q1_WG_SIZE FA_SG
 
 // The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
 // infinite operand can cause undefined behavior and miscompilation for exp.

ggml/src/ggml-opencl/kernels/flash_attn_f32.cl

@@ -11,7 +11,12 @@
 #define DK_VEC (DK/4)
 #define DV_VEC (DV/4)
 #define WG_SIZE (BLOCK_M)
-#define Q1_WG_SIZE 64
+// q1 reduces over a Q1_WG_SIZE-wide WG via work-group barriers; the launch WG
+// must match. Defaults to the Adreno sg (64); host passes -D FA_SG=32 on Intel.
+#ifndef FA_SG
+#define FA_SG 64
+#endif
+#define Q1_WG_SIZE FA_SG
 
 // The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
 // infinite operand can cause undefined behavior and miscompilation for exp.
@@ -114,6 +119,15 @@ __kernel void flash_attn_f32(
     __local DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
 
     for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+#if FA_SG < 64
+        // WAR on l_k/l_v: threads with my_query_row >= n_q skip the compute below
+        // (continue) and would race ahead to reload the tiles while active threads
+        // still read them. A single 64-wide Adreno subgroup (WG == sg) runs lockstep
+        // and hides this; a WG that spans multiple narrower subgroups (Intel sg=32)
+        // corrupts the result. All threads reach this each iteration (no-op on the
+        // first), so it does not diverge with the continue. Compiled out at sg=64.
+        barrier(CLK_LOCAL_MEM_FENCE);
+#endif
         for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
             const int row = i / DK_VEC;
             const int col = i % DK_VEC;

ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl

@@ -27,7 +27,11 @@
 
 #define DK_VEC (DK/4)
 #define DV_VEC (DV/4)
-#define Q1_WG_SIZE 64
+
+#ifndef FA_SG
+#define FA_SG 64
+#endif
+#define Q1_WG_SIZE FA_SG
 
 // The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
 // infinite operand can cause undefined behavior and miscompilation for exp.
@@ -365,6 +369,263 @@ __kernel void flash_attn_f32_q4_0_q1(
     }
 }
 
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
+#else
+#define REQD_SUBGROUP_SIZE_64
+#endif
+
+#define VEC_NSG          4
+#define VEC_WG_SIZE      (Q1_WG_SIZE * VEC_NSG)
+#define Q1V_DV_PER_THREAD ((DV_VEC + Q1_WG_SIZE - 1) / Q1_WG_SIZE)
+
+// Dequant one float4 lane (0..7) from a q4_0 block.
+// Lanes 0..3 → low nibbles of qs[0..15], lanes 4..7 → high nibbles.
+inline float4 dequant_q4_0_lane(const global char * block_ptr, int lane) {
+    const float d = vload_half(0, (const global half *)block_ptr);
+    const global uchar * qs = (const global uchar *)(block_ptr + 2);
+    const int g     = lane & 3;
+    const int shift = (lane < 4) ? 0 : 4;
+    return d * (float4)((float)((qs[g*4+0] >> shift) & 0x0F) - 8.0f,
+                        (float)((qs[g*4+1] >> shift) & 0x0F) - 8.0f,
+                        (float)((qs[g*4+2] >> shift) & 0x0F) - 8.0f,
+                        (float)((qs[g*4+3] >> shift) & 0x0F) - 8.0f);
+}
+
+REQD_SUBGROUP_SIZE_64
+__kernel void flash_attn_f32_q4_0_q1_vec(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
+) {
+    const int tid             = get_local_id(0);
+    const int sgid            = tid / Q1_WG_SIZE;
+    const int tid_sg          = tid % Q1_WG_SIZE;
+    const int head_batch_idx  = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx  = head_batch_idx % n_head;
+
+    const int gqa_ratio   = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+    global       char * o_base = (global       char *) o_void + o_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx  = head_idx  % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    __local ACC_TYPE4 q_shared[DK_VEC];
+    {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+        const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+        for (int i = tid; i < DK_VEC; i += VEC_WG_SIZE) {
+            q_shared[i] = CONVERT_Q_ACC4(q_ptr[i]);
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+#ifdef FA_HAVE_INT_DOT
+    // quantize Q to int8-packed uints + per-block (qd, q_sum) once per WG for dp4a
+    // one thread per Q block, remaining threads idle this step
+    __local uint  q_packed_shared[DK_Q4_BLOCKS * 8];
+    __local float q_d_shared[DK_Q4_BLOCKS];
+    __local int   q_sum_shared[DK_Q4_BLOCKS];
+    if (tid < DK_Q4_BLOCKS) {
+        ACC_TYPE4 q_block[8];
+        #pragma unroll
+        for (int i = 0; i < 8; ++i) q_block[i] = q_shared[tid * 8 + i];
+        uint packed[8];
+        q4_q_block_info info = quant_q_block_int8_packed_q4(q_block, packed);
+        #pragma unroll
+        for (int i = 0; i < 8; ++i) q_packed_shared[tid * 8 + i] = packed[i];
+        q_d_shared[tid]   = info.qd;
+        q_sum_shared[tid] = info.q_sum;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+#endif
+
+    const float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    const global ACC_TYPE * sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+    }
+
+    ACC_TYPE4 o_acc[Q1V_DV_PER_THREAD];
+    #pragma unroll
+    for (int i = 0; i < Q1V_DV_PER_THREAD; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    ACC_TYPE m_i = FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+
+    const int kv_per_sg = (n_kv + VEC_NSG - 1) / VEC_NSG;
+    const int kv_start  = sgid * kv_per_sg;
+    const int kv_end    = min(n_kv, kv_start + kv_per_sg);
+
+    for (int k_idx = kv_start; k_idx < kv_end; ++k_idx) {
+        const global char * k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char * v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+
+#ifdef FA_HAVE_INT_DOT
+        // per-lane dp4a: each lane packs 4 raw q4_0 nibbles into a uint,
+        // then dot_acc_sat_4x8packed_ss_int against the matching uint.
+        ACC_TYPE lane_contrib = 0.0f;
+        for (int qk = tid_sg; qk < DK_VEC; qk += Q1_WG_SIZE) {
+            const int block_idx     = qk / 8;
+            const int lane_in_block = qk % 8;
+            const int g             = lane_in_block & 3;
+            const int shift         = (lane_in_block < 4) ? 0 : 4;
+            const global char *  k_block = k_row + block_idx * Q4_0_BLOCK_SIZE;
+            const float kd = vload_half(0, (const global half *)k_block);
+            const global uchar * k_qs = (const global uchar *)(k_block + 2);
+            const uchar b0 = k_qs[g*4 + 0];
+            const uchar b1 = k_qs[g*4 + 1];
+            const uchar b2 = k_qs[g*4 + 2];
+            const uchar b3 = k_qs[g*4 + 3];
+            const uint k_packed = ((uint)((b0 >> shift) & 0x0F))       |
+                                  ((uint)((b1 >> shift) & 0x0F)) <<  8 |
+                                  ((uint)((b2 >> shift) & 0x0F)) << 16 |
+                                  ((uint)((b3 >> shift) & 0x0F)) << 24;
+            const uint q_packed_lane = q_packed_shared[block_idx * 8 + lane_in_block];
+            const int  raw_dot = dot_acc_sat_4x8packed_ss_int(q_packed_lane, k_packed, 0);
+            const float qd          = q_d_shared[block_idx];
+            const float block_scale = qd * kd;
+            float contrib = (float)raw_dot * block_scale;
+            if (lane_in_block == 0) {
+                // block bias correction is per-block
+                const int q_sum_b = q_sum_shared[block_idx];
+                contrib -= 8.0f * block_scale * (float)q_sum_b;
+            }
+            lane_contrib += contrib;
+        }
+        ACC_TYPE score = sub_group_reduce_add(lane_contrib) * scale;
+#else
+        ACC_TYPE4 dot4 = (ACC_TYPE4)(0.0f);
+        for (int qk = tid_sg; qk < DK_VEC; qk += Q1_WG_SIZE) {
+            const int block_idx = qk / 8;
+            const int lane      = qk % 8;
+            const float4 k_v = dequant_q4_0_lane(k_row + block_idx * Q4_0_BLOCK_SIZE, lane);
+            dot4 = mad(q_shared[qk], k_v, dot4);
+        }
+        ACC_TYPE dot_partial = dot4.s0 + dot4.s1 + dot4.s2 + dot4.s3;
+        ACC_TYPE score = sub_group_reduce_add(dot_partial) * scale;
+#endif
+
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) mask_base;
+            score += slope * (ACC_TYPE) mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+
+        const ACC_TYPE m_new      = max(m_i, score);
+        const ACC_TYPE scale_prev = native_exp(m_i - m_new);
+        const ACC_TYPE p          = native_exp(score - m_new);
+
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            const int block_idx = dv / 8;
+            const int lane      = dv % 8;
+            const float4 v_v = dequant_q4_0_lane(v_row + block_idx * Q4_0_BLOCK_SIZE, lane);
+            o_acc[idx] = mad(p, v_v, o_acc[idx] * scale_prev);
+        }
+        l_i = l_i * scale_prev + p;
+        m_i = m_new;
+    }
+
+    __local ACC_TYPE  sg_m[VEC_NSG];
+    __local ACC_TYPE  sg_l[VEC_NSG];
+    __local ACC_TYPE4 sg_o[VEC_NSG][DV_VEC];
+
+    if (tid_sg == 0) {
+        sg_m[sgid] = m_i;
+        sg_l[sgid] = l_i;
+    }
+    {
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            sg_o[sgid][dv] = o_acc[idx];
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (sgid == 0) {
+        ACC_TYPE m_final = sg_m[0];
+        #pragma unroll
+        for (int s = 1; s < VEC_NSG; ++s) {
+            m_final = max(m_final, sg_m[s]);
+        }
+        if (sinks_ptr != NULL) {
+            m_final = max(m_final, sinks_ptr[head_idx]);
+        }
+
+        ACC_TYPE l_final = 0.0f;
+        #pragma unroll
+        for (int s = 0; s < VEC_NSG; ++s) {
+            l_final += sg_l[s] * native_exp(sg_m[s] - m_final);
+        }
+        if (sinks_ptr != NULL) {
+            l_final += native_exp(sinks_ptr[head_idx] - m_final);
+        }
+        const ACC_TYPE l_inv = (l_final > 0.0f) ? (1.0f / l_final) : 0.0f;
+
+        const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+        global O_DATA_TYPE4 * o_row = (global O_DATA_TYPE4 *) (o_base + o_row_offset);
+
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            ACC_TYPE4 o_merged = (ACC_TYPE4)(0.0f);
+            #pragma unroll
+            for (int s = 0; s < VEC_NSG; ++s) {
+                const ACC_TYPE alpha = native_exp(sg_m[s] - m_final);
+                o_merged = mad((ACC_TYPE4)(alpha), sg_o[s][dv], o_merged);
+            }
+            o_row[dv] = CONVERT_O_DATA4(o_merged * l_inv);
+        }
+    }
+}
+
 // Flash-decoding split pass for q4_0 KV. Merge kernel is type-agnostic and
 // shared with the f16/q8_0 FA kernels.
 #define FA_PARTIAL_FLOATS (2 + DV)
@@ -583,6 +844,319 @@ __kernel void flash_attn_f32_q4_0_q1_split(
 #define WG_SIZE             BLOCK_M
 #endif
 
+#ifndef MQ_GQA
+#define MQ_GQA 4
+#endif
+#ifndef MQ_NSG_SPLIT
+#define MQ_NSG_SPLIT 4
+#endif
+#define MQ_SPLIT_WG_SIZE_Q4 (Q1_WG_SIZE * MQ_NSG_SPLIT)
+
+REQD_SUBGROUP_SIZE_64
+__kernel void flash_attn_f32_q4_0_q1_vec_mq_split(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void * mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    global float * partial_void,
+    const int n_splits,
+    const int kv_per_split
+) {
+    const int tid              = get_local_id(0);
+    const int sgid             = tid / Q1_WG_SIZE;
+    const int tid_sg           = tid % Q1_WG_SIZE;
+    const int kvhead_batch_idx = get_global_id(1);
+    const int split_q_idx      = get_global_id(2);
+    const int split_idx        = split_q_idx % n_splits;
+    const int q_idx            = split_q_idx / n_splits;
+
+    const int batch_idx   = kvhead_batch_idx / n_head_kv;
+    const int head_kv_idx = kvhead_batch_idx % n_head_kv;
+
+    const int kv_start = split_idx * kv_per_split;
+    const int kv_end   = min(kv_start + kv_per_split, n_kv);
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+
+    if (kv_start >= kv_end) {
+        if (tid == 0) {
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                const int head_idx = head_kv_idx * MQ_GQA + h;
+                const ulong rec_idx = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                       * n_splits + split_idx);
+                global float * rec = partial_void + rec_idx * record_stride;
+                rec[0] = FA_M_INIT;
+                rec[1] = 0.0f;
+            }
+        }
+        return;
+    }
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+
+    __local ACC_TYPE4 q_shared[MQ_GQA * DK_VEC];
+    for (int i = tid; i < MQ_GQA * DK_VEC; i += MQ_SPLIT_WG_SIZE_Q4) {
+        const int h        = i / DK_VEC;
+        const int k        = i % DK_VEC;
+        const int head_idx = head_kv_idx * MQ_GQA + h;
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + (ulong) q_idx * q_nb1;
+        const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+        q_shared[h * DK_VEC + k] = CONVERT_Q_ACC4(q_ptr[k]);
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+#ifdef FA_HAVE_INT_DOT
+    __local uint  q_packed_shared[MQ_GQA * DK_Q4_BLOCKS * 8];
+    __local float q_d_shared[MQ_GQA * DK_Q4_BLOCKS];
+    __local int   q_sum_shared[MQ_GQA * DK_Q4_BLOCKS];
+    {
+        const int active = MQ_GQA * DK_Q4_BLOCKS;
+        if (tid < active) {
+            const int h        = tid / DK_Q4_BLOCKS;
+            const int block_id = tid % DK_Q4_BLOCKS;
+            ACC_TYPE4 q_block[8];
+            #pragma unroll
+            for (int i = 0; i < 8; ++i) q_block[i] = q_shared[h * DK_VEC + block_id * 8 + i];
+            uint packed[8];
+            q4_q_block_info info = quant_q_block_int8_packed_q4(q_block, packed);
+            #pragma unroll
+            for (int i = 0; i < 8; ++i) q_packed_shared[(h * DK_Q4_BLOCKS + block_id) * 8 + i] = packed[i];
+            q_d_shared[h * DK_Q4_BLOCKS + block_id]   = info.qd;
+            q_sum_shared[h * DK_Q4_BLOCKS + block_id] = info.q_sum;
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+#endif
+
+    float slope[MQ_GQA];
+    #pragma unroll
+    for (int h = 0; h < MQ_GQA; ++h) {
+        slope[h] = get_alibi_slope(max_bias, head_kv_idx * MQ_GQA + h, n_head_log2, m0, m1);
+    }
+
+    const global char * mask_base[MQ_GQA];
+    if (mask_void != NULL) {
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        const global char * mask_base_b = (const global char *) mask_void + mask_offset +
+                                          mask_batch_idx * mask_nb3 +
+                                          (ulong) q_idx * mask_nb1;
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            const int head_idx      = head_kv_idx * MQ_GQA + h;
+            const int mask_head_idx = head_idx % mask_ne2;
+            mask_base[h] = mask_base_b + mask_head_idx * mask_nb2;
+        }
+    } else {
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) mask_base[h] = NULL;
+    }
+
+    ACC_TYPE4 o_acc[MQ_GQA][Q1V_DV_PER_THREAD];
+    ACC_TYPE  m_i[MQ_GQA];
+    ACC_TYPE  l_i[MQ_GQA];
+    #pragma unroll
+    for (int h = 0; h < MQ_GQA; ++h) {
+        m_i[h] = FA_M_INIT;
+        l_i[h] = 0.0f;
+        #pragma unroll
+        for (int i = 0; i < Q1V_DV_PER_THREAD; ++i) o_acc[h][i] = (ACC_TYPE4)(0.0f);
+    }
+
+    const int kv_len    = kv_end - kv_start;
+    const int kv_per_sg = (kv_len + MQ_NSG_SPLIT - 1) / MQ_NSG_SPLIT;
+    const int kv_lo     = kv_start + sgid * kv_per_sg;
+    const int kv_hi     = min(kv_end, kv_lo + kv_per_sg);
+
+    for (int k_idx = kv_lo; k_idx < kv_hi; ++k_idx) {
+        const global char * k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char * v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+
+#ifdef FA_HAVE_INT_DOT
+        ACC_TYPE lane_contrib[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) lane_contrib[h] = 0.0f;
+
+        for (int qk = tid_sg; qk < DK_VEC; qk += Q1_WG_SIZE) {
+            const int block_idx     = qk / 8;
+            const int lane_in_block = qk % 8;
+            const int g             = lane_in_block & 3;
+            const int shift         = (lane_in_block < 4) ? 0 : 4;
+            const global char *  k_block = k_row + block_idx * Q4_0_BLOCK_SIZE;
+            const float kd = vload_half(0, (const global half *)k_block);
+            const global uchar * k_qs = (const global uchar *)(k_block + 2);
+            const uchar b0 = k_qs[g*4 + 0];
+            const uchar b1 = k_qs[g*4 + 1];
+            const uchar b2 = k_qs[g*4 + 2];
+            const uchar b3 = k_qs[g*4 + 3];
+            const uint k_packed = ((uint)((b0 >> shift) & 0x0F))       |
+                                  ((uint)((b1 >> shift) & 0x0F)) <<  8 |
+                                  ((uint)((b2 >> shift) & 0x0F)) << 16 |
+                                  ((uint)((b3 >> shift) & 0x0F)) << 24;
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                const uint  q_packed_lane = q_packed_shared[(h * DK_Q4_BLOCKS + block_idx) * 8 + lane_in_block];
+                const int   raw_dot       = dot_acc_sat_4x8packed_ss_int(q_packed_lane, k_packed, 0);
+                const float qd            = q_d_shared[h * DK_Q4_BLOCKS + block_idx];
+                const float block_scale   = qd * kd;
+                float contrib = (float) raw_dot * block_scale;
+                if (lane_in_block == 0) {
+                    const int q_sum_b = q_sum_shared[h * DK_Q4_BLOCKS + block_idx];
+                    contrib -= 8.0f * block_scale * (float) q_sum_b;
+                }
+                lane_contrib[h] += contrib;
+            }
+        }
+
+        ACC_TYPE score[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            ACC_TYPE s = sub_group_reduce_add(lane_contrib[h]) * scale;
+            if (mask_base[h] != NULL) {
+                const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) mask_base[h];
+                s += slope[h] * (ACC_TYPE) mask_ptr[k_idx];
+            }
+            if (logit_softcap > 0.0f) {
+                s = logit_softcap * tanh(s / logit_softcap);
+            }
+            score[h] = s;
+        }
+#else
+        // fallback float-dequant K dot
+        ACC_TYPE4 dot4[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) dot4[h] = (ACC_TYPE4)(0.0f);
+
+        for (int qk = tid_sg; qk < DK_VEC; qk += Q1_WG_SIZE) {
+            const int block_idx = qk / 8;
+            const int lane      = qk % 8;
+            const float4 k_v = dequant_q4_0_lane(k_row + block_idx * Q4_0_BLOCK_SIZE, lane);
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                dot4[h] = mad(q_shared[h * DK_VEC + qk], k_v, dot4[h]);
+            }
+        }
+
+        ACC_TYPE score[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            const ACC_TYPE dot_partial = dot4[h].s0 + dot4[h].s1 + dot4[h].s2 + dot4[h].s3;
+            ACC_TYPE s = sub_group_reduce_add(dot_partial) * scale;
+            if (mask_base[h] != NULL) {
+                const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) mask_base[h];
+                s += slope[h] * (ACC_TYPE) mask_ptr[k_idx];
+            }
+            if (logit_softcap > 0.0f) {
+                s = logit_softcap * tanh(s / logit_softcap);
+            }
+            score[h] = s;
+        }
+#endif
+
+        ACC_TYPE p_h[MQ_GQA];
+        ACC_TYPE sp_h[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            const ACC_TYPE m_new = max(m_i[h], score[h]);
+            sp_h[h] = native_exp(m_i[h] - m_new);
+            p_h[h]  = native_exp(score[h] - m_new);
+            l_i[h]  = l_i[h] * sp_h[h] + p_h[h];
+            m_i[h]  = m_new;
+        }
+
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            const int block_idx = dv / 8;
+            const int lane      = dv % 8;
+            const float4 v_v = dequant_q4_0_lane(v_row + block_idx * Q4_0_BLOCK_SIZE, lane);
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                o_acc[h][idx] = mad(p_h[h], v_v, o_acc[h][idx] * sp_h[h]);
+            }
+        }
+    }
+
+    // per-h cross-subgroup merge
+    __local ACC_TYPE  sg_m[MQ_GQA][MQ_NSG_SPLIT];
+    __local ACC_TYPE  sg_l[MQ_GQA][MQ_NSG_SPLIT];
+    __local ACC_TYPE4 sg_o[MQ_NSG_SPLIT][DV_VEC];
+
+    if (tid_sg == 0) {
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            sg_m[h][sgid] = m_i[h];
+            sg_l[h][sgid] = l_i[h];
+        }
+    }
+
+    #pragma unroll
+    for (int h = 0; h < MQ_GQA; ++h) {
+        {
+            int idx = 0;
+            for (int dv_idx = tid_sg; dv_idx < DV_VEC; dv_idx += Q1_WG_SIZE, ++idx) {
+                sg_o[sgid][dv_idx] = o_acc[h][idx];
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (sgid == 0) {
+            const int head_idx = head_kv_idx * MQ_GQA + h;
+
+            ACC_TYPE m_c = sg_m[h][0];
+            #pragma unroll
+            for (int s = 1; s < MQ_NSG_SPLIT; ++s) {
+                m_c = max(m_c, sg_m[h][s]);
+            }
+            ACC_TYPE l_c = 0.0f;
+            #pragma unroll
+            for (int s = 0; s < MQ_NSG_SPLIT; ++s) {
+                l_c += sg_l[h][s] * native_exp(sg_m[h][s] - m_c);
+            }
+
+            const ulong rec_idx = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                   * n_splits + split_idx);
+            global float  * rec   = partial_void + rec_idx * record_stride;
+            global float4 * rec_o = (global float4 *) (rec + 2);
+
+            if (tid_sg == 0) {
+                rec[0] = (float) m_c;
+                rec[1] = (float) l_c;
+            }
+            for (int dv_idx = tid_sg; dv_idx < DV_VEC; dv_idx += Q1_WG_SIZE) {
+                ACC_TYPE4 o_merged = (ACC_TYPE4)(0.0f);
+                #pragma unroll
+                for (int s = 0; s < MQ_NSG_SPLIT; ++s) {
+                    const ACC_TYPE alpha = native_exp(sg_m[h][s] - m_c);
+                    o_merged = mad((ACC_TYPE4)(alpha), sg_o[s][dv_idx], o_merged);
+                }
+                rec_o[dv_idx] = o_merged;
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+}
+
 __kernel void flash_attn_f32_q4_0(
     const global void * q_void, ulong q_offset,
     const global void * k_void, ulong k_offset,

ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl

@@ -24,7 +24,11 @@
 
 #define DK_VEC (DK/4)
 #define DV_VEC (DV/4)
-#define Q1_WG_SIZE 64
+
+#ifndef FA_SG
+#define FA_SG 64
+#endif
+#define Q1_WG_SIZE FA_SG
 
 // The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
 // infinite operand can cause undefined behavior and miscompilation for exp.
@@ -310,6 +314,201 @@ __kernel void flash_attn_f32_q8_0_q1(
     }
 }
 
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
+#else
+#define REQD_SUBGROUP_SIZE_64
+#endif
+
+#define VEC_NSG          4
+#define VEC_WG_SIZE      (Q1_WG_SIZE * VEC_NSG)
+#define Q1V_DV_PER_THREAD ((DV_VEC + Q1_WG_SIZE - 1) / Q1_WG_SIZE)
+
+inline float4 dequant_q8_0_lane(const global char * block_ptr, int lane) {
+    const float d = vload_half(0, (const global half *)block_ptr);
+    const global char * qs = block_ptr + 2 + lane * 4;
+    return d * (float4)((float)qs[0], (float)qs[1], (float)qs[2], (float)qs[3]);
+}
+
+REQD_SUBGROUP_SIZE_64
+__kernel void flash_attn_f32_q8_0_q1_vec(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
+) {
+    const int tid             = get_local_id(0);
+    const int sgid            = tid / Q1_WG_SIZE;
+    const int tid_sg          = tid % Q1_WG_SIZE;
+    const int head_batch_idx  = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx  = head_batch_idx % n_head;
+
+    const int gqa_ratio   = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+    global       char * o_base = (global       char *) o_void + o_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx  = head_idx  % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    __local ACC_TYPE4 q_shared[DK_VEC];
+    {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+        const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+        for (int i = tid; i < DK_VEC; i += VEC_WG_SIZE) {
+            q_shared[i] = CONVERT_Q_ACC4(q_ptr[i]);
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    const global ACC_TYPE * sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+    }
+
+    ACC_TYPE4 o_acc[Q1V_DV_PER_THREAD];
+    #pragma unroll
+    for (int i = 0; i < Q1V_DV_PER_THREAD; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    ACC_TYPE m_i = FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+
+    const int kv_per_sg = (n_kv + VEC_NSG - 1) / VEC_NSG;
+    const int kv_start  = sgid * kv_per_sg;
+    const int kv_end    = min(n_kv, kv_start + kv_per_sg);
+
+    for (int k_idx = kv_start; k_idx < kv_end; ++k_idx) {
+        const global char * k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char * v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+
+        ACC_TYPE4 dot4 = (ACC_TYPE4)(0.0f);
+        for (int qk = tid_sg; qk < DK_VEC; qk += Q1_WG_SIZE) {
+            const int block_idx = qk / 8;
+            const int lane      = qk % 8;
+            const float4 k_v = dequant_q8_0_lane(k_row + block_idx * Q8_0_BLOCK_SIZE, lane);
+            dot4 = mad(q_shared[qk], k_v, dot4);
+        }
+        ACC_TYPE dot_partial = dot4.s0 + dot4.s1 + dot4.s2 + dot4.s3;
+        ACC_TYPE score = sub_group_reduce_add(dot_partial) * scale;
+
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) mask_base;
+            score += slope * (ACC_TYPE) mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+
+        const ACC_TYPE m_new      = max(m_i, score);
+        const ACC_TYPE scale_prev = native_exp(m_i - m_new);
+        const ACC_TYPE p          = native_exp(score - m_new);
+
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            const int block_idx = dv / 8;
+            const int lane      = dv % 8;
+            const float4 v_v = dequant_q8_0_lane(v_row + block_idx * Q8_0_BLOCK_SIZE, lane);
+            o_acc[idx] = mad(p, v_v, o_acc[idx] * scale_prev);
+        }
+        l_i = l_i * scale_prev + p;
+        m_i = m_new;
+    }
+
+    __local ACC_TYPE  sg_m[VEC_NSG];
+    __local ACC_TYPE  sg_l[VEC_NSG];
+    __local ACC_TYPE4 sg_o[VEC_NSG][DV_VEC];
+
+    if (tid_sg == 0) {
+        sg_m[sgid] = m_i;
+        sg_l[sgid] = l_i;
+    }
+    {
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            sg_o[sgid][dv] = o_acc[idx];
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (sgid == 0) {
+        ACC_TYPE m_final = sg_m[0];
+        #pragma unroll
+        for (int s = 1; s < VEC_NSG; ++s) {
+            m_final = max(m_final, sg_m[s]);
+        }
+        if (sinks_ptr != NULL) {
+            m_final = max(m_final, sinks_ptr[head_idx]);
+        }
+
+        ACC_TYPE l_final = 0.0f;
+        #pragma unroll
+        for (int s = 0; s < VEC_NSG; ++s) {
+            l_final += sg_l[s] * native_exp(sg_m[s] - m_final);
+        }
+        if (sinks_ptr != NULL) {
+            l_final += native_exp(sinks_ptr[head_idx] - m_final);
+        }
+        const ACC_TYPE l_inv = (l_final > 0.0f) ? (1.0f / l_final) : 0.0f;
+
+        const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+        global O_DATA_TYPE4 * o_row = (global O_DATA_TYPE4 *) (o_base + o_row_offset);
+
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            ACC_TYPE4 o_merged = (ACC_TYPE4)(0.0f);
+            #pragma unroll
+            for (int s = 0; s < VEC_NSG; ++s) {
+                const ACC_TYPE alpha = native_exp(sg_m[s] - m_final);
+                o_merged = mad((ACC_TYPE4)(alpha), sg_o[s][dv], o_merged);
+            }
+            o_row[dv] = CONVERT_O_DATA4(o_merged * l_inv);
+        }
+    }
+}
+
 // Flash-decoding split pass for q8_0 KV. Partial record: [m, l, O[DV]].
 // Merge kernel from flash_attn_f32_f16.cl is type-agnostic and reused.
 #define FA_PARTIAL_FLOATS (2 + DV)
@@ -533,6 +732,244 @@ __kernel void flash_attn_f32_q8_0_q1_split(
 #define FA_V_STRATEGY 0
 #endif
 
+#ifndef MQ_GQA
+#define MQ_GQA 4
+#endif
+#ifndef MQ_NSG_SPLIT
+#define MQ_NSG_SPLIT 4
+#endif
+#define MQ_SPLIT_WG_SIZE_Q8 (Q1_WG_SIZE * MQ_NSG_SPLIT)
+
+REQD_SUBGROUP_SIZE_64
+__kernel void flash_attn_f32_q8_0_q1_vec_mq_split(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void * mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    global float * partial_void,
+    const int n_splits,
+    const int kv_per_split
+) {
+    const int tid              = get_local_id(0);
+    const int sgid             = tid / Q1_WG_SIZE;
+    const int tid_sg           = tid % Q1_WG_SIZE;
+    const int kvhead_batch_idx = get_global_id(1);
+    const int split_q_idx      = get_global_id(2);
+    const int split_idx        = split_q_idx % n_splits;
+    const int q_idx            = split_q_idx / n_splits;
+
+    const int batch_idx   = kvhead_batch_idx / n_head_kv;
+    const int head_kv_idx = kvhead_batch_idx % n_head_kv;
+
+    const int kv_start = split_idx * kv_per_split;
+    const int kv_end   = min(kv_start + kv_per_split, n_kv);
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+
+    if (kv_start >= kv_end) {
+        // Empty split — write sentinel for each of the MQ_GQA Q-heads.
+        if (tid == 0) {
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                const int head_idx = head_kv_idx * MQ_GQA + h;
+                const ulong rec_idx = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                       * n_splits + split_idx);
+                global float * rec = partial_void + rec_idx * record_stride;
+                rec[0] = FA_M_INIT;
+                rec[1] = 0.0f;
+            }
+        }
+        return;
+    }
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+
+    __local ACC_TYPE4 q_shared[MQ_GQA * DK_VEC];
+    for (int i = tid; i < MQ_GQA * DK_VEC; i += MQ_SPLIT_WG_SIZE_Q8) {
+        const int h        = i / DK_VEC;
+        const int k        = i % DK_VEC;
+        const int head_idx = head_kv_idx * MQ_GQA + h;
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + (ulong) q_idx * q_nb1;
+        const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+        q_shared[h * DK_VEC + k] = CONVERT_Q_ACC4(q_ptr[k]);
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    float slope[MQ_GQA];
+    #pragma unroll
+    for (int h = 0; h < MQ_GQA; ++h) {
+        slope[h] = get_alibi_slope(max_bias, head_kv_idx * MQ_GQA + h, n_head_log2, m0, m1);
+    }
+
+    const global char * mask_base[MQ_GQA];
+    if (mask_void != NULL) {
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        const global char * mask_base_b = (const global char *) mask_void + mask_offset +
+                                          mask_batch_idx * mask_nb3 +
+                                          (ulong) q_idx * mask_nb1;
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            const int head_idx      = head_kv_idx * MQ_GQA + h;
+            const int mask_head_idx = head_idx % mask_ne2;
+            mask_base[h] = mask_base_b + mask_head_idx * mask_nb2;
+        }
+    } else {
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) mask_base[h] = NULL;
+    }
+
+    ACC_TYPE4 o_acc[MQ_GQA][Q1V_DV_PER_THREAD];
+    ACC_TYPE  m_i[MQ_GQA];
+    ACC_TYPE  l_i[MQ_GQA];
+    #pragma unroll
+    for (int h = 0; h < MQ_GQA; ++h) {
+        m_i[h] = FA_M_INIT;
+        l_i[h] = 0.0f;
+        #pragma unroll
+        for (int i = 0; i < Q1V_DV_PER_THREAD; ++i) o_acc[h][i] = (ACC_TYPE4)(0.0f);
+    }
+
+    const int kv_len    = kv_end - kv_start;
+    const int kv_per_sg = (kv_len + MQ_NSG_SPLIT - 1) / MQ_NSG_SPLIT;
+    const int kv_lo     = kv_start + sgid * kv_per_sg;
+    const int kv_hi     = min(kv_end, kv_lo + kv_per_sg);
+
+    for (int k_idx = kv_lo; k_idx < kv_hi; ++k_idx) {
+        const global char * k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char * v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+
+        ACC_TYPE4 dot4[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) dot4[h] = (ACC_TYPE4)(0.0f);
+
+        for (int qk = tid_sg; qk < DK_VEC; qk += Q1_WG_SIZE) {
+            const int block_idx = qk / 8;
+            const int lane      = qk % 8;
+            const float4 k_v = dequant_q8_0_lane(k_row + block_idx * Q8_0_BLOCK_SIZE, lane);
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                dot4[h] = mad(q_shared[h * DK_VEC + qk], k_v, dot4[h]);
+            }
+        }
+
+        ACC_TYPE score[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            const ACC_TYPE dot_partial = dot4[h].s0 + dot4[h].s1 + dot4[h].s2 + dot4[h].s3;
+            ACC_TYPE s = sub_group_reduce_add(dot_partial) * scale;
+            if (mask_base[h] != NULL) {
+                const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) mask_base[h];
+                s += slope[h] * (ACC_TYPE) mask_ptr[k_idx];
+            }
+            if (logit_softcap > 0.0f) {
+                s = logit_softcap * tanh(s / logit_softcap);
+            }
+            score[h] = s;
+        }
+
+        ACC_TYPE p_h[MQ_GQA];
+        ACC_TYPE sp_h[MQ_GQA];
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            const ACC_TYPE m_new = max(m_i[h], score[h]);
+            sp_h[h] = native_exp(m_i[h] - m_new);
+            p_h[h]  = native_exp(score[h] - m_new);
+            l_i[h]  = l_i[h] * sp_h[h] + p_h[h];
+            m_i[h]  = m_new;
+        }
+
+        int idx = 0;
+        for (int dv = tid_sg; dv < DV_VEC; dv += Q1_WG_SIZE, ++idx) {
+            const int block_idx = dv / 8;
+            const int lane      = dv % 8;
+            const float4 v_v = dequant_q8_0_lane(v_row + block_idx * Q8_0_BLOCK_SIZE, lane);
+            #pragma unroll
+            for (int h = 0; h < MQ_GQA; ++h) {
+                o_acc[h][idx] = mad(p_h[h], v_v, o_acc[h][idx] * sp_h[h]);
+            }
+        }
+    }
+
+    __local ACC_TYPE  sg_m[MQ_GQA][MQ_NSG_SPLIT];
+    __local ACC_TYPE  sg_l[MQ_GQA][MQ_NSG_SPLIT];
+    __local ACC_TYPE4 sg_o[MQ_NSG_SPLIT][DV_VEC];
+
+    if (tid_sg == 0) {
+        #pragma unroll
+        for (int h = 0; h < MQ_GQA; ++h) {
+            sg_m[h][sgid] = m_i[h];
+            sg_l[h][sgid] = l_i[h];
+        }
+    }
+
+    #pragma unroll
+    for (int h = 0; h < MQ_GQA; ++h) {
+        {
+            int idx = 0;
+            for (int dv_idx = tid_sg; dv_idx < DV_VEC; dv_idx += Q1_WG_SIZE, ++idx) {
+                sg_o[sgid][dv_idx] = o_acc[h][idx];
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (sgid == 0) {
+            const int head_idx = head_kv_idx * MQ_GQA + h;
+
+            ACC_TYPE m_c = sg_m[h][0];
+            #pragma unroll
+            for (int s = 1; s < MQ_NSG_SPLIT; ++s) {
+                m_c = max(m_c, sg_m[h][s]);
+            }
+            ACC_TYPE l_c = 0.0f;
+            #pragma unroll
+            for (int s = 0; s < MQ_NSG_SPLIT; ++s) {
+                l_c += sg_l[h][s] * native_exp(sg_m[h][s] - m_c);
+            }
+
+            const ulong rec_idx = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                   * n_splits + split_idx);
+            global float  * rec   = partial_void + rec_idx * record_stride;
+            global float4 * rec_o = (global float4 *) (rec + 2);
+
+            if (tid_sg == 0) {
+                rec[0] = (float) m_c;
+                rec[1] = (float) l_c;
+            }
+            for (int dv_idx = tid_sg; dv_idx < DV_VEC; dv_idx += Q1_WG_SIZE) {
+                ACC_TYPE4 o_merged = (ACC_TYPE4)(0.0f);
+                #pragma unroll
+                for (int s = 0; s < MQ_NSG_SPLIT; ++s) {
+                    const ACC_TYPE alpha = native_exp(sg_m[h][s] - m_c);
+                    o_merged = mad((ACC_TYPE4)(alpha), sg_o[s][dv_idx], o_merged);
+                }
+                rec_o[dv_idx] = o_merged;
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+}
+
 __kernel void flash_attn_f32_q8_0(
     const global void * q_void, ulong q_offset,
     const global void * k_void, ulong k_offset,

ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl

@@ -18,6 +18,14 @@
 #define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
 #endif
 
+#ifdef cl_khr_subgroup_shuffle
+#pragma OPENCL EXTENSION cl_khr_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#elif defined(cl_qcom_subgroup_shuffle)
+#pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#endif
+
 // Assumes row size (ne00) is a multiple of 4
 #ifdef ADRENO_GPU
 REQD_SUBGROUP_SIZE_64
@@ -378,3 +386,848 @@ kernel void kernel_mul_mat_f16_f32_l4_dr_lq(
     }
 }
 #endif // ADRENO_GPU
+
+#define N_ROWS_PER_WG 8
+#define N_OUTS_PER_WG 8
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_x8(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char *)((global char *)src0 + offset0);
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int sgs_sz  = get_max_sub_group_size();
+
+    const int r0_base = get_group_id(0) * N_ROWS_PER_WG;
+    const int im      = get_group_id(2);
+
+    const int i12 = im % ne12;
+    const int i13 = im / ne12;
+
+    const ulong offset_src1 = (i12) * nb12 + (i13) * nb13;
+    global float4 * y4 = (global float4 *)(src1 + offset_src1);
+
+    __local float4 q_loc[64];   // ne00/4 max for sub_group_size 64
+    if (sgs_lid < ne00 / 4) {
+        q_loc[sgs_lid] = y4[sgs_lid];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    #pragma unroll
+    for (int dr = 0; dr < N_ROWS_PER_WG; ++dr) {
+        const int r0 = r0_base + dr;
+        if (r0 >= ne01) return;
+
+        const ulong offset_src0 = r0 * nb01 + (i12 / r2) * nb02 + (i13 / r3) * nb03;
+        global half4 * x4 = (global half4 *)(src0 + offset_src0);
+
+        float sumf = 0.0f;
+        for (int i = sgs_lid; i < ne00 / 4; i += sgs_sz) {
+            const half4   k4 = x4[i];
+            const float4  q  = q_loc[i];
+            sumf += convert_float(k4.s0) * q.s0
+                  + convert_float(k4.s1) * q.s1
+                  + convert_float(k4.s2) * q.s2
+                  + convert_float(k4.s3) * q.s3;
+        }
+
+        const float all_sum = sub_group_reduce_add(sumf);
+        if (sgs_lid == 0) {
+            dst[im * ne1 * ne0 + r0] = all_sum;  // ne11 == 1, so r1==0
+        }
+    }
+}
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_y8(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char *)((global char *)src0 + offset0);
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int sgs_sz  = get_max_sub_group_size();
+
+    const int r0_base = get_group_id(0) * N_OUTS_PER_WG;
+    const int im      = get_group_id(2);
+
+    const int i12 = im % ne12;
+    const int i13 = im / ne12;
+
+    const ulong offset_src1 = (i12) * nb12 + (i13) * nb13;
+    global float4 * y4 = (global float4 *)(src1 + offset_src1);
+
+    global half4 * x4_o[N_OUTS_PER_WG];
+    #pragma unroll
+    for (int o = 0; o < N_OUTS_PER_WG; ++o) {
+        const int r0 = r0_base + o;
+        const int r0c = (r0 < ne01) ? r0 : 0;
+        const ulong off = r0c * nb01 + (i12 / r2) * nb02 + (i13 / r3) * nb03;
+        x4_o[o] = (global half4 *)(src0 + off);
+    }
+
+    float sum[N_OUTS_PER_WG] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };
+
+    for (int i = sgs_lid; i < ne00 / 4; i += sgs_sz) {
+        const float4 q4 = y4[i];
+        #pragma unroll
+        for (int o = 0; o < N_OUTS_PER_WG; ++o) {
+            const half4 v4 = x4_o[o][i];
+            sum[o] += convert_float(v4.s0) * q4.s0
+                    + convert_float(v4.s1) * q4.s1
+                    + convert_float(v4.s2) * q4.s2
+                    + convert_float(v4.s3) * q4.s3;
+        }
+    }
+
+    #pragma unroll
+    for (int o = 0; o < N_OUTS_PER_WG; ++o) {
+        const int r0 = r0_base + o;
+        const float s = sub_group_reduce_add(sum[o]);
+        if (sgs_lid == 0 && r0 < ne01) {
+            dst[im * ne1 * ne0 + r0] = s;
+        }
+    }
+}
+
+#define N_OUTS_PAIR  8
+#define N_PAIRS_PAIR (N_OUTS_PAIR / 2)
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_x8_pair(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char *)((global char *)src0 + offset0);
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int half_id = sgs_lid >> 5;     // 0 = lower half, 1 = upper half
+    const int lane_h  = sgs_lid & 31;     // lane 0..31 within half
+
+    const int r0_base = get_group_id(0) * N_OUTS_PAIR;
+    const int im      = get_group_id(2);
+
+    const int i12 = im % ne12;
+    const int i13 = im / ne12;
+
+    const ulong offset_src1 = (i12) * nb12 + (i13) * nb13;
+    global float4 * y4 = (global float4 *)(src1 + offset_src1);
+
+    __local float4 q_loc[64];   // ne00/4 max for sub_group_size 64
+    if (sgs_lid < ne00 / 4) {
+        q_loc[sgs_lid] = y4[sgs_lid];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const int dk_vec = ne00 / 4;
+
+    #pragma unroll
+    for (int p = 0; p < N_PAIRS_PAIR; ++p) {
+        const int r0 = r0_base + 2 * p + half_id;
+
+        const ulong offset_src0 = r0 * nb01 + (i12 / r2) * nb02 + (i13 / r3) * nb03;
+        global half4 * x4 = (global half4 *)(src0 + offset_src0);
+
+        float sumf = 0.0f;
+        for (int i = lane_h; i < dk_vec; i += 32) {
+            const half4  k4 = x4[i];
+            const float4 q  = q_loc[i];
+            sumf += convert_float(k4.s0) * q.s0
+                  + convert_float(k4.s1) * q.s1
+                  + convert_float(k4.s2) * q.s2
+                  + convert_float(k4.s3) * q.s3;
+        }
+
+        sumf += sub_group_shuffle_xor(sumf, 16);
+        sumf += sub_group_shuffle_xor(sumf, 8);
+        sumf += sub_group_shuffle_xor(sumf, 4);
+        sumf += sub_group_shuffle_xor(sumf, 2);
+        sumf += sub_group_shuffle_xor(sumf, 1);
+
+        if (lane_h == 0) {
+            dst[im * ne1 * ne0 + r0] = sumf;
+        }
+    }
+}
+
+#define N_K_ROWS_GQA   16
+#define GQA_RATIO_GQA  8
+#define LANES_PER_QH   8    // 64 / GQA_RATIO_GQA
+#define DK_VEC_GQA     32   // DK / 4 for DK=128
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_x8_gqa4(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char *)((global char *)src0 + offset0);
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int q_id    = sgs_lid >> 3;       // 0..7: which Q-head (8 per WG)
+    const int lane_q  = sgs_lid & 7;        // 0..7: lane within Q-head partition
+
+    const int r0_base = get_group_id(0) * N_K_ROWS_GQA;
+    const int im_kv   = get_group_id(2);
+
+    const int i02 = im_kv % ne02;           // K-head index (also K2 batch)
+    const int i03 = im_kv / ne02;           // n13 batch index
+
+    const int q_head_lo = i02 * GQA_RATIO_GQA;
+
+    __local float4 q_loc[GQA_RATIO_GQA * DK_VEC_GQA];   // 4 × 32 = 128 float4
+    #pragma unroll
+    for (int qh = 0; qh < GQA_RATIO_GQA; ++qh) {
+        const int qh_idx = q_head_lo + qh;
+        global float4 * y4 = (global float4 *)(src1 + qh_idx * nb12 + i03 * nb13);
+
+        if (sgs_lid < DK_VEC_GQA) {
+            q_loc[qh * DK_VEC_GQA + sgs_lid] = y4[sgs_lid];
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // K base offset for this WG. All 8 K-rows × 4 Q-heads share this K-head.
+    const ulong offset_src0_base = (i02) * nb02 + (i03 / r3) * nb03;
+
+    #pragma unroll
+    for (int dr = 0; dr < N_K_ROWS_GQA; ++dr) {
+        const int r0 = r0_base + dr;
+
+        const ulong offset_src0 = r0 * nb01 + offset_src0_base;
+        global half4 * x4 = (global half4 *)(src0 + offset_src0);
+
+        float sumf = 0.0f;
+        #pragma unroll
+        for (int t = 0; t < 4; ++t) {
+            const int i = lane_q + t * LANES_PER_QH;   // 8, 16, 24-step
+            const half4  k4 = x4[i];
+            const float4 q  = q_loc[q_id * DK_VEC_GQA + i];
+            sumf += convert_float(k4.s0) * q.s0
+                  + convert_float(k4.s1) * q.s1
+                  + convert_float(k4.s2) * q.s2
+                  + convert_float(k4.s3) * q.s3;
+        }
+
+        sumf += sub_group_shuffle_xor(sumf, 4);
+        sumf += sub_group_shuffle_xor(sumf, 2);
+        sumf += sub_group_shuffle_xor(sumf, 1);
+
+        if (lane_q == 0) {
+            const int im_out = i03 * ne12 + (q_head_lo + q_id);
+            dst[im_out * ne1 * ne0 + r0] = sumf;
+        }
+    }
+}
+
+#define N_DV_ROWS_Y8GQA  8
+#define GQA_RATIO_Y8GQA  8
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_y8_gqa(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char *)((global char *)src0 + offset0);
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int sgs_sz  = get_max_sub_group_size();
+
+    const int r0_base = get_group_id(0) * N_DV_ROWS_Y8GQA;
+    const int im_kv   = get_group_id(2);
+
+    const int i02 = im_kv % ne02;           // K-head index
+    const int i03 = im_kv / ne02;           // n13 batch index
+
+    // GQA Q-heads sharing this K-head.
+    const int q_head_lo = i02 * GQA_RATIO_Y8GQA;
+
+    global float4 * y4_q[GQA_RATIO_Y8GQA];
+    #pragma unroll
+    for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+        const int qh_idx = q_head_lo + qh;
+        y4_q[qh] = (global float4 *)(src1 + qh_idx * nb12 + i03 * nb13);
+    }
+
+    global half4 * x4_o[N_DV_ROWS_Y8GQA];
+    #pragma unroll
+    for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+        const int r0 = r0_base + o;
+        const int r0c = (r0 < ne01) ? r0 : 0;
+        const ulong off = r0c * nb01 + (i02) * nb02 + (i03 / r3) * nb03;
+        x4_o[o] = (global half4 *)(src0 + off);
+    }
+
+    float sum[N_DV_ROWS_Y8GQA][GQA_RATIO_Y8GQA] = { {0.0f} };
+
+    for (int i = sgs_lid; i < ne00 / 4; i += sgs_sz) {
+        // load 8 V values (one per DV row), same K-head, K-pos = i.
+        half4 v[N_DV_ROWS_Y8GQA];
+        #pragma unroll
+        for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+            v[o] = x4_o[o][i];
+        }
+
+        // load 8 softmax values (one per Q-head).
+        float4 q[GQA_RATIO_Y8GQA];
+        #pragma unroll
+        for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+            q[qh] = y4_q[qh][i];
+        }
+
+        #pragma unroll
+        for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+            const float4 vf = (float4)(convert_float(v[o].s0),
+                                       convert_float(v[o].s1),
+                                       convert_float(v[o].s2),
+                                       convert_float(v[o].s3));
+            #pragma unroll
+            for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+                sum[o][qh] += vf.s0 * q[qh].s0
+                            + vf.s1 * q[qh].s1
+                            + vf.s2 * q[qh].s2
+                            + vf.s3 * q[qh].s3;
+            }
+        }
+    }
+
+    #pragma unroll
+    for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+        const int r0 = r0_base + o;
+        #pragma unroll
+        for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+            const float s = sub_group_reduce_add(sum[o][qh]);
+            if (sgs_lid == 0 && r0 < ne01) {
+                const int im_out = i03 * ne12 + (q_head_lo + qh);
+                dst[im_out * ne1 * ne0 + r0] = s;
+            }
+        }
+    }
+}
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_x8_gqa4_img(
+        __read_only image1d_buffer_t src0_img,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int q_id    = sgs_lid >> 3;       // 0..7: which Q-head (8 per WG)
+    const int lane_q  = sgs_lid & 7;        // 0..7: lane within Q-head partition
+
+    const int r0_base = get_group_id(0) * N_K_ROWS_GQA;
+    const int im_kv   = get_group_id(2);
+
+    const int i02 = im_kv % ne02;
+    const int i03 = im_kv / ne02;
+
+    const int q_head_lo = i02 * GQA_RATIO_GQA;
+
+    __local float4 q_loc[GQA_RATIO_GQA * DK_VEC_GQA];
+    #pragma unroll
+    for (int qh = 0; qh < GQA_RATIO_GQA; ++qh) {
+        const int qh_idx = q_head_lo + qh;
+        global float4 * y4 = (global float4 *)(src1 + qh_idx * nb12 + i03 * nb13);
+        if (sgs_lid < DK_VEC_GQA) {
+            q_loc[qh * DK_VEC_GQA + sgs_lid] = y4[sgs_lid];
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const int pitch_px_row  = (int)(nb01 >> 4);
+    const int pitch_px_head = (int)(nb02 >> 4);
+    const int pitch_px_n13  = (int)(nb03 >> 4);
+
+    const int head_px_base = i02 * pitch_px_head + (i03 / r3) * pitch_px_n13;
+
+    #pragma unroll
+    for (int dr = 0; dr < N_K_ROWS_GQA; ++dr) {
+        const int r0 = r0_base + dr;
+        const int row_px_base = r0 * pitch_px_row + head_px_base;
+
+        float sumf = 0.0f;
+        #pragma unroll
+        for (int t = 0; t < 2; ++t) {
+            const int p = lane_q + t * LANES_PER_QH;          // pixel idx in row, 0..15
+            const half8 k8 = as_half8(read_imagef(src0_img, row_px_base + p));
+            const int   i0 = 2 * p;                            // first half4 idx
+            const float4 qa = q_loc[q_id * DK_VEC_GQA + i0    ];
+            const float4 qb = q_loc[q_id * DK_VEC_GQA + i0 + 1];
+            sumf += convert_float(k8.s0) * qa.s0
+                  + convert_float(k8.s1) * qa.s1
+                  + convert_float(k8.s2) * qa.s2
+                  + convert_float(k8.s3) * qa.s3
+                  + convert_float(k8.s4) * qb.s0
+                  + convert_float(k8.s5) * qb.s1
+                  + convert_float(k8.s6) * qb.s2
+                  + convert_float(k8.s7) * qb.s3;
+        }
+
+        sumf += sub_group_shuffle_xor(sumf, 4);
+        sumf += sub_group_shuffle_xor(sumf, 2);
+        sumf += sub_group_shuffle_xor(sumf, 1);
+
+        if (lane_q == 0) {
+            const int im_out = i03 * ne12 + (q_head_lo + q_id);
+            dst[im_out * ne1 * ne0 + r0] = sumf;
+        }
+    }
+}
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_y8_gqa_img(
+        __read_only image1d_buffer_t src0_img,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int sgs_sz  = get_max_sub_group_size();
+
+    const int r0_base = get_group_id(0) * N_DV_ROWS_Y8GQA;
+    const int im_kv   = get_group_id(2);
+
+    const int i02 = im_kv % ne02;
+    const int i03 = im_kv / ne02;
+
+    const int q_head_lo = i02 * GQA_RATIO_Y8GQA;
+
+    // Q (= softmax(KQ)) base pointers per Q-head
+    global float4 * y4_q[GQA_RATIO_Y8GQA];
+    #pragma unroll
+    for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+        const int qh_idx = q_head_lo + qh;
+        y4_q[qh] = (global float4 *)(src1 + qh_idx * nb12 + i03 * nb13);
+    }
+
+    const int pitch_px_row  = (int)(nb01 >> 3);
+    const int pitch_px_head = (int)(nb02 >> 3);
+    const int pitch_px_n13  = (int)(nb03 >> 3);
+
+    const int head_px_base = i02 * pitch_px_head + (i03 / r3) * pitch_px_n13;
+
+    // per-DV-row pixel base
+    int row_px_base[N_DV_ROWS_Y8GQA];
+    #pragma unroll
+    for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+        const int r0  = r0_base + o;
+        const int r0c = (r0 < ne01) ? r0 : 0;
+        row_px_base[o] = r0c * pitch_px_row + head_px_base;
+    }
+
+    float sum[N_DV_ROWS_Y8GQA][GQA_RATIO_Y8GQA] = { {0.0f} };
+
+    for (int i = sgs_lid; i < ne00 / 4; i += sgs_sz) {
+        half4 v[N_DV_ROWS_Y8GQA];
+
+        #pragma unroll
+        for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+            v[o] = read_imageh(src0_img, row_px_base[o] + i);
+        }
+
+        float4 q[GQA_RATIO_Y8GQA];
+        #pragma unroll
+        for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+            q[qh] = y4_q[qh][i];
+        }
+        // 64 mads.
+        #pragma unroll
+        for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+            const float4 vf = (float4)(convert_float(v[o].s0),
+                                       convert_float(v[o].s1),
+                                       convert_float(v[o].s2),
+                                       convert_float(v[o].s3));
+            #pragma unroll
+            for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+                sum[o][qh] += vf.s0 * q[qh].s0
+                            + vf.s1 * q[qh].s1
+                            + vf.s2 * q[qh].s2
+                            + vf.s3 * q[qh].s3;
+            }
+        }
+    }
+
+    #pragma unroll
+    for (int o = 0; o < N_DV_ROWS_Y8GQA; ++o) {
+        const int r0 = r0_base + o;
+        #pragma unroll
+        for (int qh = 0; qh < GQA_RATIO_Y8GQA; ++qh) {
+            const float s = sub_group_reduce_add(sum[o][qh]);
+            if (sgs_lid == 0 && r0 < ne01) {
+                const int im_out = i03 * ne12 + (q_head_lo + qh);
+                dst[im_out * ne1 * ne0 + r0] = s;
+            }
+        }
+    }
+}
+
+#define N_K_ROWS_GQA_R4   16
+#define GQA_RATIO_R4      4
+#define LANES_PER_QH_R4   16    // = 64 / GQA_RATIO_R4
+#define DK_VEC_R4         32    // DK / 4 for DK=128
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_x8_gqa_r4_img(
+        __read_only image1d_buffer_t src0_img,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int q_id    = sgs_lid >> 4;       // 0..3
+    const int lane_q  = sgs_lid & 15;       // 0..15
+
+    const int r0_base = get_group_id(0) * N_K_ROWS_GQA_R4;
+    const int im_kv   = get_group_id(2);
+
+    const int i02 = im_kv % ne02;
+    const int i03 = im_kv / ne02;
+
+    const int q_head_lo = i02 * GQA_RATIO_R4;
+
+    __local float4 q_loc[GQA_RATIO_R4 * DK_VEC_R4];
+    #pragma unroll
+    for (int qh = 0; qh < GQA_RATIO_R4; ++qh) {
+        const int qh_idx = q_head_lo + qh;
+        global float4 * y4 = (global float4 *)(src1 + qh_idx * nb12 + i03 * nb13);
+        if (sgs_lid < DK_VEC_R4) {
+            q_loc[qh * DK_VEC_R4 + sgs_lid] = y4[sgs_lid];
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const int pitch_px_row  = (int)(nb01 >> 4);
+    const int pitch_px_head = (int)(nb02 >> 4);
+    const int pitch_px_n13  = (int)(nb03 >> 4);
+
+    const int head_px_base = i02 * pitch_px_head + (i03 / r3) * pitch_px_n13;
+
+    #pragma unroll
+    for (int dr = 0; dr < N_K_ROWS_GQA_R4; ++dr) {
+        const int r0 = r0_base + dr;
+        const int row_px_base = r0 * pitch_px_row + head_px_base;
+
+        const int p = lane_q;
+        const half8 k8 = as_half8(read_imagef(src0_img, row_px_base + p));
+        const int   i0 = 2 * p;
+        const float4 qa = q_loc[q_id * DK_VEC_R4 + i0    ];
+        const float4 qb = q_loc[q_id * DK_VEC_R4 + i0 + 1];
+
+        float sumf =
+              convert_float(k8.s0) * qa.s0
+            + convert_float(k8.s1) * qa.s1
+            + convert_float(k8.s2) * qa.s2
+            + convert_float(k8.s3) * qa.s3
+            + convert_float(k8.s4) * qb.s0
+            + convert_float(k8.s5) * qb.s1
+            + convert_float(k8.s6) * qb.s2
+            + convert_float(k8.s7) * qb.s3;
+
+        sumf += sub_group_shuffle_xor(sumf, 8);
+        sumf += sub_group_shuffle_xor(sumf, 4);
+        sumf += sub_group_shuffle_xor(sumf, 2);
+        sumf += sub_group_shuffle_xor(sumf, 1);
+
+        if (lane_q == 0) {
+            const int im_out = i03 * ne12 + (q_head_lo + q_id);
+            dst[im_out * ne1 * ne0 + r0] = sumf;
+        }
+    }
+}
+
+#define N_K_ROWS_GQA_R2_DK256   16
+#define GQA_RATIO_R2            2
+#define LANES_PER_QH_R2         32    // = 64 / GQA_RATIO_R2
+#define DK_VEC_DK256            64    // DK / 4 for DK=256
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_x8_gqa_r2_dk256_img(
+        __read_only image1d_buffer_t src0_img,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global char *)((global char *)src1 + offset1);
+    dst  = (global float*)((global char *)dst  + offsetd);
+
+    const int sgs_lid = get_sub_group_local_id();
+    const int q_id    = sgs_lid >> 5;       // 0..1
+    const int lane_q  = sgs_lid & 31;       // 0..31
+
+    const int r0_base = get_group_id(0) * N_K_ROWS_GQA_R2_DK256;
+    const int im_kv   = get_group_id(2);
+
+    const int i02 = im_kv % ne02;
+    const int i03 = im_kv / ne02;
+
+    const int q_head_lo = i02 * GQA_RATIO_R2;
+
+    __local float4 q_loc[GQA_RATIO_R2 * DK_VEC_DK256];
+    #pragma unroll
+    for (int qh = 0; qh < GQA_RATIO_R2; ++qh) {
+        const int qh_idx = q_head_lo + qh;
+        global float4 * y4 = (global float4 *)(src1 + qh_idx * nb12 + i03 * nb13);
+        q_loc[qh * DK_VEC_DK256 + sgs_lid] = y4[sgs_lid];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const int pitch_px_row  = (int)(nb01 >> 4);
+    const int pitch_px_head = (int)(nb02 >> 4);
+    const int pitch_px_n13  = (int)(nb03 >> 4);
+
+    const int head_px_base = i02 * pitch_px_head + (i03 / r3) * pitch_px_n13;
+
+    #pragma unroll
+    for (int dr = 0; dr < N_K_ROWS_GQA_R2_DK256; ++dr) {
+        const int r0 = r0_base + dr;
+        const int row_px_base = r0 * pitch_px_row + head_px_base;
+
+        const int p = lane_q;
+        const half8 k8 = as_half8(read_imagef(src0_img, row_px_base + p));
+        const int   i0 = 2 * p;
+        const float4 qa = q_loc[q_id * DK_VEC_DK256 + i0    ];
+        const float4 qb = q_loc[q_id * DK_VEC_DK256 + i0 + 1];
+
+        float sumf =
+              convert_float(k8.s0) * qa.s0
+            + convert_float(k8.s1) * qa.s1
+            + convert_float(k8.s2) * qa.s2
+            + convert_float(k8.s3) * qa.s3
+            + convert_float(k8.s4) * qb.s0
+            + convert_float(k8.s5) * qb.s1
+            + convert_float(k8.s6) * qb.s2
+            + convert_float(k8.s7) * qb.s3;
+
+        sumf += sub_group_shuffle_xor(sumf, 16);
+        sumf += sub_group_shuffle_xor(sumf, 8);
+        sumf += sub_group_shuffle_xor(sumf, 4);
+        sumf += sub_group_shuffle_xor(sumf, 2);
+        sumf += sub_group_shuffle_xor(sumf, 1);
+
+        if (lane_q == 0) {
+            const int im_out = i03 * ne12 + (q_head_lo + q_id);
+            dst[im_out * ne1 * ne0 + r0] = sumf;
+        }
+    }
+}

ggml/src/ggml-quants.c

@@ -71,6 +71,44 @@ void quantize_row_q1_0_ref(const float * GGML_RESTRICT x, block_q1_0 * GGML_REST
     }
 }
 
+void quantize_row_q2_0_ref(const float * GGML_RESTRICT x, block_q2_0 * GGML_RESTRICT y, int64_t k) {
+    static const int qk = QK2_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        // Compute scale as max absolute value in the block
+        float amax = 0.0f;
+        for (int j = 0; j < qk; j++) {
+            const float a = fabsf(x[i*qk + j]);
+            if (a > amax) amax = a;
+        }
+        const float d = amax;
+        const float id = d > 0.0f ? 1.0f / d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        // Clear quant bytes
+        for (int j = 0; j < qk / 4; ++j) {
+            y[i].qs[j] = 0;
+        }
+
+        // Encode 2-bit values: round(w/d) clamped to [-1, 2], then add 1
+        // 00 (-1) = -scale, 01 (0) = 0, 10 (+1) = +scale, 11 (+2) = 2*scale
+        for (int j = 0; j < qk; ++j) {
+            const float w = x[i*qk + j];
+            int q = (int)roundf(w * id) + 1;
+            if (q < 0) q = 0;
+            if (q > 3) q = 3;
+            const int byte_index = j / 4;
+            const int bit_offset = (j % 4) * 2;
+            y[i].qs[byte_index] |= ((uint8_t)q << bit_offset);
+        }
+    }
+}
+
 // reference implementation for deterministic creation of model files
 void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k) {
     static const int qk = QK4_0;
@@ -398,6 +436,26 @@ void dequantize_row_q1_0(const block_q1_0 * GGML_RESTRICT x, float * GGML_RESTRI
     }
 }
 
+void dequantize_row_q2_0(const block_q2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
+    static const int qk = QK2_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        for (int j = 0; j < qk; ++j) {
+            const int byte_index = j / 4;
+            const int bit_offset = (j % 4) * 2;
+            const uint8_t q = (x[i].qs[byte_index] >> bit_offset) & 0x03;
+            // 00=-1, 01=0, 10=+1, 11=+2
+            y[i*qk + j] = ((int)q - 1) * d;
+        }
+    }
+}
+
 void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
     static const int qk = QK4_0;
 
@@ -2052,6 +2110,20 @@ size_t quantize_q1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,
     return nrow * row_size;
 }
 
+size_t quantize_q2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        quantize_row_q2_0_ref(src, dst, (int64_t)nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q2_0, n_per_row);
+    }
+    size_t row_size = ggml_row_size(GGML_TYPE_Q2_0, n_per_row);
+    char * qrow = (char *)dst;
+    for (int64_t row = 0; row < nrow; ++row) {
+        quantize_row_q2_0_ref(src, (block_q2_0*)qrow, n_per_row);
+        src += n_per_row;
+        qrow += row_size;
+    }
+    return nrow * row_size;
+}
 
 size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
@@ -5461,6 +5533,10 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
             {
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_q1_0, data, nb);
             } break;
+        case GGML_TYPE_Q2_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q2_0, data, nb);
+            } break;
         case GGML_TYPE_Q4_0:
             {
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);

ggml/src/ggml-quants.h

@@ -15,6 +15,7 @@ extern "C" {
 
 // Quantization
 GGML_API void quantize_row_q1_0_ref(const float * GGML_RESTRICT x, block_q1_0 * GGML_RESTRICT y, int64_t k);
+GGML_API void quantize_row_q2_0_ref(const float * GGML_RESTRICT x, block_q2_0 * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
@@ -43,6 +44,7 @@ GGML_API void quantize_row_iq2_s_ref  (const float * GGML_RESTRICT x, block_iq2_
 
 // Dequantization
 GGML_API void dequantize_row_q1_0(const block_q1_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+GGML_API void dequantize_row_q2_0(const block_q2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
@@ -93,6 +95,7 @@ GGML_API size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTR
 GGML_API size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+GGML_API size_t quantize_q2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);

ggml/src/ggml-sycl/backend.hpp

@@ -14,6 +14,7 @@
 #define GGML_SYCL_BACKEND_HPP
 
 #include "binbcast.hpp"
+#include "col2im-1d.hpp"
 #include "common.hpp"
 #include "concat.hpp"
 #include "conv.hpp"

ggml/src/ggml-sycl/col2im-1d.cpp

@@ -0,0 +1,102 @@
+#include "col2im-1d.hpp"
+
+template <typename T>
+static void col2im_1d_sycl(
+        const T * col,
+        T * dst,
+        const int T_in,
+        const sycl::uint3 T_out_fd,
+        const int K,
+        const int K_OC,
+        const int32_t s0,
+        const int32_t p0,
+        const int total,
+        dpct::queue_ptr stream) {
+
+    const uint32_t block_size = SYCL_COL2IM_1D_BLOCK_SIZE;
+    const uint32_t num_blocks = (uint32_t) ((total + block_size - 1) / block_size);
+
+    stream->parallel_for(
+        sycl::nd_range<3>(
+            sycl::range<3>(1, 1, num_blocks * block_size),
+            sycl::range<3>(1, 1, block_size)),
+        [=](sycl::nd_item<3> item_ct1) {
+            const int idx = (int) item_ct1.get_global_id(2);
+            if (idx >= total) {
+                return;
+            }
+
+            const sycl::uint2 qr = fast_div_modulo((uint32_t) idx, T_out_fd);
+            const int oc    = (int) qr.x();
+            const int t_out = (int) qr.y();
+            const int t_abs = t_out + p0;
+
+            int t_in_min = (t_abs - K + s0) / s0;
+            if (t_in_min < 0) {
+                t_in_min = 0;
+            }
+            int t_in_max = t_abs / s0;
+            if (t_in_max >= T_in) {
+                t_in_max = T_in - 1;
+            }
+
+            float sum = 0.0f;
+            for (int t_in = t_in_min; t_in <= t_in_max; ++t_in) {
+                const int k = t_abs - t_in * s0;
+                sum += static_cast<float>(col[(oc * K + k) + t_in * K_OC]);
+            }
+
+            dst[idx] = static_cast<T>(sum);
+        });
+}
+
+void ggml_sycl_op_col2im_1d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0 != nullptr);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int32_t s0 = ((const int32_t *) dst->op_params)[0];
+    const int32_t OC = ((const int32_t *) dst->op_params)[1];
+    const int32_t p0 = ((const int32_t *) dst->op_params)[2];
+
+    const int K_OC  = (int) src0->ne[0];
+    const int T_in  = (int) src0->ne[1];
+    const int K     = K_OC / OC;
+    const int T_out = (int) dst->ne[0];
+
+    GGML_ASSERT(OC > 0);
+    GGML_ASSERT(K_OC % OC == 0);
+
+    const sycl::uint3 T_out_fd = init_fastdiv_values((uint32_t) T_out);
+
+    const int total = T_out * OC;
+
+    dpct::queue_ptr stream = ctx.stream();
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            col2im_1d_sycl<float>(
+                (const float *) src0->data,
+                (float *) dst->data,
+                T_in, T_out_fd, K, K_OC, s0, p0, total, stream);
+            break;
+        case GGML_TYPE_F16:
+            col2im_1d_sycl<sycl::half>(
+                (const sycl::half *) src0->data,
+                (sycl::half *) dst->data,
+                T_in, T_out_fd, K, K_OC, s0, p0, total, stream);
+            break;
+#ifdef GGML_SYCL_HAS_BF16
+        case GGML_TYPE_BF16:
+            col2im_1d_sycl<sycl::ext::oneapi::bfloat16>(
+                (const sycl::ext::oneapi::bfloat16 *) src0->data,
+                (sycl::ext::oneapi::bfloat16 *) dst->data,
+                T_in, T_out_fd, K, K_OC, s0, p0, total, stream);
+            break;
+#endif
+        default:
+            GGML_ABORT("col2im_1d: unsupported type %d", src0->type);
+    }
+}

ggml/src/ggml-sycl/col2im-1d.hpp

@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_COL2IM_1D_HPP
+#define GGML_SYCL_COL2IM_1D_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_col2im_1d(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_COL2IM_1D_HPP

ggml/src/ggml-sycl/common.hpp

@@ -59,7 +59,7 @@ void ggml_sycl_host_free(void* ptr);
 
 
 extern int g_ggml_sycl_debug;
-extern int g_ggml_sycl_disable_optimize;
+extern int g_ggml_sycl_enable_optimize;
 extern int g_ggml_sycl_prioritize_dmmv;
 extern int g_ggml_sycl_enable_flash_attention;
 extern int g_ggml_sycl_dev2dev_memcpy;

ggml/src/ggml-sycl/cpy.cpp

@@ -1,6 +1,7 @@
 #include "cpy.hpp"
 
 #include <float.h>
+#include <vector>
 
 #include "dequantize.hpp"
 #include "ggml-sycl/common.hpp"
@@ -50,6 +51,57 @@ static void cpy_1_i32_i32(const char * cxi, char * cdsti) {
     *dsti = *xi;
 }
 
+static void cpy_1_f32_i32(const char * cxi, char * cdsti) {
+    const float * xi     = (const float *) cxi;
+    int32_t *     dsti   = (int32_t *) cdsti;
+
+    *dsti = (int32_t) *xi;
+}
+
+static void cpy_1_i32_f32(const char * cxi, char * cdsti) {
+    const int32_t * xi   = (const int32_t *) cxi;
+    float *         dsti = (float *) cdsti;
+
+    *dsti = (float) *xi;
+}
+
+#ifdef GGML_SYCL_HAS_BF16
+static void cpy_1_f32_bf16(const char * cxi, char * cdsti) {
+    const float * xi   = (const float *) cxi;
+    sycl::ext::oneapi::bfloat16 *  dsti = (sycl::ext::oneapi::bfloat16 *) cdsti;
+
+    *dsti = sycl::ext::oneapi::bfloat16(*xi);
+}
+
+static void cpy_1_bf16_f32(const char * cxi, char * cdsti) {
+    const sycl::ext::oneapi::bfloat16 * xi   = (const sycl::ext::oneapi::bfloat16 *) cxi;
+    float *            dsti = (float *) cdsti;
+
+    *dsti = static_cast<float>(*xi);
+}
+
+static void cpy_1_bf16_bf16(const char * cxi, char * cdsti) {
+    const sycl::ext::oneapi::bfloat16 * xi   = (const sycl::ext::oneapi::bfloat16 *) cxi;
+    sycl::ext::oneapi::bfloat16 *       dsti = (sycl::ext::oneapi::bfloat16 *) cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_f16_bf16(const char * cxi, char * cdsti) {
+    const sycl::half * xi   = (const sycl::half *) cxi;
+    sycl::ext::oneapi::bfloat16 *  dsti = (sycl::ext::oneapi::bfloat16 *) cdsti;
+
+    *dsti = sycl::ext::oneapi::bfloat16(static_cast<float>(*xi));
+}
+
+static void cpy_1_bf16_f16(const char * cxi, char * cdsti) {
+    const sycl::ext::oneapi::bfloat16 * xi   = (const sycl::ext::oneapi::bfloat16 *) cxi;
+    sycl::half *            dsti = (sycl::half *) cdsti;
+
+    *dsti = sycl::half(static_cast<float>(*xi));
+}
+#endif
+
 template <cpy_kernel_t cpy_1>
 static void cpy_f32_f16(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02,
                         const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11,
@@ -247,6 +299,38 @@ static void ggml_cpy_f32_f16_sycl(const char * cx, char * cdst, const int ne, co
     }
 }
 
+static void ggml_cpy_f32_i32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_i32_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_i32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+            });
+    }
+}
+
 static void ggml_cpy_f32_q8_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
                                    const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
@@ -376,6 +460,19 @@ static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, c
         });
 }
 
+static void ggml_cpy_mxfp4_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                    const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                    const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ne;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_f32<cpy_blck_q_f32<dequantize_mxfp4, QK_MXFP4>, QK_MXFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                                                             nb01, nb02, nb03, ne10, ne11, ne12,
+                                                                             nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
 static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
                                      const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
                                      const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
@@ -389,6 +486,269 @@ static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne,
         });
 }
 
+static void cpy_blck_f16_q4_0(const char * cxi, char * cdsti) {
+    const sycl::half * xi = (const sycl::half *) cxi;
+    float xf[QK4_0];
+
+    for (int j = 0; j < QK4_0; ++j) {
+        xf[j] = (float) xi[j];
+    }
+
+    cpy_blck_f32_q4_0((const char *) xf, cdsti);
+}
+
+static void cpy_blck_f16_q4_1(const char * cxi, char * cdsti) {
+    const sycl::half * xi = (const sycl::half *) cxi;
+    float xf[QK4_1];
+
+    for (int j = 0; j < QK4_1; ++j) {
+        xf[j] = (float) xi[j];
+    }
+
+    cpy_blck_f32_q4_1((const char *) xf, cdsti);
+}
+
+static void cpy_blck_f16_q5_0(const char * cxi, char * cdsti) {
+    const sycl::half * xi = (const sycl::half *) cxi;
+    float xf[QK5_0];
+
+    for (int j = 0; j < QK5_0; ++j) {
+        xf[j] = (float) xi[j];
+    }
+
+    cpy_blck_f32_q5_0((const char *) xf, cdsti);
+}
+
+static void ggml_cpy_f16_q4_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    GGML_ASSERT(ne % QK4_0 == 0);
+    const int num_blocks = ne / QK4_0;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f16_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02,
+                                                                 nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
+}
+
+static void ggml_cpy_f16_q4_1_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    GGML_ASSERT(ne % QK4_1 == 0);
+    const int num_blocks = ne / QK4_1;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f16_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02,
+                                                                 nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
+}
+
+static void ggml_cpy_f16_q5_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    GGML_ASSERT(ne % QK5_0 == 0);
+    const int num_blocks = ne / QK5_0;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f16_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02,
+                                                                 nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
+}
+
+static bool ggml_sycl_is_quantized_type(enum ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_MXFP4:
+        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+            return true;
+        default:
+            return false;
+    }
+}
+
+static bool ggml_sycl_can_quantize_rows_sycl(enum ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_MXFP4:
+        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+            return true;
+        default:
+            return false;
+    }
+}
+
+template <typename SrcScalar>
+static inline float ggml_sycl_src_to_f32(const SrcScalar & x) {
+    return (float) x;
+}
+
+#ifdef GGML_SYCL_HAS_BF16
+template <>
+inline float ggml_sycl_src_to_f32<sycl::ext::oneapi::bfloat16>(const sycl::ext::oneapi::bfloat16 & x) {
+    return static_cast<float>(x);
+}
+
+template <>
+inline float ggml_sycl_src_to_f32<ggml_bf16_t>(const ggml_bf16_t & x) {
+    union {
+        uint32_t u32;
+        float f32;
+    } value;
+
+    value.u32 = (uint32_t) x.bits << 16;
+    return value.f32;
+}
+#endif
+
+template <typename SrcScalar, cpy_kernel_t quantize_block, int qk>
+static void ggml_sycl_quantize_rows_q(const char * cx, char * cdst, const int64_t ne,
+                                      const int64_t ne00, const int64_t ne01, const int64_t ne02,
+                                      const size_t nb00, const size_t nb01, const size_t nb02, const size_t nb03,
+                                      const int64_t ne10, const int64_t ne11, const int64_t ne12,
+                                      const size_t nb10, const size_t nb11, const size_t nb12, const size_t nb13,
+                                      queue_ptr stream) {
+    GGML_ASSERT(ne % qk == 0);
+    GGML_ASSERT(ne00 % qk == 0);
+
+    const int64_t total_blocks = ne / qk;
+    constexpr int block_size = 256;
+    const int64_t grid_size = ceil_div(total_blocks, (int64_t) block_size);
+
+    stream->parallel_for(sycl::nd_range<1>(grid_size * block_size, block_size), [=](sycl::nd_item<1> item_ct1) {
+        const int64_t block_idx = item_ct1.get_global_linear_id();
+        if (block_idx >= total_blocks) {
+            return;
+        }
+
+        const int64_t i = block_idx * qk;
+
+        const int64_t i03 = i / (ne00 * ne01 * ne02);
+        const int64_t i02 = (i - i03 * ne00 * ne01 * ne02) / (ne00 * ne01);
+        const int64_t i01 = (i - i03 * ne00 * ne01 * ne02 - i02 * ne01 * ne00) / ne00;
+        const int64_t i00 = i - i03 * ne00 * ne01 * ne02 - i02 * ne01 * ne00 - i01 * ne00;
+        const size_t x_offset = i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03;
+
+        const int64_t i13 = i / (ne10 * ne11 * ne12);
+        const int64_t i12 = (i - i13 * ne10 * ne11 * ne12) / (ne10 * ne11);
+        const int64_t i11 = (i - i13 * ne10 * ne11 * ne12 - i12 * ne10 * ne11) / ne10;
+        const int64_t i10 = i - i13 * ne10 * ne11 * ne12 - i12 * ne10 * ne11 - i11 * ne10;
+        const size_t dst_offset = (i10 / qk) * nb10 + i11 * nb11 + i12 * nb12 + i13 * nb13;
+
+        float xf[qk];
+        if (nb00 == sizeof(SrcScalar)) {
+            const SrcScalar * src_row = (const SrcScalar *) (cx + x_offset);
+            for (int j = 0; j < qk; ++j) {
+                xf[j] = ggml_sycl_src_to_f32(src_row[j]);
+            }
+        } else {
+            for (int j = 0; j < qk; ++j) {
+                const SrcScalar * src_val = (const SrcScalar *) (cx + x_offset + j * nb00);
+                xf[j] = ggml_sycl_src_to_f32(*src_val);
+            }
+        }
+
+        quantize_block((const char *) xf, cdst + dst_offset);
+    });
+}
+
+template <typename SrcScalar>
+static void ggml_sycl_quantize_rows_sycl(const char * cx, char * cdst, const ggml_tensor * src0, const ggml_tensor * src1,
+                                         const int64_t ne, const int64_t ne00, const int64_t ne01, const int64_t ne02,
+                                         const size_t nb00, const size_t nb01, const size_t nb02, const size_t nb03,
+                                         const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10,
+                                         const size_t nb11, const size_t nb12, const size_t nb13, queue_ptr stream) {
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+
+    switch (src1->type) {
+        case GGML_TYPE_Q8_0:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
+        case GGML_TYPE_Q1_0:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q1_0, QK1_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
+        case GGML_TYPE_Q4_0:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                                                               nb01, nb02, nb03, ne10, ne11, ne12,
+                                                                               nb10, nb11, nb12, nb13, stream);
+            break;
+        case GGML_TYPE_MXFP4:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_mxfp4, QK_MXFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                                                                nb01, nb02, nb03, ne10, ne11, ne12,
+                                                                                nb10, nb11, nb12, nb13, stream);
+            break;
+        case GGML_TYPE_NVFP4:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_nvfp4, QK_NVFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                                                                nb01, nb02, nb03, ne10, ne11, ne12,
+                                                                                nb10, nb11, nb12, nb13, stream);
+            break;
+        default:
+            GGML_ABORT("unsupported quantized target type in sycl quantizer src1->type=%s\n",
+                       ggml_type_name(src1->type));
+    }
+}
+
 static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
@@ -509,8 +869,269 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const
         });
 }
 
+static void ggml_cpy_q1_0_q1_0(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q1_0, QK1_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_mxfp4_mxfp4(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_mxfp4, QK_MXFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_nvfp4_nvfp4(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_nvfp4, QK_NVFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_q2_K_q2_K(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q2_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_q3_K_q3_K(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q3_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_q4_K_q4_K(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q4_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_q5_K_q5_K(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q5_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_q6_K_q6_K(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q6_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq2_xxs_iq2_xxs(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq2_xxs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq2_xs_iq2_xs(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq2_xs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq2_s_iq2_s(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq2_s, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq3_xxs_iq3_xxs(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq3_xxs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq1_s_iq1_s(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq1_s, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq1_m_iq1_m(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq1_m, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq4_nl_iq4_nl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq3_s_iq3_s(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq3_s, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_iq4_xs_iq4_xs(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_iq4_xs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+#ifdef GGML_SYCL_HAS_BF16
+static void ggml_cpy_f32_bf16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            cpy_f32_f16<cpy_1_f32_bf16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_bf16_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            cpy_f32_f16<cpy_1_bf16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_bf16_bf16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            cpy_f32_f16<cpy_1_bf16_bf16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                            nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_f16_bf16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            cpy_f32_f16<cpy_1_f16_bf16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
+static void ggml_cpy_bf16_f16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                  const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                  const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                  const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            cpy_f32_f16<cpy_1_bf16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+#endif
+
 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
+    GGML_SYCL_DEBUG("ggml_sycl_cpy: src0->type=%s, src1->type=%s\n",
+        ggml_type_name(src0->type), ggml_type_name(src1->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));
@@ -525,12 +1146,31 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
     if ((src0->type == src1->type) && (ggml_is_contiguous(src0) && ggml_is_contiguous(src1))) {
         GGML_SYCL_DEBUG("%s: memcpy path\n", __func__);
         main_stream->memcpy(src1_ddc, src0_ddc, ggml_nbytes(src0));
+    } else if (src0->type == GGML_TYPE_F32 && ggml_sycl_is_quantized_type(src1->type)) {
+        GGML_ASSERT(ggml_sycl_can_quantize_rows_sycl(src1->type));
+        ggml_sycl_quantize_rows_sycl<float>(src0_ddc, src1_ddc, src0, src1, ne, ne00, ne01, ne02, nb00, nb01,
+                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && ggml_sycl_is_quantized_type(src1->type)) {
+        GGML_ASSERT(ggml_sycl_can_quantize_rows_sycl(src1->type));
+        ggml_sycl_quantize_rows_sycl<sycl::half>(src0_ddc, src1_ddc, src0, src1, ne, ne00, ne01, ne02, nb00,
+                                                 nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                                 main_stream);
+#ifdef GGML_SYCL_HAS_BF16
+    } else if (src0->type == GGML_TYPE_BF16 && ggml_sycl_is_quantized_type(src1->type)) {
+        GGML_ASSERT(ggml_sycl_can_quantize_rows_sycl(src1->type));
+        ggml_sycl_quantize_rows_sycl<ggml_bf16_t>(src0_ddc, src1_ddc, src0, src1, ne, ne00, ne01, ne02,
+                                                  nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                  nb12, nb13, main_stream);
+#endif
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
         ggml_cpy_f32_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
         ggml_cpy_f32_f16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) {
+        ggml_cpy_f32_i32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                              nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
         ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                                nb11, nb12, nb13, main_stream);
@@ -546,12 +1186,24 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
         ggml_cpy_f16_f16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_Q4_0) {
+        ggml_cpy_f16_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02,
+                               nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_Q4_1) {
+        ggml_cpy_f16_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02,
+                               nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_Q5_0) {
+        ggml_cpy_f16_q5_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02,
+                               nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
         ggml_cpy_i16_i16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) {
         ggml_cpy_i32_i32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_i32_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                              nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_Q4_0 && src1->type == GGML_TYPE_F32) {
         ggml_cpy_q4_0_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                                nb11, nb12, nb13, main_stream);
@@ -573,6 +1225,9 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
     } else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) {
         ggml_cpy_q5_1_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                                nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_MXFP4 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_mxfp4_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                                nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
         ggml_cpy_f32_iq4_nl_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                  nb10, nb11, nb12, nb13, main_stream);
@@ -586,6 +1241,57 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
         ggml_cpy_q4_0_q4_0(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_Q4_1 && src1->type == GGML_TYPE_Q4_1) {
         ggml_cpy_q4_1_q4_1(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q1_0 && src1->type == GGML_TYPE_Q1_0) {
+        ggml_cpy_q1_0_q1_0(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_MXFP4 && src1->type == GGML_TYPE_MXFP4) {
+        ggml_cpy_mxfp4_mxfp4(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_NVFP4 && src1->type == GGML_TYPE_NVFP4) {
+        ggml_cpy_nvfp4_nvfp4(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q2_K && src1->type == GGML_TYPE_Q2_K) {
+        ggml_cpy_q2_K_q2_K(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q3_K && src1->type == GGML_TYPE_Q3_K) {
+        ggml_cpy_q3_K_q3_K(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q4_K && src1->type == GGML_TYPE_Q4_K) {
+        ggml_cpy_q4_K_q4_K(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q5_K && src1->type == GGML_TYPE_Q5_K) {
+        ggml_cpy_q5_K_q5_K(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q6_K && src1->type == GGML_TYPE_Q6_K) {
+        ggml_cpy_q6_K_q6_K(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ2_XXS && src1->type == GGML_TYPE_IQ2_XXS) {
+        ggml_cpy_iq2_xxs_iq2_xxs(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ2_XS && src1->type == GGML_TYPE_IQ2_XS) {
+        ggml_cpy_iq2_xs_iq2_xs(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ2_S && src1->type == GGML_TYPE_IQ2_S) {
+        ggml_cpy_iq2_s_iq2_s(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ3_XXS && src1->type == GGML_TYPE_IQ3_XXS) {
+        ggml_cpy_iq3_xxs_iq3_xxs(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ1_S && src1->type == GGML_TYPE_IQ1_S) {
+        ggml_cpy_iq1_s_iq1_s(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ1_M && src1->type == GGML_TYPE_IQ1_M) {
+        ggml_cpy_iq1_m_iq1_m(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ4_NL && src1->type == GGML_TYPE_IQ4_NL) {
+        ggml_cpy_iq4_nl_iq4_nl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ3_S && src1->type == GGML_TYPE_IQ3_S) {
+        ggml_cpy_iq3_s_iq3_s(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_IQ4_XS && src1->type == GGML_TYPE_IQ4_XS) {
+        ggml_cpy_iq4_xs_iq4_xs(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+#ifdef GGML_SYCL_HAS_BF16
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) {
+        ggml_cpy_f32_bf16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                              nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_bf16_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                              nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) {
+        ggml_cpy_bf16_bf16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                               nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) {
+        ggml_cpy_f16_bf16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                              nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_bf16_f16_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
+                              nb11, nb12, nb13, main_stream);
+#endif
     } else {
         GGML_LOG_ERROR("%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type),
                        ggml_type_name(src1->type));

ggml/src/ggml-sycl/cpy.hpp

@@ -317,7 +317,7 @@ inline void cpy_blck_f32_nvfp4(const char * cxi, char * cdsti) {
 
         const uint8_t ue = ggml_fp32_to_ue4m3(amax / 6.0f);
         dsti->d[s] = ue;
-        const float d = ggml_ue4m3_to_fp32(ue);
+        const float d = ggml_sycl_ue4m3_to_fp32(ue);
 
         for (int j = 0; j < QK_NVFP4_SUB / 2; ++j) {
             const uint8_t x0 = best_index_mxfp4(xb[0 + j], d);

ggml/src/ggml-sycl/cross_entropy_loss.cpp

@@ -0,0 +1,255 @@
+#include "cross_entropy_loss.hpp"
+
+#include <cstdint>
+#include <cmath>
+
+template <bool has_shared>
+static __dpct_inline__ void cross_entropy_loss_f32_kernel(
+        const float * __restrict__ logits,
+        const float * __restrict__ labels,
+        float * __restrict__ row_loss,
+        const int nclasses,
+        const int nrows,
+        float * __restrict__ smem,
+        const sycl::nd_item<3> & item) {
+
+    const int row = item.get_group(2);
+    const int tid = item.get_local_id(2);
+
+    logits += (int64_t) row * nclasses;
+    labels += (int64_t) row * nclasses;
+
+    float max_logit = -INFINITY;
+    for (int i = tid; i < nclasses; i += WARP_SIZE) {
+        const float v = logits[i];
+        max_logit = sycl::fmax(max_logit, v);
+        if (has_shared) {
+            smem[i] = v;
+        }
+    }
+    max_logit = warp_reduce_max<WARP_SIZE>(max_logit);
+
+    float sum_exp = 0.0f;
+    for (int i = tid; i < nclasses; i += WARP_SIZE) {
+        const float v = has_shared ? smem[i] : logits[i];
+        sum_exp += sycl::exp(v - max_logit);
+    }
+    sum_exp = warp_reduce_sum<WARP_SIZE>(sum_exp);
+    const float log_sum = sycl::log(sum_exp);
+
+    float loss = 0.0f;
+    for (int i = tid; i < nclasses; i += WARP_SIZE) {
+        const float v = has_shared ? smem[i] : logits[i];
+        loss += (v - max_logit - log_sum) * labels[i];
+    }
+    loss = -warp_reduce_sum<WARP_SIZE>(loss) / (float) nrows;
+
+    if (tid == 0) {
+        row_loss[row] = loss;
+    }
+}
+
+template <bool has_shared>
+static __dpct_inline__ void cross_entropy_loss_back_f32_kernel(
+        const float * __restrict__ grad,
+        const float * __restrict__ logits,
+        const float * __restrict__ labels,
+        float * __restrict__ dst,
+        const int nclasses,
+        const int nrows,
+        float * __restrict__ smem,
+        const sycl::nd_item<3> & item) {
+
+    const int row = item.get_group(2);
+    const int tid = item.get_local_id(2);
+
+    logits += (int64_t) row * nclasses;
+    labels += (int64_t) row * nclasses;
+    dst    += (int64_t) row * nclasses;
+
+    float max_logit = -INFINITY;
+    for (int i = tid; i < nclasses; i += WARP_SIZE) {
+        const float v = logits[i];
+        max_logit = sycl::fmax(max_logit, v);
+        if (has_shared) {
+            smem[i] = v;
+        }
+    }
+    max_logit = warp_reduce_max<WARP_SIZE>(max_logit);
+
+    float sum_exp = 0.0f;
+    for (int i = tid; i < nclasses; i += WARP_SIZE) {
+        const float v = sycl::exp((has_shared ? smem[i] : logits[i]) - max_logit);
+        sum_exp += v;
+        if (has_shared) {
+            smem[i] = v;
+        } else {
+            dst[i] = v;
+        }
+    }
+    sum_exp = warp_reduce_sum<WARP_SIZE>(sum_exp);
+    const float inv_sum = 1.0f / sum_exp;
+
+    const float d_by_nrows = grad[0] / (float) nrows;
+    for (int i = tid; i < nclasses; i += WARP_SIZE) {
+        const float sm_num = has_shared ? smem[i] : dst[i];
+        dst[i] = (sm_num * inv_sum - labels[i]) * d_by_nrows;
+    }
+}
+
+static void cross_entropy_reduce_rows(
+        ggml_backend_sycl_context & ctx,
+        const float * row_loss,
+        float * dst,
+        const int64_t nrows) {
+    if (nrows == 1) {
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            ctx.stream()->memcpy(dst, row_loss, sizeof(float))));
+        return;
+    }
+
+    ggml_sycl_pool_alloc<float> tmp_alloc(ctx.pool(), nrows);
+    float * tmp = tmp_alloc.get();
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        ctx.stream()->memcpy(tmp, row_loss, nrows * sizeof(float))));
+
+    int64_t cur = nrows;
+    while (cur > 1) {
+        const int64_t out = (cur + WARP_SIZE - 1) / WARP_SIZE;
+        const sycl::range<3> block(1, 1, WARP_SIZE);
+        const sycl::range<3> grid(1, 1, out);
+        ctx.stream()->parallel_for(
+            sycl::nd_range<3>(grid * block, block),
+            [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                const int row = item.get_group(2);
+                const int tid = item.get_local_id(2);
+                const int64_t i = (int64_t) row * WARP_SIZE + tid;
+                float v = i < cur ? tmp[i] : 0.0f;
+                v = warp_reduce_sum<WARP_SIZE>(v);
+                if (tid == 0) {
+                    tmp[row] = v;
+                }
+            });
+        cur = out;
+    }
+
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        ctx.stream()->memcpy(dst, tmp, sizeof(float))));
+}
+
+void ggml_sycl_cross_entropy_loss(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, src1));
+    GGML_ASSERT(ggml_is_scalar(dst));
+
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    const int64_t nclasses = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    const float * logits_d = (const float *) src0->data;
+    const float * labels_d = (const float *) src1->data;
+    float * dst_d = (float *) dst->data;
+
+    ggml_sycl_pool_alloc<float> row_loss_alloc(ctx.pool(), nrows);
+    float * row_loss = row_loss_alloc.get();
+
+    const sycl::range<3> block(1, 1, WARP_SIZE);
+    const sycl::range<3> grid(1, 1, nrows);
+    const size_t nbytes_shared = (size_t) nclasses * sizeof(float);
+    const size_t smpbo = ggml_sycl_info().devices[ctx.device].smpbo;
+
+    if (nbytes_shared <= smpbo) {
+        ctx.stream()->submit([&](sycl::handler & cgh) {
+            sycl::local_accessor<float, 1> smem(sycl::range<1>(nclasses), cgh);
+            cgh.parallel_for(
+                sycl::nd_range<3>(grid * block, block),
+                [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    cross_entropy_loss_f32_kernel<true>(
+                        logits_d, labels_d, row_loss,
+                        (int) nclasses, (int) nrows,
+                        get_pointer(smem), item);
+                });
+        });
+    } else {
+        ctx.stream()->parallel_for(
+            sycl::nd_range<3>(grid * block, block),
+            [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                cross_entropy_loss_f32_kernel<false>(
+                    logits_d, labels_d, row_loss,
+                    (int) nclasses, (int) nrows,
+                    nullptr, item);
+            });
+    }
+
+    cross_entropy_reduce_rows(ctx, row_loss, dst_d, nrows);
+}
+
+void ggml_sycl_cross_entropy_loss_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
+
+    const ggml_tensor * grad  = dst->src[0];
+    const ggml_tensor * src0f = dst->src[1];
+    const ggml_tensor * src1f = dst->src[2];
+
+    GGML_ASSERT(grad->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0f->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1f->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_scalar(grad));
+    GGML_ASSERT(ggml_is_contiguous(grad));
+    GGML_ASSERT(ggml_is_contiguous(src0f));
+    GGML_ASSERT(ggml_is_contiguous(src1f));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0f, src1f));
+    GGML_ASSERT(ggml_are_same_shape(src0f, dst));
+
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    const int64_t nclasses = src0f->ne[0];
+    const int64_t nrows = ggml_nrows(src0f);
+
+    const float * grad_d  = (const float *) grad->data;
+    const float * logits_d = (const float *) src0f->data;
+    const float * labels_d = (const float *) src1f->data;
+    float * dst_d = (float *) dst->data;
+
+    const sycl::range<3> block(1, 1, WARP_SIZE);
+    const sycl::range<3> grid(1, 1, nrows);
+    const size_t nbytes_shared = (size_t) nclasses * sizeof(float);
+    const size_t smpbo = ggml_sycl_info().devices[ctx.device].smpbo;
+
+    if (nbytes_shared <= smpbo) {
+        ctx.stream()->submit([&](sycl::handler & cgh) {
+            sycl::local_accessor<float, 1> smem(sycl::range<1>(nclasses), cgh);
+            cgh.parallel_for(
+                sycl::nd_range<3>(grid * block, block),
+                [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    cross_entropy_loss_back_f32_kernel<true>(
+                        grad_d, logits_d, labels_d, dst_d,
+                        (int) nclasses, (int) nrows,
+                        get_pointer(smem), item);
+                });
+        });
+    } else {
+        ctx.stream()->parallel_for(
+            sycl::nd_range<3>(grid * block, block),
+            [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                cross_entropy_loss_back_f32_kernel<false>(
+                    grad_d, logits_d, labels_d, dst_d,
+                    (int) nclasses, (int) nrows,
+                    nullptr, item);
+            });
+    }
+}

ggml/src/ggml-sycl/cross_entropy_loss.hpp

@@ -0,0 +1,7 @@
+#pragma once
+
+#include "common.hpp"
+
+void ggml_sycl_cross_entropy_loss(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+void ggml_sycl_cross_entropy_loss_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

ggml/src/ggml-sycl/dmmv.cpp

@@ -680,14 +680,14 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
             q16[2] = q2[0] & 0x0f0f;
             q16[3] = q2[0] & 0xf0f0;
 
-            float4 s = {0.f, 0.f, 0.f, 0.f};
+            sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
             float smin = 0;
             for (int l = 0; l < 2; ++l) {
-                s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
-                s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+                s.x() += y1[l] * q4[l+0]; s.y() += y1[l+32] * q4[l+2];
+                s.z() += y2[l] * q4[l+4]; s.w() += y2[l+32] * q4[l+6];
                 smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
             }
-            tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+            tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f/16.f + s.z() * sc[4] + s.w() * sc[5] * 1.f/16.f) - dmin * smin;
 #endif
         }
 
@@ -835,14 +835,14 @@ static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx,
             q16[2] = q2[0] & 0x0f0f;
             q16[3] = q2[0] & 0xf0f0;
 
-            float4 s = {0.f, 0.f, 0.f, 0.f};
+            sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
             float smin = 0;
             for (int l = 0; l < 2; ++l) {
-                s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
-                s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+                s.x() += y1[l] * q4[l+0]; s.y() += y1[l+32] * q4[l+2];
+                s.z() += y2[l] * q4[l+4]; s.w() += y2[l+32] * q4[l+6];
                 smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
             }
-            tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+            tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f/16.f + s.z() * sc[4] + s.w() * sc[5] * 1.f/16.f) - dmin * smin;
 #endif
         }
 
@@ -1126,7 +1126,7 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -1762,10 +1762,13 @@ static void dequantize_mul_mat_vec_q5_K_sycl_reorder(const void *vx, const float
                                                      const int nrows,
                                                      dpct::queue_ptr stream) {
     GGML_ASSERT(ncols % QK_K == 0);
-    const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, WARP_SIZE);
     stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
             dequantize_mul_mat_vec_q5_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
         });
 }

ggml/src/ggml-sycl/element_wise.cpp

@@ -9,9 +9,12 @@
 #define SYCL_LOCAL_ID_CALC(ITEM, IDX) \
     (ITEM.get_local_range(IDX) * ITEM.get_group(IDX) + ITEM.get_local_id(IDX))
 
-static void acc_f32(const float * x, const float * y, float * dst, const int64_t ne,
-        const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
-        const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) {
+static void acc_f32(const char * x, const char * y, float * dst, const int64_t ne,
+    const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3,
+    const int64_t nb00, const int64_t nb01, const int64_t nb02, const int64_t nb03,
+    const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+    const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
+    const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) {
     auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
     const int64_t i = SYCL_LOCAL_ID_CALC(item_ct1, 2);
 
@@ -30,9 +33,18 @@ static void acc_f32(const float * x, const float * y, float * dst, const int64_t
     tmp -= i11 * s11;
     const int64_t i10 = tmp;
 
-    float val = x[i];
+    int64_t tmp_dst = i;
+    const int64_t i3 = tmp_dst / (ne2*ne1*ne0);
+    tmp_dst -= i3 * (ne2*ne1*ne0);
+    const int64_t i2 = tmp_dst / (ne1*ne0);
+    tmp_dst -= i2 * (ne1*ne0);
+    const int64_t i1 = tmp_dst / ne0;
+    tmp_dst -= i1 * ne0;
+    const int64_t i0 = tmp_dst;
+
+    float val = *(const float *) (x + i0*nb00 + i1*nb01 + i2*nb02 + i3*nb03);
     if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) {
-        val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10];
+        val += *(const float *) (y + i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13);
     }
     dst[i] = val;
 }
@@ -422,15 +434,24 @@ static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const
 }
 
 namespace ggml_sycl_detail {
-static void acc_f32_sycl(const float *x, const float *y, float *dst,
-                         const int64_t n_elements, const int64_t ne10, const int64_t ne11,
-                         const int64_t ne12, const int64_t ne13, const int64_t s1, const int64_t s2, const int64_t s3,
+static void acc_f32_sycl(const char *x, const char *y, float *dst,
+                         const int64_t n_elements,
+                         const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3,
+                         const int64_t nb00, const int64_t nb01, const int64_t nb02, const int64_t nb03,
+                         const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+                         const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
+                         const int64_t s1, const int64_t s2, const int64_t s3,
                          const int64_t offset, queue_ptr stream) {
     const int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
     stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
                                            sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
                          [=](sycl::nd_item<3> /*item_ct1*/) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                             acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset);
+                            acc_f32(x, y, dst, n_elements,
+                                ne0, ne1, ne2, ne3,
+                                nb00, nb01, nb02, nb03,
+                                ne10, ne11, ne12, ne13,
+                                nb10, nb11, nb12, nb13,
+                                s1, s2, s3, offset);
                          });
 }
 
@@ -843,8 +864,8 @@ static inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
 
-    const float * src0_d = (const float *) src0->data;
-    const float * src1_d = (const float *) src1->data;
+    const char  * src0_d = (const char  *) src0->data;
+    const char  * src1_d = (const char  *) src1->data;
     float       * dst_d  = (float       *)  dst->data;
 
     dpct::queue_ptr stream = ctx.stream();
@@ -853,17 +874,20 @@ static inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    GGML_ASSERT(ggml_is_contiguous(src1));
     GGML_ASSERT(dst->nb[0] == ggml_element_size(dst));
     GGML_ASSERT(ggml_is_contiguously_allocated(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    const int64_t s1     = dst->op_params[0] / sizeof(float);
-    const int64_t s2     = dst->op_params[1] / sizeof(float);
-    const int64_t s3     = dst->op_params[2] / sizeof(float);
-    const int64_t offset = dst->op_params[3] / sizeof(float);
+    const int64_t s1     = (int64_t) ((const int32_t *) dst->op_params)[0] / (int64_t) sizeof(float);
+    const int64_t s2     = (int64_t) ((const int32_t *) dst->op_params)[1] / (int64_t) sizeof(float);
+    const int64_t s3     = (int64_t) ((const int32_t *) dst->op_params)[2] / (int64_t) sizeof(float);
+    const int64_t offset = (int64_t) ((const int32_t *) dst->op_params)[3] / (int64_t) sizeof(float);
 
     ggml_sycl_detail::acc_f32_sycl(src0_d, src1_d, dst_d, ggml_nelements(dst),
+        dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+        src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
         src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+        src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
         s1, s2, s3, offset, stream);
 }
 

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

@@ -41,7 +41,7 @@
 #if SYCL_EXT_ONEAPI_VIRTUAL_MEM
 #    include <sycl/ext/oneapi/virtual_mem/physical_mem.hpp>
 #    include <sycl/ext/oneapi/virtual_mem/virtual_mem.hpp>
-#    define GGML_SYCL_USE_VMM
+#    define GGML_SYCL_SUPPORT_VMM
 #endif
 #include <sycl/half_type.hpp>
 
@@ -74,15 +74,16 @@
 #include "ggml-sycl/solve_tri.hpp"
 #include "ggml-sycl/gated_delta_net.hpp"
 #include "ggml-sycl/pool.hpp"
+#include "ggml-sycl/cross_entropy_loss.hpp"
 
 #define MEM_SIZE_2M	0x00200000
 #define MEM_SIZE_1G	0x40000000
 
 static bool g_sycl_loaded = false;
 int g_ggml_sycl_debug = 0;
-int g_ggml_sycl_disable_optimize = 0;
-int g_ggml_sycl_disable_graph = 0;
-int g_ggml_sycl_disable_dnn = 0;
+int g_ggml_sycl_enable_optimize = 1;
+int g_ggml_sycl_enable_graph = 0;
+int g_ggml_sycl_enable_dnn = 1;
 int g_ggml_sycl_enable_vmm = 1;
 int g_ggml_sycl_prioritize_dmmv = 0;
 int g_ggml_sycl_use_async_mem_op = 0;
@@ -117,7 +118,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
             prop, device)));
 
-#if !defined(GGML_SYCL_USE_VMM)
+#if !defined(GGML_SYCL_SUPPORT_VMM)
         info.devices[i].vmm = 0;
 #else
         info.devices[i].vmm = device.has(sycl::aspect::ext_oneapi_virtual_mem);
@@ -265,14 +266,24 @@ void ggml_backend_sycl_print_sycl_devices() {
     print_device_opt_feature(device_count);
 }
 
+static const char* dev2dev_int2str(int dev2dev) {
+    if (dev2dev == DEV2DEV_MEMCPY_SYCL) {
+        return "SYCL API";
+    } else if (dev2dev == DEV2DEV_MEMCPY_L0) {
+        return "Level Zero API";
+    } else {
+        return "Unknown";
+    }
+}
+
 static void ggml_check_sycl() try {
     static bool initialized = false;
 
     if (!initialized) {
         g_ggml_sycl_debug = ggml_sycl_get_env("GGML_SYCL_DEBUG", 0);
-        g_ggml_sycl_disable_optimize = ggml_sycl_get_env("GGML_SYCL_DISABLE_OPT", 0);
-        g_ggml_sycl_disable_graph = ggml_sycl_get_env("GGML_SYCL_DISABLE_GRAPH", 1);
-        g_ggml_sycl_disable_dnn = ggml_sycl_get_env("GGML_SYCL_DISABLE_DNN", 0);
+        g_ggml_sycl_enable_optimize = ggml_sycl_get_env("GGML_SYCL_ENABLE_OPT", 1);
+        g_ggml_sycl_enable_graph = ggml_sycl_get_env("GGML_SYCL_ENABLE_GRAPH", 0);
+        g_ggml_sycl_enable_dnn = ggml_sycl_get_env("GGML_SYCL_ENABLE_DNN", 1);
         g_ggml_sycl_enable_vmm = ggml_sycl_get_env("GGML_SYCL_ENABLE_VMM", 1);
         g_ggml_sycl_prioritize_dmmv = ggml_sycl_get_env("GGML_SYCL_PRIORITIZE_DMMV", 0);
 
@@ -292,66 +303,56 @@ static void ggml_check_sycl() try {
         GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n");
 
         GGML_LOG_INFO("Build with Macros:\n");
-#if defined(GGML_SYCL_FORCE_MMQ)
-        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: yes\n");
-#else
-        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: no\n");
-#endif
-#if defined(GGML_SYCL_F16)
-        GGML_LOG_INFO("  GGML_SYCL_F16: yes\n");
-#else
-        GGML_LOG_INFO("  GGML_SYCL_F16: no\n");
-#endif
-#if defined(GGML_SYCL_GRAPH)
-        GGML_LOG_INFO("  GGML_SYCL_GRAPH: yes\n");
-#else
-        GGML_LOG_INFO("  GGML_SYCL_GRAPH: no\n");
-#endif
 #if defined(GGML_SYCL_DNNL)
         GGML_LOG_INFO("  GGML_SYCL_DNNL: yes\n");
 #else
         GGML_LOG_INFO("  GGML_SYCL_DNNL: no\n");
 #endif
+
+#if defined(GGML_SYCL_F16)
+        GGML_LOG_INFO("  GGML_SYCL_F16: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_F16: no\n");
+#endif
+
+#if defined(GGML_SYCL_FORCE_MMQ)
+        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: no\n");
+#endif
+
+#if defined(GGML_SYCL_GRAPH)
+        GGML_LOG_INFO("  GGML_SYCL_GRAPH: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_GRAPH: no\n");
+#endif
+
 #if defined(GGML_SYCL_SUPPORT_LEVEL_ZERO_API)
         GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO_API: yes\n");
 #else
         GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO_API: no\n");
 #endif
-#if defined(GGML_SYCL_USE_VMM)
-        GGML_LOG_INFO("  GGML_SYCL_USE_VMM: yes\n");
+#if defined(GGML_SYCL_SUPPORT_VMM)
+        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_VMM: yes\n");
 #else
-        GGML_LOG_INFO("  GGML_SYCL_USE_VMM: no\n");
+        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_VMM: no\n");
 #endif
 
         GGML_LOG_INFO("Running with Environment Variables:\n");
         GGML_LOG_INFO("  GGML_SYCL_DEBUG: %d\n", g_ggml_sycl_debug);
-        GGML_LOG_INFO("  GGML_SYCL_DISABLE_OPT: %d\n", g_ggml_sycl_disable_optimize);
-#ifdef GGML_SYCL_GRAPH
-        GGML_LOG_INFO("  GGML_SYCL_DISABLE_GRAPH: %d\n", g_ggml_sycl_disable_graph);
-#else
-        GGML_LOG_INFO("  GGML_SYCL_DISABLE_GRAPH: graph disabled by compile flag\n");
-#endif
+
 #ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
-        GGML_LOG_INFO("  GGML_SYCL_USE_LEVEL_ZERO_API: %d\n", g_ggml_sycl_use_level_zero_api);
-        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d\n", g_ggml_sycl_dev2dev_memcpy);
+        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d (%s)\n", g_ggml_sycl_dev2dev_memcpy, dev2dev_int2str(g_ggml_sycl_dev2dev_memcpy));
 #else
-        GGML_LOG_INFO("  GGML_SYCL_USE_LEVEL_ZERO_API: Disable Level Zero API usage by compile flag\n");
-        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d, enable to SYCL API since missing GGML_SYCL_SUPPORT_LEVEL_ZERO_API\n",
-                      g_ggml_sycl_dev2dev_memcpy);
+        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d (%s), enable to SYCL API since missing GGML_SYCL_SUPPORT_LEVEL_ZERO_API\n",
+                      g_ggml_sycl_dev2dev_memcpy, dev2dev_int2str(g_ggml_sycl_dev2dev_memcpy));
 #endif
-#if GGML_SYCL_DNNL
-        GGML_LOG_INFO("  GGML_SYCL_DISABLE_DNN: %d\n", g_ggml_sycl_disable_dnn);
+
+#if defined(GGML_SYCL_DNNL)
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_DNN: %d\n", g_ggml_sycl_enable_dnn);
 #else
-        GGML_LOG_INFO("  GGML_SYCL_DISABLE_DNN: DNN disabled by compile flag\n");
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_DNN: DNN disabled by compile flag\n");
 #endif
-#if defined(GGML_SYCL_USE_VMM)
-        GGML_LOG_INFO("  GGML_SYCL_ENABLE_VMM: %d\n", g_ggml_sycl_enable_vmm);
-#else
-        GGML_LOG_INFO("  GGML_SYCL_ENABLE_VMM: virtual memory extension is not available\n");
-#endif
-        GGML_LOG_INFO("  GGML_SYCL_PRIORITIZE_DMMV: %d\n", g_ggml_sycl_prioritize_dmmv);
-        g_ggml_sycl_use_async_mem_op_requested = ggml_sycl_get_env("GGML_SYCL_USE_ASYNC_MEM_OP", 1);
-        GGML_LOG_INFO("  GGML_SYCL_USE_ASYNC_MEM_OP: %d\n", g_ggml_sycl_use_async_mem_op_requested);
 
 #ifdef SYCL_FLASH_ATTN
         GGML_LOG_INFO("  GGML_SYCL_ENABLE_FLASH_ATTN: %d\n", g_ggml_sycl_enable_flash_attention);
@@ -360,6 +361,31 @@ static void ggml_check_sycl() try {
             g_ggml_sycl_enable_flash_attention);
 #endif
 
+#ifdef GGML_SYCL_GRAPH
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_GRAPH: %d\n", g_ggml_sycl_enable_graph);
+#else
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_GRAPH: graph disabled by compile flag\n");
+#endif
+
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_OPT: %d\n", g_ggml_sycl_enable_optimize);
+
+#if defined(GGML_SYCL_SUPPORT_VMM)
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_VMM: %d\n", g_ggml_sycl_enable_vmm);
+#else
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_VMM: virtual memory extension is not available\n");
+#endif
+
+        GGML_LOG_INFO("  GGML_SYCL_PRIORITIZE_DMMV: %d\n", g_ggml_sycl_prioritize_dmmv);
+
+        g_ggml_sycl_use_async_mem_op_requested = ggml_sycl_get_env("GGML_SYCL_USE_ASYNC_MEM_OP", 1);
+        GGML_LOG_INFO("  GGML_SYCL_USE_ASYNC_MEM_OP: %d\n", g_ggml_sycl_use_async_mem_op_requested);
+
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
+        GGML_LOG_INFO("  GGML_SYCL_USE_LEVEL_ZERO_API: %d\n", g_ggml_sycl_use_level_zero_api);
+#else
+        GGML_LOG_INFO("  GGML_SYCL_USE_LEVEL_ZERO_API: Disable Level Zero API usage by compile flag\n");
+#endif
+
         GGML_LOG_INFO("  GGML_SYCL_USM_SYSTEM: %d\n", g_ggml_sycl_usm_system);
 
 /* NOT REMOVE, keep it for next optimize for XMX.
@@ -373,7 +399,7 @@ static void ggml_check_sycl() try {
         // staging path while preserving queue ordering semantics. Graph support still depends on the extension being
         // available, but it no longer needs to control the non-graph fast path.
 #if defined(GGML_SYCL_GRAPH) && SYCL_EXT_ONEAPI_ASYNC_MEMORY_ALLOC
-        g_ggml_sycl_use_async_mem_op = g_ggml_sycl_use_async_mem_op_requested || !g_ggml_sycl_disable_graph;
+        g_ggml_sycl_use_async_mem_op = g_ggml_sycl_use_async_mem_op_requested || g_ggml_sycl_enable_graph;
         if (g_ggml_sycl_use_async_mem_op) {
             for (unsigned int i = 0; i < dpct::dev_mgr::instance().device_count(); ++i) {
                 if (!dpct::dev_mgr::instance().get_device(i).has(sycl::aspect::ext_oneapi_async_memory_alloc)) {
@@ -516,12 +542,14 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
         return GGML_STATUS_SUCCESS;
     }
 
-    if (!g_ggml_sycl_disable_optimize) {
+    if (g_ggml_sycl_enable_optimize) {
         // set reorder extra buffer based on supported type
         switch (tensor->type) {
             case GGML_TYPE_Q4_0:
             case GGML_TYPE_Q8_0:
+            case GGML_TYPE_Q3_K:
             case GGML_TYPE_Q4_K:
+            case GGML_TYPE_Q5_K:
             case GGML_TYPE_Q6_K:{
                 ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
                 tensor->extra                 = extra;
@@ -1562,7 +1590,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
 };
 
 // pool with virtual memory management
-#if defined(GGML_SYCL_USE_VMM)
+#if defined(GGML_SYCL_SUPPORT_VMM)
 struct ggml_sycl_pool_vmm : public ggml_sycl_pool {
     static const size_t SYCL_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
 
@@ -1674,7 +1702,7 @@ struct ggml_sycl_pool_vmm : public ggml_sycl_pool {
         GGML_ASSERT(ptr == reinterpret_cast<void *>(pool_addr + pool_used));
     }
 };
-#endif // defined(GGML_SYCL_USE_VMM)
+#endif // defined(GGML_SYCL_SUPPORT_VMM)
 
 struct ggml_sycl_pool_host : public ggml_sycl_pool {
     queue_ptr qptr;
@@ -1756,11 +1784,11 @@ std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_host(que
 }
 
 std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) {
-#if defined(GGML_SYCL_USE_VMM)
+#if defined(GGML_SYCL_SUPPORT_VMM)
     if (g_ggml_sycl_enable_vmm && ggml_sycl_info().devices[device].vmm) {
         return std::unique_ptr<ggml_sycl_pool>(new ggml_sycl_pool_vmm(qptr, device));
     }
-#endif // defined(GGML_SYCL_USE_VMM)
+#endif // defined(GGML_SYCL_SUPPORT_VMM)
     return std::unique_ptr<ggml_sycl_pool>(new ggml_sycl_pool_leg(qptr, device));
 }
 
@@ -2088,11 +2116,148 @@ static int next_power_of_2(int x) {
     return n;
 }
 
+static void init_argsort_indices_padded(
+        int * idx,
+        const int nrows,
+        const int ncols_pad,
+        const sycl::nd_item<1> & item_ct1) {
+    const size_t gid = item_ct1.get_local_range(0) * item_ct1.get_group(0) + item_ct1.get_local_id(0);
+    const size_t total = (size_t) nrows * (size_t) ncols_pad;
+
+    if (gid >= total) {
+        return;
+    }
+
+    idx[gid] = (int) (gid % (size_t) ncols_pad);
+}
+
+template <ggml_sort_order order>
+static void argsort_f32_i32_global_pass(const float *            x,
+                                        int *                    idx,
+                                        const int                ncols,
+                                        const int                nrows,
+                                        const int                ncols_pad,
+                                        const int                j,
+                                        const int                k,
+                                        const sycl::nd_item<1> & item_ct1) {
+    const size_t gid   = item_ct1.get_local_range(0) * item_ct1.get_group(0) + item_ct1.get_local_id(0);
+    const size_t total = (size_t) nrows * (size_t) ncols_pad;
+
+    if (gid >= total) {
+        return;
+    }
+
+    const int row = (int) (gid / (size_t) ncols_pad);
+    const int col = (int) (gid % (size_t) ncols_pad);
+    const int ixj = col ^ j;
+
+    if (ixj <= col || ixj >= ncols_pad) {
+        return;
+    }
+
+    const size_t base  = (size_t) row * (size_t) ncols_pad;
+    const size_t pos_a = base + (size_t) col;
+    const size_t pos_b = base + (size_t) ixj;
+
+    const int a = idx[pos_a];
+    const int b = idx[pos_b];
+
+    bool do_swap = false;
+
+    if ((col & k) == 0) {
+        if (a >= ncols ||
+            (b < ncols &&
+             (order == GGML_SORT_ORDER_ASC ?
+                  x[(size_t) row * (size_t) ncols + (size_t) a] > x[(size_t) row * (size_t) ncols + (size_t) b] :
+                  x[(size_t) row * (size_t) ncols + (size_t) a] < x[(size_t) row * (size_t) ncols + (size_t) b]))) {
+            do_swap = true;
+        }
+    } else {
+        if (b >= ncols ||
+            (a < ncols &&
+             (order == GGML_SORT_ORDER_ASC ?
+                  x[(size_t) row * (size_t) ncols + (size_t) a] < x[(size_t) row * (size_t) ncols + (size_t) b] :
+                  x[(size_t) row * (size_t) ncols + (size_t) a] > x[(size_t) row * (size_t) ncols + (size_t) b]))) {
+            do_swap = true;
+        }
+    }
+
+    if (do_swap) {
+        idx[pos_a] = b;
+        idx[pos_b] = a;
+    }
+}
+
+static void copy_argsort_indices_unpadded(const int *              idx_padded,
+                                          int *                    dst,
+                                          const int                nrows,
+                                          const int                ncols,
+                                          const int                ncols_pad,
+                                          const sycl::nd_item<1> & item_ct1) {
+    const size_t gid   = item_ct1.get_local_range(0) * item_ct1.get_group(0) + item_ct1.get_local_id(0);
+    const size_t total = (size_t) nrows * (size_t) ncols;
+
+    if (gid >= total) {
+        return;
+    }
+
+    const int row = (int) (gid / (size_t) ncols);
+    const int col = (int) (gid % (size_t) ncols);
+
+    dst[(size_t) row * (size_t) ncols + (size_t) col] = idx_padded[(size_t) row * (size_t) ncols_pad + (size_t) col];
+}
+
 static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
                                  const int nrows, ggml_sort_order order,
-                                 queue_ptr stream, int device) {
+                                 queue_ptr stream, int device, ggml_sycl_pool & pool) {
     // bitonic sort requires ncols to be power of 2
     const int ncols_pad = next_power_of_2(ncols);
+    const size_t shared_mem = (size_t) ncols_pad * sizeof(int);
+    const size_t smpbo = ggml_sycl_info().devices[device].smpbo;
+
+    if (shared_mem > smpbo) {
+        ggml_sycl_pool_alloc<int> idx_padded_alloc(pool, (size_t) nrows * (size_t) ncols_pad);
+        int *                     idx_padded = idx_padded_alloc.get();
+
+        constexpr size_t block_size     = 256;
+        const size_t     total_padded   = (size_t) nrows * (size_t) ncols_pad;
+        const size_t     nblocks_padded = (total_padded + block_size - 1) / block_size;
+
+        stream->parallel_for(
+            sycl::nd_range<1>(sycl::range<1>(nblocks_padded * block_size), sycl::range<1>(block_size)),
+            [=](sycl::nd_item<1> item_ct1) { init_argsort_indices_padded(idx_padded, nrows, ncols_pad, item_ct1); });
+
+        for (int k = 2; k <= ncols_pad; k *= 2) {
+            for (int j = k / 2; j > 0; j /= 2) {
+                if (order == GGML_SORT_ORDER_ASC) {
+                    stream->parallel_for(
+                        sycl::nd_range<1>(sycl::range<1>(nblocks_padded * block_size), sycl::range<1>(block_size)),
+                        [=](sycl::nd_item<1> item_ct1) {
+                            argsort_f32_i32_global_pass<GGML_SORT_ORDER_ASC>(x, idx_padded, ncols, nrows, ncols_pad, j,
+                                                                             k, item_ct1);
+                        });
+                } else if (order == GGML_SORT_ORDER_DESC) {
+                    stream->parallel_for(
+                        sycl::nd_range<1>(sycl::range<1>(nblocks_padded * block_size), sycl::range<1>(block_size)),
+                        [=](sycl::nd_item<1> item_ct1) {
+                            argsort_f32_i32_global_pass<GGML_SORT_ORDER_DESC>(x, idx_padded, ncols, nrows, ncols_pad, j,
+                                                                              k, item_ct1);
+                        });
+                } else {
+                    GGML_ABORT("invalid sort order");
+                }
+            }
+        }
+
+        const size_t total   = (size_t) nrows * (size_t) ncols;
+        const size_t nblocks = (total + block_size - 1) / block_size;
+        stream->parallel_for(sycl::nd_range<1>(sycl::range<1>(nblocks * block_size), sycl::range<1>(block_size)),
+                             [=](sycl::nd_item<1> item_ct1) {
+                                 copy_argsort_indices_unpadded(idx_padded, dst, nrows, ncols, ncols_pad, item_ct1);
+                             });
+
+        return;
+    }
 
     int nth = 1;
     int max_block_size = ggml_sycl_info().max_work_group_sizes[device];
@@ -2105,8 +2270,6 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
 
     const sycl::range<3> block_dims(1, 1, nth);
     const sycl::range<3> block_nums(1, nrows, 1);
-    const size_t shared_mem = ncols_pad * sizeof(int);
-    GGML_ASSERT(shared_mem<=ggml_sycl_info().devices[device].smpbo);
 
     if (order == GGML_SORT_ORDER_ASC) {
         stream->submit([&](sycl::handler &cgh) {
@@ -2429,7 +2592,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
 
 #if GGML_SYCL_DNNL && defined(GGML_SYCL_HAS_BF16)
     // Fast path for bf16 src0
-    if (src0->type == GGML_TYPE_BF16 && !g_ggml_sycl_disable_dnn && ggml_is_contiguous(src0) &&
+    if (src0->type == GGML_TYPE_BF16 && g_ggml_sycl_enable_dnn && ggml_is_contiguous(src0) &&
         row_diff == src0->ne[1]) {
         using bf16_t = sycl::ext::oneapi::bfloat16;
         ggml_sycl_pool_alloc<bf16_t> src1_as_bf16(ctx.pool(), src1_ncols*ne10);
@@ -2482,7 +2645,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
                                          : src1_as_f16.get();
 
 #if GGML_SYCL_DNNL
-        if (!g_ggml_sycl_disable_dnn) {
+        if (g_ggml_sycl_enable_dnn) {
                 DnnlGemmWrapper::row_gemm(ctx,row_diff, src1_ncols , ne10, src0_ptr,
                                      DnnlGemmWrapper::to_dt<sycl::half>(), src1_ptr, DnnlGemmWrapper::to_dt<sycl::half>(),
                                       dst_dd_i, DnnlGemmWrapper::to_dt<float>(), stream);
@@ -2532,7 +2695,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
             const int64_t gemm_flops = (int64_t)row_diff * src1_ncols * ne10;
             const bool use_mkl_direct = gemm_flops < 256 * 256 * 256;
 #if GGML_SYCL_DNNL
-            if (!g_ggml_sycl_disable_dnn && !use_mkl_direct) {
+            if (g_ggml_sycl_enable_dnn && !use_mkl_direct) {
                 DnnlGemmWrapper::row_gemm(ctx, row_diff, src1_ncols, ne10, src0_ddf_i,
                                           DnnlGemmWrapper::to_dt<float>(), src1_ddf1_i, DnnlGemmWrapper::to_dt<float>(),
                                           dst_dd_i, DnnlGemmWrapper::to_dt<float>(), stream);
@@ -2625,7 +2788,7 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor *
     enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
 
     argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order,
-                         main_stream, ctx.device);
+                         main_stream, ctx.device, ctx.pool());
 }
 
 static void ggml_sycl_op_top_k(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
@@ -3352,7 +3515,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
     const int64_t r3 = ne13 / ne03;
 
 #if GGML_SYCL_DNNL
-    if (!g_ggml_sycl_disable_dnn) {
+    if (g_ggml_sycl_enable_dnn) {
             int64_t str_a0 = nb00 / type_size_src0;
             int64_t str_a1 = nb01 / type_size_src0;
             int64_t str_a2 = nb02 / type_size_src0;
@@ -3527,6 +3690,10 @@ inline bool ggml_sycl_supports_reorder_dmmv(enum ggml_type type) {
         case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
             return true;
         default:
             return false;
@@ -4092,12 +4259,12 @@ static bool reorder_qw(const ggml_tensor * src0, dpct::queue_ptr stream) {
 }
 
 static bool should_reorder_tensor(ggml_backend_sycl_context& ctx, const ggml_tensor * dst) {
-    return !g_ggml_sycl_disable_optimize && //allow optimize, controlled by $GGML_SYCL_DISABLE_OPT
-            ctx.opt_feature.reorder &&      //allow this device due to good perf, skip the devices with bad perf.
-            dst->op == GGML_OP_MUL_MAT &&   //limit to some supported cases of Q4_0, to do for more cases.
-            // ne[1] <= 8 so multi-column decode (spec / MTP verify) also bootstraps the reorder;
-            // all reorderable types have a _switch_ncols kernel.
-            dst->src[1]->ne[1] <= 8 && dst->src[1]->ne[2]==1 && dst->src[1]->ne[3]==1;
+    return g_ggml_sycl_enable_optimize && //allow optimize, controlled by $GGML_SYCL_ENABLE_OPT
+           ctx.opt_feature.reorder &&      //allow this device due to good perf, skip the devices with bad perf.
+           dst->op == GGML_OP_MUL_MAT &&   //limit to some supported cases of Q4_0, to do for more cases.
+           // ne[1] <= 8 so multi-column decode (spec / MTP verify) also bootstraps the reorder;
+           // all reorderable types have a _switch_ncols kernel.
+           dst->src[1]->ne[1] <= 8 && dst->src[1]->ne[2]==1 && dst->src[1]->ne[3]==1;
 }
 
 static void opt_for_reorder(ggml_backend_sycl_context * ctx, const ggml_tensor * src0, const ggml_tensor * /* src1 */,
@@ -4136,7 +4303,7 @@ static void opt_for_reorder(ggml_backend_sycl_context * ctx, const ggml_tensor *
 
 // Lazily reorder supported MoE expert weights once their fused path is used.
 static void opt_for_reorder_id(ggml_backend_sycl_context * ctx, const ggml_tensor * src0) {
-    if (g_ggml_sycl_disable_optimize || !ctx->opt_feature.reorder) {
+    if (!g_ggml_sycl_enable_optimize || !ctx->opt_feature.reorder) {
         return;
     }
     if (src0->type != GGML_TYPE_Q4_K && src0->type != GGML_TYPE_Q5_K && src0->type != GGML_TYPE_Q6_K) {
@@ -4604,6 +4771,11 @@ static void ggml_sycl_im2col_3d(ggml_backend_sycl_context & ctx, ggml_tensor * d
     ggml_sycl_op_im2col_3d(ctx, dst);
 }
 
+static void ggml_sycl_col2im_1d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    ggml_sycl_op_col2im_1d(ctx, dst);
+}
+
 static void ggml_sycl_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
     ggml_sycl_op_conv_3d(ctx, dst);
@@ -4912,6 +5084,12 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_SOFT_MAX_BACK:
             ggml_sycl_op_soft_max_back(ctx, dst);
             break;
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+            ggml_sycl_cross_entropy_loss(ctx, dst);
+            break;
+        case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+            ggml_sycl_cross_entropy_loss_back(ctx, dst);
+            break;
         case GGML_OP_ROPE:
             ggml_sycl_rope(ctx, dst);
             break;
@@ -4924,6 +5102,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_IM2COL_3D:
             ggml_sycl_im2col_3d(ctx, dst);
             break;
+        case GGML_OP_COL2IM_1D:
+            ggml_sycl_col2im_1d(ctx, dst);
+            break;
         case GGML_OP_POOL_2D:
             ggml_sycl_pool2d(ctx, dst);
             break;
@@ -5204,7 +5385,10 @@ static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_
     auto * sycl_ctx = static_cast<ggml_backend_sycl_context *>(backend->context);
 
 #ifdef GGML_SYCL_GRAPH
-    bool use_sycl_graph = !g_ggml_sycl_disable_graph && check_graph_compatibility(cgraph);
+    bool use_sycl_graph = false;
+    if (g_ggml_sycl_enable_graph) {
+        use_sycl_graph = check_graph_compatibility(cgraph);
+    }
     if (use_sycl_graph) {
         const bool graph_support = dpct::get_device(sycl_ctx->device).has(sycl::aspect::ext_oneapi_limited_graph);
         if (!graph_support) {
@@ -5470,7 +5654,6 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
                 // TODO: This specific configuration can fail with oneDNN and needs more debugging
                 if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 &&
                     a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) {
-                        printf("zjy 2\n");
                     return false;
                 }
                 return true;
@@ -5538,70 +5721,99 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
             {
                 ggml_type src0_type = op->src[0]->type;
                 ggml_type src1_type = op->src[1]->type;
-                if (src0_type == src1_type && (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])) && src0_type != GGML_TYPE_BF16) {
-                    return true;
+
+                if (src0_type == GGML_TYPE_F16) {
+                    if (src1_type == GGML_TYPE_Q2_K ||
+                        src1_type == GGML_TYPE_Q3_K ||
+                        src1_type == GGML_TYPE_Q4_K ||
+                        src1_type == GGML_TYPE_Q5_K ||
+                        src1_type == GGML_TYPE_Q6_K ||
+                        src1_type == GGML_TYPE_IQ2_XXS ||
+                        src1_type == GGML_TYPE_IQ2_XS ||
+                        src1_type == GGML_TYPE_IQ2_S ||
+                        src1_type == GGML_TYPE_IQ3_XXS ||
+                        src1_type == GGML_TYPE_IQ1_S ||
+                        src1_type == GGML_TYPE_IQ1_M ||
+                        src1_type == GGML_TYPE_IQ3_S ||
+                        src1_type == GGML_TYPE_IQ4_XS) {
+                        return false;
+                    }
                 }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
-                    return true;
+
+                if (src0_type == GGML_TYPE_BF16) {
+                    if (src1_type == GGML_TYPE_Q4_0 || //big error in ut
+                        src1_type == GGML_TYPE_Q4_1 || //big error in ut
+                        src1_type == GGML_TYPE_Q8_0 || //big error in ut
+                        src1_type == GGML_TYPE_Q2_K ||
+                        src1_type == GGML_TYPE_Q3_K ||
+                        src1_type == GGML_TYPE_Q4_K ||
+                        src1_type == GGML_TYPE_Q5_K ||
+                        src1_type == GGML_TYPE_Q6_K ||
+                        src1_type == GGML_TYPE_IQ2_XXS ||
+                        src1_type == GGML_TYPE_IQ2_XS ||
+                        src1_type == GGML_TYPE_IQ2_S ||
+                        src1_type == GGML_TYPE_IQ3_XXS ||
+                        src1_type == GGML_TYPE_IQ1_S ||
+                        src1_type == GGML_TYPE_IQ1_M ||
+                        src1_type == GGML_TYPE_IQ3_S ||
+                        src1_type == GGML_TYPE_IQ4_XS) {
+                        return false;
+                    }
                 }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
-                    return true;
+
+                if (src0_type == GGML_TYPE_F32) {
+                    if (src1_type == GGML_TYPE_Q2_K ||
+                        src1_type == GGML_TYPE_Q3_K ||
+                        src1_type == GGML_TYPE_Q4_K ||
+                        src1_type == GGML_TYPE_Q5_K ||
+                        src1_type == GGML_TYPE_Q6_K ||
+                        src1_type == GGML_TYPE_IQ2_XXS ||
+                        src1_type == GGML_TYPE_IQ2_XS ||
+                        src1_type == GGML_TYPE_IQ2_S ||
+                        src1_type == GGML_TYPE_IQ3_XXS ||
+                        src1_type == GGML_TYPE_IQ1_S ||
+                        src1_type == GGML_TYPE_IQ1_M ||
+                        src1_type == GGML_TYPE_IQ3_S ||
+                        src1_type == GGML_TYPE_IQ4_XS) {
+                        return false;
+                    }
                 }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
-                    return true;
+
+                if (src1_type == GGML_TYPE_F32) {
+                    if (src0_type == GGML_TYPE_Q1_0 ||
+                        src0_type == GGML_TYPE_NVFP4 ||
+                        src0_type == GGML_TYPE_Q2_K ||
+                        src0_type == GGML_TYPE_Q3_K ||
+                        src0_type == GGML_TYPE_Q4_K ||
+                        src0_type == GGML_TYPE_Q5_K ||
+                        src0_type == GGML_TYPE_Q6_K ||
+                        src0_type == GGML_TYPE_IQ2_XXS ||
+                        src0_type == GGML_TYPE_IQ2_XS ||
+                        src0_type == GGML_TYPE_IQ2_S ||
+                        src0_type == GGML_TYPE_IQ3_XXS ||
+                        src0_type == GGML_TYPE_IQ1_S ||
+                        src0_type == GGML_TYPE_IQ1_M ||
+                        src0_type == GGML_TYPE_IQ3_S ||
+                        src0_type == GGML_TYPE_IQ4_NL ||
+                        src0_type == GGML_TYPE_IQ4_XS
+                    ) {
+                        return false;
+                    }
                 }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
-                    return true;
+
+                if (src0_type == src1_type) {
+                    if (src1_type == GGML_TYPE_IQ2_XXS ||
+                        src1_type == GGML_TYPE_IQ2_XS ||
+                        src1_type == GGML_TYPE_IQ2_S ||
+                        src1_type == GGML_TYPE_IQ3_XXS ||
+                        src1_type == GGML_TYPE_IQ3_S ||
+                        src1_type == GGML_TYPE_IQ1_S ||
+                        src1_type == GGML_TYPE_IQ1_M) {
+                        return false;
+                    }
                 }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_Q8_0 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_Q4_0 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_Q4_1 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_0) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_Q5_0 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_1) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_Q5_1 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_IQ4_NL) {
-                    return true;
-                }
-                if(src0_type == GGML_TYPE_Q8_0 && src1_type == GGML_TYPE_Q8_0) {
-                    return true;
-                }
-                if(src0_type == GGML_TYPE_Q5_0 && src1_type == GGML_TYPE_Q5_0) {
-                    return true;
-                }
-                if(src0_type == GGML_TYPE_Q5_1 && src1_type == GGML_TYPE_Q5_1) {
-                    return true;
-                }
-                if(src0_type == GGML_TYPE_Q4_0 && src1_type == GGML_TYPE_Q4_0) {
-                    return true;
-                }
-                if(src0_type == GGML_TYPE_Q4_1 && src1_type == GGML_TYPE_Q4_1) {
-                    return true;
-                }
-                return false;
+
+                return true;
             }
         case GGML_OP_REPEAT_BACK:
             {
@@ -5643,7 +5855,7 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_SCALE:
             return true;
         case GGML_OP_CONT:
-            return op->src[0]->type != GGML_TYPE_BF16;
+            return true;
         case GGML_OP_TRI:
             {
                 const ggml_tensor * src0 = op->src[0];
@@ -5666,6 +5878,14 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_IM2COL_3D:
         case GGML_OP_UPSCALE:
             return true;
+        case GGML_OP_COL2IM_1D:
+            return ggml_is_contiguous(op->src[0]) &&
+                   (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16
+#ifdef GGML_SYCL_HAS_BF16
+                    || op->type == GGML_TYPE_BF16
+#endif
+                   ) &&
+                   op->src[0]->type == op->type;
         case GGML_OP_CONV_3D:
             return op->type == GGML_TYPE_F32 &&
                    (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
@@ -5677,8 +5897,7 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_MEAN:
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_ARGSORT:
-            return op->src[0]->ne[0] * sizeof(int) <=
-                   ggml_sycl_info().devices[device].smpbo;
+            return true;
         case GGML_OP_TOP_K: {
             const ggml_tensor * src0 = op->src[0];
             const int k = op->ne[0];
@@ -5690,9 +5909,8 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
         }
         case GGML_OP_POOL_2D:
         case GGML_OP_POOL_1D:
-            return true;
         case GGML_OP_ACC:
-            return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
+            return true;
         case GGML_OP_PAD:
             if (ggml_get_op_params_i32(op, 8) != 0) {
                 return false;
@@ -5725,6 +5943,8 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_FILL:
         case GGML_OP_CUMSUM:
         case GGML_OP_DIAG:
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+        case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             return true;
         case GGML_OP_SOLVE_TRI:
             return op->src[0]->ne[0] <= SYCL_SOLVE_TRI_MAX_N && op->src[1]->ne[0] <= SYCL_SOLVE_TRI_MAX_K;

ggml/src/ggml-sycl/presets.hpp

@@ -19,6 +19,7 @@
 #define WARP_SIZE GGML_SYCL_WARP_SIZE
 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
 
+#define SYCL_COL2IM_1D_BLOCK_SIZE 256
 #define SYCL_GELU_BLOCK_SIZE 256
 #define SYCL_SILU_BLOCK_SIZE 256
 #define SYCL_TANH_BLOCK_SIZE 256
@@ -62,7 +63,7 @@
 #endif
 
 #ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 2
+#define K_QUANTS_PER_ITERATION 1
 #else
 static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
 #endif

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

@@ -17370,21 +17370,24 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
             return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SET_ROWS:
             {
-                switch (op->type) {
-                    case GGML_TYPE_F32:
-                    case GGML_TYPE_F16:
-                    case GGML_TYPE_BF16:
-                    case GGML_TYPE_Q1_0:
-                    case GGML_TYPE_Q4_0:
-                    case GGML_TYPE_Q4_1:
-                    case GGML_TYPE_Q5_0:
-                    case GGML_TYPE_Q5_1:
-                    case GGML_TYPE_Q8_0:
-                    case GGML_TYPE_IQ4_NL:
-                        return true;
-                    default:
-                        return false;
+                if (op->src[0]->type == GGML_TYPE_F32) {
+                    switch (op->type) {
+                        case GGML_TYPE_F32:
+                        case GGML_TYPE_F16:
+                        case GGML_TYPE_BF16:
+                        case GGML_TYPE_Q1_0:
+                        case GGML_TYPE_Q4_0:
+                        case GGML_TYPE_Q4_1:
+                        case GGML_TYPE_Q5_0:
+                        case GGML_TYPE_Q5_1:
+                        case GGML_TYPE_Q8_0:
+                        case GGML_TYPE_IQ4_NL:
+                            return true;
+                        default:
+                            return false;
+                    }
                 }
+                return false;
             }
         case GGML_OP_CONT:
         case GGML_OP_CPY:

ggml/src/ggml.c

@@ -525,7 +525,11 @@ const char * ggml_commit(void) {
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
 static int64_t timer_freq, timer_start;
-void ggml_time_init(void) {
+static BOOL CALLBACK ggml_time_init_once(PINIT_ONCE once, PVOID param, PVOID *ctx) {
+    UNUSED(once);
+    UNUSED(param);
+    UNUSED(ctx);
+
     LARGE_INTEGER t;
     QueryPerformanceFrequency(&t);
     timer_freq = t.QuadPart;
@@ -535,6 +539,12 @@ void ggml_time_init(void) {
     // We subtract the program start time to reduce the likelihood of that happening.
     QueryPerformanceCounter(&t);
     timer_start = t.QuadPart;
+
+    return TRUE;
+}
+void ggml_time_init(void) {
+    static INIT_ONCE once = INIT_ONCE_STATIC_INIT;
+    InitOnceExecuteOnce(&once, ggml_time_init_once, NULL, NULL);
 }
 int64_t ggml_time_ms(void) {
     LARGE_INTEGER t;
@@ -671,6 +681,14 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = {
         .to_float                 = (ggml_to_float_t) dequantize_row_q1_0,
         .from_float_ref           = (ggml_from_float_t) quantize_row_q1_0_ref,
     },
+    [GGML_TYPE_Q2_0] = {
+        .type_name                = "q2_0",
+        .blck_size                = QK2_0,
+        .type_size                = sizeof(block_q2_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q2_0,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q2_0_ref,
+    },
     [GGML_TYPE_Q4_0] = {
         .type_name                = "q4_0",
         .blck_size                = QK4_0,
@@ -1407,6 +1425,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_Q4_0:          wtype = GGML_TYPE_Q4_0;  break;
         case GGML_FTYPE_MOSTLY_Q4_1:          wtype = GGML_TYPE_Q4_1;  break;
         case GGML_FTYPE_MOSTLY_Q1_0:          wtype = GGML_TYPE_Q1_0;  break;
+        case GGML_FTYPE_MOSTLY_Q2_0:          wtype = GGML_TYPE_Q2_0;  break;
         case GGML_FTYPE_MOSTLY_Q5_0:          wtype = GGML_TYPE_Q5_0;  break;
         case GGML_FTYPE_MOSTLY_Q5_1:          wtype = GGML_TYPE_Q5_1;  break;
         case GGML_FTYPE_MOSTLY_Q8_0:          wtype = GGML_TYPE_Q8_0;  break;
@@ -3907,7 +3926,7 @@ struct ggml_tensor * ggml_set_rows(
     GGML_ASSERT(b->ne[2] % c->ne[1] == 0);
     GGML_ASSERT(b->ne[3] % c->ne[2] == 0);
     GGML_ASSERT(c->ne[3] == 1);
-    GGML_ASSERT(b->type == GGML_TYPE_F32);
+    GGML_ASSERT(b->type == GGML_TYPE_F32 || b->type == GGML_TYPE_F16);
     GGML_ASSERT(c->type == GGML_TYPE_I64 || c->type == GGML_TYPE_I32);
 
     GGML_ASSERT(ggml_is_contiguous_rows(a));
@@ -7409,6 +7428,10 @@ static int ggml_node_list_find_tensor(const struct ggml_cgraph * cgraph,
     return -1;
 }
 
+static bool ggml_is_constant(const struct ggml_tensor * tensor) {
+    return tensor->buffer != NULL && ggml_backend_buffer_get_usage(tensor->buffer) == GGML_BACKEND_BUFFER_USAGE_WEIGHTS && (tensor->flags & GGML_TENSOR_FLAG_PARAM) == 0;
+}
+
 bool ggml_can_fuse_subgraph_ext(const struct ggml_cgraph * cgraph,
                                 const int *                node_idxs,
                                 int                        count,
@@ -7454,10 +7477,11 @@ bool ggml_can_fuse_subgraph_ext(const struct ggml_cgraph * cgraph,
             return false;
         }
 
-        // if node is a view, check if the view_src and all it's parent view_srcs are within the subgraph
+        // if node is a view, check if the view_src and all its parent view_srcs are within the subgraph.
+        // external view sources are allowed only for weight tensors, which are constant for this graph execution.
         struct ggml_tensor * view_src = node->view_src;
         while (view_src) {
-            if (ggml_node_list_find_tensor(cgraph, node_idxs, count, view_src) == -1) {
+            if (ggml_node_list_find_tensor(cgraph, node_idxs, count, view_src) == -1 && !ggml_is_constant(view_src)) {
                 return false;
             }
             view_src = view_src->view_src;
@@ -7729,6 +7753,7 @@ size_t ggml_quantize_chunk(
 
     switch (type) {
         case GGML_TYPE_Q1_0:    result = quantize_q1_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q2_0:    result = quantize_q2_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q4_0:    result = quantize_q4_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q4_1:    result = quantize_q4_1   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q5_0:    result = quantize_q5_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

gguf-py/gguf/constants.py

@@ -4533,6 +4533,7 @@ class GGMLQuantizationType(IntEnum):
     MXFP4   = 39
     NVFP4   = 40
     Q1_0    = 41
+    Q2_0    = 42
 
 
 class ExpertGatingFuncType(IntEnum):
@@ -4588,6 +4589,7 @@ class LlamaFileType(IntEnum):
     MOSTLY_MXFP4_MOE     = 38  # except 1d tensors
     MOSTLY_NVFP4         = 39  # except 1d tensors
     MOSTLY_Q1_0          = 40  # except 1d tensors
+    MOSTLY_Q2_0          = 41  # except 1d tensors
 
     GUESSED              = 1024  # not specified in the model file
 
@@ -4713,6 +4715,7 @@ GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
     GGMLQuantizationType.MXFP4:   (32, 1 + 16),
     GGMLQuantizationType.NVFP4:   (64, 4 + 32),
     GGMLQuantizationType.Q1_0:    (128, 2 + 16),
+    GGMLQuantizationType.Q2_0:    (64, 2 + 16),
 }
 
 

include/llama.h

@@ -155,6 +155,7 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_MXFP4_MOE     = 38, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_NVFP4         = 39, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q1_0          = 40, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q2_0          = 41, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };

src/llama-batch.cpp

@@ -505,7 +505,7 @@ llama_ubatch llama_batch_allocr::split_simple(uint32_t n_ubatch) {
     return ubatch_add(idxs, idxs.size(), false);
 }
 
-llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential) {
+llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential, uint32_t n_keep_tail) {
     if (sequential && has_cpl) {
         LLAMA_LOG_ERROR("%s: sequential split is not supported when there are coupled sequences in the input batch (you may need to use the -kvu flag)\n", __func__);
 
@@ -548,7 +548,7 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential)
         }
     }
 
-    const uint32_t n_seqs = cur_seq_set.size();
+    uint32_t n_seqs = cur_seq_set.size();
 
     // we are done
     if (n_seqs == 0) {
@@ -569,7 +569,7 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential)
     std::vector<idx_vec_t> idxs_per_seq(n_seqs);
 
     while (true) {
-        // we can only add new n_seq_tokens tokens if all the sequence sets have at least one more unused token and
+        // we can only add new n_seq_tokens tokens if all the sequence sets have at least 1 more unused tokens and
         //   if we haven't reached n_ubatch
         bool can_expand = true;
 
@@ -600,6 +600,72 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential)
         }
     }
 
+    // if n_keep_tail > 0, keep only the seqs that either finish in this ubatch or have at least
+    //   n_keep_tail tokens remaining for a future ubatch, so that the trailing n_keep_tail tokens
+    //   of each seq are never split across ubatches
+    if (n_keep_tail > 0) {
+        GGML_ASSERT(n_ubatch > n_keep_tail);
+
+        auto n_remaining = [&](uint32_t s) {
+            return (uint32_t) (seq_set_map[cur_seq_set[s]].size() - cur_idx[s]);
+        };
+
+        // keep the longest prefix of seqs that satisfy the constraint, to preserve sequential seq ids
+        uint32_t n_keep = 0;
+        while (n_keep < n_seqs) {
+            const uint32_t remaining = n_remaining(n_keep);
+
+            if (remaining != 0 && remaining < n_keep_tail) {
+                break;
+            }
+
+            n_keep++;
+        }
+
+        // all seqs violate the constraint - resolve the first one directly and emit it alone
+        if (n_keep == 0) {
+            auto & idxs = idxs_per_seq[0];
+
+            const auto & seq_idxs = seq_set_map[cur_seq_set[0]];
+
+            if (idxs.size() + n_remaining(0) <= n_ubatch) {
+                // extend the seq to completion
+                while (n_remaining(0) > 0) {
+                    const int32_t idx = seq_idxs[cur_idx[0]];
+
+                    idxs.push_back(idx);
+
+                    used[idx] = true;
+                    ++n_used;
+
+                    ++cur_idx[0];
+                }
+            } else {
+                // truncate the seq so that at least n_keep_tail tokens remain
+                while (n_remaining(0) < n_keep_tail) {
+                    used[idxs.back()] = false;
+                    --n_used;
+
+                    idxs.pop_back();
+
+                    --cur_idx[0];
+                }
+            }
+
+            n_keep = 1;
+        }
+
+        // return the tokens of the deferred seqs back to the pool
+        for (uint32_t s = n_keep; s < n_seqs; ++s) {
+            for (const int32_t idx : idxs_per_seq[s]) {
+                used[idx] = false;
+                --n_used;
+            }
+        }
+
+        n_seqs = n_keep;
+    }
+
     // concat the per-sequence-set lists
     std::vector<int32_t> idxs;
 
@@ -814,7 +880,7 @@ void llama_batch_allocr::ubatch_print(const llama_ubatch & ubatch, int debug) {
         LLAMA_LOG_DEBUG("%s:   output     = %p\n", __func__, (void *) ubatch.output);
         LLAMA_LOG_DEBUG("%s:   n_outputs  = %d\n", __func__, n_outputs);
 
-        if (debug > 1) {
+        if (debug > 0) {
             int seq_id_max = 0;
             for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
                 for (int s = 0; s < ubatch.n_seq_id[i]; ++s) {

src/llama-batch.h

@@ -104,7 +104,8 @@ public:
 
     // make ubatches of equal-length sequences sets
     // if sequential == true, the tokens in the ubatch will have increasing sequential sequence ids
-    llama_ubatch split_equal(uint32_t n_ubatch, bool sequential);
+    // n_keep_tail = minimum trailing tokens of a seq that must land in the same ubatch
+    llama_ubatch split_equal(uint32_t n_ubatch, bool sequential, uint32_t n_keep_tail);
 
     // sequence-set-wise split - each ubatch contains a single sequence-set
     llama_ubatch split_seq(uint32_t n_ubatch);

src/llama-context.cpp

@@ -17,6 +17,7 @@
 #include <cstring>
 #include <limits>
 #include <stdexcept>
+#include <string>
 
 //
 // llama_context
@@ -30,6 +31,30 @@ static llm_graph_type ctx_type_to_graph_type(llama_context_type ctx_type) {
     throw std::runtime_error("Unsupported ctx type");
 }
 
+struct llm_fused_op_probe {
+    llm_fused_op op;
+    const char * name;
+    uint32_t n_tokens_per_seq;
+};
+
+static const llm_fused_op_probe llm_fused_op_flash_attn_probe = {
+    /*.op               =*/ LLM_FUSED_OP_FLASH_ATTN,
+    /*.name             =*/ "Flash Attention",
+    /*.n_tokens_per_seq =*/ 1,
+};
+
+static const llm_fused_op_probe llm_fused_op_gdn_ar_probe = {
+    /*.op               =*/ LLM_FUSED_OP_GDN_AR,
+    /*.name             =*/ "fused Gated Delta Net (autoregressive)",
+    /*.n_tokens_per_seq =*/ 1,
+};
+
+static const llm_fused_op_probe llm_fused_op_gdn_ch_probe = {
+    /*.op               =*/ LLM_FUSED_OP_GDN_CH,
+    /*.name             =*/ "fused Gated Delta Net (chunked)",
+    /*.n_tokens_per_seq =*/ 16,
+};
+
 llama_context::llama_context(
         const llama_model & model,
               llama_context_params params) :
@@ -436,6 +461,69 @@ llama_context::~llama_context() {
     ggml_opt_free(opt_ctx);
 }
 
+void llama_context::resolve_fused_ops(const llama_memory_context_i * mctx, uint32_t n_seqs) {
+    const char * func = __func__;
+    auto resolve = [&](const llm_fused_op_probe & probe, bool & enabled) {
+        if (!enabled) {
+            return;
+        }
+
+        const uint32_t n_tokens_probe = probe.n_tokens_per_seq*n_seqs;
+
+        auto * gf = graph_reserve(n_tokens_probe, n_seqs, n_tokens_probe, mctx, true);
+        if (!gf) {
+            throw std::runtime_error(std::string("failed to reserve graph for ") + probe.name + " check");
+        }
+
+        bool device_mismatch = false;
+        for (const auto & node : get_gf_res_reserve()->get_fused_nodes()) {
+            if (node.op != probe.op) {
+                continue;
+            }
+
+            GGML_ASSERT(node.il >= 0);
+
+            ggml_backend_t backend_fused = ggml_backend_sched_get_tensor_backend(sched.get(), node.tensor);
+            ggml_backend_dev_t device_fused = backend_fused ? ggml_backend_get_device(backend_fused) : nullptr;
+
+            // TODO: make this descriptor-specific; model.dev_layer() preserves the current behavior,
+            // but is still wrong for cases like --no-kv-offload.
+            ggml_backend_dev_t device_layer = model.dev_layer(node.il);
+
+            if (device_fused != device_layer) {
+                LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but %s "
+                        "is assigned to device %s (usually due to missing support)\n",
+                        func, node.il,
+                        device_layer ? ggml_backend_dev_name(device_layer) : "none",
+                        probe.name,
+                        device_fused ? ggml_backend_dev_name(device_fused) : "none");
+                device_mismatch = true;
+                break;
+            }
+        }
+
+        if (device_mismatch) {
+            enabled = false;
+            LLAMA_LOG_WARN("%s: %s not supported, set to disabled\n", func, probe.name);
+        } else {
+            enabled = true;
+            LLAMA_LOG_INFO("%s: %s enabled\n", func, probe.name);
+        }
+    };
+
+    if (cparams.auto_fa) {
+        resolve(llm_fused_op_flash_attn_probe, cparams.flash_attn);
+        cparams.auto_fa = false;
+    }
+
+    if (cparams.auto_fgdn) {
+        LLAMA_LOG_INFO("%s: resolving fused Gated Delta Net support:\n", func);
+        resolve(llm_fused_op_gdn_ar_probe, cparams.fused_gdn_ar);
+        resolve(llm_fused_op_gdn_ch_probe, cparams.fused_gdn_ch);
+        cparams.auto_fgdn = false;
+    }
+}
+
 void llama_context::sched_reserve() {
     if (!sched_need_reserve) {
         return;
@@ -475,128 +563,7 @@ void llama_context::sched_reserve() {
 
     LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
 
-    // resolve automatic Flash Attention use
-    if (cparams.auto_fa) {
-        auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
-        if (!gf) {
-            throw std::runtime_error("failed to reserve graph for Flash Attention check");
-        }
-
-        const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
-        bool fa_device_mismatch = false;
-        for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
-            ggml_tensor * n = ggml_graph_node(gf, i);
-            if (n->op != GGML_OP_FLASH_ATTN_EXT) {
-                continue;
-            }
-            ggml_backend_dev_t device_fa = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
-
-            // TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
-            GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
-            const int il = std::stoi(n->name + prefix_len);
-            ggml_backend_dev_t device_kv = model.dev_layer(il);
-            if (device_fa != device_kv) {
-                LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the Flash Attention tensor "
-                        "is assigned to device %s (usually due to missing support)\n",
-                        __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_fa));
-                // FIXME: fa_device_mismatch logic is wrong for --no-kv-offload, but this is broken anyways
-                fa_device_mismatch = true;
-                break;
-            }
-        }
-
-        if (fa_device_mismatch) {
-            cparams.flash_attn = false;
-            LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
-        } else {
-            cparams.flash_attn = true;
-            LLAMA_LOG_INFO("%s: Flash Attention was auto, set to enabled\n", __func__);
-        }
-
-        cparams.auto_fa = false;
-    }
-
-    if (cparams.auto_fgdn) {
-        LLAMA_LOG_INFO("%s: resolving fused Gated Delta Net support:\n", __func__);
-
-        if (cparams.fused_gdn_ar) {
-            auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
-            if (!gf) {
-                throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check (autoregressive)");
-            }
-
-            const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDN_AR) + 1;
-            bool gdn_device_mismatch = false;
-            for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
-                ggml_tensor * n = ggml_graph_node(gf, i);
-                if (n->op != GGML_OP_GATED_DELTA_NET) {
-                    continue;
-                }
-                ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
-
-                GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDN_AR "-", prefix_len) == 0);
-                const int il = std::stoi(n->name + prefix_len);
-                ggml_backend_dev_t device_kv = model.dev_layer(il);
-                if (device_gdn != device_kv) {
-                    LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor "
-                            "is assigned to device %s (usually due to missing support)\n",
-                            __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn));
-                    gdn_device_mismatch = true;
-                    break;
-                }
-            }
-
-            if (gdn_device_mismatch) {
-                cparams.fused_gdn_ar = false;
-                LLAMA_LOG_WARN("%s: fused Gated Delta Net (autoregressive) not supported, set to disabled\n", __func__);
-            } else {
-                LLAMA_LOG_INFO("%s: fused Gated Delta Net (autoregressive) enabled\n", __func__);
-            }
-        }
-
-        if (cparams.fused_gdn_ch) {
-            // more than one token in the batch per sequence in order to take the chunked path
-            // note: n_outputs must match n_tokens for embedding models with mean/rank pooling,
-            // because build_pooling creates inp_mean with shape [n_tokens, n_seqs] and multiplies
-            // it with t_embd which is reduced to [n_outputs, ...] via out_ids. if n_outputs != n_tokens,
-            // the ggml_mul_mat assertion fails.
-            const uint32_t n_tokens_ch = 16*n_seqs;
-            auto * gf = graph_reserve(n_tokens_ch, n_seqs, n_tokens_ch, mctx.get(), true);
-            if (!gf) {
-                throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check (chunked)");
-            }
-
-            const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDN_CH) + 1;
-            bool gdn_device_mismatch = false;
-            for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
-                ggml_tensor * n = ggml_graph_node(gf, i);
-                if (n->op != GGML_OP_GATED_DELTA_NET) {
-                    continue;
-                }
-                ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
-
-                GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDN_CH "-", prefix_len) == 0);
-                const int il = std::stoi(n->name + prefix_len);
-                ggml_backend_dev_t device_kv = model.dev_layer(il);
-                if (device_gdn != device_kv) {
-                    LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor "
-                            "is assigned to device %s (usually due to missing support)\n",
-                            __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn));
-                    gdn_device_mismatch = true;
-                    break;
-                }
-            }
-
-            if (gdn_device_mismatch) {
-                cparams.fused_gdn_ch = false;
-                LLAMA_LOG_WARN("%s: fused Gated Delta Net (chunked) not supported, set to disabled\n", __func__);
-            } else {
-                LLAMA_LOG_INFO("%s: fused Gated Delta Net (chunked) enabled\n", __func__);
-            }
-        }
-
-        cparams.auto_fgdn = false;
-    }
+    resolve_fused_ops(mctx.get(), n_seqs);
 
     // reserve worst-case graph
     int n_splits_pp = -1;

src/llama-context.h

@@ -262,6 +262,10 @@ private:
 
     llm_graph_cb graph_get_cb() const;
 
+    // disable auto fused ops (Flash Attention, Gated Delta Net) whose op lands on a device
+    // that differs from the layer it belongs to (usually due to missing backend support)
+    void resolve_fused_ops(const llama_memory_context_i * mctx, uint32_t n_seqs);
+
     // TODO: read/write lora adapters and cvec
     size_t state_write_data(llama_io_write_i & io);
     size_t state_read_data (llama_io_read_i  & io);

src/llama-graph.cpp

@@ -63,26 +63,6 @@ static bool can_reuse_kq_mask(
 
 // impl
 
-static ggml_tensor * ggml_mul_mat_aux(
-        ggml_context * ctx,
-        ggml_tensor * cur,
-        ggml_tensor * rot) {
-    const auto n = rot->ne[0];
-
-    ggml_tensor * res;
-
-    if (!ggml_is_contiguous(cur)) {
-        res = ggml_cont_2d   (ctx, cur, n, ggml_nelements(cur)/n);
-    } else {
-        res = ggml_reshape_2d(ctx, cur, n, ggml_nelements(cur)/n);
-    }
-    res = ggml_mul_mat   (ctx, rot, res);
-    ggml_mul_mat_set_hint(res, GGML_HINT_SRC0_IS_HADAMARD);
-    res = ggml_reshape_4d(ctx, res, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3]);
-
-    return res;
-}
-
 void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
     if (ubatch->token) {
         const int64_t n_tokens = ubatch->n_tokens;
@@ -881,6 +861,14 @@ void llm_graph_input_dsv4::set_input(const llama_ubatch * ubatch) {
     dsv4_set_comp_inputs(inp_hca, plan_hca, "hca", debug > 0, ubatch->n_tokens, n_stream);
     dsv4_set_comp_inputs(inp_lid, plan_lid, "lid", debug > 0, ubatch->n_tokens, n_stream);
 
+    if (inp_csa.k_rot && inp_csa.k_rot->buffer) {
+        mctx->get_csa()->set_input_k_rot(inp_csa.k_rot);
+    }
+
+    if (inp_hca.k_rot && inp_hca.k_rot->buffer) {
+        mctx->get_hca()->set_input_k_rot(inp_hca.k_rot);
+    }
+
     if (inp_lid.k_rot && inp_lid.k_rot->buffer) {
         mctx->get_lid()->set_input_k_rot(inp_lid.k_rot);
     }
@@ -1204,6 +1192,7 @@ void llm_graph_result::reset() {
     params = {};
 
     inputs.clear();
+    fused_nodes.clear();
 
     buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
 
@@ -1305,6 +1294,10 @@ llm_graph_input_i * llm_graph_result::add_input(llm_graph_input_ptr input) {
     return inputs.back().get();
 }
 
+void llm_graph_result::add_fused_node(llm_graph_fused_node result) {
+    fused_nodes.push_back(result);
+}
+
 void llm_graph_result::set_params(const llm_graph_params & params) {
     this->params = params;
 }
@@ -1364,6 +1357,8 @@ void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
     }
 }
 
+
+
 ggml_tensor * llm_graph_context::build_cvec(
          ggml_tensor * cur,
                  int   il) const {
@@ -2414,7 +2409,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
 
         cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
                                   hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
-        cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
+        res->add_fused_node({LLM_FUSED_OP_FLASH_ATTN, cur, il});
 
         ggml_flash_attn_ext_add_sinks(cur, sinks);
         ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
@@ -2633,12 +2628,12 @@ ggml_tensor * llm_graph_context::build_attn(
     GGML_ASSERT(v_mla == nullptr);
 
     if (inp->self_k_rot) {
-        q_cur = ggml_mul_mat_aux(ctx0, q_cur, inp->self_k_rot);
-        k_cur = ggml_mul_mat_aux(ctx0, k_cur, inp->self_k_rot);
+        q_cur = llama_mul_mat_hadamard(ctx0, q_cur, inp->self_k_rot);
+        k_cur = llama_mul_mat_hadamard(ctx0, k_cur, inp->self_k_rot);
     }
 
     if (inp->self_v_rot) {
-        v_cur = ggml_mul_mat_aux(ctx0, v_cur, inp->self_v_rot);
+        v_cur = llama_mul_mat_hadamard(ctx0, v_cur, inp->self_v_rot);
     }
 
     // these nodes are added to the graph together so that they are not reordered
@@ -2669,7 +2664,7 @@ ggml_tensor * llm_graph_context::build_attn(
     cb(cur, "kqv_out", il);
 
     if (inp->self_v_rot) {
-        cur = ggml_mul_mat_aux(ctx0, cur, inp->self_v_rot);
+        cur = llama_mul_mat_hadamard(ctx0, cur, inp->self_v_rot);
     }
 
     if (wo) {
@@ -2874,14 +2869,14 @@ ggml_tensor * llm_graph_context::build_attn(
     auto * v_rot = is_swa ? inp->self_v_rot_swa : inp->self_v_rot;
 
     if (k_rot) {
-        q_cur = ggml_mul_mat_aux(ctx0, q_cur, k_rot);
+        q_cur = llama_mul_mat_hadamard(ctx0, q_cur, k_rot);
         if (k_cur) {
-            k_cur = ggml_mul_mat_aux(ctx0, k_cur, k_rot);
+            k_cur = llama_mul_mat_hadamard(ctx0, k_cur, k_rot);
         }
     }
     if (v_rot) {
         if (v_cur) {
-            v_cur = ggml_mul_mat_aux(ctx0, v_cur, v_rot);
+            v_cur = llama_mul_mat_hadamard(ctx0, v_cur, v_rot);
         }
     }
 
@@ -2924,7 +2919,7 @@ ggml_tensor * llm_graph_context::build_attn(
     cb(cur, "kqv_out", il);
 
     if (v_rot) {
-        cur = ggml_mul_mat_aux(ctx0, cur, v_rot);
+        cur = llama_mul_mat_hadamard(ctx0, cur, v_rot);
     }
 
     if (wo) {
@@ -3084,6 +3079,8 @@ llm_graph_input_dsv4 * llm_graph_context::build_inp_dsv4() const {
     dsv4_build_comp_inputs(ctx0, inp->inp_csa, mctx_cur->get_csa_plan(ubatch), "csa", n_stream);
     dsv4_build_comp_inputs(ctx0, inp->inp_hca, mctx_cur->get_hca_plan(ubatch), "hca", n_stream);
     dsv4_build_comp_inputs(ctx0, inp->inp_lid, mctx_cur->get_lid_plan(ubatch), "lid", n_stream);
+    inp->inp_csa.k_rot = mctx_cur->get_csa()->build_input_k_rot(ctx0);
+    inp->inp_hca.k_rot = mctx_cur->get_hca()->build_input_k_rot(ctx0);
     inp->inp_lid.k_rot = mctx_cur->get_lid()->build_input_k_rot(ctx0);
 
     return (llm_graph_input_dsv4 *) res->add_input(std::move(inp));

src/llama-graph.h

@@ -38,6 +38,12 @@ enum llm_graph_type {
     LLM_GRAPH_TYPE_DECODER_MTP,
 };
 
+enum llm_fused_op {
+    LLM_FUSED_OP_FLASH_ATTN,
+    LLM_FUSED_OP_GDN_AR,
+    LLM_FUSED_OP_GDN_CH,
+};
+
 enum llm_ffn_op_type : int {
     LLM_FFN_NONE = 0,           // sentinel: unset; archs must assign before use
     LLM_FFN_SILU,
@@ -775,6 +781,12 @@ struct llm_graph_params {
     }
 };
 
+struct llm_graph_fused_node {
+    llm_fused_op op;
+    ggml_tensor * tensor;
+    int il;
+};
+
 class llm_graph_result {
 public:
     llm_graph_result(int64_t max_nodes);
@@ -808,6 +820,10 @@ public:
 
     llm_graph_input_i * add_input(llm_graph_input_ptr input);
 
+    void add_fused_node(llm_graph_fused_node result);
+
+    const std::vector<llm_graph_fused_node> & get_fused_nodes() const { return fused_nodes; }
+
     void set_params(const llm_graph_params & params);
 
     // important graph nodes
@@ -826,6 +842,7 @@ public:
     std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
 
     std::vector<llm_graph_input_ptr> inputs;
+    std::vector<llm_graph_fused_node> fused_nodes;
 
     ggml_context_ptr ctx_compute;
 

src/llama-impl.h

@@ -54,6 +54,26 @@ static inline dst_t llama_cast(src_t v) {
     }
 }
 
+static inline ggml_tensor * llama_mul_mat_hadamard(
+        ggml_context * ctx,
+        ggml_tensor * cur,
+        ggml_tensor * rot) {
+    const auto n = rot->ne[0];
+
+    ggml_tensor * res;
+
+    if (!ggml_is_contiguous(cur)) {
+        res = ggml_cont_2d(ctx, cur, n, ggml_nelements(cur)/n);
+    } else {
+        res = ggml_reshape_2d(ctx, cur, n, ggml_nelements(cur)/n);
+    }
+    res = ggml_mul_mat(ctx, rot, res);
+    ggml_mul_mat_set_hint(res, GGML_HINT_SRC0_IS_HADAMARD);
+    res = ggml_reshape_4d(ctx, res, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3]);
+
+    return res;
+}
+
 struct time_meas {
     time_meas(int64_t & t_acc, bool disable = false);
     ~time_meas();
@@ -83,7 +103,3 @@ std::string llama_format_tensor_shape(const std::vector<int64_t> & ne);
 std::string llama_format_tensor_shape(const struct ggml_tensor * t);
 
 std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i);
-
-#define LLAMA_TENSOR_NAME_FATTN   "__fattn__"
-#define LLAMA_TENSOR_NAME_FGDN_AR "__fgdn_ar__"
-#define LLAMA_TENSOR_NAME_FGDN_CH "__fgdn_ch__"

src/llama-kv-cache-dsa.cpp

@@ -113,7 +113,7 @@ llama_memory_context_ptr llama_kv_cache_dsa::init_batch(
 
         std::vector<llama_ubatch> ubatches;
         while (true) {
-            auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true);
+            auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true, 0);
 
             if (ubatch.n_tokens == 0) {
                 break;

src/llama-kv-cache-dsv4.cpp

@@ -1110,7 +1110,7 @@ llama_memory_context_ptr llama_kv_cache_dsv4::init_batch(
             if (has_coupled) {
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq);
+                ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq, 0);
             }
 
             if (ubatch.n_tokens == 0) {

src/llama-kv-cache-iswa.cpp

@@ -206,7 +206,7 @@ llama_memory_context_ptr llama_kv_cache_iswa::init_batch(llama_batch_allocr & ba
 
         std::vector<llama_ubatch> ubatches;
         while (true) {
-            auto ubatch = balloc.split_equal(n_ubatch, !unified);
+            auto ubatch = balloc.split_equal(n_ubatch, !unified, 0);
 
             if (ubatch.n_tokens == 0) {
                 break;

src/llama-kv-cache.cpp

@@ -57,22 +57,6 @@ static void ggml_gen_hadamard(ggml_tensor * tensor) {
     }
 }
 
-static ggml_tensor * ggml_mul_mat_aux(
-        ggml_context * ctx,
-        ggml_tensor * cur,
-        ggml_tensor * rot) {
-    const auto n = rot->ne[0];
-
-    ggml_tensor * res;
-
-    res = ggml_reshape_2d(ctx, cur, n, ggml_nelements(cur)/n);
-    res = ggml_mul_mat   (ctx, rot, res);
-    ggml_mul_mat_set_hint(res, GGML_HINT_SRC0_IS_HADAMARD);
-    res = ggml_reshape_4d(ctx, res, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3]);
-
-    return res;
-}
-
 //
 // llama_kv_cache
 //
@@ -722,7 +706,7 @@ llama_memory_context_ptr llama_kv_cache::init_batch(
 
         std::vector<llama_ubatch> ubatches;
         while (true) {
-            auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true);
+            auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true, 0);
 
             if (ubatch.n_tokens == 0) {
                 break;
@@ -1875,14 +1859,14 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
         tmp = ggml_cast(ctx, cur, GGML_TYPE_F32);
 
         // rotate back
-        tmp = ggml_mul_mat_aux(ctx, tmp, rot);
+        tmp = llama_mul_mat_hadamard(ctx, tmp, rot);
 
         tmp = ggml_rope_ext(ctx, tmp,
                 shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                 yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow);
 
         // rotate fwd
-        tmp = ggml_mul_mat_aux(ctx, tmp, rot);
+        tmp = llama_mul_mat_hadamard(ctx, tmp, rot);
 
         tmp = ggml_cpy(ctx, tmp, cur);
     } else {

src/llama-memory-hybrid-iswa.cpp

@@ -77,15 +77,15 @@ llama_memory_context_ptr llama_memory_hybrid_iswa::init_batch(llama_batch_allocr
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                if (mem_recr->n_rs_seq > 0) {
-                    // [TAG_RECURRENT_ROLLBACK_SPLITS]
-                    // TODO: recurrent state rollback does not support equal splits
-                    ubatch = balloc.split_seq(n_ubatch);
-                } else {
-                    // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
-                    const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
-                    ubatch = balloc.split_equal(n_ubatch, !unified);
-                }
+                // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
+                const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
+
+                // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                // the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
+                //   so that the rollback snapshots remain valid
+                const uint32_t n_rs_seq = mem_recr->n_rs_seq;
+
+                ubatch = balloc.split_equal(n_ubatch, !unified, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
             }
 
             if (ubatch.n_tokens == 0) {

src/llama-memory-hybrid.cpp

@@ -78,15 +78,15 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                if (mem_recr->n_rs_seq > 0) {
-                    // [TAG_RECURRENT_ROLLBACK_SPLITS]
-                    // TODO: recurrent state rollback does not support equal splits
-                    ubatch = balloc.split_seq(n_ubatch);
-                } else {
-                    // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
-                    const bool unified = (mem_attn->get_n_stream() == 1);
-                    ubatch = balloc.split_equal(n_ubatch, !unified);
-                }
+                // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
+                const bool unified = (mem_attn->get_n_stream() == 1);
+
+                // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                // the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
+                //   so that the rollback snapshots remain valid
+                const uint32_t n_rs_seq = mem_recr->n_rs_seq;
+
+                ubatch = balloc.split_equal(n_ubatch, !unified, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
             }
 
             if (ubatch.n_tokens == 0) {

src/llama-memory-recurrent.cpp

@@ -416,15 +416,12 @@ llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr &
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                if (n_rs_seq > 0) {
-                    // [TAG_RECURRENT_ROLLBACK_SPLITS]
-                    // TODO: recurrent state rollback does not support equal splits
-                    ubatch = balloc.split_seq(n_ubatch);
-                } else {
-                    // TODO: non-sequential equal split can be done if using unified KV cache
-                    //       for simplicity, we always use sequential equal split for now
-                    ubatch = balloc.split_equal(n_ubatch, true);
-                }
+                // TODO: non-sequential equal split can be done if using unified KV cache
+                //       for simplicity, we always use sequential equal split for now
+                // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                // the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
+                //   so that the rollback snapshots remain valid
+                ubatch = balloc.split_equal(n_ubatch, true, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
             }
 
             if (ubatch.n_tokens == 0) {

src/llama-model-loader.cpp

@@ -37,6 +37,7 @@ const char * llama_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_F16:       name = LLAMA_FTYPE_PREFIX "F16"; break;
         case LLAMA_FTYPE_MOSTLY_BF16:      name = LLAMA_FTYPE_PREFIX "BF16"; break;
         case LLAMA_FTYPE_MOSTLY_Q1_0:      name = LLAMA_FTYPE_PREFIX "Q1_0"; break;
+        case LLAMA_FTYPE_MOSTLY_Q2_0:      name = LLAMA_FTYPE_PREFIX "Q2_0"; break;
         case LLAMA_FTYPE_MOSTLY_Q4_0:      name = LLAMA_FTYPE_PREFIX "Q4_0"; break;
         case LLAMA_FTYPE_MOSTLY_Q4_1:      name = LLAMA_FTYPE_PREFIX "Q4_1"; break;
         case LLAMA_FTYPE_MOSTLY_Q5_0:      name = LLAMA_FTYPE_PREFIX "Q5_0"; break;
@@ -767,6 +768,7 @@ llama_model_loader::llama_model_loader(
             case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
             case GGML_TYPE_NVFP4:   ftype = LLAMA_FTYPE_MOSTLY_NVFP4;   break;
             case GGML_TYPE_Q1_0:    ftype = LLAMA_FTYPE_MOSTLY_Q1_0;    break;
+            case GGML_TYPE_Q2_0:    ftype = LLAMA_FTYPE_MOSTLY_Q2_0;    break;
             default:
                 {
                     LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));

src/llama-quant.cpp

@@ -380,6 +380,7 @@ static ggml_type tensor_type_fallback(quantize_state_impl & qs, const ggml_tenso
             case GGML_TYPE_IQ3_XXS:
             case GGML_TYPE_IQ3_S:   // types on the right: block size 32
             case GGML_TYPE_IQ4_XS:  return_type = GGML_TYPE_IQ4_NL; break;
+            case GGML_TYPE_Q2_0:
             case GGML_TYPE_Q2_K:
             case GGML_TYPE_Q3_K:
             case GGML_TYPE_TQ1_0:
@@ -480,7 +481,7 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
             else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
                 new_type = GGML_TYPE_IQ3_S;
             }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0) {
+            else if (ftype == LLAMA_FTYPE_MOSTLY_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0 || ftype == LLAMA_FTYPE_MOSTLY_Q2_0) {
                 new_type = GGML_TYPE_Q4_K;
             }
         }
@@ -800,6 +801,7 @@ ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_BF16: return GGML_TYPE_BF16;
         case LLAMA_FTYPE_ALL_F32:     return GGML_TYPE_F32;
         case LLAMA_FTYPE_MOSTLY_Q1_0: return GGML_TYPE_Q1_0;
+        case LLAMA_FTYPE_MOSTLY_Q2_0: return GGML_TYPE_Q2_0;
 
         case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: return GGML_TYPE_MXFP4;
 

src/llama-vocab.cpp

@@ -887,9 +887,6 @@ struct llm_tokenizer_ugm : llm_tokenizer {
             // blob containing XOR-compressed compact double array (XCDA) entries
             uint32_t xcda_blob_size = *(const uint32_t *) &precompiled_charsmap[0];
             charsmap_offset += sizeof(xcda_blob_size);
-            if (xcda_blob_size + charsmap_offset >= precompiled_charsmap.size()) {
-                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
-            }
 
             // Next xcda_blob_size bytes contain entries of XOR-compressed compact
             // double array (XCDA). Each entry is bit-packed into a 32-bit integer.
@@ -1205,7 +1202,15 @@ private:
                 throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
             }
             const char * prefix_replacement = &(tokenizer.prefix_replacements)[longest_prefix_offset];
-            return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
+            size_t max_len = tokenizer.prefix_replacements_size - longest_prefix_offset;
+            size_t repl_len = 0;
+            while (repl_len < max_len && prefix_replacement[repl_len] != '\0') {
+                repl_len++;
+            }
+            if (repl_len == max_len) {
+                throw std::runtime_error("Unterminated string in precompiled charsmap!");
+            }
+            return { prefix_replacement, repl_len, longest_prefix_length };
         }
 
         // check if the input prefix contains a valid sequence of UTF-8 code units
@@ -2018,11 +2023,18 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
                 const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
                 precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
+                if (precompiled_charsmap.size() < sizeof(uint32_t)) {
+                    throw std::runtime_error("precompiled_charsmap too small for xcda_blob_size header!");
+                }
+                uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0];
+#if defined(__BYTE_ORDER__) && defined(__ORDER_BIG_ENDIAN__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
+#endif
+                if (*xcda_blob_size + sizeof(uint32_t) >= precompiled_charsmap.size()) {
+                    throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
+                }
 #if defined(__BYTE_ORDER__) && defined(__ORDER_BIG_ENDIAN__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
                 // correct endianness of data in precompiled_charsmap binary blob
-                uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0];
-                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
-                assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
                 size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
                 uint32_t * xcda_array = (uint32_t *) &precompiled_charsmap[sizeof(uint32_t)];
                 for (size_t i = 0; i < xcda_array_size; ++i) {

src/models/deepseek4.cpp

@@ -557,7 +557,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_lid_top_k(
     cb(indexer_q_pe, "lid_q_pe", il);
 
     indexer_q = ggml_concat(ctx0, indexer_q_nope, indexer_q_pe, 0);
-    indexer_q = ggml_mul_mat(ctx0, inp_lid.k_rot, indexer_q);
+    indexer_q = llama_mul_mat_hadamard(ctx0, indexer_q, inp_lid.k_rot);
     cb(indexer_q, "lid_q_rot", il);
 
     ggml_tensor * indexer_weights = build_lora_mm(layer.indexer_proj, cur);
@@ -652,10 +652,15 @@ ggml_tensor * llama_model_deepseek4::graph::build_csa_lid_attention(
         int il) const {
     const auto & inp_csa = inp_dsv4->get_csa();
     GGML_ASSERT(inp_csa.kq_mask);
-    GGML_ASSERT(inp_attn->self_k_rot == nullptr);
 
     ggml_tensor * top_k = build_lid_top_k(model, inp_dsv4, qr, cur, inp_pos, il);
 
+    ggml_tensor * k_rot = inp_attn->self_k_rot;
+    if (k_rot) {
+        q  = llama_mul_mat_hadamard(ctx0, q, k_rot);
+        kv = llama_mul_mat_hadamard(ctx0, kv, k_rot);
+    }
+
     ggml_build_forward_expand(gf, q);
     ggml_build_forward_expand(gf, kv);
 
@@ -696,6 +701,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_csa_lid_attention(
 
     ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
     ggml_tensor * out = build_attn_mha(q, k_all, k_all, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
+    if (k_rot) {
+        out = llama_mul_mat_hadamard(ctx0, out, k_rot);
+    }
     cb(out, "attn_csa_lid", il);
 
     return out;
@@ -711,7 +719,12 @@ ggml_tensor * llama_model_deepseek4::graph::build_hca_attention(
         int il) const {
     const auto & inp_hca = inp_dsv4->get_hca();
     GGML_ASSERT(inp_hca.kq_mask);
-    GGML_ASSERT(inp_attn->self_k_rot == nullptr);
+
+    ggml_tensor * k_rot = inp_attn->self_k_rot;
+    if (k_rot) {
+        q  = llama_mul_mat_hadamard(ctx0, q, k_rot);
+        kv = llama_mul_mat_hadamard(ctx0, kv, k_rot);
+    }
 
     ggml_build_forward_expand(gf, q);
     ggml_build_forward_expand(gf, kv);
@@ -753,6 +766,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_hca_attention(
 
     ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
     ggml_tensor * out = build_attn_mha(q, k_all, k_all, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
+    if (k_rot) {
+        out = llama_mul_mat_hadamard(ctx0, out, k_rot);
+    }
     cb(out, "attn_hca", il);
 
     return out;
@@ -770,8 +786,8 @@ ggml_tensor * llama_model_deepseek4::graph::build_raw_attention(
     ggml_tensor * k_rot = inp_attn->self_k_rot;
 
     if (k_rot) {
-        q  = ggml_mul_mat(ctx0, k_rot, q);
-        kv = ggml_mul_mat(ctx0, k_rot, kv);
+        q  = llama_mul_mat_hadamard(ctx0, q, k_rot);
+        kv = llama_mul_mat_hadamard(ctx0, kv, k_rot);
     }
 
     ggml_build_forward_expand(gf, q);
@@ -788,6 +804,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_raw_attention(
 
     ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
     ggml_tensor * out = build_attn_mha(q, k, k, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
+    if (k_rot) {
+        out = llama_mul_mat_hadamard(ctx0, out, k_rot);
+    }
     cb(out, "attn_raw", il);
 
     return out;
@@ -917,6 +936,11 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
                 "csa_state_compress",
                 il);
 
+        if (inp_dsv4->get_csa().k_rot) {
+            kv_comp_csa_state = llama_mul_mat_hadamard(ctx0, kv_comp_csa_state, inp_dsv4->get_csa().k_rot);
+            cb(kv_comp_csa_state, "csa_state_compress_rot", il);
+        }
+
         ggml_build_forward_expand(gf, inp_dsv4->mctx->get_csa()->cpy_k(ctx0,
                     kv_comp_csa_state, inp_dsv4->get_csa().state_write_idxs, il));
 
@@ -965,7 +989,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
                 il);
 
         if (inp_dsv4->get_lid().k_rot) {
-            kv_comp_lid_state = ggml_mul_mat(ctx0, inp_dsv4->get_lid().k_rot, kv_comp_lid_state);
+            kv_comp_lid_state = llama_mul_mat_hadamard(ctx0, kv_comp_lid_state, inp_dsv4->get_lid().k_rot);
             cb(kv_comp_lid_state, "lid_state_compress_rot", il);
         }
 
@@ -1007,6 +1031,11 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
                 "hca_state_compress",
                 il);
 
+        if (inp_dsv4->get_hca().k_rot) {
+            kv_comp_hca = llama_mul_mat_hadamard(ctx0, kv_comp_hca, inp_dsv4->get_hca().k_rot);
+            cb(kv_comp_hca, "hca_state_compress_rot", il);
+        }
+
         ggml_build_forward_expand(gf, inp_dsv4->mctx->get_hca()->cpy_k(ctx0,
                     kv_comp_hca, inp_dsv4->get_hca().state_write_idxs, il));
         hca_state_dep = kv_comp_hca;
@@ -1035,13 +1064,11 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
     if (ratio == DSV4_CSA_RATIO &&
             inp_dsv4->get_csa().kq_mask &&
             inp_dsv4->get_lid().kq_mask &&
-            inp_dsv4->get_lid().k_rot &&
-            inp_attn->self_k_rot == nullptr) {
+            inp_dsv4->get_lid().k_rot) {
         out = build_csa_lid_attention(model, inp_dsv4, inp_attn, q, kv, qr, cur, inp_pos, layer.attn_sinks,
                 1.0f/sqrtf(float(n_embd_head)), il);
     } else if (ratio == DSV4_HCA_RATIO &&
-            inp_dsv4->get_hca().kq_mask &&
-            inp_attn->self_k_rot == nullptr) {
+            inp_dsv4->get_hca().kq_mask) {
         out = build_hca_attention(inp_dsv4, inp_attn, q, kv, layer.attn_sinks,
                 1.0f/sqrtf(float(n_embd_head)), il);
     } else {

src/models/delta-net-base.cpp

@@ -401,9 +401,9 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_delta_net_base::build_delta_ne
     // K=1: output carries the final state only. state s is 4D [S_v, S_v, H_v, n_seqs].
     ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, /*K=*/1);
     if (n_tokens == 1) {
-        cb(result, LLAMA_TENSOR_NAME_FGDN_AR, il);
+        res->add_fused_node({LLM_FUSED_OP_GDN_AR, result, il});
     } else {
-        cb(result, LLAMA_TENSOR_NAME_FGDN_CH, il);
+        res->add_fused_node({LLM_FUSED_OP_GDN_CH, result, il});
     }
 
     ggml_tensor * output = ggml_view_4d(ctx0, result,
@@ -496,8 +496,8 @@ ggml_tensor * llm_build_delta_net_base::build_conv_state(
         ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_state_last, conv_state_update));
     } else {
         // [TAG_RECURRENT_ROLLBACK_SPLITS]
-        // TODO: this logic incorrectly assumes that the last (n_rs_seq + 1) tokens of a sequence in a batch are
-        //       inside the same ubatch. currently with `split_equal()` this is not correct
+        // this logic assumes that the last (n_rs_seq + 1) tokens of a sequence in a batch are inside
+        //   the same ubatch, which `split_equal()` guarantees via its n_keep_tail argument
 
         const int64_t K = (int64_t) cparams.n_rs_seq + 1;
 
@@ -566,9 +566,9 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
     // state s is 4D [S_v, S_v, H_v, n_seqs]; K snapshot slots are written into the output.
     ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, K);
     if (n_seq_tokens > 1) {
-        cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
+        res->add_fused_node({LLM_FUSED_OP_GDN_CH, gdn_out, il});
     } else {
-        cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_AR, il);
+        res->add_fused_node({LLM_FUSED_OP_GDN_AR, gdn_out, il});
     }
 
     const int64_t attn_score_elems    = S_v * H_v * n_seq_tokens * n_seqs;

tests/test-backend-ops.cpp

@@ -1137,6 +1137,10 @@ struct test_case {
     }
 
     virtual ggml_tensor * build_graph(ggml_context * ctx) = 0;
+    virtual ggml_tensor * build_graph(ggml_context * ctx, ggml_context * ctx_weights) {
+        GGML_UNUSED(ctx_weights);
+        return build_graph(ctx);
+    }
 
     virtual double max_nmse_err() {
         return 1e-7;
@@ -1213,6 +1217,7 @@ struct test_case {
 
     virtual bool run_whole_graph() { return false; }
     virtual std::vector<ggml_tensor *> fusion_test_nodes() { return {}; }
+    virtual bool use_weight_context() { return false; }
 
     ggml_cgraph * gf = nullptr;
     ggml_cgraph * gb = nullptr;
@@ -1319,20 +1324,28 @@ struct test_case {
             /* .mem_base = */ NULL,
             /* .no_alloc = */ true,
         };
+        const bool use_weights = use_weight_context();
+
         ggml_context * ctx = ggml_init(params);
         GGML_ASSERT(ctx);
+        ggml_context * ctx_weights = use_weights ? ggml_init(params) : nullptr;
+        GGML_ASSERT(!use_weights || ctx_weights);
 
         gf = ggml_new_graph(ctx);
 
         // pre-graph sentinel
         add_sentinel(ctx);
+        if (ctx_weights) {
+            add_sentinel(ctx_weights);
+        }
 
-        ggml_tensor * out = build_graph(ctx);
+        ggml_tensor * out = build_graph(ctx, ctx_weights);
         current_op_name   = op_desc(out);
         check_for_f16_tensor(ctx);
 
         if (!matches_filter(out, op_names_filter)) {
             //printf("  %s: skipping\n", op_desc(out).c_str());
+            ggml_free(ctx_weights);
             ggml_free(ctx);
             return test_status_t::SKIPPED;
         }
@@ -1355,18 +1368,36 @@ struct test_case {
 
             print_test_result_locked(output_printer, result);
 
+            ggml_free(ctx_weights);
             ggml_free(ctx);
             return test_status_t::NOT_SUPPORTED;
         }
 
         // post-graph sentinel
         add_sentinel(ctx);
+        if (ctx_weights) {
+            add_sentinel(ctx_weights);
+        }
+
+        ggml_backend_buffer_t buf_weights = nullptr;
+        if (ctx_weights) {
+            buf_weights = ggml_backend_alloc_ctx_tensors(ctx_weights, backend1);
+            if (buf_weights == NULL) {
+                printf("failed to allocate weight tensors [%s] ", ggml_backend_name(backend1));
+                ggml_free(ctx_weights);
+                ggml_free(ctx);
+                return test_status_t::FAIL;
+            }
+            ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        }
 
         // allocate
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
 
         if (buf == NULL) {
             printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1));
+            ggml_backend_buffer_free(buf_weights);
+            ggml_free(ctx_weights);
             ggml_free(ctx);
             return test_status_t::FAIL;
         }
@@ -1381,6 +1412,9 @@ struct test_case {
 
         // randomize tensors
         initialize_tensors(ctx);
+        if (ctx_weights) {
+            initialize_tensors(ctx_weights);
+        }
 
         // compare
         struct callback_userdata {
@@ -1466,7 +1500,8 @@ struct test_case {
                                                                fused_nodes_to_verify.size());
 
         ggml_backend_buffer_free(buf);
-
+        ggml_backend_buffer_free(buf_weights);
+        ggml_free(ctx_weights);
         ggml_free(ctx);
 
         // Create test result
@@ -1490,10 +1525,14 @@ struct test_case {
             /* .mem_base = */ NULL,
             /* .no_alloc = */ true,
         };
+        const bool use_weights = use_weight_context();
+
         ggml_context_ptr ctx(ggml_init(params)); // smart ptr
         GGML_ASSERT(ctx);
+        ggml_context_ptr ctx_weights(use_weights ? ggml_init(params) : nullptr);
+        GGML_ASSERT(!use_weights || ctx_weights);
 
-        ggml_tensor * out             = build_graph(ctx.get());
+        ggml_tensor * out             = build_graph(ctx.get(), ctx_weights.get());
         current_op_name               = op_desc(out);
         if (!matches_filter(out, op_names_filter)) {
             //printf("  %s: skipping\n", op_desc(out).c_str());
@@ -1510,6 +1549,16 @@ struct test_case {
             return true;
         }
 
+        ggml_backend_buffer_ptr buf_weights(nullptr);
+        if (ctx_weights) {
+            buf_weights.reset(ggml_backend_alloc_ctx_tensors(ctx_weights.get(), backend));
+            if (buf_weights == NULL) {
+                printf("failed to allocate weight tensors\n");
+                return false;
+            }
+            ggml_backend_buffer_set_usage(buf_weights.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        }
+
         // allocate
         ggml_backend_buffer_ptr buf(ggml_backend_alloc_ctx_tensors(ctx.get(), backend)); // smart ptr
 
@@ -1520,6 +1569,9 @@ struct test_case {
 
         // randomize tensors
         initialize_tensors(ctx.get());
+        if (ctx_weights) {
+            initialize_tensors(ctx_weights.get());
+        }
 
         // build graph
         ggml_cgraph * gf = ggml_new_graph_custom(ctx.get(), graph_nodes, false);
@@ -2341,7 +2393,8 @@ static void init_set_rows_row_ids(ggml_tensor * t, int num_rows) {
 
 // GGML_OP_SET_ROWS
 struct test_set_rows : public test_case {
-    const ggml_type type;
+    const ggml_type type_src;
+    const ggml_type type_dst;
     const ggml_type type_idx;
     const std::array<int64_t, 4> ne;
     const std::array<int, 2> nr23; // broadcast only dims 2 and 3
@@ -2349,21 +2402,22 @@ struct test_set_rows : public test_case {
     const bool v; // view (non-contiguous src1)
 
     std::string vars() override {
-        return VARS_TO_STR6(type, type_idx, ne, nr23, r, v);
+        return VARS_TO_STR7(type_src, type_dst, type_idx, ne, nr23, r, v);
     }
 
-    test_set_rows(ggml_type type,
+    test_set_rows(ggml_type type_src,
+            ggml_type type_dst,
             ggml_type type_idx,
             std::array<int64_t, 4> ne,
             std::array<int, 2> nr23,
             int r, bool v = false)
-        : type(type), type_idx(type_idx), ne(ne), nr23(nr23), r(r), v(v) {}
+        : type_src(type_src), type_dst(type_dst), type_idx(type_idx), ne(ne), nr23(nr23), r(r), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * dst = ggml_new_tensor_4d(ctx, type,          ne[0], ne[1], ne[2]*nr23[0], ne[3]*nr23[1]);
+        ggml_tensor * dst = ggml_new_tensor_4d(ctx, type_dst, ne[0], ne[1], ne[2]*nr23[0], ne[3]*nr23[1]);
         ggml_set_name(dst, "dst");
 
-        ggml_tensor * src = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], r,     ne[2]*nr23[0], ne[3]*nr23[1]);
+        ggml_tensor * src = ggml_new_tensor_4d(ctx, type_src, ne[0], r,     ne[2]*nr23[0], ne[3]*nr23[1]);
         ggml_set_name(src, "src");
 
         ggml_tensor * row_idxs = ggml_new_tensor_3d(ctx, type_idx, r, ne[2], ne[3]);
@@ -2396,17 +2450,17 @@ struct test_set_rows : public test_case {
     }
 
     double max_nmse_err() override {
-        if (type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 || type == GGML_TYPE_IQ4_NL ||
-            type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1 || type == GGML_TYPE_Q8_0) {
+        if (type_dst == GGML_TYPE_Q4_0 || type_dst == GGML_TYPE_Q4_1 || type_dst == GGML_TYPE_IQ4_NL ||
+            type_dst == GGML_TYPE_Q5_0 || type_dst == GGML_TYPE_Q5_1 || type_dst == GGML_TYPE_Q8_0) {
             // estimate what the max nmse error would be if one quantized value is
             // off by one. The test values are distributed in [-1,1], so it'll be
             // roughly (2.0 / 2^bits)^2, divided by the mean square value of the reference,
             // which is roughly 0.25 times the number of elements.
             double err_estimate = 1.0f/8.0f;
-            if (type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1) {
+            if (type_dst == GGML_TYPE_Q5_0 || type_dst == GGML_TYPE_Q5_1) {
                 err_estimate /= 2.0f;
             }
-            if (type == GGML_TYPE_Q8_0) {
+            if (type_dst == GGML_TYPE_Q8_0) {
                 err_estimate /= 8.0f;
             }
             err_estimate *= err_estimate;
@@ -2419,7 +2473,7 @@ struct test_set_rows : public test_case {
     // See dicussion here: https://github.com/ggml-org/llama.cpp/pull/23760#issuecomment-4566312209
     double max_nmse_err(ggml_backend_t backend) override {
         ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend));
-        if (type == GGML_TYPE_Q8_0 && strcmp(ggml_backend_reg_name(reg), "WebGPU") == 0) {
+        if (type_dst == GGML_TYPE_Q8_0 && strcmp(ggml_backend_reg_name(reg), "WebGPU") == 0) {
             return std::max(test_case::max_nmse_err(backend), 2e-7);
         }
         return test_case::max_nmse_err(backend);
@@ -5848,19 +5902,21 @@ struct test_mul_mat_vec_fusion : public test_case {
     const bool b;        // broadcast b matrix (only for use_id)
     const bool with_bias;
     const bool with_gate;
+    const bool with_lane_scale;
     std::array<int64_t, 2> batch_dims;
 
     test_mul_mat_vec_fusion(ggml_type type, ggml_glu_op op, int64_t m, int64_t n, int64_t k,
                         bool use_id = false, int n_mats = 1, int n_used = 1, bool b = false, bool with_bias = false, bool with_gate = true,
-                        std::array<int64_t, 2> batch_dims = {4, 2})
-    : type(type), glu_op(op), m(m), n(n), k(k), use_id(use_id), n_mats(n_mats), n_used(n_used), b(b), with_bias(with_bias), with_gate(with_gate), batch_dims(batch_dims) {
+                        bool with_lane_scale = false, std::array<int64_t, 2> batch_dims = {4, 2})
+    : type(type), glu_op(op), m(m), n(n), k(k), use_id(use_id), n_mats(n_mats), n_used(n_used), b(b), with_bias(with_bias),
+        with_gate(with_gate), with_lane_scale(with_lane_scale), batch_dims(batch_dims) {
         if (use_id) {
             GGML_ASSERT(n_used <= n_mats);
         }
     }
 
     std::string vars() override {
-        return VARS_TO_STR12(type, glu_op, m, n, k, use_id, n_mats, n_used, b, with_bias, with_gate, batch_dims);
+        return VARS_TO_STR13(type, glu_op, m, n, k, use_id, n_mats, n_used, b, with_bias, with_gate, with_lane_scale, batch_dims);
     }
 
     std::string op_desc(ggml_tensor * t) override {
@@ -5869,6 +5925,7 @@ struct test_mul_mat_vec_fusion : public test_case {
     }
 
     bool run_whole_graph() override { return true; }
+    bool use_weight_context() override { return use_id && with_lane_scale; }
 
     ggml_tensor * build_gate(ggml_context * ctx, ggml_tensor * ffn_gate, ggml_tensor * ffn_up) {
         ggml_tensor * out = nullptr;
@@ -5884,7 +5941,26 @@ struct test_mul_mat_vec_fusion : public test_case {
         return out;
     }
 
+    ggml_tensor * build_lane_scale_dense(ggml_context * ctx, ggml_tensor * out) {
+        ggml_tensor * scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+        return ggml_mul(ctx, out, scale);
+    }
+
+    ggml_tensor * build_lane_scale_id(ggml_context * ctx, ggml_context * ctx_weights, ggml_tensor * out, ggml_tensor * ids) {
+        GGML_ASSERT(ctx_weights);
+        ggml_tensor * scale = ggml_new_tensor_1d(ctx_weights, GGML_TYPE_F32, n_mats);
+        ggml_tensor * s = ggml_reshape_3d(ctx, scale, 1, n_mats, 1);
+        s = ggml_repeat_4d(ctx, s, 1, n_mats, m, 1);
+        s = ggml_get_rows(ctx, s, ids);
+        return ggml_mul(ctx, out, s);
+    }
+
     ggml_tensor * build_graph(ggml_context * ctx) override {
+        GGML_ASSERT(!use_weight_context());
+        return build_graph(ctx, nullptr);
+    }
+
+    ggml_tensor * build_graph(ggml_context * ctx, ggml_context * ctx_weights) override {
         if (!use_id) {
             const int              channels = batch_dims[0];
             const int              samples  = batch_dims[1];
@@ -5895,19 +5971,34 @@ struct test_mul_mat_vec_fusion : public test_case {
             ggml_tensor * gate = with_gate ? ggml_new_tensor(ctx, type, 4, ne0.data()) : nullptr;
             ggml_tensor * up   = ggml_new_tensor(ctx, type, 4, ne0.data());
 
-            ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
-            if (with_bias) {
-                std::array<int64_t, 4> bias_ne = { ffn_up->ne[0], 1, channels, samples };
-                ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
-                ffn_up = ggml_add(ctx, ffn_up, up_bias);
-            }
+            auto build_lane_up = [&]() {
+                ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
+                if (with_lane_scale) {
+                    ffn_up = build_lane_scale_dense(ctx, ffn_up);
+                }
+                if (with_bias) {
+                    std::array<int64_t, 4> bias_ne = { ffn_up->ne[0], 1, channels, samples };
+                    ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
+                    ffn_up = ggml_add(ctx, ffn_up, up_bias);
+                }
+                return ffn_up;
+            };
 
-            ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr;
-            if (with_bias && with_gate) {
-                std::array<int64_t, 4> bias_ne   = { ffn_gate->ne[0], 1, channels, samples };
-                ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
-                ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
-            }
+            auto build_lane_gate = [&]() {
+                ggml_tensor * ffn_gate = ggml_mul_mat(ctx, gate, cur);
+                if (with_lane_scale) {
+                    ffn_gate = build_lane_scale_dense(ctx, ffn_gate);
+                }
+                if (with_bias) {
+                    std::array<int64_t, 4> bias_ne   = { ffn_gate->ne[0], 1, channels, samples };
+                    ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
+                    ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
+                }
+                return ffn_gate;
+            };
+
+            ggml_tensor * ffn_up = build_lane_up();
+            ggml_tensor * ffn_gate = with_gate ? build_lane_gate() : nullptr;
 
             ggml_tensor * out = with_gate ? build_gate(ctx, ffn_gate, ffn_up) : ffn_up;
 
@@ -5929,17 +6020,32 @@ struct test_mul_mat_vec_fusion : public test_case {
             ggml_tensor * cur = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, k, this->b ? 1 : n_used, m);
             ggml_set_name(cur, "cur");
 
-            ggml_tensor * ffn_up = ggml_mul_mat_id(ctx, ups, cur, ids);
-            if (with_bias) {
-                ggml_tensor * up_bias_param = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ffn_up->ne[0], n_mats);
-                ffn_up = ggml_add_id(ctx, ffn_up, up_bias_param, ids);
-            }
+            auto build_lane_up = [&]() {
+                ggml_tensor * ffn_up = ggml_mul_mat_id(ctx, ups, cur, ids);
+                if (with_lane_scale) {
+                    ffn_up = build_lane_scale_id(ctx, ctx_weights, ffn_up, ids);
+                }
+                if (with_bias) {
+                    ggml_tensor * up_bias_param = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ffn_up->ne[0], n_mats);
+                    ffn_up = ggml_add_id(ctx, ffn_up, up_bias_param, ids);
+                }
+                return ffn_up;
+            };
 
-            ggml_tensor * ffn_gate = with_gate? ggml_mul_mat_id(ctx, gates, cur, ids) : nullptr;
-            if (with_bias && with_gate) {
-                ggml_tensor * gate_bias_param = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ffn_gate->ne[0], n_mats);
-                ffn_gate = ggml_add_id(ctx, ffn_gate, gate_bias_param, ids);
-            }
+            auto build_lane_gate = [&]() {
+                ggml_tensor * ffn_gate = ggml_mul_mat_id(ctx, gates, cur, ids);
+                if (with_lane_scale) {
+                    ffn_gate = build_lane_scale_id(ctx, ctx_weights, ffn_gate, ids);
+                }
+                if (with_bias) {
+                    ggml_tensor * gate_bias_param = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ffn_gate->ne[0], n_mats);
+                    ffn_gate = ggml_add_id(ctx, ffn_gate, gate_bias_param, ids);
+                }
+                return ffn_gate;
+            };
+
+            ggml_tensor * ffn_up = build_lane_up();
+            ggml_tensor * ffn_gate = with_gate ? build_lane_gate() : nullptr;
 
             ggml_tensor * out = with_gate ? build_gate(ctx, ffn_gate, ffn_up) : ffn_up;
 
@@ -7769,24 +7875,28 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_I32, 256, 5, 4, 1, v));
     }
 
-    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, GGML_TYPE_I64, { 1, 8, 1, 3 }, { 1, 1 }, 2, false));
-    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, GGML_TYPE_I32, { 1, 8, 1, 3 }, { 1, 1 }, 2, false));
-    test_cases.emplace_back(new test_set_rows(GGML_TYPE_Q8_0, GGML_TYPE_I32, { 256, 5, 1, 3 }, { 1, 1, }, 1, false));
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_I64, { 1, 8, 1, 3 }, { 1, 1 }, 2, false));
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_I32, { 1, 8, 1, 3 }, { 1, 1 }, 2, false));
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, GGML_TYPE_Q8_0, GGML_TYPE_I32, { 256, 5, 1, 3 }, { 1, 1, }, 1, false));
     for (ggml_type type : all_types) {
         for (int b : {1, 7}) {
             for (bool v : {false, true}) {
-                test_cases.emplace_back(new test_set_rows(type, GGML_TYPE_I64, { 256, 5,  b, 3 }, { 1, 1, }, 1, v));
-                test_cases.emplace_back(new test_set_rows(type, GGML_TYPE_I64, { 256, 11, 1, b }, { 2, 3, }, 7, v));
+                test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, type, GGML_TYPE_I64, { 256, 5,  b, 3 }, { 1, 1, }, 1, v));
+                test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, type, GGML_TYPE_I64, { 256, 11, 1, b }, { 2, 3, }, 7, v));
 
-                test_cases.emplace_back(new test_set_rows(type, GGML_TYPE_I64, { 3*ggml_blck_size(type), 3, b, 1 }, { 2, 3, }, 2, v));
+                test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, type, GGML_TYPE_I64, { 3*ggml_blck_size(type), 3, b, 1 }, { 2, 3, }, 2, v));
 
                 if (ggml_blck_size(type) == 1) {
-                    test_cases.emplace_back(new test_set_rows(type, GGML_TYPE_I64, { 31, 3, b, 1 }, { 2, 3, }, 2, v));
-                    test_cases.emplace_back(new test_set_rows(type, GGML_TYPE_I64, { 33, 5, 1, b }, { 2, 3, }, 1, v));
+                    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, type, GGML_TYPE_I64, { 31, 3, b, 1 }, { 2, 3, }, 2, v));
+                    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F32, type, GGML_TYPE_I64, { 33, 5, 1, b }, { 2, 3, }, 1, v));
                 }
             }
         }
     }
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F16, GGML_TYPE_F16, GGML_TYPE_I64, { 1, 8, 1, 3 }, { 1, 1 }, 2, false));
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F16, GGML_TYPE_F16, GGML_TYPE_I32, { 1, 8, 1, 3 }, { 1, 1 }, 2, false));
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F16, GGML_TYPE_F16, GGML_TYPE_I64, { 1, 8, 1, 3 }, { 1, 1 }, 2, true));
+    test_cases.emplace_back(new test_set_rows(GGML_TYPE_F16, GGML_TYPE_F16, GGML_TYPE_I32, { 1, 8, 1, 3 }, { 1, 1 }, 2, true));
 
     for (int mode : { GGML_ROPE_TYPE_NORMAL, GGML_ROPE_TYPE_NEOX, GGML_ROPE_TYPE_MROPE, GGML_ROPE_TYPE_VISION }) {
         for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
@@ -9202,10 +9312,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                             if (!with_gate && glu_op != GGML_GLU_OP_SWIGLU) {
                                 continue;
                             }
-                            test_cases.emplace_back(new test_mul_mat_vec_fusion(type, glu_op, 1, 32, 256,
-                                use_id, 16, 8, b, with_bias, with_gate));
-                            test_cases.emplace_back(new test_mul_mat_vec_fusion(type, glu_op, 1, 32, 256,
-                                use_id, 16, 8, b, with_bias, with_gate, {1, 1}));
+                            for (bool with_lane_scale : {false, true}) {
+                                if (with_lane_scale && type != GGML_TYPE_NVFP4) {
+                                    continue;
+                                }
+                                test_cases.emplace_back(new test_mul_mat_vec_fusion(type, glu_op, 1, 32, 256,
+                                    use_id, 16, 8, b, with_bias, with_gate, with_lane_scale));
+                                test_cases.emplace_back(new test_mul_mat_vec_fusion(type, glu_op, 1, 32, 256,
+                                    use_id, 16, 8, b, with_bias, with_gate, with_lane_scale, {1, 1}));
+                            }
                         }
                     }
                 }
@@ -9823,6 +9938,13 @@ static bool test_backend(ggml_backend_t backend, ggml_backend_dev_t dev, test_mo
     }
 
     if (mode == MODE_GRAD) {
+        test_cases.erase(
+            std::remove_if(test_cases.begin(), test_cases.end(), [](const std::unique_ptr<test_case> & tc) {
+                return tc->run_whole_graph();
+            }),
+            test_cases.end()
+        );
+
         size_t n_ok = 0;
         for (auto & test : test_cases) {
             if (test->eval_grad(backend, op_names_filter, output_printer)) {

tests/test-quantize-fns.cpp

@@ -158,6 +158,7 @@ static int test_vec_dot_q(bool verbose) {
                 type == GGML_TYPE_Q1_0    ? MAX_QUANTIZATION_TOTAL_ERROR_BINARY :
                 type == GGML_TYPE_TQ1_0   ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
                 type == GGML_TYPE_TQ2_0   ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
+                type == GGML_TYPE_Q2_0    ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
                 type == GGML_TYPE_Q2_K    ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
                 type == GGML_TYPE_IQ2_S   ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
                 type == GGML_TYPE_Q3_K    ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
@@ -183,7 +184,7 @@ static int test_vec_dot_q(bool verbose) {
                 ? MAX_DOT_PRODUCT_ERROR_LOWBIT
                 : type == GGML_TYPE_Q1_0
                 ? MAX_DOT_PRODUCT_ERROR_BINARY
-                : type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0
+                : type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0 || type == GGML_TYPE_Q2_0
                 ? MAX_DOT_PRODUCT_ERROR_TERNARY
                 : type == GGML_TYPE_NVFP4
                 ? MAX_DOT_PRODUCT_ERROR_FP4

tools/cli/CMakeLists.txt

@@ -2,11 +2,13 @@
 
 set(TARGET llama-cli-impl)
 
-add_library(${TARGET} cli.cpp)
+add_library(${TARGET} cli.cpp
+                      cli-client.cpp
+                      cli-context.cpp)
 set_target_properties(${TARGET} PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS ON)
 
 target_include_directories(${TARGET} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR} ../server)
-target_link_libraries(${TARGET} PUBLIC server-context llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PUBLIC llama-server-impl llama-common ${CMAKE_THREAD_LIBS_INIT})
 
 if(LLAMA_TOOLS_INSTALL)
     install(TARGETS ${TARGET} LIBRARY)

tools/cli/cli-client.cpp

@@ -0,0 +1,130 @@
+#include "cli-client.h"
+
+#include "http.h"
+
+#include <algorithm>
+#include <chrono>
+#include <thread>
+
+// generation can stall for a long time during prompt processing, so the
+// read timeout must be generous
+static constexpr time_t CLI_HTTP_READ_TIMEOUT_SEC = 3600;
+
+// upper bound for the accumulated response body kept for error reporting
+static constexpr size_t CLI_HTTP_MAX_ERROR_BODY = 1024 * 1024;
+
+// returns the path with the base url's path prefix prepended (if any)
+static std::string join_path(const common_http_url & parts, const std::string & path) {
+    if (parts.path.empty() || parts.path == "/") {
+        return path;
+    }
+    std::string prefix = parts.path;
+    if (prefix.back() == '/') {
+        prefix.pop_back();
+    }
+    return prefix + path;
+}
+
+std::string cli_client::get(const std::string & path) {
+    auto [cli, parts] = common_http_client(server_base);
+    cli.set_read_timeout(CLI_HTTP_READ_TIMEOUT_SEC, 0);
+    auto path_with_model = path + (model.empty() ? "" : ("?model=" + model));
+    auto res = cli.Get(join_path(parts, path_with_model));
+    if (!res) {
+        throw std::runtime_error("failed to connect to " + server_base + ": " + httplib::to_string(res.error()));
+    }
+    if (res->status < 200 || res->status >= 300) {
+        throw std::runtime_error("GET " + path + " failed with status " + std::to_string(res->status) + ": " + res->body);
+    }
+    return res->body;
+}
+
+std::string cli_client::post(const std::string & path, const std::string & body) {
+    auto [cli, parts] = common_http_client(server_base);
+    cli.set_read_timeout(CLI_HTTP_READ_TIMEOUT_SEC, 0);
+    auto res = cli.Post(join_path(parts, path), body, "application/json");
+    if (!res) {
+        throw std::runtime_error("failed to connect to " + server_base + ": " + httplib::to_string(res.error()));
+    }
+    if (res->status < 200 || res->status >= 300) {
+        throw std::runtime_error("POST " + path + " failed with status " + std::to_string(res->status) + ": " + res->body);
+    }
+    return res->body;
+}
+
+std::string cli_client::post_sse(const std::string & path,
+                                  const std::string & body,
+                                  const std::function<bool()> & should_stop,
+                                  const std::function<void(const std::string &)> & on_data) {
+    auto [cli, parts] = common_http_client(server_base);
+    cli.set_read_timeout(CLI_HTTP_READ_TIMEOUT_SEC, 0);
+
+    std::string pending;  // buffer for incomplete SSE lines
+    std::string raw_body; // accumulated body, used only for error reporting
+
+    auto receiver = [&](const char * data, size_t len) -> bool {
+        if (should_stop()) {
+            return false; // aborts the request
+        }
+        if (raw_body.size() < CLI_HTTP_MAX_ERROR_BODY) {
+            raw_body.append(data, std::min(len, CLI_HTTP_MAX_ERROR_BODY - raw_body.size()));
+        }
+        pending.append(data, len);
+        size_t pos;
+        while ((pos = pending.find('\n')) != std::string::npos) {
+            std::string line = pending.substr(0, pos);
+            pending.erase(0, pos + 1);
+            if (!line.empty() && line.back() == '\r') {
+                line.pop_back();
+            }
+            if (line.rfind("data: ", 0) != 0) {
+                continue;
+            }
+            std::string payload = line.substr(6);
+            if (payload == "[DONE]") {
+                continue;
+            }
+            on_data(payload);
+        }
+        return true;
+    };
+
+    httplib::Headers headers = {{"Accept", "text/event-stream"}};
+    auto res = cli.Post(join_path(parts, path), headers, body, "application/json", receiver);
+
+    if (!res) {
+        if (res.error() == httplib::Error::Canceled && should_stop()) {
+            return ""; // cancelled by the user
+        }
+        return "failed to connect to " + server_base + ": " + httplib::to_string(res.error());
+    }
+    if (res->status < 200 || res->status >= 300) {
+        if (!raw_body.empty()) {
+            return raw_body;
+        }
+        return "request failed with status " + std::to_string(res->status);
+    }
+    return "";
+}
+
+bool cli_client::wait_health(const std::function<bool()> & is_aborted) {
+    int connect_attempts = 0;
+    while (!is_aborted()) {
+        auto [cli, parts] = common_http_client(server_base);
+        cli.set_connection_timeout(1, 0);
+        auto res = cli.Get(join_path(parts, "/health"));
+        if (res) {
+            if (res->status == 200) {
+                return true;
+            }
+            // any other status means the server is up but not ready yet
+            // (e.g. 503 while the model is still loading)
+        } else if (++connect_attempts >= 10) {
+            last_error = "failed to connect to " + server_base + ": " + httplib::to_string(res.error());
+            return false;
+        }
+        std::this_thread::sleep_for(std::chrono::milliseconds(300));
+    }
+    last_error = "aborted while waiting for the server to become ready";
+    return false;
+}

tools/cli/cli-client.h

@@ -0,0 +1,33 @@
+#pragma once
+
+#include <functional>
+#include <string>
+
+// openai-like client for CLI
+struct cli_client {
+    std::string server_base; // base url, for example "http://127.0.0.1:8080"
+    std::string last_error;  // set when wait_health() fails
+
+    std::string model; // optional, set when the server has multiple models (router mode)
+
+    // simple GET request, returns the raw response body
+    // throws std::runtime_error on transport error or non-2xx status
+    std::string get(const std::string & path);
+
+    // simple POST request, returns the raw response body
+    // throws std::runtime_error on transport error or non-2xx status
+    std::string post(const std::string & path, const std::string & body);
+
+    // POST request with an SSE streaming response
+    // on_data is invoked per "data:" event with the raw event payload
+    // returns after the stream is finished (empty string on graceful exit)
+    // otherwise, the raw error response body
+    std::string post_sse(const std::string & path,
+                          const std::string & body,
+                          const std::function<bool()> & should_stop,
+                          const std::function<void(const std::string &)> & on_data);
+
+    // poll /health until the server is ready to accept requests
+    // returns false if is_aborted returned true or the server is unreachable
+    bool wait_health(const std::function<bool()> & is_aborted);
+};

tools/cli/cli-context.cpp

@@ -0,0 +1,622 @@
+#include "cli-context.h"
+#include "cli-ui.h"
+
+#include "arg.h"
+#include "base64.hpp"
+#include "log.h"
+#include "console.h"
+
+#define JSON_ASSERT GGML_ASSERT
+#include <nlohmann/json.hpp>
+
+#include <algorithm>
+#include <cctype>
+#include <filesystem>
+#include <fstream>
+#include <map>
+#include <set>
+
+using json = nlohmann::ordered_json;
+
+struct cli_context_impl {
+    json messages      = json::array();
+    json pending_media = json::array(); // staged multimodal content parts
+};
+
+cli_context::cli_context(const common_params & params) : params(params), impl(new cli_context_impl()) {}
+
+cli_context::~cli_context() {
+    shutdown();
+}
+
+std::atomic<bool> & cli_context::interrupted() {
+    static std::atomic<bool> flag = false;
+    return flag;
+}
+
+static bool should_stop() {
+    return cli_context::interrupted().load();
+}
+
+static constexpr size_t FILE_GLOB_MAX_RESULTS = 100;
+
+const char * LLAMA_ASCII_LOGO = R"(
+▄▄ ▄▄
+██ ██
+██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
+██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
+██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
+                                    ██    ██
+                                    ▀▀    ▀▀
+)";
+
+// number of values an arg consumes on the command line
+static int arg_num_values(const common_arg & opt) {
+    if (opt.value_hint_2 != nullptr) {
+        return 2;
+    }
+    if (opt.value_hint != nullptr) {
+        return 1;
+    }
+    return 0;
+}
+
+static std::string format_error_message(const json & err) {
+    if (err.contains("error") && err.at("error").is_object()) {
+        const auto & e = err.at("error");
+        if (e.contains("message") && e.at("message").is_string()) {
+            return e.at("message").get<std::string>();
+        }
+    }
+    return err.dump();
+}
+
+// err is the raw response body of a failed request; it may or may not be JSON
+static std::string format_error_message(const std::string & err) {
+    json parsed = json::parse(err, nullptr, false);
+    if (!parsed.is_discarded()) {
+        return format_error_message(parsed);
+    }
+    return err;
+}
+
+static std::string media_type_from_ext(const std::string & fname) {
+    std::string ext = std::filesystem::path(fname).extension().string();
+    std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c) { return std::tolower(c); });
+    if (ext == ".wav" || ext == ".mp3") {
+        return "audio";
+    }
+    if (ext == ".mp4" || ext == ".avi" || ext == ".mkv" || ext == ".mov" || ext == ".webm") {
+        return "video";
+    }
+    return "image";
+}
+
+bool cli_context::init() {
+    ui::init(params);
+
+    std::optional<ui::spinner> spinner;
+
+    bool use_external_server = !params.server_base.empty();
+    if (use_external_server) {
+        std::string base = params.server_base;
+        while (!base.empty() && base.back() == '/') {
+            base.pop_back();
+        }
+        client.server_base = base;
+
+        spinner.emplace("Connecting to server at " + base);
+    } else {
+        if (params.model.path.empty() && params.model.url.empty() &&
+                params.model.hf_repo.empty() && params.model.docker_repo.empty()) {
+            ui::show_error(
+                "no model specified",
+                "use -m <file.gguf> or -hf <user/repo> to run a local model,\n"
+                "or --server-base <url> to connect to a running llama-server"
+            );
+            return false;
+        }
+
+        spinner.emplace("\n\nLoading model...");
+
+        server.emplace();
+        if (!server->start(params)) {
+            ui::show_error("server start failed");
+            return false;
+        }
+        if (!server->wait_ready(should_stop)) {
+            if (!should_stop()) {
+                ui::show_error("the server exited before becoming ready");
+            }
+            return false;
+        }
+        client.server_base = server->address();
+    }
+
+    // for --server-base this is the main availability check; for a spawned
+    // server it is a cheap sanity check on top of the ready signal
+    auto is_aborted = [this]() {
+        return should_stop() || (server && !server->alive());
+    };
+    bool healthy = false;
+    try {
+        healthy = client.wait_health(is_aborted);
+    } catch (const std::exception & e) {
+        client.last_error = e.what();
+    }
+    if (!healthy) {
+        if (!should_stop()) {
+            ui::show_error(client.last_error);
+        }
+        return false;
+    }
+
+    if (use_external_server) {
+        spinner.reset();
+        if (!list_and_ask_models()) {
+            return false;
+        }
+        // restore the spinner for the next step
+        spinner.emplace("Waiting for server...");
+    }
+
+    fetch_server_props();
+
+    return true;
+}
+
+void cli_context::fetch_server_props() {
+    try {
+        json props = json::parse(client.get("/props"));
+        model_name = props.value("model_alias", "");
+        if (model_name.empty()) {
+            const std::string path = props.value("model_path", "");
+            if (!path.empty()) {
+                model_name = std::filesystem::path(path).filename().string();
+            }
+        }
+        model_ftype = props.value("model_ftype", "");
+        build_info = props.value("build_info", "");
+        if (props.contains("modalities") && props.at("modalities").is_object()) {
+            const auto & modalities = props.at("modalities");
+            has_vision = modalities.value("vision", false);
+            has_audio  = modalities.value("audio", false);
+            has_video  = modalities.value("video", false);
+        }
+    } catch (const std::exception & e) {
+        // /props can be disabled on remote servers; not fatal
+        LOG_DBG("failed to fetch /props: %s\n", e.what());
+    }
+}
+
+bool cli_context::list_and_ask_models() {
+    json resp = json::parse(client.get("/v1/models"));
+    if (!resp.contains("data") || !resp.at("data").is_array()) {
+        throw std::runtime_error("invalid response from /v1/models");
+    }
+    std::vector<std::string> models;
+    std::vector<std::string> models_display;
+    for (const auto & m : resp.at("data")) {
+        if (!m.contains("id") || !m.at("id").is_string()) {
+            continue;
+        }
+        std::string name = m.at("id").get<std::string>();
+        std::string display = name;
+        if (m.contains("aliases") && m.at("aliases").is_array()) {
+            std::vector<std::string> aliases;
+            for (const auto & a : m.at("aliases")) {
+                if (a.is_string()) {
+                    aliases.push_back(a.get<std::string>());
+                }
+            }
+            if (!aliases.empty()) {
+                display += " (" + string_join(aliases, ", ") + ")";
+            }
+        }
+        models.push_back(name);
+        models_display.push_back(display);
+    }
+
+    // only one model: use it without asking
+    if (models.size() == 1) {
+        model_name = models[0];
+        client.model = model_name;
+        return true;
+    }
+
+    std::string message = "\nAvailable models:";
+    for (size_t i = 0; i < models_display.size(); ++i) {
+        message += "\n  " + std::to_string(i + 1) + ". " + models_display[i];
+    }
+    message += "\n";
+    ui::show_message(message);
+    std::string selection;
+    while (selection.empty()) {
+        if (should_stop()) {
+            return false;
+        }
+        ui::user_turn user_turn;
+        selection = user_turn.read_input(false, "Select model by number: ");
+        if (selection.empty()) {
+            continue;
+        }
+        try {
+            size_t idx = std::stoul(selection);
+            if (idx > 0 && idx <= models.size()) {
+                model_name = models[idx - 1];
+                client.model = model_name;
+                ui::show_message("Selected model: " + model_name);
+                break;
+            }
+        } catch (...) {
+            // ignore
+        }
+        ui::show_error("Invalid selection. Please enter a valid number.");
+        selection.clear();
+        continue;
+    }
+    return true;
+}
+
+void cli_context::add_system_prompt() {
+    if (!params.system_prompt.empty()) {
+        impl->messages.push_back({
+            {"role",    "system"},
+            {"content", params.system_prompt}
+        });
+    }
+}
+
+void cli_context::push_user_message(const std::string & text) {
+    json content;
+    if (impl->pending_media.empty()) {
+        content = text;
+    } else {
+        // multimodal message: media parts first, then the text
+        content = impl->pending_media;
+        content.push_back({
+            {"type", "text"},
+            {"text", text}
+        });
+        impl->pending_media = json::array();
+    }
+    impl->messages.push_back({
+        {"role",    "user"},
+        {"content", content}
+    });
+}
+
+bool cli_context::stage_media_file(const std::string & fname, const std::string & type) {
+    std::ifstream file(fname, std::ios::binary);
+    if (!file) {
+        return false;
+    }
+    std::string data((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+    std::string encoded = base64::encode(data);
+
+    if (type == "audio") {
+        std::string ext = std::filesystem::path(fname).extension().string();
+        std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c) { return std::tolower(c); });
+        impl->pending_media.push_back({
+            {"type", "input_audio"},
+            {"input_audio", {
+                {"data",   encoded},
+                {"format", ext == ".mp3" ? "mp3" : "wav"}
+            }}
+        });
+    } else if (type == "video") {
+        impl->pending_media.push_back({
+            {"type", "input_video"},
+            {"input_video", {
+                {"data", encoded}
+            }}
+        });
+    } else {
+        // the server detects the actual image type from the data
+        impl->pending_media.push_back({
+            {"type", "image_url"},
+            {"image_url", {
+                {"url", "data:image/unknown;base64," + encoded}
+            }}
+        });
+    }
+    return true;
+}
+
+bool cli_context::generate_completion(std::string & assistant_content, cli_timings & timings) {
+    json body = {
+        {"messages",          impl->messages},
+        {"stream",            true},
+        // in order to get timings even when we cancel mid-way
+        {"timings_per_token", true},
+    };
+    if (!client.model.empty()) {
+        body["model"] = client.model;
+    }
+
+    bool stream_error = false;
+
+    ui::assistant_turn a;
+
+    std::string err = client.post_sse("/v1/chat/completions", body.dump(), should_stop, [&](const std::string & payload) {
+        json chunk = json::parse(payload, nullptr, false);
+        if (chunk.is_discarded()) {
+            return;
+        }
+        if (chunk.contains("error")) {
+            stream_error = true;
+            ui::show_error(format_error_message(chunk));
+            return;
+        }
+        if (chunk.contains("timings")) {
+            const auto & t = chunk.at("timings");
+            timings.prompt_per_second    = t.value("prompt_per_second",    0.0);
+            timings.predicted_per_second = t.value("predicted_per_second", 0.0);
+        }
+        if (!chunk.contains("choices") || !chunk.at("choices").is_array() || chunk.at("choices").empty()) {
+            return;
+        }
+        const auto & choice = chunk.at("choices").at(0);
+        if (!choice.contains("delta")) {
+            return;
+        }
+        const auto & delta = choice.at("delta");
+        if (delta.contains("reasoning_content") && delta.at("reasoning_content").is_string()) {
+            const std::string text = delta.at("reasoning_content").get<std::string>();
+            if (!text.empty()) {
+                a.push(ui::ASSISTANT_DISPLAY_MODE_REASONING, text);
+            }
+        }
+        if (delta.contains("content") && delta.at("content").is_string()) {
+            const std::string text = delta.at("content").get<std::string>();
+            if (!text.empty()) {
+                assistant_content += text;
+                a.push(ui::ASSISTANT_DISPLAY_MODE_CONTENT, text);
+            }
+        }
+    });
+
+    cli_context::interrupted().store(false);
+
+    if (!err.empty()) {
+        ui::show_error(format_error_message(err));
+        return false;
+    }
+    return !stream_error;
+}
+
+int cli_context::run() {
+    add_system_prompt();
+
+    std::string modalities = "text";
+    if (has_vision) {
+        modalities += ", vision";
+    }
+    if (has_audio) {
+        modalities += ", audio";
+    }
+    if (has_video) {
+        modalities += ", video";
+    }
+
+    std::string banner;
+    banner += "\n";
+    banner += LLAMA_ASCII_LOGO;
+    banner += "\n";
+    banner += "build      : " + build_info + "\n";
+    banner += "model      : " + model_name + "\n";
+    if (!model_ftype.empty()) {
+        banner += "ftype      : " + model_ftype + "\n";
+    }
+    banner += "modalities : " + modalities + "\n";
+    if (!params.system_prompt.empty()) {
+        banner += "using custom system prompt\n";
+    }
+    banner += "\n";
+    banner += "available commands:\n";
+    banner += "  /exit or Ctrl+C     stop or exit\n";
+    banner += "  /regen              regenerate the last response\n";
+    banner += "  /clear              clear the chat history\n";
+    banner += "  /read <file>        add a text file\n";
+    banner += "  /glob <pattern>     add text files using globbing pattern\n";
+    if (has_vision) {
+        banner += "  /image <file>       add an image file\n";
+    }
+    if (has_audio) {
+        banner += "  /audio <file>       add an audio file\n";
+    }
+    if (has_video) {
+        banner += "  /video <file>       add a video file\n";
+    }
+    banner += "\n";
+
+    ui::show_message(banner);
+
+    // interactive loop
+    std::string cur_msg;
+
+    auto add_text_file = [&](const std::string & fname) -> bool {
+        std::ifstream file(fname, std::ios::binary);
+        if (!file) {
+            ui::show_error(string_format("file does not exist or cannot be opened: '%s'", fname.c_str()));
+            return false;
+        }
+        std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+        cur_msg += "--- File: ";
+        cur_msg += fname;
+        cur_msg += " ---\n";
+        cur_msg += content;
+        ui::show_message(string_format("Loaded text from '%s'", fname.c_str()));
+        return true;
+    };
+
+    while (true) {
+        std::string buffer;
+        {
+            ui::user_turn user_turn;
+
+            if (params.prompt.empty()) {
+                buffer = user_turn.read_input(params.multiline_input);
+            } else {
+                // process input prompt from args
+                for (auto & fname : params.image) {
+                    if (!stage_media_file(fname, media_type_from_ext(fname))) {
+                        ui::show_error(string_format("file does not exist or cannot be opened: '%s'", fname.c_str()));
+                        break;
+                    }
+                    ui::show_message(string_format("Loaded media from '%s'", fname.c_str()));
+                }
+                buffer = params.prompt;
+                user_turn.echo(buffer);
+                params.prompt.clear(); // only use it once
+            }
+        }
+
+        if (should_stop()) {
+            cli_context::interrupted().store(false);
+            break;
+        }
+
+        // remove trailing newline
+        if (!buffer.empty() && buffer.back() == '\n') {
+            buffer.pop_back();
+        }
+
+        // skip empty messages
+        if (buffer.empty()) {
+            continue;
+        }
+
+        bool add_user_msg = true;
+
+        // process commands
+        if (string_starts_with(buffer, "/exit")) {
+            break;
+        } else if (string_starts_with(buffer, "/regen")) {
+            if (impl->messages.size() >= 2) {
+                size_t last_idx = impl->messages.size() - 1;
+                impl->messages.erase(last_idx);
+                add_user_msg = false;
+            } else {
+                ui::show_error("No message to regenerate.");
+                continue;
+            }
+        } else if (string_starts_with(buffer, "/clear")) {
+            impl->messages.clear();
+            add_system_prompt();
+
+            impl->pending_media = json::array();
+            ui::show_message("Chat history cleared.");
+            continue;
+        } else if (
+                (string_starts_with(buffer, "/image ") && has_vision) ||
+                (string_starts_with(buffer, "/audio ") && has_audio) ||
+                (string_starts_with(buffer, "/video ") && has_video)) {
+            std::string type = buffer.substr(1, 5);
+            // just in case (bad copy-paste for example), we strip all trailing/leading spaces
+            std::string fname = string_strip(buffer.substr(7));
+            if (!stage_media_file(fname, type)) {
+                ui::show_error(string_format("file does not exist or cannot be opened: '%s'", fname.c_str()));
+                continue;
+            }
+            ui::show_message(string_format("Loaded media from '%s'", fname.c_str()));
+            continue;
+        } else if (string_starts_with(buffer, "/read ")) {
+            std::string fname = string_strip(buffer.substr(6));
+            add_text_file(fname);
+            continue;
+        } else if (string_starts_with(buffer, "/glob ")) {
+            std::error_code ec;
+            size_t count = 0;
+            auto curdir = std::filesystem::current_path();
+            std::string pattern = string_strip(buffer.substr(6));
+            std::filesystem::path rel_path;
+
+            auto startglob = pattern.find_first_of("![*?");
+            if (startglob != std::string::npos && startglob != 0) {
+                auto endpath = pattern.substr(0, startglob).find_last_of('/');
+                if (endpath != std::string::npos) {
+                    std::string rel_pattern = pattern.substr(0, endpath);
+#if !defined(_WIN32)
+                    if (string_starts_with(rel_pattern, '~')) {
+                        const char * home = std::getenv("HOME");
+                        if (home && home[0]) {
+                            rel_pattern = home + rel_pattern.substr(1);
+                        }
+                    }
+#endif
+                    rel_path = rel_pattern;
+                    pattern.erase(0, endpath + 1);
+                    curdir /= rel_path;
+                }
+            }
+
+            for (const auto & entry : std::filesystem::recursive_directory_iterator(curdir,
+                    std::filesystem::directory_options::skip_permission_denied, ec)) {
+                if (!entry.is_regular_file()) {
+                    continue;
+                }
+
+                std::string rel = std::filesystem::relative(entry.path(), curdir, ec).string();
+                if (ec) {
+                    ec.clear();
+                    continue;
+                }
+                std::replace(rel.begin(), rel.end(), '\\', '/');
+
+                if (!glob_match(pattern, rel)) {
+                    continue;
+                }
+
+                if (!add_text_file((rel_path / rel).string())) {
+                    continue;
+                }
+
+                if (++count >= FILE_GLOB_MAX_RESULTS) {
+                    ui::show_error(string_format("Maximum number of globbed files allowed (%zu) reached.", FILE_GLOB_MAX_RESULTS));
+                    break;
+                }
+            }
+            continue;
+        } else {
+            // not a command
+            cur_msg += buffer;
+        }
+
+        // generate response
+        if (add_user_msg) {
+            push_user_message(cur_msg);
+            cur_msg.clear();
+        }
+        cli_timings timings;
+        std::string assistant_content;
+        generate_completion(assistant_content, timings);
+        impl->messages.push_back({
+            {"role",    "assistant"},
+            {"content", assistant_content}
+        });
+
+        if (params.show_timings) {
+            ui::show_info(string_format(
+                "\n[ Prompt: %.1f t/s | Generation: %.1f t/s ]",
+                timings.prompt_per_second,
+                timings.predicted_per_second
+            ));
+        }
+
+        if (params.single_turn) {
+            break;
+        }
+    }
+
+    ui::show_message("\n\nExiting...");
+
+    return 0;
+}
+
+void cli_context::shutdown() {
+    if (server) {
+        server->stop();
+        server.reset();
+    }
+}

tools/cli/cli-context.h

@@ -0,0 +1,66 @@
+#pragma once
+
+#include "common.h"
+
+#include "cli-client.h"
+#include "cli-server.h"
+
+#include <atomic>
+#include <memory>
+#include <optional>
+#include <string>
+
+struct cli_timings {
+    double prompt_per_second    = 0.0;
+    double predicted_per_second = 0.0;
+};
+
+struct cli_context_impl;
+
+struct cli_context {
+    common_params params;
+
+    cli_client client;                // always initialized
+    std::optional<cli_server> server; // only set when no --server-base is given
+
+    // properties of the connected server
+    // will be populated by fetch_server_props()
+    std::string model_name;
+    std::string model_ftype;
+    std::string build_info;
+    bool has_vision = false;
+    bool has_audio  = false;
+    bool has_video  = false;
+
+    cli_context(const common_params & params);
+    ~cli_context();
+
+    // connect to --server-base or spawn a local llama-server child;
+    // argc/argv are needed to forward the server-relevant args to the child
+    bool init();
+
+    // run the interactive chat loop, returns the process exit code
+    int run();
+
+    // stop the local server child (if any)
+    void shutdown();
+
+    // set by the SIGINT handler; cleared once the interrupt has been handled
+    static std::atomic<bool> & interrupted();
+
+private:
+    bool generate_completion(std::string & assistant_content, cli_timings & timings);
+    void fetch_server_props();
+    void add_system_prompt();
+    void push_user_message(const std::string & text);
+
+    // check if server have multiple models (router mode)
+    // if yes, list them then ask; do nothing otherwise
+    bool list_and_ask_models();
+
+    // read a file and stage it as a multimodal content part; type is one of
+    // "image", "audio", "video"; returns false if the file cannot be read
+    bool stage_media_file(const std::string & fname, const std::string & type);
+
+    std::unique_ptr<cli_context_impl> impl;
+};

tools/cli/cli-server.h

@@ -0,0 +1,89 @@
+#pragma once
+
+#include <thread>
+
+#include "http.h"
+
+// llama_server will be available as a dynamic library symbol
+int llama_server(common_params & params, int argc, char ** argv);
+void llama_server_terminate();
+
+struct cli_server {
+    std::thread th;
+    int port = -1;
+    std::atomic<bool> is_alive = false;
+    std::atomic<bool> is_stopping = false;
+
+    ~cli_server() {
+        stop();
+    }
+
+    void stop() {
+        if (is_stopping.exchange(true)) {
+            return;
+        }
+        if (alive()) {
+            llama_server_terminate();
+        }
+        if (th.joinable()) {
+            th.join();
+        }
+    }
+
+    // spawn llama-server in a thread and interact with it via a random port
+    bool start(common_params & params) {
+        port = common_http_get_free_port();
+        if (port <= 0) {
+            fprintf(stderr, "failed to get a free port\n");
+            exit(1);
+        }
+
+        is_alive.store(true, std::memory_order_release);
+
+        common_params server_params = params; // copy
+        server_params.port = port;
+
+        th = std::thread([this, server_params]() mutable {
+            // argc / argv are only used in router mode, we can skip them for now
+            int res = llama_server(server_params, 0, nullptr);
+            if (res != 0) {
+                fprintf(stderr, "llama_server exited with code %d\n", res);
+            }
+            is_alive.store(false, std::memory_order_release);
+        });
+
+        return true;
+    }
+
+    std::string address() const {
+        return "http://127.0.0.1:" + std::to_string(port);
+    }
+
+    bool wait_ready(std::function<bool()> should_stop) {
+        if (!alive()) {
+            return false;
+        }
+        while (!should_stop()) {
+            auto [cli, parts] = common_http_client(address());
+            cli.set_connection_timeout(1, 0);
+            auto res = cli.Get("/health");
+            if (res) {
+                if (res->status == 200) {
+                    return true;
+                }
+                // any other status means the server is up but not ready yet
+                // (e.g. 503 while the model is still loading)
+            }
+            if (!alive()) {
+                // in case server die permanently
+                return false;
+            }
+            std::this_thread::sleep_for(std::chrono::milliseconds(200));
+        }
+        return true;
+    }
+
+    bool alive() const {
+        return is_alive.load(std::memory_order_acquire);
+    }
+};

tools/cli/cli-ui.h

@@ -0,0 +1,251 @@
+#pragma once
+
+#include "common.h"
+#include "console.h"
+
+#include <array>
+#include <algorithm>
+#include <cctype>
+#include <filesystem>
+#include <string_view>
+
+// TODO?: Make this reusable, enums, docs
+static const std::array<std::string_view, 8> cmds = {
+    "/audio ",
+    "/clear",
+    "/exit",
+    "/glob ",
+    "/image ",
+    "/read ",
+    "/regen",
+    "/video ",
+};
+
+static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std::string_view line, size_t cursor_byte_pos) {
+    std::vector<std::pair<std::string, size_t>> matches;
+    std::string cmd;
+
+    if (line.length() > 1 && line.front() == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
+        return string_starts_with(line, prefix);
+    })) {
+        auto it = cmds.begin();
+
+        while ((it = std::find_if(it, cmds.end(), [line](std::string_view cmd_line) {
+            return string_starts_with(cmd_line, line);
+        })) != cmds.end()) {
+            matches.emplace_back(*it, it->length());
+            ++it;
+        }
+    } else {
+        auto it = std::find_if(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
+            return prefix.back() == ' ' && string_starts_with(line, prefix);
+        });
+
+        if (it != cmds.end()) {
+            cmd = *it;
+        }
+    }
+
+    if (!cmd.empty() && cmd != "/glob " && line.length() >= cmd.length() && cursor_byte_pos >= cmd.length()) {
+        const std::string path_prefix  = std::string(line.substr(cmd.length(), cursor_byte_pos - cmd.length()));
+        const std::string path_postfix = std::string(line.substr(cursor_byte_pos));
+        auto cur_dir = std::filesystem::current_path();
+        std::string cur_dir_str = cur_dir.string();
+        std::string expanded_prefix = path_prefix;
+
+#if !defined(_WIN32)
+        if (string_starts_with(path_prefix, '~')) {
+            const char * home = std::getenv("HOME");
+            if (home && home[0]) {
+                expanded_prefix = home + path_prefix.substr(1);
+            }
+        }
+        if (string_starts_with(expanded_prefix, '/')) {
+#else
+        if (std::isalpha(static_cast<unsigned char>(expanded_prefix[0])) && expanded_prefix.find(':') == 1) {
+#endif
+            cur_dir = std::filesystem::path(expanded_prefix).parent_path();
+            cur_dir_str.clear();
+        } else if (!path_prefix.empty()) {
+            cur_dir /= std::filesystem::path(path_prefix).parent_path();
+        }
+
+        std::error_code ec;
+        for (const auto & entry : std::filesystem::directory_iterator(cur_dir, ec)) {
+            if (ec) {
+                break;
+            }
+            if (!entry.exists(ec)) {
+                ec.clear();
+                continue;
+            }
+
+            const std::string path_full = entry.path().string();
+            std::string path_entry = !cur_dir_str.empty() && string_starts_with(path_full, cur_dir_str) ? path_full.substr(cur_dir_str.length() + 1) : path_full;
+
+            if (entry.is_directory(ec)) {
+                path_entry.push_back(std::filesystem::path::preferred_separator);
+            }
+
+            if (expanded_prefix.empty() || string_starts_with(path_entry, expanded_prefix)) {
+                const std::string updated_line = cmd + path_entry;
+                matches.emplace_back(updated_line + path_postfix, updated_line.length());
+            }
+
+            if (ec) {
+                ec.clear();
+            }
+        }
+
+        if (matches.empty()) {
+            const std::string updated_line = cmd + path_prefix;
+            matches.emplace_back(updated_line + path_postfix, updated_line.length());
+        }
+
+        // Add the longest common prefix
+        if (!expanded_prefix.empty() && matches.size() > 1) {
+            const std::string_view match0(matches[0].first);
+            const std::string_view match1(matches[1].first);
+            auto it = std::mismatch(match0.begin(), match0.end(), match1.begin(), match1.end());
+            size_t len = it.first - match0.begin();
+
+            for (size_t i = 2; i < matches.size(); ++i) {
+                const std::string_view matchi(matches[i].first);
+                auto cmp = std::mismatch(match0.begin(), match0.end(), matchi.begin(), matchi.end());
+                len = std::min(len, static_cast<size_t>(cmp.first - match0.begin()));
+            }
+
+            const std::string updated_line = std::string(match0.substr(0, len));
+            matches.emplace_back(updated_line + path_postfix, updated_line.length());
+        }
+
+        std::sort(matches.begin(), matches.end(), [](const auto & a, const auto & b) {
+            return a.first.compare(0, a.second, b.first, 0, b.second) < 0;
+        });
+    }
+
+    return matches;
+}
+
+// note: make this view implementation generic, so that we can move to TUI in the future if we want to
+namespace ui {
+    static void init(const common_params & params) {
+        // TODO: avoid using atexit() here by making `console` a singleton
+        console::init(params.simple_io, params.use_color);
+        atexit([]() { console::cleanup(); });
+
+        console::set_completion_callback(auto_completion_callback);
+    }
+
+    struct spinner {
+        spinner(const std::string & message) {
+            if (!message.empty()) {
+                console::log("%s ", message.c_str());
+            }
+            console::spinner::start();
+        }
+        ~spinner() {
+            console::spinner::stop();
+        }
+    };
+
+    struct user_turn {
+        user_turn() {
+            console::set_display(DISPLAY_TYPE_USER_INPUT);
+        }
+        ~user_turn() {
+            console::set_display(DISPLAY_TYPE_RESET);
+        }
+        void echo(const std::string & buffer) {
+            if (buffer.size() > 500) {
+                console::log("\n> %s ... (truncated)\n", buffer.substr(0, 500).c_str());
+            } else {
+                console::log("\n> %s\n", buffer.c_str());
+            }
+        }
+        std::string read_input(bool multiline_input, const char * prompt = nullptr) {
+            if (prompt) {
+                console::log("%s", prompt);
+            } else {
+                console::log("\n> ");
+            }
+            std::string buffer;
+            std::string line;
+            bool another_line = true;
+            do {
+                another_line = console::readline(line, multiline_input);
+                buffer += line;
+            } while (another_line);
+            return buffer;
+        }
+    };
+
+    enum assistant_display_mode {
+        ASSISTANT_DISPLAY_MODE_REASONING,
+        ASSISTANT_DISPLAY_MODE_CONTENT,
+    };
+    struct assistant_turn {
+        assistant_display_mode mode = ASSISTANT_DISPLAY_MODE_CONTENT;
+        bool trailing_newline = true;
+        bool is_inside_reasoning = false;
+        assistant_turn() {
+            console::set_display(DISPLAY_TYPE_RESET);
+        }
+        ~assistant_turn() {
+            console::set_display(DISPLAY_TYPE_RESET);
+            add_newline_if_needed();
+        }
+        void push(assistant_display_mode m, const std::string & buffer) {
+            if (m != mode) {
+                add_newline_if_needed();
+                switch (m) {
+                    case ASSISTANT_DISPLAY_MODE_CONTENT:
+                        {
+                            if (is_inside_reasoning) {
+                                console::log("[End thinking]\n\n");
+                                is_inside_reasoning = false;
+                            }
+                            console::set_display(DISPLAY_TYPE_RESET);
+                        } break;
+                    case ASSISTANT_DISPLAY_MODE_REASONING:
+                        {
+                            console::set_display(DISPLAY_TYPE_REASONING);
+                            is_inside_reasoning = true;
+                            console::log("\n[Start thinking]\n\n");
+                        } break;
+                }
+            }
+            mode = m;
+            if (buffer.empty()) {
+                return;
+            }
+            trailing_newline = buffer.back() == '\n';
+            console::log("%s", buffer.c_str());
+            console::flush();
+        }
+        void add_newline_if_needed() {
+            if (!trailing_newline) {
+                console::log("\n");
+                console::flush();
+            }
+        }
+    };
+
+    static void show_error(const std::string & title, const std::string & message = "") {
+        console::spinner::stop();
+        console::error("Error: %s\n", title.c_str());
+        if (!message.empty()) {
+            console::log("%s\n", message.c_str());
+        }
+    }
+
+    static void show_message(const std::string & message) {
+        console::log("%s\n", message.c_str());
+    }
+
+    static void show_info(const std::string & message) {
+        console::set_display(DISPLAY_TYPE_INFO);
+        console::log("%s\n", message.c_str());
+        console::set_display(DISPLAY_TYPE_RESET);
+    }
+}

tools/cli/cli.cpp

@@ -1,20 +1,9 @@
-#include "chat.h"
-#include "common.h"
 #include "arg.h"
-#include "console.h"
-#include "fit.h"
-// #include "log.h"
+#include "common.h"
+#include "log.h"
 
-#include "server-common.h"
-#include "server-context.h"
-#include "server-task.h"
+#include "cli-context.h"
 
-#include <array>
-#include <atomic>
-#include <algorithm>
-#include <filesystem>
-#include <fstream>
-#include <thread>
 #include <signal.h>
 
 #if defined(_WIN32)
@@ -25,342 +14,19 @@
 #include <windows.h>
 #endif
 
-const char * LLAMA_ASCII_LOGO = R"(
-▄▄ ▄▄
-██ ██
-██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
-██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
-██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
-                                    ██    ██
-                                    ▀▀    ▀▀
-)";
-
-static std::atomic<bool> g_is_interrupted = false;
-static bool should_stop() {
-    return g_is_interrupted.load();
-}
-
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
 static void signal_handler(int) {
-    if (g_is_interrupted.load()) {
+    if (cli_context::interrupted().load()) {
         // second Ctrl+C - exit immediately
         // make sure to clear colors before exiting (not using LOG or console.cpp here to avoid deadlock)
         fprintf(stdout, "\033[0m\n");
         fflush(stdout);
         std::exit(130);
     }
-    g_is_interrupted.store(true);
+    cli_context::interrupted().store(true);
 }
 #endif
 
-struct cli_context {
-    server_context ctx_server;
-    json messages = json::array();
-    std::vector<raw_buffer> input_files;
-    task_params defaults;
-    bool verbose_prompt;
-
-    // thread for showing "loading" animation
-    std::atomic<bool> loading_show;
-
-    cli_context(const common_params & params) {
-        defaults.sampling    = params.sampling;
-        defaults.speculative = params.speculative;
-        defaults.n_keep      = params.n_keep;
-        defaults.n_predict   = params.n_predict;
-        defaults.antiprompt  = params.antiprompt;
-
-        defaults.stream = true; // make sure we always use streaming mode
-        defaults.timings_per_token = true; // in order to get timings even when we cancel mid-way
-        // defaults.return_progress = true; // TODO: show progress
-
-        verbose_prompt = params.verbose_prompt;
-    }
-
-    std::string generate_completion(result_timings & out_timings) {
-        server_response_reader rd = ctx_server.get_response_reader();
-        auto chat_params = format_chat();
-        {
-            // TODO: reduce some copies here in the future
-            server_task task = server_task(SERVER_TASK_TYPE_COMPLETION);
-            task.id         = rd.get_new_id();
-            task.index      = 0;
-            task.params     = defaults;           // copy
-            task.cli_prompt = chat_params.prompt; // copy
-            task.cli_files  = input_files;        // copy
-            task.cli        = true;
-
-            // chat template settings
-            task.params.chat_parser_params = common_chat_parser_params(chat_params);
-            task.params.chat_parser_params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
-            if (!chat_params.parser.empty()) {
-                task.params.chat_parser_params.parser.load(chat_params.parser);
-            }
-
-            // Copy the preserved tokens into the sampling params
-            const llama_vocab * vocab = llama_model_get_vocab(
-                llama_get_model(ctx_server.get_llama_context()));
-            for (const auto & token : chat_params.preserved_tokens) {
-                auto ids = common_tokenize(vocab, token, false, true);
-                if (ids.size() == 1) {
-                    task.params.sampling.preserved_tokens.insert(ids[0]);
-                }
-            }
-
-            // reasoning budget sampler
-            if (!chat_params.thinking_end_tag.empty()) {
-                task.params.sampling.reasoning_budget_tokens = defaults.sampling.reasoning_budget_tokens;
-                task.params.sampling.generation_prompt = chat_params.generation_prompt;
-
-                if (!chat_params.thinking_start_tag.empty()) {
-                    task.params.sampling.reasoning_budget_start =
-                        common_tokenize(vocab, chat_params.thinking_start_tag, false, true);
-                }
-                task.params.sampling.reasoning_budget_end =
-                    common_tokenize(vocab, chat_params.thinking_end_tag, false, true);
-                task.params.sampling.reasoning_budget_forced =
-                    common_tokenize(vocab, defaults.sampling.reasoning_budget_message + chat_params.thinking_end_tag, false, true);
-            }
-
-            rd.post_task({std::move(task)});
-        }
-
-        if (verbose_prompt) {
-            console::set_display(DISPLAY_TYPE_PROMPT);
-            console::log("%s\n\n", chat_params.prompt.c_str());
-            console::set_display(DISPLAY_TYPE_RESET);
-        }
-
-        // wait for first result
-        console::spinner::start();
-        server_task_result_ptr result = rd.next(should_stop);
-
-        while (true) {
-            auto res_partial = dynamic_cast<server_task_result_cmpl_partial *>(result.get());
-            if (res_partial && res_partial->is_begin) {
-                // this is the "send 200 status to client" signal in streaming mode
-                // skip, do not stop the spinner
-                result = rd.next(should_stop);
-            } else {
-                console::spinner::stop();
-                break;
-            }
-        }
-
-        std::string curr_content;
-        bool is_thinking = false;
-
-        while (result) {
-            if (should_stop()) {
-                break;
-            }
-            if (result->is_error()) {
-                json err_data = result->to_json();
-                if (err_data.contains("message")) {
-                    console::error("Error: %s\n", err_data["message"].get<std::string>().c_str());
-                } else {
-                    console::error("Error: %s\n", err_data.dump().c_str());
-                }
-                return curr_content;
-            }
-            auto res_partial = dynamic_cast<server_task_result_cmpl_partial *>(result.get());
-            if (res_partial) {
-                out_timings = std::move(res_partial->timings);
-                for (const auto & diff : res_partial->oaicompat_msg_diffs) {
-                    if (!diff.content_delta.empty()) {
-                        if (is_thinking) {
-                            console::log("\n[End thinking]\n\n");
-                            console::set_display(DISPLAY_TYPE_RESET);
-                            is_thinking = false;
-                        }
-                        curr_content += diff.content_delta;
-                        console::log("%s", diff.content_delta.c_str());
-                        console::flush();
-                    }
-                    if (!diff.reasoning_content_delta.empty()) {
-                        console::set_display(DISPLAY_TYPE_REASONING);
-                        if (!is_thinking) {
-                            console::log("[Start thinking]\n");
-                        }
-                        is_thinking = true;
-                        console::log("%s", diff.reasoning_content_delta.c_str());
-                        console::flush();
-                    }
-                }
-            }
-            auto res_final = dynamic_cast<server_task_result_cmpl_final *>(result.get());
-            if (res_final) {
-                out_timings = std::move(res_final->timings);
-                break;
-            }
-            result = rd.next(should_stop);
-        }
-        g_is_interrupted.store(false);
-        // server_response_reader automatically cancels pending tasks upon destruction
-        return curr_content;
-    }
-
-    // TODO: support remote files in the future (http, https, etc)
-    std::string load_input_file(const std::string & fname, bool is_media) {
-        std::ifstream file = fs_open_ifstream(fname, std::ios::binary);
-        if (!file) {
-            return "";
-        }
-        if (is_media) {
-            raw_buffer buf;
-            buf.assign((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
-            input_files.push_back(std::move(buf));
-            return get_media_marker();
-        } else {
-            std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
-            return content;
-        }
-    }
-
-    common_chat_params format_chat() {
-        auto meta = ctx_server.get_meta();
-        auto & chat_params = meta.chat_params;
-
-        auto caps = common_chat_templates_get_caps(chat_params.tmpls.get());
-
-        common_chat_templates_inputs inputs;
-        inputs.messages              = common_chat_msgs_parse_oaicompat(messages);
-        inputs.tools                 = {}; // TODO
-        inputs.tool_choice           = COMMON_CHAT_TOOL_CHOICE_NONE;
-        inputs.json_schema           = ""; // TODO
-        inputs.grammar               = ""; // TODO
-        inputs.use_jinja             = chat_params.use_jinja;
-        inputs.parallel_tool_calls   = caps["supports_parallel_tool_calls"];
-        inputs.add_generation_prompt = true;
-        inputs.reasoning_format      = COMMON_REASONING_FORMAT_DEEPSEEK;
-        inputs.force_pure_content    = chat_params.force_pure_content;
-        inputs.enable_thinking       = chat_params.enable_thinking ? common_chat_templates_support_enable_thinking(chat_params.tmpls.get()) : false;
-
-        // Apply chat template to the list of messages
-        return common_chat_templates_apply(chat_params.tmpls.get(), inputs);
-    }
-};
-
-// TODO?: Make this reusable, enums, docs
-static const std::array<std::string_view, 8> cmds = {
-    "/audio ",
-    "/clear",
-    "/exit",
-    "/glob ",
-    "/image ",
-    "/read ",
-    "/regen",
-    "/video ",
-};
-
-static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std::string_view line, size_t cursor_byte_pos) {
-    std::vector<std::pair<std::string, size_t>> matches;
-    std::string cmd;
-
-    if (line.length() > 1 && line.front() == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
-        return string_starts_with(line, prefix);
-    })) {
-        auto it = cmds.begin();
-
-        while ((it = std::find_if(it, cmds.end(), [line](std::string_view cmd_line) {
-            return string_starts_with(cmd_line, line);
-        })) != cmds.end()) {
-            matches.emplace_back(*it, it->length());
-            ++it;
-        }
-    } else {
-        auto it = std::find_if(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
-            return prefix.back() == ' ' && string_starts_with(line, prefix);
-        });
-
-        if (it != cmds.end()) {
-            cmd = *it;
-        }
-    }
-
-    if (!cmd.empty() && cmd != "/glob " && line.length() >= cmd.length() && cursor_byte_pos >= cmd.length()) {
-        const std::string path_prefix  = std::string(line.substr(cmd.length(), cursor_byte_pos - cmd.length()));
-        const std::string path_postfix = std::string(line.substr(cursor_byte_pos));
-        auto cur_dir = std::filesystem::current_path();
-        std::string cur_dir_str = cur_dir.string();
-        std::string expanded_prefix = path_prefix;
-
-#if !defined(_WIN32)
-        if (string_starts_with(path_prefix, '~')) {
-            const char * home = std::getenv("HOME");
-            if (home && home[0]) {
-                expanded_prefix = home + path_prefix.substr(1);
-            }
-        }
-        if (string_starts_with(expanded_prefix, '/')) {
-#else
-        if (std::isalpha(expanded_prefix[0]) && expanded_prefix.find(':') == 1) {
-#endif
-            cur_dir = std::filesystem::path(expanded_prefix).parent_path();
-            cur_dir_str.clear();
-        } else if (!path_prefix.empty()) {
-            cur_dir /= std::filesystem::path(path_prefix).parent_path();
-        }
-
-        std::error_code ec;
-        for (const auto & entry : std::filesystem::directory_iterator(cur_dir, ec)) {
-            if (ec) {
-                break;
-            }
-            if (!entry.exists(ec)) {
-                ec.clear();
-                continue;
-            }
-
-            const std::string path_full = entry.path().string();
-            std::string path_entry = !cur_dir_str.empty() && string_starts_with(path_full, cur_dir_str) ? path_full.substr(cur_dir_str.length() + 1) : path_full;
-
-            if (entry.is_directory(ec)) {
-                path_entry.push_back(std::filesystem::path::preferred_separator);
-            }
-
-            if (expanded_prefix.empty() || string_starts_with(path_entry, expanded_prefix)) {
-                const std::string updated_line = cmd + path_entry;
-                matches.emplace_back(updated_line + path_postfix, updated_line.length());
-            }
-
-            if (ec) {
-                ec.clear();
-            }
-        }
-
-        if (matches.empty()) {
-            const std::string updated_line = cmd + path_prefix;
-            matches.emplace_back(updated_line + path_postfix, updated_line.length());
-        }
-
-        // Add the longest common prefix
-        if (!expanded_prefix.empty() && matches.size() > 1) {
-            const std::string_view match0(matches[0].first);
-            const std::string_view match1(matches[1].first);
-            auto it = std::mismatch(match0.begin(), match0.end(), match1.begin(), match1.end());
-            size_t len = it.first - match0.begin();
-
-            for (size_t i = 2; i < matches.size(); ++i) {
-                const std::string_view matchi(matches[i].first);
-                auto cmp = std::mismatch(match0.begin(), match0.end(), matchi.begin(), matchi.end());
-                len = std::min(len, static_cast<size_t>(cmp.first - match0.begin()));
-            }
-
-            const std::string updated_line = std::string(match0.substr(0, len));
-            matches.emplace_back(updated_line + path_postfix, updated_line.length());
-        }
-
-        std::sort(matches.begin(), matches.end(), [](const auto & a, const auto & b) {
-            return a.first.compare(0, a.second, b.first, 0, b.second) < 0;
-        });
-    }
-
-    return matches;
-}
-
-static constexpr size_t FILE_GLOB_MAX_RESULTS = 100;
-
 // satisfies -Wmissing-declarations
 int llama_cli(int argc, char ** argv);
 
@@ -375,25 +41,6 @@ int llama_cli(int argc, char ** argv) {
         return 1;
     }
 
-    // TODO: maybe support it later?
-    if (params.conversation_mode == COMMON_CONVERSATION_MODE_DISABLED) {
-        console::error("--no-conversation is not supported by llama-cli\n");
-        console::error("please use llama-completion instead\n");
-    }
-
-    // struct that contains llama context and inference
-    cli_context ctx_cli(params);
-
-    llama_backend_init();
-    llama_numa_init(params.numa);
-
-    // TODO: avoid using atexit() here by making `console` a singleton
-    console::init(params.simple_io, params.use_color);
-    atexit([]() { console::cleanup(); });
-
-    console::set_display(DISPLAY_TYPE_RESET);
-    console::set_completion_callback(auto_completion_callback);
-
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
     struct sigaction sigint_action;
     sigint_action.sa_handler = signal_handler;
@@ -408,276 +55,11 @@ int llama_cli(int argc, char ** argv) {
     SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
 
-    console::log("\nLoading model... "); // followed by loading animation
-    console::spinner::start();
-    if (!ctx_cli.ctx_server.load_model(params)) {
-        console::spinner::stop();
-        console::error("\nFailed to load the model\n");
+    cli_context ctx_cli(params);
+
+    if (!ctx_cli.init()) {
         return 1;
     }
 
-    ctx_cli.defaults.sampling = params.sampling;
-
-    console::spinner::stop();
-    console::log("\n");
-
-    std::thread inference_thread([&ctx_cli]() {
-        ctx_cli.ctx_server.start_loop();
-    });
-
-    auto inf = ctx_cli.ctx_server.get_meta();
-    std::string modalities = "text";
-    if (inf.has_inp_image) {
-        modalities += ", vision";
-    }
-    if (inf.has_inp_audio) {
-        modalities += ", audio";
-    }
-
-    auto add_system_prompt = [&]() {
-        if (!params.system_prompt.empty()) {
-            ctx_cli.messages.push_back({
-                {"role",    "system"},
-                {"content", params.system_prompt}
-            });
-        }
-    };
-    add_system_prompt();
-
-    console::log("\n");
-    console::log("%s\n", LLAMA_ASCII_LOGO);
-    console::log("build      : %s\n", inf.build_info.c_str());
-    console::log("model      : %s\n", inf.model_name.c_str());
-    if (!inf.model_ftype.empty()) {
-        console::log("ftype      : %s\n", inf.model_ftype.c_str());
-    }
-    console::log("modalities : %s\n", modalities.c_str());
-    if (!params.system_prompt.empty()) {
-        console::log("using custom system prompt\n");
-    }
-    console::log("\n");
-    console::log("available commands:\n");
-    console::log("  /exit or Ctrl+C     stop or exit\n");
-    console::log("  /regen              regenerate the last response\n");
-    console::log("  /clear              clear the chat history\n");
-    console::log("  /read <file>        add a text file\n");
-    console::log("  /glob <pattern>     add text files using globbing pattern\n");
-    if (inf.has_inp_image) {
-        console::log("  /image <file>       add an image file\n");
-    }
-    if (inf.has_inp_audio) {
-        console::log("  /audio <file>       add an audio file\n");
-    }
-    if (inf.has_inp_video) {
-        console::log("  /video <file>       add a video file\n");
-    }
-    console::log("\n");
-
-    // interactive loop
-    std::string cur_msg;
-
-    auto add_text_file = [&](const std::string & fname) -> bool {
-        std::string marker = ctx_cli.load_input_file(fname, false);
-        if (marker.empty()) {
-            console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
-            return false;
-        }
-        if (inf.fim_sep_token != LLAMA_TOKEN_NULL) {
-            cur_msg += common_token_to_piece(ctx_cli.ctx_server.get_llama_context(), inf.fim_sep_token, true);
-            cur_msg += fname;
-            cur_msg.push_back('\n');
-        } else {
-            cur_msg += "--- File: ";
-            cur_msg += fname;
-            cur_msg += " ---\n";
-        }
-        cur_msg += marker;
-        console::log("Loaded text from '%s'\n", fname.c_str());
-        return true;
-    };
-
-    while (true) {
-        std::string buffer;
-        console::set_display(DISPLAY_TYPE_USER_INPUT);
-        if (params.prompt.empty()) {
-            console::log("\n> ");
-            std::string line;
-            bool another_line = true;
-            do {
-                another_line = console::readline(line, params.multiline_input);
-                buffer += line;
-            } while (another_line);
-        } else {
-            // process input prompt from args
-            for (auto & fname : params.image) {
-                std::string marker = ctx_cli.load_input_file(fname, true);
-                if (marker.empty()) {
-                    console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
-                    break;
-                }
-                console::log("Loaded media from '%s'\n", fname.c_str());
-                cur_msg += marker;
-            }
-            buffer = params.prompt;
-            if (buffer.size() > 500) {
-                console::log("\n> %s ... (truncated)\n", buffer.substr(0, 500).c_str());
-            } else {
-                console::log("\n> %s\n", buffer.c_str());
-            }
-            params.prompt.clear(); // only use it once
-        }
-        console::set_display(DISPLAY_TYPE_RESET);
-        console::log("\n");
-
-        if (should_stop()) {
-            g_is_interrupted.store(false);
-            break;
-        }
-
-        // remove trailing newline
-        if (!buffer.empty() &&buffer.back() == '\n') {
-            buffer.pop_back();
-        }
-
-        // skip empty messages
-        if (buffer.empty()) {
-            continue;
-        }
-
-        bool add_user_msg = true;
-
-        // process commands
-        if (string_starts_with(buffer, "/exit")) {
-            break;
-        } else if (string_starts_with(buffer, "/regen")) {
-            if (ctx_cli.messages.size() >= 2) {
-                size_t last_idx = ctx_cli.messages.size() - 1;
-                ctx_cli.messages.erase(last_idx);
-                add_user_msg = false;
-            } else {
-                console::error("No message to regenerate.\n");
-                continue;
-            }
-        } else if (string_starts_with(buffer, "/clear")) {
-            ctx_cli.messages.clear();
-            add_system_prompt();
-
-            ctx_cli.input_files.clear();
-            console::log("Chat history cleared.\n");
-            continue;
-        } else if (
-                (string_starts_with(buffer, "/image ") && inf.has_inp_image) ||
-                (string_starts_with(buffer, "/audio ") && inf.has_inp_audio) ||
-                (string_starts_with(buffer, "/video ") && inf.has_inp_video)) {
-            // just in case (bad copy-paste for example), we strip all trailing/leading spaces
-            std::string fname = string_strip(buffer.substr(7));
-            std::string marker = ctx_cli.load_input_file(fname, true);
-            if (marker.empty()) {
-                console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
-                continue;
-            }
-            cur_msg += marker;
-            console::log("Loaded media from '%s'\n", fname.c_str());
-            continue;
-        } else if (string_starts_with(buffer, "/read ")) {
-            std::string fname = string_strip(buffer.substr(6));
-            add_text_file(fname);
-            continue;
-        } else if (string_starts_with(buffer, "/glob ")) {
-            std::error_code ec;
-            size_t count = 0;
-            auto curdir = std::filesystem::current_path();
-            std::string pattern = string_strip(buffer.substr(6));
-            std::filesystem::path rel_path;
-
-            auto startglob = pattern.find_first_of("![*?");
-            if (startglob != std::string::npos && startglob != 0) {
-                auto endpath = pattern.substr(0, startglob).find_last_of('/');
-                if (endpath != std::string::npos) {
-                    std::string rel_pattern = pattern.substr(0, endpath);
-#if !defined(_WIN32)
-                    if (string_starts_with(rel_pattern, '~')) {
-                        const char * home = std::getenv("HOME");
-                        if (home && home[0]) {
-                            rel_pattern = home + rel_pattern.substr(1);
-                        }
-                    }
-#endif
-                    rel_path = rel_pattern;
-                    pattern.erase(0, endpath + 1);
-                    curdir /= rel_path;
-                }
-            }
-
-            for (const auto & entry : std::filesystem::recursive_directory_iterator(curdir,
-                    std::filesystem::directory_options::skip_permission_denied, ec)) {
-                if (!entry.is_regular_file()) {
-                    continue;
-                }
-
-                std::string rel = std::filesystem::relative(entry.path(), curdir, ec).string();
-                if (ec) {
-                    ec.clear();
-                    continue;
-                }
-                std::replace(rel.begin(), rel.end(), '\\', '/');
-
-                if (!glob_match(pattern, rel)) {
-                    continue;
-                }
-
-                if (!add_text_file((rel_path / rel).string())) {
-                    continue;
-                }
-
-                if (++count >= FILE_GLOB_MAX_RESULTS) {
-                    console::error("Maximum number of globbed files allowed (%zu) reached.\n", FILE_GLOB_MAX_RESULTS);
-                    break;
-                }
-            }
-            continue;
-        } else {
-            // not a command
-            cur_msg += buffer;
-        }
-
-        // generate response
-        if (add_user_msg) {
-            ctx_cli.messages.push_back({
-                {"role",    "user"},
-                {"content", cur_msg}
-            });
-            cur_msg.clear();
-        }
-        result_timings timings;
-        std::string assistant_content = ctx_cli.generate_completion(timings);
-        ctx_cli.messages.push_back({
-            {"role",    "assistant"},
-            {"content", assistant_content}
-        });
-        console::log("\n");
-
-        if (params.show_timings) {
-            console::set_display(DISPLAY_TYPE_INFO);
-            console::log("\n");
-            console::log("[ Prompt: %.1f t/s | Generation: %.1f t/s ]\n", timings.prompt_per_second, timings.predicted_per_second);
-            console::set_display(DISPLAY_TYPE_RESET);
-        }
-
-        if (params.single_turn) {
-            break;
-        }
-    }
-
-    console::set_display(DISPLAY_TYPE_RESET);
-
-    console::log("\nExiting...\n");
-    ctx_cli.ctx_server.terminate();
-    inference_thread.join();
-
-    // bump the log level to display timings
-    common_log_set_verbosity_thold(LOG_LEVEL_INFO);
-    common_memory_breakdown_print(ctx_cli.ctx_server.get_llama_context());
-
-    return 0;
+    return ctx_cli.run();
 }

tools/quantize/quantize.cpp

@@ -33,6 +33,7 @@ struct quant_option {
 
 static const std::vector<quant_option> QUANT_OPTIONS = {
     { "Q1_0",     LLAMA_FTYPE_MOSTLY_Q1_0,     " 1.125 bpw quantization",           },
+    { "Q2_0",     LLAMA_FTYPE_MOSTLY_Q2_0,     " 2.25 bpw quantization (group 64)",  },
     { "Q4_0",     LLAMA_FTYPE_MOSTLY_Q4_0,     " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
     { "Q4_1",     LLAMA_FTYPE_MOSTLY_Q4_1,     " 4.78G, +0.4511 ppl @ Llama-3-8B",  },
     { "MXFP4_MOE",LLAMA_FTYPE_MOSTLY_MXFP4_MOE," MXFP4 MoE",  },

tools/server/README-dev.md

@@ -57,7 +57,7 @@ The core architecture consists of the following components:
 - `server_tokens`: Unified representation of token sequences (supports both text and multimodal tokens); used by `server_task` and `server_slot`.
 - `server_prompt_checkpoint`: For recurrent (e.g., RWKV) and SWA models, stores snapshots of KV cache state. Enables reuse when subsequent requests share the same prompt prefix, saving redundant computation.
 - `server_models`: Standalone component for managing multiple backend instances (used in router mode). It is completely independent of `server_context`.
-- `stream_session_manager`: Process wide owner of resumable SSE stream sessions (`g_stream_sessions`), keyed by conversation id. Backs the replay buffer that lets a client reattach to a generation after an HTTP disconnect. See the "Resumable streaming" section below.
+- `stream_session_manager`: process wide owner of resumable SSE stream sessions, keyed by conversation id. A file-static singleton inside `server-stream.cpp`, driven through `server_stream_session_manager_start/stop`. Backs the replay buffer that lets a client reattach to a generation after an HTTP disconnect. See the "Resumable streaming" section below.
 
 ```mermaid
 graph TD
@@ -127,10 +127,12 @@ It is opt in via the `X-Conversation-Id` header on `POST /v1/chat/completions`.
 The feature lives entirely in `server-stream.{h,cpp}` and rests on three types:
 
 - `stream_session`: a bounded ring buffer (4 MiB cap, oldest bytes drop first) plus a condvar. `append` pushes raw SSE bytes, `read_from` drains from any offset and blocks for live bytes or finalize, `finalize` wakes readers, `cancel` stops the producer. One conv maps to at most one live session.
-- `stream_session_manager` (`g_stream_sessions`): owns all sessions keyed by conv id, enforces the one conv one session invariant via `create_or_replace`, and runs a GC thread that drops completed sessions past their TTL.
+- `stream_session_manager`: a file-static singleton (`g_stream_sessions`) inside `server-stream.cpp`, owns all sessions keyed by conv id, enforces the one conv one session invariant via `create_or_replace`, and runs a GC thread that drops completed sessions past their TTL. Exposed to main only through `server_stream_session_manager_start/stop`.
 - `stream_pipe_producer` / `stream_pipe_consumer`: the write and read ends. The producer owns the session lifetime and finalizes it on destruction; the consumer is read only and never finalizes, so a reader detaching cannot kill a running generation.
 
-Producer side: `server_res_generator` attaches a producer pipe when the header is present. The HTTP content provider mirrors every chunk into the ring before writing it to the socket. While a pipe is attached, `stream_aware_should_stop` ignores peer disconnect, so a dropped socket does not stop generation: only an explicit `DELETE` does. When the peer leaves early, `on_complete` calls `close()`, which drains the rest of the generation into the ring on the http worker.
+The implementation is hidden in `server-stream.cpp` (pimpl). The header exposes only the route handler factories, `server_stream_session_attach_pipe`, `server_stream_aware_should_stop`, `server_stream_conv_id_from_headers` and the GC lifecycle; the session, manager and consumer types stay in the `.cpp`.
+
+Producer side: `server_res_generator` attaches a producer pipe when the header is present. The HTTP content provider mirrors every chunk into the ring before writing it to the socket. While a pipe is attached, `server_stream_aware_should_stop` ignores peer disconnect, so a dropped socket does not stop generation: only an explicit `DELETE` does. When the peer leaves early, `on_complete` calls `close()`, which drains the rest of the generation into the ring on the http worker.
 
 Lifetime safety: the producer pipe holds a shared `alive` flag also captured by the session cancel hook. `~server_res_generator` calls `cleanup()` to clear that hook while the reader is still alive, so a `cancel` arriving during teardown can never call `stop()` on a freed response. This ordering is the most fragile part of the feature: finalizing or destroying the producer before `cleanup()` runs reintroduces a use after free.
 
@@ -144,7 +146,7 @@ Routes:
 
 Router mode binds the same paths to proxy handlers. A `conv_id -> child` map (`conv_models`), populated when a POST is routed, resolves the owning child in one lookup with no polling. The lookup groups ids per child; GET and DELETE proxy straight to the owner. This loopback REST hop is expected to move to a websocket IPC later, swapping only the transport.
 
-Lifecycle: `g_stream_sessions.start_gc()` runs in main after common init, `stop_gc()` runs first in `clean_up()` and finalizes every live session so no reader hangs. Reader blocking and the post drop drain both run on httplib worker threads, which block on a condvar rather than spin.
+Lifecycle: `server_stream_session_manager_start()` runs in main after common init, `server_stream_session_manager_stop()` runs first in `clean_up()` and finalizes every live session so no reader hangs. Reader blocking and the post drop drain both run on httplib worker threads, which block on a condvar rather than spin.
 
 | Constant | Value | Role |
 | --- | --- | --- |

tools/server/README.md

@@ -228,7 +228,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
 | `--sleep-idle-seconds SECONDS` | number of seconds of idleness after which the server will sleep (default: -1; -1 = disabled) |
-| `--log-prompts-dir PATH` | Log prompts to directory (only used for debugging, default: disabled) |
+| `--log-prompts-dir PATH` | Log prompts to directory (auto-created if not present; only used for debugging, default: disabled) |
 | `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
 | `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
 | `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |

tools/server/server-context.cpp

@@ -897,8 +897,10 @@ private:
 
     server_batch batch;
 
-    llama_model_ptr model_dft;
-    llama_context_ptr ctx_dft;
+    llama_model   * model_dft = nullptr;
+    llama_context * ctx_dft   = nullptr;
+
+    common_speculative_init_result_ptr spec_init;
 
     common_context_seq_rm_type ctx_tgt_seq_rm_type = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
     common_context_seq_rm_type ctx_dft_seq_rm_type = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
@@ -939,8 +941,10 @@ private:
 
     void destroy() {
         spec.reset();
-        ctx_dft.reset();
-        model_dft.reset();
+        spec_init.reset();
+
+        ctx_dft   = nullptr;
+        model_dft = nullptr;
 
         llama_init.reset();
 
@@ -1084,30 +1088,15 @@ private:
         // optionally reserve VRAM for the draft / MTP context before fitting the target model
         if (params_base.fit_params) {
             if (has_spec) {
-                common_params params_dft = params_base;
-                bool measure_model_bytes = true;
+                // MTP draft context lives on the target model, only context+compute are new
+                bool measure_model_bytes = has_draft;
 
-                if (has_draft) {
-                    const auto & params_spec = params_base.speculative.draft;
-                    params_dft.devices               = params_spec.devices;
-                    params_dft.model                 = params_spec.mparams;
-                    params_dft.n_gpu_layers          = params_spec.n_gpu_layers;
-                    params_dft.cache_type_k          = params_spec.cache_type_k;
-                    params_dft.cache_type_v          = params_spec.cache_type_v;
-                    params_dft.tensor_buft_overrides = params_spec.tensor_buft_overrides;
-                } else {
-                    // MTP draft context lives on the target model, only context+compute are new
-                    measure_model_bytes = false;
-                }
-
-                params_dft.n_outputs_max = params_base.n_parallel;
+                common_params params_dft = common_base_params_to_speculative(params_base);
 
                 auto mparams_dft = common_model_params_to_llama(params_dft);
                 auto cparams_dft = common_context_params_to_llama(params_dft);
                 if (spec_mtp) {
                     cparams_dft.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
-                    cparams_dft.type_k   = params_base.speculative.draft.cache_type_k;
-                    cparams_dft.type_v   = params_base.speculative.draft.cache_type_v;
                 }
                 cparams_dft.n_rs_seq = 0;
 
@@ -1175,82 +1164,36 @@ private:
 
         add_bos_token = llama_vocab_get_add_bos(vocab);
 
-        if (has_draft) {
-            // TODO speculative: move to common/speculative.cpp?
-            const auto & params_spec = params_base.speculative.draft;
-
-            SRV_TRC("loading draft model '%s'\n", params_spec.mparams.path.c_str());
-
-            auto params_dft = params_base;
-
-            params_dft.devices      = params_spec.devices;
-            params_dft.model        = params_spec.mparams;
-            params_dft.n_gpu_layers = params_spec.n_gpu_layers;
-            params_dft.cache_type_k = params_spec.cache_type_k;
-            params_dft.cache_type_v = params_spec.cache_type_v;
-
-            if (params_spec.cpuparams.n_threads > 0) {
-                params_dft.cpuparams.n_threads       = params_spec.cpuparams.n_threads;
-                params_dft.cpuparams_batch.n_threads = params_spec.cpuparams_batch.n_threads;
-            }
-
-            params_dft.tensor_buft_overrides = params_spec.tensor_buft_overrides;
-
-            auto mparams_dft = common_model_params_to_llama(params_dft);
-
-            // progress callback
-            mparams_dft.progress_callback           = load_progress_callback;
-            mparams_dft.progress_callback_user_data = &load_progress_spec;
-
-            model_dft.reset(llama_model_load_from_file(params_dft.model.path.c_str(), mparams_dft));
-            if (model_dft == nullptr) {
-                SRV_ERR("failed to load draft model, '%s'\n", params_dft.model.path.c_str());
-                return false;
-            }
-
-            auto cparams = common_context_params_to_llama(params_dft);
-
-            if (spec_mtp) {
-                cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
-            }
-
-            // note: for small models maybe we can set this to the maximum possible draft from all speculative types
-            //       the extra memory for small models is likely negligible?
-            cparams.n_rs_seq  = 0;
-            cparams.ctx_other = ctx_tgt;
-
-            ctx_dft.reset(llama_init_from_model(model_dft.get(), cparams));
-            if (ctx_dft == nullptr) {
-                SRV_ERR("%s", "failed to create draft context\n");
-                return false;
-            }
-
-            params_base.speculative.draft.ctx_tgt = ctx_tgt;
-            params_base.speculative.draft.ctx_dft = ctx_dft.get();
-        } else if (spec_mtp) {
-            // no new model load, so we simply report 0.0 and 1.0 progress
+        if (has_spec) {
+            // spec_mtp doesn't use load a model internally, so we report 0.0 and 1.0 manually
             load_progress_callback(0.0f, &load_progress_spec);
+            load_progress_spec.t_last_load_progress_ms = 0;  // reset so internal cbs aren't delayed
 
-            SRV_TRC("creating MTP draft context against the target model '%s'\n",
-                    params_base.model.path.c_str());
+            {
+                common_params params_dft = common_base_params_to_speculative(params_base);
 
-            auto cparams_mtp = common_context_params_to_llama(params_base);
-            cparams_mtp.ctx_type      = LLAMA_CONTEXT_TYPE_MTP;
-            cparams_mtp.type_k        = params_base.speculative.draft.cache_type_k;
-            cparams_mtp.type_v        = params_base.speculative.draft.cache_type_v;
-            cparams_mtp.n_rs_seq      = 0;
-            cparams_mtp.n_outputs_max = params_base.n_parallel;
-            cparams_mtp.ctx_other     = ctx_tgt;
+                // progress callback
+                params_dft.load_progress_callback           = load_progress_callback;
+                params_dft.load_progress_callback_user_data = &load_progress_spec;
 
-            ctx_dft.reset(llama_init_from_model(model_tgt, cparams_mtp));
-            if (ctx_dft == nullptr) {
-                SRV_ERR("%s", "failed to create MTP context\n");
-                return false;
+                spec_init = common_speculative_init_from_params(params_dft, model_tgt, ctx_tgt);
+                model_dft = spec_init->model();
+                ctx_dft   = spec_init->context();
+
+                if (has_draft && model_dft == nullptr) {
+                    SRV_ERR("failed to load draft model, '%s'\n", params_dft.model.path.c_str());
+                    return false;
+                }
+
+                if (ctx_dft == nullptr) {
+                    SRV_ERR("%s", "failed to create MTP context\n");
+                    return false;
+                }
+
+                params_base.speculative.draft.ctx_tgt = ctx_tgt;
+                params_base.speculative.draft.ctx_dft = ctx_dft;
             }
 
-            params_base.speculative.draft.ctx_tgt = ctx_tgt;
-            params_base.speculative.draft.ctx_dft = ctx_dft.get();
-
             load_progress_callback(1.0f, &load_progress_spec);
         }
 
@@ -1343,13 +1286,15 @@ private:
         }
 
         if (ctx_dft) {
-            ctx_dft_seq_rm_type = common_context_can_seq_rm(ctx_dft.get());
+            ctx_dft_seq_rm_type = common_context_can_seq_rm(ctx_dft);
         }
 
         if (spec) {
             SRV_TRC("%s", "speculative decoding context initialized\n");
         } else {
-            ctx_dft.reset();
+            spec_init.reset();
+            ctx_dft   = nullptr;
+            model_dft = nullptr;
         }
 
         for (int i = 0; i < params_base.n_parallel; i++) {
@@ -1357,7 +1302,7 @@ private:
 
             slot.id      = i;
             slot.ctx_tgt = ctx_tgt;
-            slot.ctx_dft = ctx_dft.get();
+            slot.ctx_dft = ctx_dft;
             slot.spec    = spec.get();
             slot.n_ctx   = n_ctx_slot;
 
@@ -2362,8 +2307,8 @@ private:
         //       this is not true for SWA models: https://github.com/ggml-org/llama.cpp/pull/24411#issuecomment-4677983225
         cur.update_pos(slot.prompt.n_tokens() - n_tokens_cur, pos_min, pos_max);
 
-        cur.update_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-        cur.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+        cur.update_tgt(ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+        cur.update_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
         // stash the draft's speculative state with the checkpoint
         common_speculative_get_state(spec.get(), slot.id, cur.data_spec);
 
@@ -2899,8 +2844,8 @@ private:
                 common_context_seq_add(ctx_tgt, slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
 
                 if (ctx_dft) {
-                    common_context_seq_rm (ctx_dft.get(), slot.id, n_keep            , n_keep + n_discard);
-                    common_context_seq_add(ctx_dft.get(), slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
+                    common_context_seq_rm (ctx_dft, slot.id, n_keep            , n_keep + n_discard);
+                    common_context_seq_add(ctx_dft, slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
                 }
 
                 // add generated tokens to cache
@@ -2972,7 +2917,7 @@ private:
                                 llama_memory_seq_pos_max(llama_get_memory(ctx_tgt), slot.id));
 
                         if (use_ckpt_dft) {
-                            slot.spec_ckpt.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                            slot.spec_ckpt.update_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
                         }
 
                         slot.spec_prompt = slot.prompt.tokens.get_text_tokens();
@@ -3009,10 +2954,10 @@ private:
 
             if (ctx_dft) {
                 if (use_ckpt_dft) {
-                    ckpt.load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                    ckpt.load_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
                 }
 
-                common_context_seq_rm(ctx_dft.get(), slot.id, ckpt.pos_max + 1, -1);
+                common_context_seq_rm(ctx_dft, slot.id, ckpt.pos_max + 1, -1);
             }
 
             if (!draft.empty()) {
@@ -3021,7 +2966,7 @@ private:
                    (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_tgt));
 
                 const bool use_ckpt_dft =
-                   (ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_dft.get()));
+                   (ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_dft));
 
                 if (use_ckpt_tgt) {
                     //const int64_t t_start = ggml_time_us();
@@ -3038,7 +2983,7 @@ private:
                 }
 
                 if (use_ckpt_dft) {
-                    ckpt.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                    ckpt.update_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
                 }
             }
         });
@@ -3219,8 +3164,8 @@ private:
                                             common_context_seq_add(ctx_tgt, slot.id, head_c, head_c + n_match, kv_shift);
 
                                             if (ctx_dft) {
-                                                common_context_seq_rm (ctx_dft.get(), slot.id, head_p, head_c);
-                                                common_context_seq_add(ctx_dft.get(), slot.id, head_c, head_c + n_match, kv_shift);
+                                                common_context_seq_rm (ctx_dft, slot.id, head_p, head_c);
+                                                common_context_seq_add(ctx_dft, slot.id, head_c, head_c + n_match, kv_shift);
                                             }
 
                                             for (size_t i = 0; i < n_match; i++) {
@@ -3320,8 +3265,8 @@ private:
 
                                     if (!do_reset) {
                                         // restore the context checkpoint
-                                        it->load_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-                                        it->load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                                        it->load_tgt(ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                                        it->load_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
                                         // restore the draft's speculative state
                                         common_speculative_set_state(spec.get(), slot.id, it->data_spec);
 
@@ -3395,7 +3340,7 @@ private:
 
                     common_context_seq_rm(ctx_tgt, slot.id, p0, -1);
                     if (ctx_dft) {
-                        common_context_seq_rm(ctx_dft.get(), slot.id, p0, -1);
+                        common_context_seq_rm(ctx_dft, slot.id, p0, -1);
                     }
 
                     // If using an alora, there may be uncached tokens that come
@@ -4243,7 +4188,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                 }
             };
 
-            auto effective_should_stop = stream_aware_should_stop(res_this, req.should_stop);
+            auto effective_should_stop = server_stream_aware_should_stop(res_this, req.should_stop);
 
             try {
                 if (effective_should_stop()) {
@@ -4339,7 +4284,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
 
     // attach a producer pipe to the response when X-Conversation-Id is present.
     // the pipe mirrors SSE chunks into the ring buffer and wires up the cancel hook.
-    stream_session_attach_pipe(*res, req.headers);
+    server_stream_session_attach_pipe(*res, req.headers);
 
     return res;
 }
@@ -4576,6 +4521,7 @@ void server_routes::init_routes() {
             { "default_generation_settings", default_generation_settings_for_props },
             { "total_slots",                 params.n_parallel },
             { "model_alias",                 meta->model_name },
+            { "model_ftype",                 meta->model_ftype },
             { "model_path",                  meta->model_path },
             { "modalities",                  json {
                 {"vision", meta->has_inp_image},

tools/server/server-models.cpp

@@ -7,6 +7,7 @@
 #include "build-info.h"
 #include "preset.h"
 #include "download.h"
+#include "http.h"
 
 #include <cpp-httplib/httplib.h> // TODO: remove this once we use HTTP client from download.h
 #include <optional>
@@ -28,14 +29,7 @@
 #include <sstream>
 #include <cstring>
 
-#ifdef _WIN32
-#include <winsock2.h>
-#include <windows.h>
-#else
-#include <sys/socket.h>
-#include <netinet/in.h>
-#include <arpa/inet.h>
-#include <unistd.h>
+#ifndef _WIN32
 extern char **environ;
 #endif
 
@@ -716,66 +710,6 @@ std::optional<server_model_meta> server_models::get_meta(const std::string & nam
     return std::nullopt;
 }
 
-static int get_free_port() {
-#ifdef _WIN32
-    WSADATA wsaData;
-    if (WSAStartup(MAKEWORD(2, 2), &wsaData) != 0) {
-        return -1;
-    }
-    typedef SOCKET native_socket_t;
-#define INVALID_SOCKET_VAL INVALID_SOCKET
-#define CLOSE_SOCKET(s) closesocket(s)
-#else
-    typedef int native_socket_t;
-#define INVALID_SOCKET_VAL -1
-#define CLOSE_SOCKET(s) close(s)
-#endif
-
-    native_socket_t sock = socket(AF_INET, SOCK_STREAM, 0);
-    if (sock == INVALID_SOCKET_VAL) {
-#ifdef _WIN32
-        WSACleanup();
-#endif
-        return -1;
-    }
-
-    struct sockaddr_in serv_addr;
-    std::memset(&serv_addr, 0, sizeof(serv_addr));
-    serv_addr.sin_family = AF_INET;
-    serv_addr.sin_addr.s_addr = htonl(INADDR_ANY);
-    serv_addr.sin_port = htons(0);
-
-    if (bind(sock, (struct sockaddr*)&serv_addr, sizeof(serv_addr)) != 0) {
-        CLOSE_SOCKET(sock);
-#ifdef _WIN32
-        WSACleanup();
-#endif
-        return -1;
-    }
-
-#ifdef _WIN32
-    int namelen = sizeof(serv_addr);
-#else
-    socklen_t namelen = sizeof(serv_addr);
-#endif
-    if (getsockname(sock, (struct sockaddr*)&serv_addr, &namelen) != 0) {
-        CLOSE_SOCKET(sock);
-#ifdef _WIN32
-        WSACleanup();
-#endif
-        return -1;
-    }
-
-    int port = ntohs(serv_addr.sin_port);
-
-    CLOSE_SOCKET(sock);
-#ifdef _WIN32
-    WSACleanup();
-#endif
-
-    return port;
-}
-
 // helper to convert vector<string> to char **
 // pointers are only valid as long as the original vector is valid
 static std::vector<char *> to_char_ptr_array(const std::vector<std::string> & vec) {
@@ -879,7 +813,7 @@ void server_models::load(const std::string & name, const load_options & opts) {
     // prepare new instance info
     instance_t inst;
     inst.meta             = meta;
-    inst.meta.port        = get_free_port();
+    inst.meta.port        = common_http_get_free_port();
     inst.meta.status      = SERVER_MODEL_STATUS_LOADING;
     inst.meta.loaded_info = json{};
     inst.meta.last_used   = ggml_time_ms();
@@ -1681,7 +1615,7 @@ void server_models_routes::init_routes() {
         }
         // remember which child serves this conversation so the stream routes can route straight
         // to it without polling, keyed on the exact conv id from the header
-        std::string conv_id = stream_conv_id_from_headers(req.headers);
+        std::string conv_id = server_stream_conv_id_from_headers(req.headers);
         if (!conv_id.empty()) {
             models.conv_models.remember(conv_id, name);
         }
@@ -1896,7 +1830,7 @@ void server_models_routes::init_routes() {
         if (!from.empty()) {
             child_path += "?from=" + from;
         }
-        SRV_INF("proxying stream resume to model %s on port %d, path=%s\n",
+        SRV_TRC("proxying stream resume to model %s on port %d, path=%s\n",
                 owner->name.c_str(), owner->port, child_path.c_str());
         auto proxy = std::make_unique<server_http_proxy>(
                 "GET",

tools/server/server-stream.cpp

@@ -6,6 +6,12 @@
 #include <chrono>
 #include <memory>
 #include <utility>
+#include <shared_mutex>
+
+enum class stream_read_status {
+    OK,
+    OFFSET_LOST,
+};
 
 namespace {
 constexpr int64_t STREAM_SESSION_TTL_SECONDS         = 300;
@@ -13,7 +19,6 @@ constexpr size_t  STREAM_SESSION_MAX_BYTES           = 4 * 1024 * 1024;
 constexpr int64_t STREAM_SESSION_GC_INTERVAL_SECONDS = 60;
 constexpr int64_t STREAM_READ_WAKE_INTERVAL_MS       = 200;
 
-// returns unix time in seconds
 int64_t now_seconds() {
     return std::chrono::duration_cast<std::chrono::seconds>(
         std::chrono::system_clock::now().time_since_epoch()
@@ -21,6 +26,91 @@ int64_t now_seconds() {
 }
 }
 
+// owns all live sessions keyed by conversation_id, one conv = at most one live session.
+// a periodic GC evicts expired ones
+class stream_session_manager {
+public:
+    stream_session_manager();
+    ~stream_session_manager();
+
+    stream_session_manager(const stream_session_manager &)             = delete;
+    stream_session_manager & operator=(const stream_session_manager &) = delete;
+
+    // install a new session, evicting and cancelling any previous one. conversation_id must be non empty
+    stream_session_ptr create_or_replace(const std::string & conversation_id);
+
+    stream_session_ptr get(const std::string & conversation_id);
+
+    std::vector<stream_session_ptr> list_all() const;
+
+    void evict(const std::string & conversation_id);
+
+    void evict_and_cancel(const std::string & conversation_id);
+
+    void start_gc();
+    void stop_gc();
+
+private:
+    void gc_loop();
+
+    mutable std::shared_mutex                           map_mu;
+    std::unordered_map<std::string, stream_session_ptr> sessions; // key: conversation_id
+    std::thread                                         gc_thread;
+    bool                                                running;
+    std::mutex                                          gc_wake_mu;
+    std::condition_variable                             gc_wake_cv;
+};
+
+// process wide manager, lifecycle controlled by llama-server main() via start_gc/stop_gc
+static stream_session_manager g_stream_sessions;
+
+void server_stream_session_manager_start() {
+    g_stream_sessions.start_gc();
+}
+
+void server_stream_session_manager_stop() {
+    g_stream_sessions.stop_gc();
+}
+
+struct stream_session {
+    std::string conversation_id;
+    int64_t     started_ts; // unix seconds at construction
+
+    stream_session(std::string conversation_id_, size_t max_bytes_);
+    stream_session(const stream_session &)             = delete;
+    stream_session & operator=(const stream_session &) = delete;
+
+    bool append(const char * data, size_t len);
+
+    void finalize();
+
+    // drain from offset into sink, blocking for more bytes or finalize. OFFSET_LOST if offset
+    // fell below the dropped prefix
+    stream_read_status read_from(size_t offset,
+        const std::function<bool(const char *, size_t)> & sink,
+        const std::function<bool()> & should_stop);
+
+    bool    is_done() const;
+    bool    is_cancelled() const;
+    size_t  total_size() const;     // bytes that ever entered the session
+    size_t  dropped_prefix() const; // bytes evicted from the front due to cap
+    int64_t completed_at() const;   // 0 while alive, unix seconds after finalize
+
+    void set_stop_producer(std::function<void()> fn);
+
+    void cancel();
+
+private:
+    mutable std::mutex      mu;
+    std::condition_variable cv;
+    std::vector<char>       buffer;
+    size_t                  prefix_dropped;
+    size_t                  cap_bytes;
+    bool                    done;
+    std::atomic<bool>       cancelled; // polled lock-free by the should_stop closure, no mu
+    int64_t                 completed_ts;
+    std::function<void()>   stop_producer;
+};
 stream_session::stream_session(std::string conversation_id_, size_t max_bytes_)
     : conversation_id(std::move(conversation_id_))
     , started_ts(now_seconds())
@@ -38,7 +128,7 @@ bool stream_session::append(const char * data, size_t len) {
     }
     {
         std::lock_guard<std::mutex> lock(mu);
-        if (done.load(std::memory_order_relaxed)) {
+        if (done) {
             return false;
         }
         if (len >= cap_bytes) {
@@ -62,11 +152,14 @@ bool stream_session::append(const char * data, size_t len) {
 }
 
 void stream_session::finalize() {
-    bool was_done = done.exchange(true, std::memory_order_acq_rel);
-    if (was_done) {
-        return;
+    {
+        std::lock_guard<std::mutex> lock(mu);
+        if (done) {
+            return;
+        }
+        done         = true;
+        completed_ts = now_seconds();
     }
-    completed_ts.store(now_seconds(), std::memory_order_release);
     cv.notify_all();
 }
 
@@ -96,7 +189,7 @@ stream_read_status stream_session::read_from(size_t offset,
             lock.lock();
             continue;
         }
-        if (done.load(std::memory_order_acquire)) {
+        if (done) {
             return stream_read_status::OK;
         }
         // wait for new bytes, finalize, or a periodic wake to re check should_stop
@@ -105,7 +198,8 @@ stream_read_status stream_session::read_from(size_t offset,
 }
 
 bool stream_session::is_done() const {
-    return done.load(std::memory_order_acquire);
+    std::lock_guard<std::mutex> lock(mu);
+    return done;
 }
 
 size_t stream_session::total_size() const {
@@ -119,7 +213,8 @@ size_t stream_session::dropped_prefix() const {
 }
 
 int64_t stream_session::completed_at() const {
-    return completed_ts.load(std::memory_order_acquire);
+    std::lock_guard<std::mutex> lock(mu);
+    return completed_ts;
 }
 
 void stream_session::set_stop_producer(std::function<void()> fn) {
@@ -128,7 +223,7 @@ void stream_session::set_stop_producer(std::function<void()> fn) {
 }
 
 void stream_session::cancel() {
-    // flip cancelled first so the producer-side stream_aware_should_stop can break out of the
+    // flip cancelled first so the producer-side server_stream_aware_should_stop can break out of the
     // recv() wait even if remove_waiting_task_ids does not notify the condvar (the cancel task
     // posted by rd.stop() will eventually notify, but we do not want to depend on that timing)
     cancelled.store(true, std::memory_order_release);
@@ -237,18 +332,24 @@ void stream_session_manager::evict_and_cancel(const std::string & conversation_i
 }
 
 void stream_session_manager::start_gc() {
-    if (running.exchange(true)) {
-        return;
+    {
+        std::lock_guard<std::mutex> lock(gc_wake_mu);
+        if (running) {
+            return;
+        }
+        running = true;
     }
     gc_thread = std::thread([this] { gc_loop(); });
 }
 
 void stream_session_manager::stop_gc() {
-    bool was_running = running.exchange(false);
+    bool was_running;
+    {
+        std::lock_guard<std::mutex> lock(gc_wake_mu);
+        was_running = running;
+        running = false;
+    }
     if (was_running) {
-        {
-            std::lock_guard<std::mutex> lock(gc_wake_mu);
-        }
         gc_wake_cv.notify_all();
         if (gc_thread.joinable()) {
             gc_thread.join();
@@ -270,15 +371,15 @@ void stream_session_manager::stop_gc() {
 }
 
 void stream_session_manager::gc_loop() {
-    while (running.load(std::memory_order_acquire)) {
+    while (true) {
         {
             std::unique_lock<std::mutex> lock(gc_wake_mu);
             gc_wake_cv.wait_for(lock,
                 std::chrono::seconds(STREAM_SESSION_GC_INTERVAL_SECONDS),
-                [this] { return !running.load(std::memory_order_acquire); });
-        }
-        if (!running.load(std::memory_order_acquire)) {
-            return;
+                [this] { return !running; });
+            if (!running) {
+                return;
+            }
         }
         int64_t cutoff = now_seconds() - STREAM_SESSION_TTL_SECONDS;
         std::vector<stream_session_ptr> to_drop;
@@ -301,10 +402,19 @@ void stream_session_manager::gc_loop() {
     }
 }
 
-// process wide manager, lifecycle controlled by llama-server main() via start_gc/stop_gc
-stream_session_manager g_stream_sessions;
+// stream_pipe
 
-// stream_pipe ---------------------------------------------------------------------------------
+// consumer end: read-only replay of the ring buffer, the destructor does not finalize the session
+struct stream_pipe_consumer : stream_pipe {
+    stream_read_status read(size_t & offset,
+        const std::function<bool(const char *, size_t)> & sink,
+        const std::function<bool()> & should_stop);
+
+    static std::shared_ptr<stream_pipe_consumer> create(stream_session_ptr session);
+
+private:
+    explicit stream_pipe_consumer(stream_session_ptr session);
+};
 
 stream_pipe::stream_pipe(stream_session_ptr session)
     : session_(std::move(session)) {
@@ -408,12 +518,10 @@ static server_http_res_ptr make_error_response(int status, const std::string & m
     return res;
 }
 
-server_http_context::handler_t make_stream_get_handler() {
+server_http_context::handler_t server_stream_make_get_handler() {
     return [](const server_http_req & req) -> server_http_res_ptr {
-        // GET /v1/stream/<conv_id>?from=N replays the SSE bytes already buffered for the
-        // session, blocks for more bytes when the session is still running, returns when
-        // the session is finalized. the body is streamed back as text/event-stream so the
-        // browser EventSource can attach to it like a fresh request
+        // GET /v1/stream/<conv_id>?from=N replays buffered SSE bytes then blocks for live
+        // bytes until the session finalizes, streamed as text/event-stream for EventSource
         std::string conv_id = req.get_param("conv_id");
         if (conv_id.empty()) {
             return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
@@ -459,11 +567,10 @@ server_http_context::handler_t make_stream_get_handler() {
     };
 }
 
-server_http_context::handler_t make_streams_lookup_handler() {
+server_http_context::handler_t server_stream_make_lookup_handler() {
     return [](const server_http_req & req) -> server_http_res_ptr {
-        // POST /v1/streams/lookup with body {"conversation_ids": ["X", "Y", ...]} returns the
-        // matching sessions, only for ids the caller already knows. each id matches the exact key
-        // and any "<id>::<model>" variant, so one lookup covers every per model session for a conv
+        // POST /v1/streams/lookup returns the matching sessions, only for ids the caller already
+        // knows. each id matches the exact key and any "<id>::<model>" per model variant
         std::vector<std::string> requested;
         try {
             json body = json::parse(req.body);
@@ -518,11 +625,10 @@ server_http_context::handler_t make_streams_lookup_handler() {
     };
 }
 
-server_http_context::handler_t make_stream_delete_handler() {
+server_http_context::handler_t server_stream_make_delete_handler() {
     return [](const server_http_req & req) -> server_http_res_ptr {
-        // DELETE /v1/stream/<conv_id> is the explicit user Stop, cancels the producer hook
-        // wired by handle_completions_impl and evicts the buffer. idempotent, a session that
-        // already finalized or was never created returns 204 either way
+        // DELETE /v1/stream/<conv_id> is the explicit user Stop, cancels the producer and evicts
+        // the buffer. idempotent, returns 204 even if the session was already gone
         std::string conv_id = req.get_param("conv_id");
         if (conv_id.empty()) {
             return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
@@ -536,7 +642,7 @@ server_http_context::handler_t make_stream_delete_handler() {
     };
 }
 
-std::string stream_conv_id_from_headers(const std::map<std::string, std::string> & headers) {
+std::string server_stream_conv_id_from_headers(const std::map<std::string, std::string> & headers) {
     // case-insensitive scan for x-conversation-id
     static constexpr char   target[]   = "x-conversation-id";
     static constexpr size_t target_len = sizeof(target) - 1;
@@ -555,8 +661,8 @@ std::string stream_conv_id_from_headers(const std::map<std::string, std::string>
     return std::string();
 }
 
-void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
-    std::string conversation_id = stream_conv_id_from_headers(headers);
+void server_stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
+    std::string conversation_id = server_stream_conv_id_from_headers(headers);
     SRV_TRC("conv_id=%s (empty=%d)\n", conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
     if (conversation_id.empty()) {
         return;
@@ -565,7 +671,7 @@ void stream_session_attach_pipe(server_http_res & res, const std::map<std::strin
     res.spipe = stream_pipe_producer::create(session, res);
 }
 
-std::function<bool()> stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback) {
+std::function<bool()> server_stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback) {
     return [res, fallback = std::move(fallback)]() -> bool {
         if (res->spipe) {
             return res->spipe->is_cancelled();

tools/server/server-stream.h

@@ -3,81 +3,23 @@
 #include "server-http.h"
 
 #include <atomic>
-#include <condition_variable>
 #include <cstddef>
-#include <cstdint>
 #include <functional>
 #include <memory>
-#include <mutex>
-#include <shared_mutex>
 #include <string>
-#include <thread>
-#include <unordered_map>
-#include <vector>
 
-enum class stream_read_status {
-    OK,
-    OFFSET_LOST,
-};
+// streaming buffer for one generation, survives HTTP disconnect. the producer appends SSE bytes,
+// readers drain from any offset via read_from. keyed by conversation_id, one conv = one live session
 
-// streaming buffer for one generation, survives HTTP disconnect. the producer appends raw SSE
-// bytes, readers drain from any offset via read_from and block until more bytes or finalize.
-// keyed by conversation_id: one conv = at most one live session
-struct stream_session {
-    std::string conversation_id;
-    int64_t     started_ts; // unix seconds at construction, used by /v1/streams listing
-
-    stream_session(std::string conversation_id_, size_t max_bytes_);
-    stream_session(const stream_session &)             = delete;
-    stream_session & operator=(const stream_session &) = delete;
-
-    // append raw bytes, drops from the front if the cap is reached.
-    // returns false if the session is already finalized
-    bool append(const char * data, size_t len);
-
-    // mark the session as complete, wakes all pending readers
-    void finalize();
-
-    // drain bytes from offset, calling sink for each chunk. blocks until more
-    // bytes arrive or finalize is called. returns OK on clean exit, OFFSET_LOST
-    // if offset falls below the dropped prefix
-    stream_read_status read_from(size_t offset,
-        const std::function<bool(const char *, size_t)> & sink,
-        const std::function<bool()> & should_stop);
-
-    bool    is_done() const;
-    bool    is_cancelled() const;
-    size_t  total_size() const;     // bytes that ever entered the session
-    size_t  dropped_prefix() const; // bytes evicted from the front due to cap
-    int64_t completed_at() const;   // 0 while alive, unix seconds after finalize
-
-    // attach the producer stop hook used to cancel its reader, pass an empty function to detach
-    void set_stop_producer(std::function<void()> fn);
-
-    // signal the producer to abort its inference asap via the stop hook, idempotent
-    void cancel();
-
-private:
-    mutable std::mutex      mu;
-    std::condition_variable cv;
-    std::vector<char>       buffer;
-    size_t                  prefix_dropped;
-    size_t                  cap_bytes;
-    std::atomic<bool>       done;
-    std::atomic<bool>       cancelled;
-    std::atomic<int64_t>    completed_ts;
-    std::function<void()>   stop_producer; // protected by mu
-};
+struct stream_session;
 
 using stream_session_ptr = std::shared_ptr<stream_session>;
 
-// one end of a stream_session pipe. the base holds the session and the shared query, the
-// producer and consumer ends derive from it. virtual dtor so each end runs its own teardown:
+// base of the producer/consumer pipe ends. virtual dtor so each runs its own teardown:
 // the producer finalizes the session, the consumer leaves it untouched
 struct stream_pipe {
     virtual ~stream_pipe() = default;
 
-    // true if the session was cancelled (e.g. via DELETE /v1/stream/<conv_id>)
     bool is_cancelled() const;
 
 protected:
@@ -95,7 +37,6 @@ protected:
 struct stream_pipe_producer : stream_pipe {
     ~stream_pipe_producer() override;
 
-    // append raw bytes to the session's ring buffer, returns false if already finalized
     bool write(const char * data, size_t len);
 
     // mark the natural end on the wire so a later close() is a no-op
@@ -121,83 +62,21 @@ private:
     server_http_res *                   res_ = nullptr;
 };
 
-// consumer end: read-only replay of the ring buffer, the destructor does not finalize the session
-struct stream_pipe_consumer : stream_pipe {
-    // drain bytes from offset, calling sink for each available chunk. blocks until more data
-    // arrives or the session finalizes. should_stop is polled, returns OFFSET_LOST if offset
-    // fell below the dropped prefix
-    stream_read_status read(size_t & offset,
-        const std::function<bool(const char *, size_t)> & sink,
-        const std::function<bool()> & should_stop);
+void server_stream_session_manager_start();
+void server_stream_session_manager_stop();
 
-    static std::shared_ptr<stream_pipe_consumer> create(stream_session_ptr session);
+// route handler factories wired under /v1/stream/* by server.cpp
+server_http_context::handler_t server_stream_make_get_handler();
+server_http_context::handler_t server_stream_make_lookup_handler();
+server_http_context::handler_t server_stream_make_delete_handler();
 
-private:
-    explicit stream_pipe_consumer(stream_session_ptr session);
-};
+// extract the X-Conversation-Id header value (case-insensitive), empty when absent
+std::string server_stream_conv_id_from_headers(const std::map<std::string, std::string> & headers);
 
-// owns all live sessions, runs a periodic GC to evict expired ones.
-// the map is keyed by conversation_id, so the invariant "one conv = at most one
-// live session" is enforced at the type level
-class stream_session_manager {
-public:
-    stream_session_manager();
-    ~stream_session_manager();
-
-    stream_session_manager(const stream_session_manager &)             = delete;
-    stream_session_manager & operator=(const stream_session_manager &) = delete;
-
-    // install a new session for this conversation, evicting and cancelling any previous one.
-    // the conversation_id must be non empty, the caller is responsible for that check.
-    // returns the new session
-    stream_session_ptr create_or_replace(const std::string & conversation_id);
-
-    // lookup, returns null if unknown or already evicted
-    stream_session_ptr get(const std::string & conversation_id);
-
-    // list every live or recently completed session, used by GET /v1/streams without filter
-    std::vector<stream_session_ptr> list_all() const;
-
-    // remove from the map and finalize, wakes any pending readers
-    void evict(const std::string & conversation_id);
-
-    // signal the producer to cancel asap then evict, used by the explicit user Stop path
-    void evict_and_cancel(const std::string & conversation_id);
-
-    void start_gc();
-    void stop_gc();
-
-private:
-    void gc_loop();
-
-    mutable std::shared_mutex                           map_mu;
-    std::unordered_map<std::string, stream_session_ptr> sessions; // key: conversation_id
-    std::thread                                         gc_thread;
-    std::atomic<bool>                                   running;
-    std::mutex                                          gc_wake_mu;
-    std::condition_variable                             gc_wake_cv;
-};
-
-// process wide manager, linked by both llama-server and llama-cli. llama-server main() drives
-// start_gc/stop_gc, llama-cli leaves it idle. the dtor calls stop_gc() unconditionally so exit
-// is safe whether or not the GC thread ran
-extern stream_session_manager g_stream_sessions;
-
-// route handler factories operating on g_stream_sessions, wired under /v1/stream/* by server.cpp.
-// keeps the resumable stream surface confined to server-stream
-server_http_context::handler_t make_stream_get_handler();
-server_http_context::handler_t make_streams_lookup_handler();
-server_http_context::handler_t make_stream_delete_handler();
-
-// extract the X-Conversation-Id header value (case-insensitive), empty when absent. exposed so
-// the router can track which child serves a forwarded POST
-std::string stream_conv_id_from_headers(const std::map<std::string, std::string> & headers);
-
-// on an X-Conversation-Id header, create or replace the session and attach a producer pipe to
-// res. no-op when absent, called from the server_res_generator constructor
-void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers);
+// on an X-Conversation-Id header, create or replace the session and attach a producer pipe to res
+void server_stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers);
 
 // should_stop closure that ignores peer disconnect when a pipe is attached, so only an explicit
 // DELETE stops the producer and generation keeps flowing into the ring buffer. without a pipe it
 // delegates to fallback, the legacy non-resumable flow
-std::function<bool()> stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback);
+std::function<bool()> server_stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback);

tools/server/server-task.cpp

@@ -730,6 +730,10 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
         }}
     });
 
+    if (timings.prompt_n >= 0) {
+        server_sent_events.back().at("data").push_back({"timings", timings.to_json()});
+    }
+
     return server_sent_events;
 }
 
@@ -1016,6 +1020,7 @@ void server_task_result_cmpl_partial::update(task_result_state & state) {
     thinking_block_started = state.thinking_block_started;
     text_block_started     = state.text_block_started;
 
+    oai_resp_created       = state.oai_resp_created;
     oai_resp_id            = state.oai_resp_id;
     oai_resp_reasoning_id  = state.oai_resp_reasoning_id;
     oai_resp_message_id    = state.oai_resp_message_id;
@@ -1024,6 +1029,10 @@ void server_task_result_cmpl_partial::update(task_result_state & state) {
     // track if the accumulated message has any reasoning content
     anthropic_has_reasoning = !state.chat_msg.reasoning_content.empty();
 
+    if (res_type == TASK_RESPONSE_TYPE_OAI_RESP && !state.oai_resp_created && (is_progress || n_decoded == 1)) {
+        state.oai_resp_created = true;
+    }
+
     // Pre-compute state updates based on diffs (for next chunk)
     for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
         if (!diff.reasoning_content_delta.empty() && !state.thinking_block_started) {
@@ -1181,7 +1190,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
 json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
     std::vector<json> events;
 
-    if (n_decoded == 1) {
+    if (!oai_resp_created) {
         events.push_back(json {
             {"event", "response.created"},
             {"data", json {
@@ -1204,6 +1213,18 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
                 }},
             }},
         });
+    } else if (is_progress) {
+        events.push_back(json {
+            {"event", "response.in_progress"},
+            {"data", json {
+                {"type", "response.in_progress"},
+                {"response", json {
+                    {"id",     oai_resp_id},
+                    {"object", "response"},
+                    {"status", "in_progress"},
+                }},
+            }},
+        });
     }
 
     for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
@@ -1302,6 +1323,17 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
             });
         }
     }
+
+    if (!events.empty()) {
+        json & data = events.back().at("data");
+        if (timings.prompt_n >= 0) {
+            data.push_back({"timings", timings.to_json()});
+        }
+        if (is_progress) {
+            data.push_back({"prompt_progress", progress.to_json()});
+        }
+    }
+
     return events;
 }
 
@@ -1631,7 +1663,22 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
         }
     }
 
-    // next, remove any cached prompts that are fully contained in the current prompt
+    // calculate checkpoints size to see if it will fit with the prompt
+    size_t checkpoints_size = 0;
+    for (const auto & ckpt : prompt.checkpoints) {
+        checkpoints_size += ckpt.size();
+    }
+
+    const size_t state_size_new = state_size_tgt + state_size_dft + checkpoints_size;
+
+    // skip over-limit entries to avoid disturbing the cache
+    if (limit_size > 0 && state_size_new > limit_size) {
+        SRV_WRN(" - prompt state size %.3f MiB exceeds cache size limit %.3f MiB, skipping\n",
+                state_size_new / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0));
+        return nullptr;
+    }
+
+    // remove any cached prompts that are fully contained in the current prompt
     for (auto it = states.begin(); it != states.end();) {
         const int len = it->tokens.get_common_prefix(prompt.tokens);
 
@@ -1644,6 +1691,16 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
         }
     }
 
+    if (limit_size > 0) {
+        // make room before allocating the new vectors to avoid breaching the limit
+        while (!states.empty() && size() + state_size_new > limit_size) {
+            SRV_WRN(" - making room for prompt cache entry, removing oldest entry (size = %.3f MiB)\n",
+                    states.front().size() / (1024.0 * 1024.0));
+
+            states.pop_front();
+        }
+    }
+
     std::vector<uint8_t> state_data_tgt;
     std::vector<uint8_t> state_data_dft;
 
@@ -1752,12 +1809,7 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
 
 void server_prompt_cache::update() {
     if (limit_size > 0) {
-        // always keep at least one state, regardless of the limits
-        while (states.size() > 1 && size() > limit_size) {
-            if (states.empty()) {
-                break;
-            }
-
+        while (!states.empty() && size() > limit_size) {
             SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0));
 
             states.pop_front();
@@ -1771,11 +1823,7 @@ void server_prompt_cache::update() {
     const size_t limit_tokens_cur = limit_size > 0 ? std::max<size_t>(limit_tokens, limit_size/size_per_token) : limit_tokens;
 
     if (limit_tokens > 0) {
-        while (states.size() > 1 && n_tokens() > limit_tokens_cur) {
-            if (states.empty()) {
-                break;
-            }
-
+        while (!states.empty() && n_tokens() > limit_tokens_cur) {
             SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n",
                     limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0));
 

tools/server/server-task.h

@@ -117,6 +117,7 @@ struct task_result_state {
     bool text_block_started = false;
 
     // for OpenAI Responses streaming API
+    bool oai_resp_created = false;
     const std::string oai_resp_id;
     const std::string oai_resp_reasoning_id;
     const std::string oai_resp_message_id;
@@ -440,6 +441,7 @@ struct server_task_result_cmpl_partial : server_task_result {
     bool text_block_started     = false;
 
     // for OpenAI Responses API
+    bool oai_resp_created = false;
     std::string oai_resp_id;
     std::string oai_resp_reasoning_id;
     std::string oai_resp_message_id;

tools/server/server.cpp

@@ -36,6 +36,19 @@ static inline void signal_handler(int signal) {
     shutdown_handler(signal);
 }
 
+// satisfies -Wmissing-declarations (used by llama command)
+int llama_server(int argc, char ** argv);
+
+// to be used via CLI (argc / argv are used by router mode only)
+int llama_server(common_params & params, int argc, char ** argv);
+void llama_server_terminate();
+void llama_server_terminate() {
+    if (shutdown_handler) {
+        shutdown_handler(0);
+    }
+}
+
+
 // wrapper function that handles exceptions and logs errors
 // this is to make sure handler_t never throws exceptions; instead, it returns an error response
 static server_http_context::handler_t ex_wrapper(server_http_context::handler_t func) {
@@ -72,9 +85,6 @@ static server_http_context::handler_t ex_wrapper(server_http_context::handler_t
     };
 }
 
-// satisfies -Wmissing-declarations
-int llama_server(int argc, char ** argv);
-
 int llama_server(int argc, char ** argv) {
     std::setlocale(LC_NUMERIC, "C");
 
@@ -85,7 +95,7 @@ int llama_server(int argc, char ** argv) {
 
     // start the stream session manager GC right after common init, before any HTTP route can
     // touch it. lifecycle is symmetric, stop_gc() runs in clean_up() before backend free
-    g_stream_sessions.start_gc();
+    server_stream_session_manager_start();
 
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
         return 1;
@@ -94,16 +104,26 @@ int llama_server(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
+    return llama_server(params, argc, argv);
+}
+
+int llama_server(common_params & params, int argc, char ** argv) {
+    bool is_run_by_cli = (argv == nullptr);
+
     common_models_handler models_handler;
-    try {
-        models_handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
-        if (common_models_handler_is_preset_repo(models_handler)) {
-            // apply the preset and start the server in router mode
-            common_models_handler_apply(models_handler, params);
+
+    // note: router mode also accepts -hf remote-preset, so we need to check that first
+    if (!is_run_by_cli && !params.model.hf_repo.empty()) {
+        try {
+            models_handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
+            if (common_models_handler_is_preset_repo(models_handler)) {
+                // apply the preset and start the server in router mode
+                common_models_handler_apply(models_handler, params);
+            }
+        } catch (const std::exception & e) {
+            SRV_ERR("failed to fetch model metadata: %s\n", e.what());
+            return 1;
         }
-    } catch (const std::exception & e) {
-        SRV_ERR("failed to fetch model metadata: %s\n", e.what());
-        return 1;
     }
 
     // router server never loads a model and must not touch the GPU
@@ -245,8 +265,8 @@ int llama_server(int argc, char ** argv) {
     ctx_http.post("/slots/:id_slot",           ex_wrapper(routes.post_slots));
 
     // resumable streaming, the conversation_id is the session identity end to end. router and
-    // child wire different handlers under the same paths: a child binds the local g_stream_sessions
-    // backed factories, the router binds proxies that resolve the owning child through the
+    // child wire different handlers under the same paths: a child binds the local session
+    // factories, the router binds proxies that resolve the owning child through the
     // conv_id -> model map
     server_http_context::handler_t stream_get_h;
     server_http_context::handler_t streams_lookup_h;
@@ -256,9 +276,9 @@ int llama_server(int argc, char ** argv) {
         streams_lookup_h = models_routes->router_streams_lookup;
         stream_delete_h  = models_routes->router_stream_delete;
     } else {
-        stream_get_h     = make_stream_get_handler();
-        streams_lookup_h = make_streams_lookup_handler();
-        stream_delete_h  = make_stream_delete_handler();
+        stream_get_h     = server_stream_make_get_handler();
+        streams_lookup_h = server_stream_make_lookup_handler();
+        stream_delete_h  = server_stream_make_delete_handler();
     }
     ctx_http.get ("/v1/stream/:conv_id",       ex_wrapper(stream_get_h));
     // POST /v1/streams/lookup with body {"conversation_ids": [...]}. you can only ask for ids
@@ -321,8 +341,9 @@ int llama_server(int argc, char ** argv) {
 
     if (child.is_child() && child.get_mode() == SERVER_CHILD_MODE_DOWNLOAD) {
         return child.run_download(params);
-    } else if (!is_router_server) {
+    } else if (!is_router_server && !is_run_by_cli) {
         // single-model mode (NOT spawned by router)
+        // if this is invoked by CLI, model downloading should be already handled
         try {
             common_models_handler_apply(models_handler, params);
         } catch (const std::exception & e) {
@@ -343,7 +364,7 @@ int llama_server(int argc, char ** argv) {
         clean_up = [&models_routes]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
             // stop the session GC first, it finalizes live sessions and wakes pending readers
-            g_stream_sessions.stop_gc();
+            server_stream_session_manager_stop();
             if (models_routes.has_value()) {
                 models_routes->stopping.store(true); // maybe redundant, but just to be safe
                 models_routes->models.unload_all();
@@ -371,7 +392,7 @@ int llama_server(int argc, char ** argv) {
         clean_up = [&ctx_http, &ctx_server]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
             // stop the session GC first, it finalizes live sessions and wakes pending readers
-            g_stream_sessions.stop_gc();
+            server_stream_session_manager_stop();
             ctx_http.stop();
             ctx_server.terminate();
             llama_backend_free();
@@ -411,20 +432,22 @@ int llama_server(int argc, char ** argv) {
         };
     }
 
-    // TODO: refactor in common/console
+    // register signal handler if not running by CLI
+    if (!is_run_by_cli) {
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
-    struct sigaction sigint_action;
-    sigint_action.sa_handler = signal_handler;
-    sigemptyset (&sigint_action.sa_mask);
-    sigint_action.sa_flags = 0;
-    sigaction(SIGINT, &sigint_action, NULL);
-    sigaction(SIGTERM, &sigint_action, NULL);
+        struct sigaction sigint_action;
+        sigint_action.sa_handler = signal_handler;
+        sigemptyset (&sigint_action.sa_mask);
+        sigint_action.sa_flags = 0;
+        sigaction(SIGINT, &sigint_action, NULL);
+        sigaction(SIGTERM, &sigint_action, NULL);
 #elif defined (_WIN32)
-    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
-        return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
-    };
-    SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+        auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+            return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
+        };
+        SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
+    }
 
     SRV_INF("listening on %s\n", ctx_http.listening_address.c_str());
 

tools/server/tests/unit/test_compat_oai_responses.py

@@ -71,3 +71,44 @@ def test_responses_stream_with_openai_library():
             assert r.response.output[0].id.startswith("msg_")
             assert gathered_text == r.response.output_text
             assert match_regex("(Suddenly)+", r.response.output_text)
+
+
+def test_responses_stream_with_llama_telemetry():
+    global server
+    server.n_ctx = 256
+    server.n_batch = 32
+    server.n_slots = 1
+    server.start()
+
+    saw_progress = False
+    saw_delta_timings = False
+    completed = None
+
+    res = server.make_stream_request("POST", "/responses", data={
+        "input": "This is a test" * 10,
+        "max_output_tokens": 8,
+        "temperature": 0.8,
+        "stream": True,
+        "timings_per_token": True,
+        "return_progress": True,
+    })
+
+    for data in res:
+        if "prompt_progress" in data:
+            assert data["type"] == "response.in_progress"
+            assert data["prompt_progress"]["total"] > 0
+            assert data["prompt_progress"]["processed"] >= data["prompt_progress"]["cache"]
+            saw_progress = True
+        if "timings" in data:
+            assert "prompt_per_second" in data["timings"]
+            assert "predicted_per_second" in data["timings"]
+            if data["type"] == "response.output_text.delta":
+                saw_delta_timings = True
+        if data["type"] == "response.completed":
+            completed = data
+
+    assert saw_progress
+    assert saw_delta_timings
+    assert completed is not None
+    assert "usage" in completed["response"]
+    assert "timings" in completed

tools/ui/package-lock.json

@@ -11,7 +11,7 @@
 				"@chromatic-com/storybook": "5.0.0",
 				"@eslint/compat": "1.4.1",
 				"@eslint/js": "9.39.2",
-				"@internationalized/date": "3.10.1",
+				"@internationalized/date": "3.12.2",
 				"@lucide/svelte": "0.515.0",
 				"@modelcontextprotocol/sdk": "1.26.0",
 				"@playwright/test": "1.56.1",
@@ -2981,9 +2981,9 @@
 			}
 		},
 		"node_modules/@internationalized/date": {
-			"version": "3.10.1",
-			"resolved": "https://registry.npmjs.org/@internationalized/date/-/date-3.10.1.tgz",
-			"integrity": "sha512-oJrXtQiAXLvT9clCf1K4kxp3eKsQhIaZqxEyowkBcsvZDdZkbWrVmnGknxs5flTD0VGsxrxKgBCZty1EzoiMzA==",
+			"version": "3.12.2",
+			"resolved": "https://registry.npmjs.org/@internationalized/date/-/date-3.12.2.tgz",
+			"integrity": "sha512-FY1Y+H64NDs+HAF6omlnWxm3mEpfgaCSWtL5l551ZZfImA+kGjPFgrnJrGjH6lfmLL0g8Z/mBu1R3kufeCp6Jw==",
 			"dev": true,
 			"license": "Apache-2.0",
 			"dependencies": {

tools/ui/package.json

@@ -30,7 +30,7 @@
 		"@chromatic-com/storybook": "5.0.0",
 		"@eslint/compat": "1.4.1",
 		"@eslint/js": "9.39.2",
-		"@internationalized/date": "3.10.1",
+		"@internationalized/date": "3.12.2",
 		"@lucide/svelte": "0.515.0",
 		"@modelcontextprotocol/sdk": "1.26.0",
 		"@playwright/test": "1.56.1",

tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddDropdown.svelte

@@ -11,7 +11,8 @@
 	} from '$lib/constants';
 	import {
 		ChatFormActionAddToolsSubmenu,
-		ChatFormActionAddMcpServersSubmenu
+		ChatFormActionAddMcpServersSubmenu,
+		ChatFormActionAddReasoningSubmenu
 	} from '$lib/components/app';
 	import { useAttachmentMenu } from '$lib/hooks/use-attachment-menu.svelte';
 
@@ -92,7 +93,11 @@
 			</Tooltip.Content>
 		</Tooltip.Root>
 
-		<DropdownMenu.Content align="start" class="w-48">
+		<DropdownMenu.Content align="start" class="w-52">
+			<ChatFormActionAddReasoningSubmenu />
+
+			<DropdownMenu.Separator />
+
 			<DropdownMenu.Sub>
 				<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
 					<File class="h-4 w-4" />