hexagon: fix typo in vtcm_needs_release (#19545)

* opencl: add q4_1 mv

* opencl: clean up

* opencl: add flattened q4_1 mv

* opencl: clean up

* opencl: add basic q4_1 mm

* opencl: fix whitespace

* opencl: add general q4_0 mm

.github/workflows/server-metal.yml

@@ -0,0 +1,73 @@
+name: Server-Metal
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server-metal.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
+
+env:
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+  LLAMA_LOG_VERBOSITY: 10
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  server-metal:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    name: server-metal (${{ matrix.wf_name }})
+    strategy:
+      matrix:
+        build_type: [Release]
+        wf_name: ["GPUx1"]
+        include:
+          - build_type: Release
+            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
+            wf_name:    "GPUx1, backend-sampling"
+          - build_type: Release
+            extra_args: "GGML_METAL_DEVICES=2"
+            wf_name:    "GPUx2"
+          - build_type: Release
+            extra_args: "GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1"
+            wf_name:    "GPUx2, backend-sampling"
+      fail-fast: false
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build -DGGML_SCHED_NO_REALLOC=ON
+          cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
+        run: |
+          cd tools/server/tests
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+          export ${{ matrix.extra_args }}
+          pytest -v -x -m "not slow"

CONTRIBUTING.md

@@ -20,7 +20,7 @@ If AI is used to generate any portion of the code, contributors must adhere to t
 1. Explicitly disclose the manner in which AI was employed.
 2. Perform a comprehensive manual review prior to submitting the pull request.
 3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
+4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
 
 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
 

README.md

@@ -288,6 +288,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [WebGPU [In Progress]](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 | [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
+| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
 
 ## Obtaining and quantizing models
 

build-xcframework.sh

@@ -534,7 +534,7 @@ xcodebuild -create-xcframework \
     -framework $(pwd)/build-ios-device/framework/llama.framework \
     -debug-symbols $(pwd)/build-ios-device/dSYMs/llama.dSYM \
     -framework $(pwd)/build-macos/framework/llama.framework \
-    -debug-symbols $(pwd)/build-macos/dSYMS/llama.dSYM \
+    -debug-symbols $(pwd)/build-macos/dSYMs/llama.dSYM \
     -framework $(pwd)/build-visionos/framework/llama.framework \
     -debug-symbols $(pwd)/build-visionos/dSYMs/llama.dSYM \
     -framework $(pwd)/build-visionos-sim/framework/llama.framework \

common/arg.cpp

@@ -1301,7 +1301,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.kv_unified = value;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH}));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -3437,16 +3437,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.ngram_size_m = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(common_arg(
-        {"--spec-ngram-check-rate"}, "N",
-        string_format("ngram check rate for ngram-simple/ngram-map speculative decoding (default: %d)", params.speculative.ngram_check_rate),
-        [](common_params & params, int value) {
-            if (value < 1) {
-                throw std::invalid_argument("ngram check rate must be at least 1");
-            }
-            params.speculative.ngram_check_rate = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--spec-ngram-min-hits"}, "N",
         string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits),

common/chat.cpp

@@ -380,15 +380,46 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
     return msgs;
 }
 
-json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
+static json render_message_to_json(const std::vector<common_chat_msg> & msgs, const jinja::caps & c) {
+    if (!c.supports_string_content && !c.supports_typed_content) {
+        LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
+    }
+
+    bool only_string_accepted =  c.supports_string_content && !c.supports_typed_content;
+    bool only_typed_accepted  = !c.supports_string_content &&  c.supports_typed_content;
+
     json messages = json::array();
     for (const auto & msg : msgs) {
-        json jmsg = msg.to_json_oaicompat(concat_typed_text);
-        messages.push_back(jmsg);
+        if (only_string_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
+            messages.push_back(jmsg);
+        } else if (only_typed_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            if (jmsg.at("content").is_string()) {
+                jmsg["content"] = json::array({
+                    json{
+                        {"type", "text"},
+                        {"text", jmsg.at("content").get<std::string>()},
+                    }
+                });
+            }
+            messages.push_back(jmsg);
+        } else {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            messages.push_back(jmsg);
+        }
     }
     return messages;
 }
 
+// DEPRECATED: only used in tests
+json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
+    jinja::caps c;
+    c.supports_string_content = true;
+    c.supports_typed_content = !concat_typed_text;
+    return render_message_to_json(msgs, c);
+}
+
 std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & tools) {
     std::vector<common_chat_tool> result;
 
@@ -3020,7 +3051,7 @@ static common_chat_params common_chat_templates_apply_jinja(
         : *tmpls->template_default;
     const auto & src = tmpl.source();
     const auto & caps = tmpl.original_caps();
-    params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content);
+    params.messages = render_message_to_json(inputs.messages, tmpl.original_caps());
     params.add_generation_prompt = inputs.add_generation_prompt;
     params.tool_choice = inputs.tool_choice;
     params.reasoning_format = inputs.reasoning_format;

common/chat.h

@@ -240,6 +240,8 @@ bool common_chat_templates_support_enable_thinking(const common_chat_templates *
 
 // Parses a JSON array of messages in OpenAI's chat completion API format.
 std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);
+
+// DEPRECATED: only used in tests
 nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
 
 std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);

common/common.cpp

@@ -1,7 +1,3 @@
-#if defined(_MSC_VER)
-#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
-#endif
-
 #include "ggml.h"
 #include "gguf.h"
 
@@ -9,12 +5,12 @@
 #include "log.h"
 #include "llama.h"
 #include "sampling.h"
+#include "unicode.h"
 
 #include <algorithm>
 #include <cinttypes>
 #include <climits>
 #include <cmath>
-#include <codecvt>
 #include <chrono>
 #include <cstdarg>
 #include <cstring>
@@ -706,45 +702,28 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
         return false;
     }
 
-    std::u32string filename_utf32;
-    try {
-#if defined(__clang__)
-        // disable C++17 deprecation warning for std::codecvt_utf8
-#    pragma clang diagnostic push
-#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
-#elif defined(__GNUC__)
-#    pragma GCC diagnostic push
-#    pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#endif
+    size_t offset = 0;
+    while (offset < filename.size()) {
+        utf8_parse_result result = parse_utf8_codepoint(filename, offset);
 
-        std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
-
-#if defined(__clang__)
-#    pragma clang diagnostic pop
-#elif defined(__GNUC__)
-#    pragma GCC diagnostic pop
-#endif
-
-        filename_utf32 = converter.from_bytes(filename);
-
-        // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
-        // or invalid encodings were encountered. Reject such attempts
-        std::string filename_reencoded = converter.to_bytes(filename_utf32);
-        if (filename_reencoded != filename) {
+        if (result.status != utf8_parse_result::SUCCESS) {
             return false;
         }
-    } catch (const std::exception &) {
-        return false;
-    }
+        uint32_t c = result.codepoint;
 
-    // Check for forbidden codepoints:
-    // - Control characters
-    // - Unicode equivalents of illegal characters
-    // - UTF-16 surrogate pairs
-    // - UTF-8 replacement character
-    // - Byte order mark (BOM)
-    // - Illegal characters: / \ : * ? " < > |
-    for (char32_t c : filename_utf32) {
+        if ((result.bytes_consumed == 2 && c < 0x80) ||
+            (result.bytes_consumed == 3 && c < 0x800) ||
+            (result.bytes_consumed == 4 && c < 0x10000)) {
+            return false;
+        }
+
+        // Check for forbidden codepoints:
+        // - Control characters
+        // - Unicode equivalents of illegal characters
+        // - UTF-16 surrogate pairs
+        // - UTF-8 replacement character
+        // - Byte order mark (BOM)
+        // - Illegal characters: / \ : * ? " < > |
         if (c <= 0x1F // Control characters (C0)
             || c == 0x7F // Control characters (DEL)
             || (c >= 0x80 && c <= 0x9F) // Control characters (C1)
@@ -752,6 +731,7 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
             || c == 0x2215 // Division Slash (forward slash equivalent)
             || c == 0x2216 // Set Minus (backslash equivalent)
             || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
+            || c > 0x10FFFF // Max Unicode limit
             || c == 0xFFFD // Replacement Character (UTF-8)
             || c == 0xFEFF // Byte Order Mark (BOM)
             || c == ':' || c == '*' // Illegal characters
@@ -762,6 +742,7 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
             // Subdirectories not allowed, reject path separators
             return false;
         }
+        offset += result.bytes_consumed;
     }
 
     // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
@@ -1469,66 +1450,6 @@ void common_batch_add(
     batch.n_tokens++;
 }
 
-//
-// Token utils
-//
-
-size_t common_lcp(const llama_tokens & a, const llama_tokens & b) {
-    size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
-
-    return i;
-}
-
-size_t common_lcs(const llama_tokens & a, const llama_tokens & b) {
-    // check for empty sequences
-    if (a.empty() || b.empty()) {
-        return 0;
-    }
-
-    // get the lengths of the input sequences
-    size_t a_len = a.size();
-    size_t b_len = b.size();
-
-    // initialize the maximum length of the longest common subsequence (LCS)
-    size_t max_length = 0;
-
-    // use two rows instead of a 2D matrix to optimize space
-    std::vector<size_t> prev_row(b_len + 1, 0);
-    std::vector<size_t> curr_row(b_len + 1, 0);
-
-    // iterate through the elements of a
-    for (size_t i = 1; i <= a_len; i++) {
-        // iterate through the elements of b
-        for (size_t j = 1; j <= b_len; j++) {
-            // if elements at the current positions match
-            if (a[i - 1] == b[j - 1]) {
-                // if it's the first element of either sequences, set LCS length to 1
-                if (i == 1 || j == 1) {
-                    curr_row[j] = 1;
-                } else {
-                    // increment LCS length by 1 compared to the previous element
-                    curr_row[j] = prev_row[j - 1] + 1;
-                }
-
-                // update max_length if necessary
-                if (curr_row[j] > max_length) {
-                    max_length = curr_row[j];
-                }
-            } else {
-                // reset LCS length if elements don't match
-                curr_row[j] = 0;
-            }
-        }
-
-        // update the previous row for the next iteration
-        prev_row = curr_row;
-    }
-
-    // return the maximum length of the LCS
-    return max_length;
-}
-
 //
 // Vocab utils
 //

common/common.h

@@ -269,7 +269,6 @@ struct common_params_speculative {
 
     uint16_t ngram_size_n     = 12; // ngram size for lookup
     uint16_t ngram_size_m     = 48; // mgram size for speculative tokens
-    uint16_t ngram_check_rate =  1; // check rate for ngram lookup
     uint16_t ngram_min_hits   =  1; // minimum hits at ngram/mgram lookup for mgram to be proposed
 
     std::shared_ptr<common_ngram_mod> ngram_mod;
@@ -780,16 +779,6 @@ void common_batch_add(
     const std::vector<llama_seq_id> & seq_ids,
                                bool   logits);
 
-//
-// Token utils
-//
-
-// longest common prefix
-size_t common_lcp(const llama_tokens & a, const llama_tokens & b);
-
-// longet common subsequence
-size_t common_lcs(const llama_tokens & a, const llama_tokens & b);
-
 //
 // Vocab utils
 //

common/download.cpp

@@ -305,7 +305,10 @@ static bool common_pull_file(httplib::Client & cli,
     );
 
     if (!res) {
-        LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1);
+        LOG_ERR("%s: download failed: %s (status: %d)\n",
+                __func__,
+                httplib::to_string(res.error()).c_str(),
+                res ? res->status : -1);
         return false;
     }
 

common/jinja/caps.cpp

@@ -63,7 +63,8 @@ static void caps_print_stats(value & v, const std::string & path) {
 
 std::map<std::string, bool> caps::to_map() const {
     return {
-        {"requires_typed_content", requires_typed_content},
+        {"supports_string_content", supports_string_content},
+        {"supports_typed_content", supports_typed_content},
         {"supports_tools", supports_tools},
         {"supports_tool_calls", supports_tool_calls},
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
@@ -89,7 +90,7 @@ caps caps_get(jinja::program & prog) {
         return v->stats.ops.find(op_name) != v->stats.ops.end();
     };
 
-    // case: typed content requirement
+    // case: typed content support
     caps_try_execute(
         prog,
         [&]() {
@@ -105,12 +106,16 @@ caps caps_get(jinja::program & prog) {
             // tools
             return json{nullptr};
         },
-        [&](bool, value & messages, value &) {
+        [&](bool success, value & messages, value &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (has_op(content, "selectattr") || has_op(content, "array_access")) {
                 // accessed as an array
-                result.requires_typed_content = true;
+                result.supports_typed_content = true;
+            }
+            if (!success) {
+                // failed to execute with content as string
+                result.supports_string_content = false;
             }
         }
     );

common/jinja/caps.h

@@ -14,7 +14,9 @@ struct caps {
     bool supports_parallel_tool_calls = true;
     bool supports_preserve_reasoning = false; // support assistant message with reasoning_content
 
-    bool requires_typed_content = false; // default: use string content
+    // one of the 2 content capabilities must be true
+    bool supports_string_content = true;
+    bool supports_typed_content = false;
 
     // for reporting on server
     std::map<std::string, bool> to_map() const;

common/jinja/runtime.cpp

@@ -446,6 +446,12 @@ value for_statement::execute_impl(context & ctx) {
 
     value iterable_val = iter_expr->execute(scope);
 
+    // mark the variable being iterated as used for stats
+    if (ctx.is_get_stats) {
+        iterable_val->stats.used = true;
+        iterable_val->stats.ops.insert("array_access");
+    }
+
     if (iterable_val->is_undefined()) {
         JJ_DEBUG("%s", "For loop iterable is undefined, skipping loop");
         iterable_val = mk_val<value_array>();

common/ngram-map.cpp

@@ -231,10 +231,9 @@ void common_ngram_map_draft(common_ngram_map & map,
         GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
     }
 
-    // Only check every check_rate tokens to save compute
-    // i.e., perform check if (cur_len - idx_last_check) >= check_rate
-    if (map.idx_last_check + map.check_rate > cur_len) {
-        return;
+    if (map.idx_last_check  > cur_len) {
+        // Should not happen because of common_ngram_map_begin().
+        GGML_ABORT("%s: map.idx_last_check > cur_len: %zu > %zu", __func__, map.idx_last_check, cur_len);
     }
     map.idx_last_check = cur_len;
 
@@ -462,7 +461,7 @@ void common_ngram_map_draft(common_ngram_map & map,
             slot_max = v;
         }
     }
-    // What is sum of the other occurences?
+    // What is sum of the other occurrences?
     uint32_t sum_occur = 0;
     for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
         if (v == slot_max) {

common/ngram-map.h

@@ -24,7 +24,6 @@
 struct common_ngram_simple_config {
     uint16_t   size_ngram;      // size of n-grams to lookup in self-mode
     uint16_t   size_mgram;      // size of m-grams to draft in self-mode
-    uint16_t   check_rate;      // check for speculative decoding without draft model for each check_rate token
 };
 
 // Searches for a n-gram in the history and checks whether a draft sequence should be generated.
@@ -45,7 +44,7 @@ llama_tokens common_ngram_simple_draft(
 // statistics of a m-gram after a known n-gram
 struct common_ngram_map_value {
     size_t   value_idx =  0;  // index of value m-gram in token-history (0 if unused)
-    uint16_t value_num =  0;  // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot)
+    uint16_t value_num =  0;  // number of occurrences of this value m-gram after the key n-gram (0 in an unused values-slot)
     int16_t n_accepted = -1;  // number of accepted tokens at last draft (-1 if unused)
 };
 
@@ -54,7 +53,7 @@ struct common_ngram_map_key {
     size_t   key_idx;   // index of key n-gram in token-history
     size_t   stat_idx;  // index of last token of stastistics computation (key_num, values)
 
-    uint16_t key_num;   // number of occurences of this key n-gram in token-history
+    uint16_t key_num;   // number of occurrences of this key n-gram in token-history
     common_ngram_map_value values[COMMON_NGRAM_MAX_VALUES]; // some known values after the key
 };
 
@@ -66,15 +65,14 @@ struct common_ngram_map {
     bool key_only;       // true if only key n-grams are used, no values.
 
     std::vector<common_ngram_map_key> keys; // key n-grams which occur several times in token-history
-    uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token
     uint16_t min_hits;   // minimum number of key hits to consider a draft
 
-    bool     show_key_map_stats = false; // true, if statitics of the key_map should be printed.
+    bool     show_key_map_stats = false; // true, if statistics of the key_map should be printed.
 
     common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys,
-                     uint16_t check_rate, uint16_t min_hits)
+                     uint16_t min_hits)
         : size_key(sz_key), size_value(sz_value), key_only(only_keys),
-          check_rate(check_rate), min_hits(min_hits) {
+          min_hits(min_hits) {
         key_map.resize(COMMON_NGRAM_HASH_MAP_SIZE); // 2^18 hash entries, 0 entries if key_map shouldn't be used
     }
 

common/speculative.cpp

@@ -113,13 +113,14 @@ static bool common_speculative_are_compatible(
 struct common_speculative_state {
     const enum common_speculative_type type;
 
-    // TODO: rename to n_call_draft, n_gen_drafts, n_acc_drafts, n_gen_tokens, n_acc_tokens
-    // TODO: add n_call_begin, n_call_accept
-    size_t drafts_call_count       = 0; // number of times this implementation was called.
-    size_t drafts_generated_count  = 0; // number of times a draft or part was generated by this implementation.
-    size_t drafts_accepted_count   = 0; // number of times a draft or part was accepted by the target model.
-    size_t drafts_generated_tokens = 0; // number of tokens generated by this implementation.
-    size_t drafts_accepted_tokens  = 0; // number of tokens accepted by the target model.
+    size_t n_call_begin  = 0; // number of times this implementation was called for refresh.
+    size_t n_call_draft  = 0; // number of times this implementation was called for generation.
+    size_t n_call_accept = 0; // number of times this implementation was called for accumulation.
+
+    size_t n_gen_drafts = 0; // number of times a draft or part was generated by this implementation.
+    size_t n_acc_drafts = 0; // number of times a draft or part was accepted by the target model.
+    size_t n_gen_tokens = 0; // number of tokens generated by this implementation.
+    size_t n_acc_tokens = 0; // number of tokens accepted by the target model.
 
     // TODO: track performance of most recent calls
     const bool gen_perf = true; // whether to generate performance stats.
@@ -465,8 +466,6 @@ struct common_speculative_state_eagle3 : public common_speculative_state {
 struct common_speculative_state_ngram_simple : public common_speculative_state {
     common_ngram_simple_config config;
 
-    uint16_t check_id = 0; // used to control the frequency of generating drafts
-
     common_speculative_state_ngram_simple(
             enum common_speculative_type type,
             common_ngram_simple_config config)
@@ -481,11 +480,6 @@ struct common_speculative_state_ngram_simple : public common_speculative_state {
             const llama_tokens & prompt_tgt,
             llama_token id_last,
             llama_tokens & result) override {
-        ++check_id;
-        if (check_id < config.check_rate) {
-            return;
-        }
-        check_id = 0;
 
         result = common_ngram_simple_draft(config, prompt_tgt, id_last);
         GGML_UNUSED(params);
@@ -752,10 +746,9 @@ static common_ngram_map get_common_ngram_map(const common_speculative_config & c
     uint16_t size_key   = config.params.ngram_size_n;
     uint16_t size_value = config.params.ngram_size_m;
     bool     key_only   = (config.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
-    uint16_t check_rate = config.params.ngram_check_rate;
     uint16_t min_hits   = config.params.ngram_min_hits;
 
-    return common_ngram_map(size_key, size_value, key_only, check_rate, min_hits);
+    return common_ngram_map(size_key, size_value, key_only, min_hits);
 }
 
 static common_speculative_state_ngram_cache create_state_ngram_cache(
@@ -931,12 +924,10 @@ common_speculative * common_speculative_init(
 
                 uint16_t ngram_size_key   = ngram_map.size_key;
                 uint16_t mgram_size_value = ngram_map.size_value;
-                uint16_t check_rate       = ngram_map.check_rate;
 
                 auto config_simple = common_ngram_simple_config {
                     /* .size_ngram      = */ ngram_size_key,
-                    /* .size_mgram      = */ mgram_size_value,
-                    /* .check_rate      = */ check_rate
+                    /* .size_mgram      = */ mgram_size_value
                 };
                 auto state = std::make_unique<common_speculative_state_ngram_simple>(
                     /* .type            = */ config.type,
@@ -997,6 +988,7 @@ void common_speculative_begin(common_speculative * spec, const llama_tokens & pr
     for (auto & impl : spec->impls) {
         common_time_meas tm(impl->t_begin_us, !impl->gen_perf);
         impl->begin(prompt);
+        impl->n_call_begin++;
     }
 }
 
@@ -1013,17 +1005,17 @@ llama_tokens common_speculative_draft(
         {
             common_time_meas tm(impl->t_draft_us, !impl->gen_perf);
             impl->draft(params, prompt_tgt, id_last, result);
-            impl->drafts_call_count++;
+            impl->n_call_draft++;
         }
 
         if (!result.empty()) {
             LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
                     common_speculative_type_to_str(impl.get()->type).c_str(), prompt_tgt.size(),
-                    impl.get()->drafts_call_count, result.size());
+                    impl.get()->n_call_draft, result.size());
 
             spec->curr_impl = impl.get(); // set current implementation for stats
-            impl->drafts_generated_count++;
-            impl->drafts_generated_tokens += result.size();
+            impl->n_gen_drafts++;
+            impl->n_gen_tokens += result.size();
 
             break; // We have a draft, so break out of the loop and return it.
         }
@@ -1044,11 +1036,12 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
     {
         common_time_meas tm(impl->t_accept_us, !impl->gen_perf);
         if (n_accepted > 0) {
-            impl->drafts_accepted_count++;
-            impl->drafts_accepted_tokens += n_accepted;
+            impl->n_acc_drafts++;
+            impl->n_acc_tokens += n_accepted;
         }
 
         impl->accept(n_accepted);
+        impl->n_call_accept++;
     }
 }
 
@@ -1069,13 +1062,13 @@ void common_speculative_print_stats(const common_speculative * spec) {
             str_perf = "";
         }
 
-        LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
+        LOG_INF("statistics %s: #calls(b,g,a) = %zu %zu %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
                 common_speculative_type_to_str(impl->type).c_str(),
-                impl->drafts_call_count,
-                impl->drafts_generated_count,
-                impl->drafts_accepted_count,
-                impl->drafts_generated_tokens,
-                impl->drafts_accepted_tokens,
+                impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
+                impl->n_gen_drafts,
+                impl->n_acc_drafts,
+                impl->n_gen_tokens,
+                impl->n_acc_tokens,
                 str_perf.c_str());
     }
 }

convert_hf_to_gguf.py

@@ -160,8 +160,6 @@ class ModelBase:
                 self.ftype = gguf.LlamaFileType.MOSTLY_F16
                 logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16")
 
-        self.dequant_model()
-
         # Configure GGUF Writer
         self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
                                            split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
@@ -527,6 +525,8 @@ class ModelBase:
         return ()
 
     def prepare_tensors(self):
+        self.dequant_model()
+
         # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
         if self.tensor_map.mapping:
             max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
@@ -1261,6 +1261,9 @@ class TextModel(ModelBase):
         if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
             # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
             res = "exaone-moe"
+        if chkhsh == "d30d75d9059f1aa2c19359de71047b3ae408c70875e8a3ccf8c5fba56c9d8af4":
+            # ref: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
+            res = "qwen35"
 
         if res is None:
             logger.warning("\n")
@@ -1812,7 +1815,7 @@ class MmprojModel(ModelBase):
     preprocessor_config: dict[str, Any]
     global_config: dict[str, Any]
 
-    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"]
+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers", "vt_num_hidden_layers"]
 
     has_vision_encoder: bool = True # by default
     has_audio_encoder: bool = False
@@ -1867,7 +1870,15 @@ class MmprojModel(ModelBase):
         preprocessor_config_path = self.dir_model / "preprocessor_config.json"
         if preprocessor_config_path.is_file():
             with open(preprocessor_config_path, "r", encoding="utf-8") as f:
-                self.preprocessor_config = json.load(f)
+                cfg = json.load(f)
+                # move media_proc_cfg to root level for compat
+                if "media_proc_cfg" in cfg:
+                    cfg = {
+                        **cfg,
+                        **cfg["media_proc_cfg"],
+                    }
+                # merge configs
+                self.preprocessor_config = {**self.preprocessor_config, **cfg}
 
         # prefer processor_config.json if possible
         processor_config_path = self.dir_model / "processor_config.json"
@@ -1916,10 +1927,10 @@ class MmprojModel(ModelBase):
             self.image_size = self.find_vparam(["image_size"])
             self.gguf_writer.add_vision_image_size(self.image_size)
             self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
-            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"]))
-            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"]))
+            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "vt_hidden_size"]))
+            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
             self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
-            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads"]))
+            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "vt_num_attention_heads"]))
 
             # preprocessor config
             image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
@@ -4102,22 +4113,25 @@ class Qwen2MoeModel(TextModel):
         # process the experts separately
         name = name.replace("language_model.", "") # InternVL
 
-        # handle pre-packed expert tensors (e.g. Qwen3.5 MoE, Qwen3Next)
-        # HF stores these using nn.Linear convention: [n_expert, out_features, in_features]
-        # This matches the individual expert stacking path below (which stacks
-        # per-expert [out, in] weights into [n_expert, out, in]), so no permute is needed.
+        # handle aggregated expert tensors
+        # GGUF stores dimensions reversed from PyTorch, so:
+        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
+        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
+        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
+            # HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
             yield from super().modify_tensors(data_torch, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            # HF: [n_expert, 2*n_ff, n_embd] → split on dim=1
-            n_ff = data_torch.shape[1] // 2
-            gate = data_torch[:, :n_ff, :].contiguous()
-            up = data_torch[:, n_ff:, :].contiguous()
-            # gate/up: [n_expert, n_ff, n_embd] → GGML: {n_embd, n_ff, n_expert} ✓
+            if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            # HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
+            n_ff = data_torch.shape[-2] // 2
+            gate = data_torch[..., :n_ff, :].contiguous()
+            up = data_torch[..., n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
             base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
             mapped_gate = f"{base_name}.gate_proj.weight"
             mapped_up = f"{base_name}.up_proj.weight"
@@ -4128,6 +4142,7 @@ class Qwen2MoeModel(TextModel):
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
             # skip visual tensors
             return
+
         if name.find("experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
@@ -4283,6 +4298,7 @@ class Qwen3NextModel(Qwen2MoeModel):
         self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
         self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
         self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
@@ -4332,40 +4348,6 @@ class Qwen3NextModel(Qwen2MoeModel):
             yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Qwen3_5ForCausalLM", "Qwen3_5TextForCausalLM")
-class Qwen3_5Model(Qwen3NextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3_5
-
-    # Stores whichever of in_proj_a/in_proj_b is seen first, keyed by layer
-    _pending_ba: dict[int | None, tuple[str, Tensor]] = {}
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Handle split in_proj_b + in_proj_a → concatenated SSM_BETA_ALPHA
-        # safetensors sorts alphabetically so in_proj_a arrives before in_proj_b
-        if "in_proj_a.weight" in name or "in_proj_b.weight" in name:
-            which = "a" if "in_proj_a" in name else "b"
-            if bid not in self._pending_ba:
-                self._pending_ba[bid] = (which, data_torch)
-                return
-            prev_which, prev_tensor = self._pending_ba.pop(bid)
-            assert prev_which != which, f"duplicate in_proj_{which} for layer {bid}"
-            b_tensor = prev_tensor if prev_which == "b" else data_torch
-            a_tensor = prev_tensor if prev_which == "a" else data_torch
-            ba_combined = torch.cat([b_tensor, a_tensor], dim=0)
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.SSM_BETA_ALPHA, bid, ".weight"), ba_combined)
-            return
-        else:
-            # Qwen3Next uses .qkvz tensor, so we use the super to get the other functionalities
-            # (norm correction, A_log to A etc.) for free
-            # Qwen2Moe already does the gate_up conversion properly, just use that
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3_5MoeForCausalLM", "Qwen3_5MoeTextForCausalLM")
-class Qwen3_5MoeModel(Qwen3_5Model):
-    model_arch = gguf.MODEL_ARCH.QWEN3_5_MOE
-
-
 @ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.RND1
@@ -4381,7 +4363,7 @@ class RND1Model(Qwen2MoeModel):
             self.gguf_writer.add_mask_token_id(mask_token_id)
 
 
-@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration")
+@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration")
 class Qwen3VLVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -4427,6 +4409,10 @@ class Qwen3VLVisionModel(MmprojModel):
         if name.startswith("model.language_model.") or name.startswith("lm_head."):
             return
 
+        # Skip MTP tensors
+        if name.startswith("mtp."):
+            return
+
         if name.startswith("model.visual."):
             name = name.replace("model.visual.", "visual.", 1)
 
@@ -4557,9 +4543,125 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
         if name.startswith("model.visual."):
             return
 
+        # Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
+        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
+            name = name.replace("language_model.", "")
+            mapped = f"{name}.weight" if not name.endswith(".weight") else name
+            permuted = data_torch.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
+            return
+
+        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
+            name = name.replace("language_model.", "")
+            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            split_dim = data_torch.shape[-1] // 2
+            gate = data_torch[..., :split_dim].contiguous()
+            up = data_torch[..., split_dim:].contiguous()
+            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
+            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
+            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
+            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
+            base_name = name.removesuffix(".weight")
+            base = base_name.rsplit('.', 1)[0]
+            mapped_gate = f"{base}.gate_proj.weight"
+            mapped_up = f"{base}.up_proj.weight"
+            perm_gate = gate.permute(0, 2, 1).contiguous()
+            perm_up = up.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
+            yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
+            return
+
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+class _LinearAttentionVReorderBase(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
+    """reorders V heads from grouped to tiled order for ggml broadcast
+
+    see https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
+
+    Linear attention may has num_k_heads < num_v_heads. The HF weights store
+    V heads grouped by K head: [G0_v0..v{r-1}, G1_v0..v{r-1}, ...].
+    ggml binary ops use tiled broadcast: [K0, K1, ..., K0, K1, ...].
+    We reorder V heads to tiled order so ggml_repeat can replace the expensive
+    interleaved repeat: [G0_v0, G1_v0, ..., G0_v1, G1_v1, ...].
+    """
+
+    @staticmethod
+    def _reorder_v_heads(tensor: Tensor, dim: int, num_k_heads: int, num_v_per_k: int, head_dim: int) -> Tensor:
+        """Reorder V heads from grouped (by K head) to tiled order along the given dimension."""
+        shape = list(tensor.shape)
+        if dim < 0:
+            dim += len(shape)
+        new_shape = shape[:dim] + [num_k_heads, num_v_per_k, head_dim] + shape[dim + 1:]
+        tensor = tensor.reshape(*new_shape)
+        perm = list(range(len(new_shape)))
+        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
+        return tensor.permute(*perm).contiguous().reshape(*shape)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
+        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
+
+        if num_k_heads > 0 and num_v_heads > 0 and num_k_heads != num_v_heads and "linear_attn." in name:
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_per_k = num_v_heads // num_k_heads
+
+            if ".in_proj_qkv." in name:
+                # QKV weight: reorder only the V rows
+                q_dim = head_k_dim * num_k_heads
+                k_dim = head_k_dim * num_k_heads
+                q = data_torch[:q_dim]
+                k = data_torch[q_dim:q_dim + k_dim]
+                v = data_torch[q_dim + k_dim:]
+                v = self._reorder_v_heads(v, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([q, k, v], dim=0)
+
+            elif ".in_proj_z." in name:
+                # Z gate weight: reorder rows (num_v_heads * head_v_dim)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, head_v_dim)
+
+            elif ".in_proj_b." in name or ".in_proj_a." in name:
+                # Beta/Alpha weight: reorder rows (num_v_heads, head_dim=1)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, 1)
+
+            elif ".A_log" in name or ".dt_bias" in name or ".dt_proj" in name:
+                # A_log / dt_bias: 1D parameters with num_v_heads elements
+                if data_torch.ndim == 1:
+                    data_torch = self._reorder_v_heads(
+                        data_torch.unsqueeze(-1), 0, num_k_heads, num_v_per_k, 1
+                    ).squeeze(-1)
+                else:
+                    data_torch = self._reorder_v_heads(data_torch, -1, num_k_heads, num_v_per_k, 1)
+
+            elif ".conv1d" in name:
+                # Conv1d kernel: reorder only the V channel portion
+                data = data_torch.squeeze()
+                qk_channels = head_k_dim * num_k_heads * 2
+                qk_part = data[:qk_channels]
+                v_part = data[qk_channels:]
+                v_part = self._reorder_v_heads(v_part, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([qk_part, v_part], dim=0)
+
+            elif ".out_proj." in name:
+                # Out projection weight: reorder columns (input dimension)
+                data_torch = self._reorder_v_heads(data_torch, 1, num_k_heads, num_v_per_k, head_v_dim)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3_5ForConditionalGeneration")
+class Qwen3_5TextModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35
+
+
+@ModelBase.register("Qwen3_5MoeForConditionalGeneration")
+class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35MOE
+
+
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GPT2
@@ -7601,6 +7703,7 @@ class DeepseekModel(TextModel):
     "DeepseekV2ForCausalLM",
     "DeepseekV3ForCausalLM",
     "KimiVLForConditionalGeneration",
+    "KimiK25ForConditionalGeneration",
     "YoutuForCausalLM",
     "YoutuVLForConditionalGeneration",
 )
@@ -7719,8 +7822,8 @@ class DeepseekV2Model(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # skip vision tensors and remove "language_model." for Kimi-VL
-        if "vision_tower" in name or "multi_modal_projector" in name:
+        # skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5
+        if "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name:
             return
         if name.startswith("siglip2.") or name.startswith("merger."):
             return
@@ -11082,6 +11185,103 @@ class KimiVLModel(MmprojModel):
                 yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("KimiK25ForConditionalGeneration")
+class KimiK25Model(MmprojModel):
+    """Kimi-K2.5 with MoonViT3d vision encoder"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        assert self.hparams_vision is not None, "Kimi-K2.5 requires vision_config in model config"
+
+        self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2]))
+        self.patch_size = self.hparams_vision.get("patch_size", 14)
+
+        # Set image_size for compatibility with base class
+        # Use position embedding dimensions as image_size reference
+        pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64)
+        self.hparams_vision["image_size"] = pos_emb_h * self.patch_size
+
+    def set_gguf_parameters(self):
+        # Base class MmprojModel.set_gguf_parameters() already writes:
+        # - vision_block_count, vision_head_count, vision_embedding_length
+        # - vision_feed_forward_length, vision_patch_size, image_mean, image_std
+        # via find_vparam() which handles the vt_* prefixed keys in Kimi-K2.5's config
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK25)
+
+        # Position embedding parameters (for interpolation)
+        self.gguf_writer.add_uint32("vision.pos_emb_height", self.hparams_vision.get("init_pos_emb_height", 64))
+        self.gguf_writer.add_uint32("vision.pos_emb_width", self.hparams_vision.get("init_pos_emb_width", 64))
+        self.gguf_writer.add_uint32("vision.pos_emb_time", self.hparams_vision.get("init_pos_emb_time", 4))
+
+        # Projector parameters
+        self.gguf_writer.add_vision_use_gelu(self.hparams_vision.get("projector_hidden_act", "gelu") == "gelu")
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("projector_ln_eps", 1e-5))
+        self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0])
+
+        # Image size limits
+        # Note: in_patch_limit is for images, in_patch_limit_each_frame is for video (not supported yet)
+        in_patch_limit = self.preprocessor_config.get("in_patch_limit", 16384)
+        min_patches = 8  # reasonable minimum
+        pixels_per_patch = self.patch_size ** 2
+        self.gguf_writer.add_vision_min_pixels(min_patches * pixels_per_patch)
+        self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int) -> Tensor:
+        out_dim, in_dim = weights.shape
+        head_dim = out_dim // n_head
+        w = weights.reshape(n_head, head_dim // 4, 2, 2, in_dim)
+        w = w.permute(0, 2, 1, 3, 4)
+        return w.reshape(out_dim, in_dim)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Only process vision and projector tensors
+        is_vision = any(x in name for x in ["vision_tower", "mm_projector"])
+
+        if not is_vision:
+            return
+
+        assert self.hparams_vision is not None
+        n_head = self.hparams_vision.get("num_attention_heads", 16)
+
+        # Permute Q/K weights/biases from interleaved to split RoPE format
+        # This allows using build_rope_2d at runtime without post-permutation.
+        if "wqkv" in name:
+            out_dim = data_torch.shape[0]
+            qkv_dim = out_dim // 3
+            head_dim = qkv_dim // n_head
+
+            if "weight" in name:
+                wq, wk, wv = data_torch[:qkv_dim, :], data_torch[qkv_dim:2 * qkv_dim, :], data_torch[2 * qkv_dim:, :]
+                wq = self.permute(wq, n_head)
+                wk = self.permute(wk, n_head)
+                data_torch = torch.cat([wq, wk, wv], dim=0)
+            elif "bias" in name:
+                bq, bk, bv = data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]
+                bq = bq.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
+                bk = bk.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
+                data_torch = torch.cat([bq, bk, bv], dim=0)
+
+        # Temporal embeddings: (T, 1, C) → (T, C)
+        if "pos_emb.time_weight" in name:
+            T, _, C = data_torch.shape
+            data_torch = data_torch.reshape(T, C)
+
+        # PatchMergerMLP tensor name mapping
+        # proj.0.weight → proj.linear_1.weight
+        # proj.2.weight → proj.linear_2.weight
+        if "mm_projector.proj.0." in name:
+            name = name.replace(".proj.0.", ".proj.linear_1.")
+        elif "mm_projector.proj.2." in name:
+            name = name.replace(".proj.2.", ".proj.linear_2.")
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("CogVLMForCausalLM")
 class CogVLMVisionModel(MmprojModel):
 

convert_hf_to_gguf_update.py

@@ -148,6 +148,7 @@ models = [
     {"name": "youtu",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
     {"name": "solar-open",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
     {"name": "exaone-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B", },
+    {"name": "qwen35",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3.5-9B-Instruct", }
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

docs/backend/VirtGPU.md

@@ -0,0 +1,180 @@
+# GGML-VirtGPU Backend
+
+The GGML-VirtGPU backend enables GGML applications to run machine
+learning computations on host hardware while the application itself
+runs inside a virtual machine.  It uses host-guest shared memory to
+efficiently share data buffers between the two sides.
+
+This backend relies on the virtio-gpu, and VirglRenderer API Remoting
+(APIR) component. The backend is split into two libraries:
+- a GGML implementation (the "remoting frontend"), running in the
+  guest and interacting with the virtgpu device
+- a VirglRenderer APIR compatible library (the "remoting backend"),
+  running in the host and interacting with Virglrenderer and an actual
+  GGML device backend.
+
+## OS support
+
+| OS       | Status            | Backend     | CI testing  | Notes
+| -------- | ----------------- | ----------- | ----------- | -----
+| MacOS 14 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14
+| MacOS 15 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14 or MacOS 15
+| MacOS 26 | Not tested        |             |             |
+| Linux    | Under development | ggml-vulkan | not working | Working locally, CI running into deadlocks
+
+
+## Architecture Overview
+
+The GGML-VirtGPU backend consists of three main components:
+
+```mermaid
+graph TD
+    %% Nodes
+
+ subgraph GuestVM ["Guest VM - Frontend"]
+        App([GGML Application<br/>llama.cpp, etc.])
+
+        direction TB
+        Interface[GGML Backend Interface]
+        Comm["GGML-VirtGPU<br/>(hypercalls + shared mem)"]
+
+        App --> Interface
+        Interface --> Comm
+    end
+
+    API[virtio-gpu / virglrenderer API]
+
+    subgraph HostSystem [Host System - Backend]
+        direction TB
+        Dispatcher[GGML-VirtGPU-Backend]
+        BackendLib[GGML Backend library<br/>Metal / Vulkan / CPU / ...]
+
+        Dispatcher --> BackendLib
+    end
+
+    %% Connections
+    Comm --> API
+    API --> HostSystem
+```
+
+### Key Components
+
+1. **Guest-side Frontend** (`ggml-virtgpu/`): Implements the GGML backend interface and forwards operations to the host
+2. **Host-side Backend** (`ggml-virtgpu/backend/`): Receives forwarded operations and executes them on actual hardware backends
+3. **Communication Layer**: Uses virtio-gpu hypercalls and shared memory for efficient data transfer
+
+## Features
+
+- **Dynamic backend loading** on the host side (CPU, CUDA, Metal, etc.)
+- **Zero-copy data transfer** via host-guest shared memory pages
+
+## Communication Protocol
+
+### Hypercalls and Shared Memory
+
+The backend uses two primary communication mechanisms:
+
+1. **Hypercalls (`DRM_IOCTL_VIRTGPU_EXECBUFFER`)**: Trigger remote execution from guest to host
+2. **Shared Memory Pages**: Zero-copy data transfer for tensors and parameters
+
+#### Shared Memory Layout
+
+Each connection uses two shared memory buffers:
+
+- **Data Buffer** (24 MiB): For command/response data and tensor transfers
+- **Reply Buffer** (16 KiB): For command replies and status information
+- **Data Buffers**: Dynamically allocated host-guest shared buffers
+  served as GGML buffers.
+
+### APIR Protocol
+
+The Virglrender API Remoting protocol defines three command types:
+
+- `HANDSHAKE`: Protocol version negotiation and capability discovery
+- `LOADLIBRARY`: Dynamic loading of backend libraries on the host
+- `FORWARD`: API function call forwarding
+
+### Binary Serialization
+
+Commands and data are serialized using a custom binary protocol with:
+
+- Fixed-size encoding for basic types
+- Variable-length arrays with size prefixes
+- Buffer bounds checking
+- Error recovery mechanisms
+
+## Supported Operations
+
+### Device Operations
+- Device enumeration and capability queries
+- Memory information (total/free)
+- Backend type detection
+
+### Buffer Operations
+- Buffer allocation and deallocation
+- Tensor data transfer (host ↔ guest)
+- Memory copying and clearing
+
+### Computation Operations
+- Graph execution forwarding
+
+## Build Requirements
+
+### Guest-side Dependencies
+- `libdrm` for DRM/virtio-gpu communication
+- C++20 compatible compiler
+- CMake 3.14+
+
+### Host-side Dependencies
+- virglrenderer with APIR support (pending upstream review)
+- Target backend libraries (libggml-metal, libggml-vulkan, etc.)
+
+## Configuration
+
+### Environment Variables
+
+- `GGML_VIRTGPU_BACKEND_LIBRARY`: Path to the host-side backend library
+- `GGML_VIRTGPU_DEBUG`: Enable debug logging
+
+### Build Options
+
+- `GGML_VIRTGPU`: Enable the VirtGPU backend (`ON` or `OFF`, default: `OFF`)
+- `GGML_VIRTGPU_BACKEND`: Build the host-side backend component (`ON`, `OFF` or `ONLY`, default: `OFF`)
+
+### System Requirements
+
+- VM with virtio-gpu support
+- VirglRenderer with APIR patches
+- Compatible backend libraries on host
+
+## Limitations
+
+- **VM-specific**: Only works in virtual machines with virtio-gpu support
+- **Host dependency**: Requires properly configured host-side backend
+- **Latency**: Small overhead from VM escaping for each operation
+
+
+* This work is pending upstream changes in the VirglRenderer
+  project.
+  * The backend can be tested with Virglrenderer compiled from source
+  using this PR:
+  https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590
+* This work is pending changes in the VMM/hypervisor running the
+  virtual machine, which need to know how to route the newly
+  introduced APIR capset.
+  * The environment variable `VIRGL_ROUTE_VENUS_TO_APIR=1` allows
+    using the Venus capset, until the relevant hypervisors have been
+    patched. However, setting this flag breaks the Vulkan/Venus normal
+    behavior.
+  * The environment variable `GGML_REMOTING_USE_APIR_CAPSET` tells the
+    `ggml-virtgpu` backend to use the APIR capset. This will become
+    the default when the relevant hypervisors have been patched.
+
+* This work focused on improving the performance of llama.cpp running
+  on MacOS containers, and is mainly tested on this platform. The
+  linux support (via `krun`) is in progress.
+
+## See Also
+
+- [Development and Testing](VirtGPU/development.md)
+- [Backend configuration](VirtGPU/configuration.md)

docs/backend/VirtGPU/configuration.md

@@ -0,0 +1,174 @@
+# GGML-VirtGPU Backend Configuration
+
+This document describes the environment variables used by the ggml-virtgpu backend system, covering both the frontend (guest-side) and backend (host-side) components.
+
+## Environment Variables Overview
+
+The ggml-virtgpu backend uses environment variables for configuration across three main components:
+- **Frontend (Guest)**: GGML applications running in VMs
+- **Hypervisor**: Virglrenderer/APIR system
+- **Backend (Host)**: Host-side GGML backend integration
+
+## Frontend (Guest-side) Configuration
+
+### GGML_REMOTING_USE_APIR_CAPSET
+- **Location**: `ggml/src/ggml-virtgpu/virtgpu.cpp`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Controls which virtio-gpu capability set to use for communication
+- **Values**:
+  - Set (any value): Use the APIR capset (long-term setup)
+  - Unset: Use the Venus capset (easier for testing with an unmodified hypervisor)
+- **Default**: Unset (Venus capset)
+- **Usage**:
+  ```bash
+  export GGML_REMOTING_USE_APIR_CAPSET=1  # Use APIR capset
+  # or leave unset for Venus capset
+  ```
+
+## Hypervisor (Virglrenderer/APIR) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### VIRGL_APIR_BACKEND_LIBRARY
+- **Location**: `virglrenderer/src/apir/apir-context.c`
+- **Configuration Key**: `apir.load_library.path`
+- **Type**: File path string
+- **Purpose**: Path to the APIR backend library that virglrenderer should dynamically load
+- **Required**: Yes
+- **Example**:
+  ```bash
+  export VIRGL_APIR_BACKEND_LIBRARY="/path/to/libggml-remotingbackend.so"
+  ```
+
+### VIRGL_ROUTE_VENUS_TO_APIR
+- **Location**: `virglrenderer/src/apir/apir-renderer.h`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Temporary workaround to route Venus capset calls to APIR during hypervisor transition period
+- **Status**: will be removed once hypervisors support APIR natively
+- **Warning**: Breaks normal Vulkan/Venus functionality
+- **Usage**:
+  ```bash
+  export VIRGL_ROUTE_VENUS_TO_APIR=1  # For testing with an unmodified hypervisor
+  ```
+
+### VIRGL_APIR_LOG_TO_FILE
+- **Location**: `virglrenderer/src/apir/apir-renderer.c`
+- **Environment Variable**: `VIRGL_APIR_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the VirglRenderer APIR component to specified file
+- **Required**: No (optional debugging)
+- **Default**: Logging to `stderr`
+- **Usage**:
+  ```bash
+  export VIRGL_APIR_LOG_TO_FILE="/tmp/apir-debug.log"
+  ```
+
+## Backend (Host-side) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_PATH
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+- **Configuration Key**: `ggml.library.path`
+- **Type**: File path string
+- **Purpose**: Path to the actual GGML backend library (Metal, CUDA, Vulkan, etc.)
+- **Required**: **Yes** - backend initialization fails without this
+- **Examples**:
+  ```bash
+  # macOS with Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+
+  # Linux with CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-cuda.so"
+
+  # macOS or Linux with Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-vulkan.so"
+  ```
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_REG
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+- **Configuration Key**: `ggml.library.reg`
+- **Type**: Function symbol name string
+- **Purpose**: Name of the backend registration function to call after loading the library
+- **Required**: No (defaults to `ggml_backend_init`)
+- **Default**: `ggml_backend_init`
+- **Examples**:
+  ```bash
+  # Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+  # CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_cuda_reg"
+
+  # Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_vulkan_reg"
+
+  # Generic fallback (default)
+  # export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_init"
+  ```
+
+### APIR_LLAMA_CPP_LOG_TO_FILE
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp:62`
+- **Environment Variable**: `APIR_LLAMA_CPP_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the GGML backend to specified file
+- **Required**: No (optional debugging)
+- **Usage**:
+  ```bash
+  export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml-backend-debug.log"
+  ```
+
+## Configuration Flow
+
+The configuration system works as follows:
+
+1. **Hypervisor Setup**: Virglrenderer loads the APIR backend library specified by `VIRGL_APIR_BACKEND_LIBRARY`
+
+2. **Context Creation**: When an APIR context is created, it populates a configuration table with environment variables:
+   - `apir.load_library.path` ← `VIRGL_APIR_BACKEND_LIBRARY`
+   - `ggml.library.path` ← `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+   - `ggml.library.reg` ← `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+   - this step will eventually be performed by the hypervisor itself, with command-line arguments instead of environment variables.
+
+3. **Backend Initialization**: The backend queries the configuration via callbacks:
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.path")` returns the library path
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.reg")` returns the registration function
+
+4. **Library Loading**: The backend dynamically loads and initializes the specified GGML library
+
+## Error Messages
+
+Common error scenarios and their messages:
+
+- **Missing library path**: `"cannot open the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_PATH' not defined"`
+- **Missing registration function**: `"cannot register the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_REG' not defined"`
+
+## Example Complete Configuration
+
+Here's an example configuration for a macOS host with Metal backend:
+
+```bash
+# Hypervisor environment
+export VIRGL_APIR_BACKEND_LIBRARY="/opt/llama.cpp/lib/libggml-virtgpu-backend.dylib"
+
+# Backend configuration
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+# Optional logging
+export VIRGL_APIR_LOG_TO_FILE="/tmp/apir.log"
+export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml.log"
+
+# Guest configuration
+export GGML_REMOTING_USE_APIR_CAPSET=1
+```

docs/backend/VirtGPU/development.md

@@ -0,0 +1,220 @@
+# Development and Testing
+
+## Development
+
+### Code Generation
+
+The backend uses code generation from YAML configuration:
+
+```bash
+# Regenerate protocol code
+cd ggml-virtgpu/
+python regenerate_remoting.py
+```
+
+### Adding New Operations
+
+1. Add function definition to `ggmlremoting_functions.yaml`
+2. Regenerate code with `regenerate_remoting.py`
+3. Implement guest-side forwarding in `virtgpu-forward-*.cpp`
+4. Implement host-side handling in `backend-dispatched-*.cpp`
+
+## Testing
+
+This document provides instructions for building and testing the GGML-VirtGPU backend on macOS with containers.
+
+### Prerequisites
+
+The testing setup requires:
+
+- macOS host system
+- Container runtime with `libkrun` provider (podman machine)
+- Access to development patchset for VirglRenderer
+
+### Required Patchsets
+
+The backend requires patches that are currently under review:
+
+- **Virglrenderer APIR upstream PR**: https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590 (for reference)
+- **MacOS Virglrenderer (for krunkit)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-macos
+- **Linux Virglrenderer (for krun)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-linux
+
+### Build Instructions
+
+#### 1. Build ggml-virtgpu-backend (Host-side, macOS)
+
+```bash
+# Build the backend that runs natively on macOS
+mkdir llama.cpp
+cd llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
+
+cmake -S . -B $LLAMA_MAC_BUILD \
+      -DGGML_NATIVE=OFF \
+      -DLLAMA_CURL=ON \
+      -DGGML_REMOTINGBACKEND=ONLY \
+      -DGGML_METAL=ON
+
+TARGETS="ggml-metal"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $TARGETS
+
+# Build additional tools for native benchmarking
+EXTRA_TARGETS="llama-run llama-bench"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
+```
+
+#### 2. Build virglrenderer (Host-side, macOS)
+
+```bash
+# Build virglrenderer with APIR support
+mkdir virglrenderer
+git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
+cd src
+
+VIRGL_BUILD_DIR=$PWD/build
+
+# -Dvenus=true and VIRGL_ROUTE_VENUS_TO_APIR=1 route the APIR requests via the Venus backend, for easier testing without a patched hypervisor
+
+meson setup $VIRGL_BUILD_DIR \
+      -Dvenus=true \
+      -Dapir=true
+
+ninja -C $VIRGL_BUILD_DIR
+```
+
+#### 3. Build ggml-virtgpu (Guest-side, Linux)
+
+Option A: Build from a script:
+
+```bash
+# Inside a Linux container
+mkdir llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_LINUX_BUILD=$PWD//build-virtgpu
+
+cmake -S . -B $LLAMA_LINUX_BUILD \
+      -DGGML_VIRTGPU=ON
+
+ninja -C $LLAMA_LINUX_BUILD
+```
+
+Option B: Build container image with frontend:
+
+```bash
+cat << EOF > remoting.containerfile
+FROM quay.io/fedora/fedora:43
+USER 0
+
+WORKDIR /app/remoting
+
+ARG LLAMA_CPP_REPO="https://github.com/ggml-org/llama.cpp.git"
+ARG LLAMA_CPP_VERSION="master"
+ARG LLAMA_CPP_CMAKE_FLAGS="-DGGML_VIRTGPU=ON"
+ARG LLAMA_CPP_CMAKE_BUILD_FLAGS="--parallel 4"
+
+RUN dnf install -y git cmake gcc gcc-c++ libcurl-devel libdrm-devel
+
+RUN git clone "\${LLAMA_CPP_REPO}" src \\
+ && git -C src fetch origin \${LLAMA_CPP_VERSION} \\
+ && git -C src reset --hard FETCH_HEAD
+
+RUN mkdir -p build \\
+ && cd src \\
+ && set -o pipefail \\
+ && cmake -S . -B ../build \${LLAMA_CPP_CMAKE_FLAGS} \\
+ && cmake --build ../build/ \${LLAMA_CPP_CMAKE_BUILD_FLAGS}
+
+ENTRYPOINT ["/app/remoting/src/build/bin/llama-server"]
+EOF
+
+mkdir -p empty_dir
+podman build -f remoting.containerfile ./empty_dir -t localhost/llama-cpp.virtgpu
+```
+
+### Environment Setup
+
+#### Set krunkit Environment Variables
+
+```bash
+# Define the base directories (adapt these paths to your system)
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build
+LLAMA_MAC_BUILD=$HOME/remoting/llama.cpp/build-backend
+
+# For krunkit to load the custom virglrenderer library
+export DYLD_LIBRARY_PATH=$VIRGL_BUILD_DIR/src
+
+# For Virglrenderer to load the ggml-remotingbackend library
+export VIRGL_APIR_BACKEND_LIBRARY="$LLAMA_MAC_BUILD/bin/libggml-virtgpu-backend.dylib"
+
+# For llama.cpp remotingbackend to load the ggml-metal backend
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="$LLAMA_MAC_BUILD/bin/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG=ggml_backend_metal_reg
+```
+
+#### Launch Container Environment
+
+```bash
+# Set container provider to libkrun
+export CONTAINERS_MACHINE_PROVIDER=libkrun
+podman machine start
+```
+
+#### Verify Environment
+
+Confirm that krunkit is using the correct virglrenderer library:
+
+```bash
+lsof -c krunkit | grep virglrenderer
+# Expected output:
+# krunkit 50574 user  txt  REG  1,14  2273912  10849442 ($VIRGL_BUILD_DIR/src)/libvirglrenderer.1.dylib
+```
+
+### Running Tests
+
+#### Launch Test Container
+
+```bash
+# Optional model caching
+mkdir -p models
+PODMAN_CACHE_ARGS="-v models:/models --user root:root --cgroupns host --security-opt label=disable -w /models"
+
+podman run $PODMAN_CACHE_ARGS -it --rm --device /dev/dri localhost/llama-cpp.virtgpu
+```
+
+#### Test llama.cpp in Container
+
+```bash
+
+# Run performance benchmark
+/app/remoting/build/bin/llama-bench -m ./llama3.2
+```
+
+Expected output (performance may vary):
+```
+| model                          |       size |     params | backend    | ngl |          test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------: | -------------------: |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         pp512 |        991.30 ± 0.66 |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         tg128 |         85.71 ± 0.11 |
+```
+
+### Troubleshooting
+
+#### SSH Environment Variable Issues
+
+⚠️ **Warning**: Setting `DYLD_LIBRARY_PATH` from SSH doesn't work on macOS. Here is a workaround:
+
+**Workaround 1: Replace system library**
+```bash
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build  # ⚠️ adapt to your system
+BREW_VIRGL_DIR=/opt/homebrew/Cellar/virglrenderer/0.10.4d/lib
+VIRGL_LIB=libvirglrenderer.1.dylib
+
+cd $BREW_VIRGL_DIR
+mv $VIRGL_LIB ${VIRGL_LIB}.orig
+ln -s $VIRGL_BUILD_DIR/src/$VIRGL_LIB
+```

docs/backend/snapdragon/README.md

@@ -35,7 +35,7 @@ Adapt below build commands accordingly.
 Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
 
 ```
-[d]/workspace> cp docs/backend/hexagon/CMakeUserPresets.json .
+[d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json .
 
 [d]/workspace> cmake --preset arm64-android-snapdragon-release -B build-snapdragon
 Preset CMake variables:

docs/speculative.md

@@ -119,8 +119,6 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
                                         of lookup n-gram (default: 12)
 --spec-ngram-size-m N                   ngram size M for ngram-simple/ngram-map speculative decoding, length
                                         of draft m-gram (default: 48)
---spec-ngram-check-rate N               ngram check rate for ngram-simple/ngram-map speculative decoding
-                                        (default: 1)
 --spec-ngram-min-hits N                 minimum hits for ngram-map speculative decoding (default: 1)
 ```
 
@@ -153,10 +151,6 @@ Sets the size M of the draft m-gram for n-gram map based speculative decoding.
 The m-gram size determines how many tokens to draft when a match is found.
 Larger values can provide more speedup but may reduce acceptance rate.
 
-### `--spec-ngram-check-rate R`
-
-This option aims at performance if the n-gram lookup in history is to costly. A lookup will be executed at every R tokens (default is 1, every token).
-
 ### `--spec-ngram-min-hits H`
 
 This option defines how often a key has to appear in the token history to be used as a draft (default is 1).
@@ -175,7 +169,12 @@ draft acceptance rate = 0.70312 (   90 accepted /   128 generated)
 statistics ngram_mod: #calls = 810, #gen drafts = 15, #acc drafts = 15, #gen tokens = 960, #acc tokens = 730, dur(b,g,a) = 0.149, 0.347, 0.005 ms
 ```
 
-- `#calls`: number of calls of this implementations
+```
+statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts = 26, #gen tokens = 1248, #acc tokens = 968, dur(b,g,a) = 2.234, 1.427, 0.016 ms
+```
+
+
+- `#calls(b,g,a)`: number of calls of begin (new prompt), generation and accumulation of this implementations
 - `#gen drafts`: number of drafts generated by this implementation
 - `#acc drafts`: number of drafts accepted (partially) by the main model
 - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)

ggml/src/ggml-backend-reg.cpp

@@ -471,9 +471,10 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
 
     int best_score = 0;
     fs::path best_path;
+    std::error_code ec;
 
     for (const auto & search_path : search_paths) {
-        if (std::error_code ec; !fs::exists(search_path, ec)) {
+        if (!fs::exists(search_path, ec)) {
             if (ec) {
                 GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str());
             } else {
@@ -483,7 +484,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         }
         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
         for (const auto & entry : dir_it) {
-            if (entry.is_regular_file()) {
+            if (entry.is_regular_file(ec)) {
                 auto filename = entry.path().filename();
                 auto ext = entry.path().extension();
                 if (filename.native().find(file_prefix) == 0 && ext == file_extension) {

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -3286,130 +3286,223 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor
 }
 
 /**
- * @brief Performs expert-specific matrix multiplication (MoE) with
- * quantized precision using the CANN backend.
+ * @brief Performs quantized matrix multiplication for Mixture of Experts (MoE)
+ * models using the CANN backend.
  *
- * This function executes a matrix multiplication operation tailored for
- * Mixture of Experts (MoE) models, where the input tensor is multiplied
- * with expert-specific quantized weight matrices. It leverages the CANN
- * backend to perform efficient low-precision computations and stores the
- * quantized result in the destination tensor `dst`.
+ * This function implements MUL_MAT_ID operation for quantized weight matrices
+ * (Q4_0 and Q8_0 formats). It selects expert-specific weight matrices based on
+ * the provided expert indices, and computes matrix multiplication using CANN's
+ * WeightQuantBatchMatmulV2 operator.
  *
- * Quantization techniques reduce memory footprint and improve performance
- * by using lower-bit representations (e.g., int8) instead of floating-point.
- * This function is designed to work with such formats and may incorporate
- * optimizations like identity-based fast paths or routing masks for sparse
- * expert selection.
+ * The function performs the following steps:
+ * 1. Converts input/output tensors to F16 format if necessary
+ * 2. Uses IndexSelect to extract expert-specific weights and scales based on indices
+ * 3. Performs quantized matrix multiplication for each expert using WeightQuantBatchMatmulV2
+ * 4. Converts output back to the target type if needed
  *
- * @param ctx The context for executing CANN backend operations.
- * @param dst The destination tensor where the quantized MoE multiplication result
- * will be stored.
+ * Tensor shapes:
+ * - dst:  [M, K, N, 1] - output tensor
+ * - src0: [D, M, A, 1] - quantized weight matrices (Q4_0 or Q8_0)
+ * - src1: [D, B, N, 1] - input activations (B = K for per-expert input, or B = 1 for broadcast)
+ * - ids:  [K, N] - expert indices for routing
  *
- * @note This function assumes quantized data types and is designed for
- * MoE architectures with potential sparse expert routing.
+ * @param ctx The CANN backend context for operation execution.
+ * @param dst The destination tensor where the multiplication result will be stored.
+ *
+ * @note Only Q4_0 and Q8_0 quantization formats are supported.
+ * @note The function handles automatic type conversion to/from F16 as needed by the hardware.
  */
 static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    // TODO: Use aclnnGroupedMatMul
-    //dst   [M, K, N, 1]
-    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]
-    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1
-    ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
+    // dst:  [M, K, N, 1]
+    // src0: [D, M, A, 1] - quantized weights
+    // src1: [D, B, N, 1] - input activations, B = K or B = 1
+    // ids:  [K, N] - expert indices
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+    ggml_tensor * ids  = dst->src[2];
 
-    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);
+    GGML_ASSERT(dst->ne[3] == 1);
+    GGML_ASSERT(src1->ne[2] == ids->ne[1]);
 
-    // copy index from npu to cpu
-    int64_t n_as  = ne02;        // A
-    int64_t n_ids = ids->ne[0];  // K
+    const int64_t        n_batches        = ids->ne[1];
+    const int64_t        n_select_experts = ids->ne[0];
+    const enum ggml_type type             = src0->type;
 
-    std::vector<char> ids_host(ggml_nbytes(ids));
-    ACL_CHECK(aclrtMemcpyAsync(ids_host.data(), ggml_nbytes(ids), ids->data, ggml_nbytes(ids),
-                               ACL_MEMCPY_DEVICE_TO_HOST, ctx.stream()));
-    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
+    const int32_t group_size = QK8_0;  // Both Q4_0 and Q8_0 use group size of 32
+    GGML_ASSERT(group_size == QK4_0);
 
-    char * src0_original = (char *) src0->data;
-    char * src1_original = (char *) src1->data;
-    char * dst_original  = (char *) dst->data;
+    // Calculate element size for quantized weights
+    const float weight_elem_size =
+        (type == GGML_TYPE_Q4_0) ? 0.5f :
+        (type == GGML_TYPE_Q8_0) ? 1.0f :
+                                   (GGML_ABORT("MUL_MAT_ID only supports Q4_0 and Q8_0"), 0.0f);
 
-    ggml_tensor src0_row = *src0;
-    ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row  = *dst;
+    // Calculate scale offset in memory
+    const size_t weight_size     = src0->ne[0] * src0->ne[1] * src0->ne[2] * weight_elem_size;
+    const size_t scale_elem_size = sizeof(uint16_t);
+    char *       scale_data      = (char *) src0->data + weight_size;
 
-    const enum ggml_type type = dst->src[0]->type;
-    float                weight_elem_size;
-    if (type == GGML_TYPE_Q4_0) {
-        weight_elem_size = float(sizeof(uint8_t)) / 2;
-    } else if (type == GGML_TYPE_Q8_0) {
-        weight_elem_size = float(sizeof(uint8_t));
-    } else {
-        GGML_ABORT("MUL_MAT_ID only support quant type Q4_0 and Q8_0 ");
-    }
+    // Allocate buffers for selected expert weights and scales
+    const size_t         selected_weight_size = src0->ne[0] * src0->ne[1] * n_select_experts * weight_elem_size;
+    ggml_cann_pool_alloc selected_weight_alloc(ctx.pool(), selected_weight_size);
+    void *               selected_weight_buffer = selected_weight_alloc.get();
 
-    // src0_row [D, M, 1, 1] weight without permute
-    src0_row.ne[2]       = 1;
-    src0_row.ne[3]       = 1;
-    src0_row.nb[0]       = weight_elem_size;
-    src0_row.nb[1]       = weight_elem_size * ne00;
-    src0_row.nb[2]       = weight_elem_size * ne00;
-    src0_row.nb[3]       = weight_elem_size * ne00;
-    size_t weight_stride = ne00 * ne01 * weight_elem_size;
-    size_t weight_size   = weight_stride * ne02 * ne03;
+    const size_t selected_scale_size = (src0->ne[0] / group_size) * src0->ne[1] * n_select_experts * scale_elem_size;
+    ggml_cann_pool_alloc selected_scale_alloc(ctx.pool(), selected_scale_size);
+    void *               selected_scale_buffer = selected_scale_alloc.get();
 
-    // scale [D, M, 1, 1] -> scale && permute
-    size_t scale_elem_size = sizeof(uint16_t);
-    size_t scale_stride    = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
+    // Helper lambda to allocate and cast tensor to F16 if needed
+    constexpr size_t f16_elem_size      = sizeof(uint16_t);
+    auto             prepare_f16_buffer = [&](ggml_tensor * tensor, ggml_cann_pool_alloc & allocator,
+                                  bool need_cast = false) -> void * {
+        if (tensor->type == GGML_TYPE_F16) {
+            return tensor->data;
+        }
 
-    // src1_row [D, 1, 1, 1] -> input
-    src1_row.ne[1] = 1;
-    src1_row.ne[2] = 1;
-    src1_row.ne[3] = 1;
-    src1_row.nb[2] = nb11;
-    src1_row.nb[3] = nb11;
+        size_t total_size = f16_elem_size;
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            total_size *= tensor->ne[i];
+        }
+        void * buffer = allocator.alloc(total_size);
 
-    // dst_row [M, 1, 1, 1] -> out
-    dst_row.ne[1] = 1;
-    dst_row.ne[2] = 1;
-    dst_row.ne[3] = 1;
-    dst_row.nb[2] = nb1;
-    dst_row.nb[3] = nb1;
+        if (need_cast == false) {
+            return buffer;
+        }
 
-    //create weight for one row
-    ggml_cann_pool_alloc weight_allocator(ctx.pool());
-    void *               weight_buffer = weight_allocator.alloc(nb02);
-    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
-        for (int64_t id = 0; id < n_ids; id++) {
-            // expert index
-            int32_t i02 = *(int32_t *) (ids_host.data() + iid1 * ids->nb[1] + id * ids->nb[0]);
-            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = tensor->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
 
-            // If B = 1 (broadcast), always use 0; otherwise, use id.
-            int64_t i11 = (ne11 == 1 ? 0 : id);
-            int64_t i12 = iid1;
+        acl_tensor_ptr src_tensor = ggml_cann_create_tensor(tensor);
+        acl_tensor_ptr f16_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, src_tensor.get(), f16_tensor.get(), ACL_FLOAT16);
 
-            int64_t i1 = id;
-            int64_t i2 = i12;
+        return buffer;
+    };
 
-            void * src0_tmp_ptr  = src0_original + i02 * weight_stride;
-            void * scale_tmp_ptr = src0_original + weight_size + i02 * scale_stride;
-            void * src1_tmp_ptr  = src1_original + i11 * nb11 + i12 * nb12;
-            void * dst_tmp_ptr   = dst_original + i1 * nb1 + i2 * nb2;
+    // Prepare input and output buffers
+    ggml_cann_pool_alloc input_alloc(ctx.pool());
+    void *               input_buffer = prepare_f16_buffer(src1, input_alloc, true);
 
-            // mem cpy
-            ACL_CHECK(aclrtMemcpyAsync(weight_buffer, weight_stride, src0_tmp_ptr, weight_stride,
-                                       ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
-            void * scale_buffer = (char *) weight_buffer + weight_stride;
-            ACL_CHECK(aclrtMemcpyAsync(scale_buffer, scale_stride, scale_tmp_ptr, scale_stride,
-                                       ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    ggml_cann_pool_alloc output_alloc(ctx.pool());
+    void *               output_buffer = prepare_f16_buffer(dst, output_alloc, false);
 
-            src0_row.data  = weight_buffer;
-            src1_row.data  = src1_tmp_ptr;
-            dst_row.data   = dst_tmp_ptr;
-            dst_row.src[0] = &src0_row;
-            dst_row.src[1] = &src1_row;
+    // Process each batch
+    for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+        // Create index tensor for current batch
+        const size_t   index_offset  = batch_idx * ids->nb[1];
+        acl_tensor_ptr batch_indices = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, index_offset);
 
-            ggml_cann_mul_mat(ctx, &dst_row);
+        // Select quantized weights using expert indices
+        // Q4_0 stores 2 values per byte, Q8_0 stores 1 value per byte
+        const int64_t weight_d         = (type == GGML_TYPE_Q4_0) ? src0->ne[0] / 2 : src0->ne[0];
+        const int64_t weight_m         = src0->ne[1];
+        const int64_t weight_n_experts = src0->ne[2];
+
+        int64_t weight_ne[3] = { weight_d, weight_m, weight_n_experts };
+        size_t  weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t), weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr all_weights =
+            ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, 3);
+
+        int64_t selected_weight_ne[3] = { weight_d, weight_m, n_select_experts };
+        size_t  selected_weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t),
+                                          weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr selected_weights = ggml_cann_create_tensor(selected_weight_buffer, ACL_INT8, sizeof(int8_t),
+                                                                  selected_weight_ne, selected_weight_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_weights.get(), 0, batch_indices.get(), selected_weights.get());
+
+        // Select scales using the same expert indices
+        const int64_t scale_d     = src0->ne[0] / group_size;
+        int64_t       scale_ne[3] = { scale_d, weight_m, weight_n_experts };
+        size_t scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size, scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr all_scales =
+            ggml_cann_create_tensor(scale_data, ACL_FLOAT16, scale_elem_size, scale_ne, scale_nb, 3);
+
+        int64_t selected_scale_ne[3] = { scale_d, weight_m, n_select_experts };
+        size_t  selected_scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size,
+                                         scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr selected_scales = ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size,
+                                                                 selected_scale_ne, selected_scale_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_scales.get(), 0, batch_indices.get(), selected_scales.get());
+
+        // Process each expert for current batch
+        // IndexSelect output layout: [D, M, K] in contiguous format
+        // WeightQuantBatchMatmulV2 expects: [M, D] with row-major stride
+        for (int64_t expert_idx = 0; expert_idx < n_select_experts; expert_idx++) {
+            // Determine input offset: broadcast if src1->ne[1]==1, otherwise use per-expert input
+            const size_t input_offset =
+                (batch_idx * src1->ne[1] + (src1->ne[1] == 1 ? 0 : expert_idx)) * src1->ne[0] * f16_elem_size;
+            const size_t output_offset = (batch_idx * dst->ne[1] + expert_idx) * dst->ne[0] * f16_elem_size;
+
+            // Create weight view for current expert: [D, M, K] -> [M, D]
+            int64_t      weight_view_ne[2]  = { weight_m, src0->ne[0] };
+            float        weight_view_nb[2]  = { src0->ne[0] * weight_elem_size, weight_elem_size };
+            const size_t weight_view_offset = expert_idx * selected_weight_nb[2];
+
+            acl_tensor_ptr weight_view =
+                ggml_cann_create_tensor(selected_weight_buffer, ggml_cann_type_mapping(type), weight_elem_size,
+                                        weight_view_ne, weight_view_nb, 2, ACL_FORMAT_ND, weight_view_offset);
+
+            // Create scale view for current expert: [D, M, K] -> [M, D]
+            int64_t      scale_view_ne[2]  = { weight_m, scale_d };
+            size_t       scale_view_nb[2]  = { selected_scale_nb[1], selected_scale_nb[0] };
+            const size_t scale_view_offset = expert_idx * selected_scale_nb[2];
+
+            acl_tensor_ptr scale_view =
+                ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size, scale_view_ne,
+                                        scale_view_nb, 2, ACL_FORMAT_ND, scale_view_offset);
+
+            // Create input activation tensor [D, 1]
+            int64_t input_ne[2] = { src1->ne[0], 1 };
+            size_t  input_nb[2] = { f16_elem_size, src1->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, f16_elem_size, input_ne,
+                                                                  input_nb, 2, ACL_FORMAT_ND, input_offset);
+
+            // Create output tensor [M, 1]
+            int64_t output_ne[2] = { dst->ne[0], 1 };
+            size_t  output_nb[2] = { f16_elem_size, dst->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, output_ne,
+                                                                   output_nb, 2, ACL_FORMAT_ND, output_offset);
+
+            // Perform quantized matrix multiplication
+            GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, input_tensor.get(), weight_view.get(),
+                                    scale_view.get(), nullptr, nullptr, nullptr, nullptr, group_size,
+                                    output_tensor.get());
         }
     }
-    return;
+
+    // Cast output back to original type if we used a temporary F16 buffer
+    if (dst->type != GGML_TYPE_F16) {
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = dst->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
+
+        acl_tensor_ptr f16_output =
+            ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        acl_tensor_ptr dst_tensor = ggml_cann_create_tensor(dst);
+
+        aclnn_cast(ctx, f16_output.get(), dst_tensor.get(), ggml_cann_type_mapping(dst->type));
+    }
 }
 
 void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-cann/ggml-cann.cpp

@@ -794,19 +794,44 @@ struct ggml_backend_cann_buffer_context {
     ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
 };
 
+// cann buffer type
 /**
- * @brief Check if a buffer is a CANN buffer.
- *
- * This function checks if a given buffer is a CANN buffer by comparing its
- * `get_name` function pointer to `ggml_backend_cann_buffer_get_name`.
- *
- * @param buffer The buffer to check.
- * @return true if the buffer is a CANN buffer, false otherwise.
+ * @brief Structure representing context information for a specific backend
+ * buffer type.
  */
-static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft);
+struct ggml_backend_cann_buffer_type_context {
+    int32_t     device; /**< Device identifier associated with the buffer context. */
+    std::string name;   /**< Name associated with the buffer context. */
+};
 
-static bool ggml_backend_buffer_is_cann(ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_is_cann(buffer->buft);
+/**
+ * @brief Retrieves the name associated with a CANN buffer type.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN buffer type context.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
+
+    return buft_ctx->name.c_str();
+}
+
+/**
+ * @brief Checks if the backend buffer type is associated with the CANN backend.
+ *
+ * This function checks whether the provided backend buffer type is associated
+ * with the CANN backend based on the comparison of its name retrieval function
+ * pointer.
+ *
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the buffer type is associated with the CANN
+ * backend, otherwise false.
+ */
+static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
 }
 
 /**
@@ -1271,7 +1296,7 @@ static void ggml_backend_cann_buffer_get_tensor(ggml_backend_buffer_t buffer,
 static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
                                                 const ggml_tensor *   src,
                                                 ggml_tensor *         dst) {
-    if (ggml_backend_buffer_is_cann(src->buffer)) {
+    if (ggml_backend_buft_is_cann(src->buffer->buft)) {
         ggml_backend_cann_buffer_context * src_ctx = (ggml_backend_cann_buffer_context *) src->buffer->context;
         ggml_backend_cann_buffer_context * dst_ctx = (ggml_backend_cann_buffer_context *) buffer->context;
 
@@ -1335,31 +1360,6 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
     /* .reset           = */ NULL,
 };
 
-// cann buffer type
-/**
- * @brief Structure representing context information for a specific backend
- * buffer type.
- */
-struct ggml_backend_cann_buffer_type_context {
-    int32_t     device; /**< Device identifier associated with the buffer context. */
-    std::string name;   /**< Name associated with the buffer context. */
-};
-
-/**
- * @brief Retrieves the name associated with a CANN buffer type.
- *
- * This function returns the descriptive name associated with the specified
- * CANN buffer type context.
- *
- * @param buft Pointer to the buffer type context.
- * @return Const pointer to the C-style string containing the name.
- */
-static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
-
-    return buft_ctx->name.c_str();
-}
-
 /**
  * @brief Allocates a new CANN buffer of the specified type and size.
  *
@@ -1997,7 +1997,7 @@ static bool ggml_backend_cann_cpy_tensor_async(ggml_backend_t      backend_src,
 
     GGML_ASSERT(!is_matmul_weight((const ggml_tensor *) src));
 
-    if (!ggml_backend_buffer_is_cann(src->buffer) || !ggml_backend_buffer_is_cann(dst->buffer)) {
+    if (!ggml_backend_buft_is_cann(src->buffer->buft) || !ggml_backend_buft_is_cann(dst->buffer->buft)) {
         return false;
     }
 
@@ -2523,21 +2523,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
     GGML_UNUSED(dev);
 }
 
-/**
- * @brief Checks if the backend buffer type is associated with the CANN backend.
- *
- * This function checks whether the provided backend buffer type is associated
- * with the CANN backend based on the comparison of its name retrieval function
- * pointer.
- *
- * @param buft Pointer to the backend buffer type to check.
- * @return bool Returns true if the buffer type is associated with the CANN
- * backend, otherwise false.
- */
-static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
-    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
-}
-
 /**
  * @brief Records an event on the CANN backend stream.
  *

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

@@ -43,6 +43,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -55,7 +56,8 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
-#    define ggml_gemm_q6_K_8x8_q8_K_generic   ggml_gemm_q6_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
+#define ggml_gemm_q6_K_8x8_q8_K_generic   ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
@@ -76,6 +78,7 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
@@ -84,6 +87,7 @@
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
@@ -107,6 +111,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -119,6 +124,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -143,6 +149,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -155,6 +162,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -186,6 +194,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -197,6 +206,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -227,6 +237,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -239,6 +250,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -271,6 +283,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -283,6 +296,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0

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

@@ -1072,6 +1072,195 @@ void ggml_gemv_q5_K_8x8_q8_K(int                        n,
     ggml_gemv_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
 }
 
+void ggml_gemv_q6_K_8x4_q8_K(int                        n,
+                             float * GGML_RESTRICT      s,
+                             size_t                     bs,
+                             const void * GGML_RESTRICT vx,
+                             const void * GGML_RESTRICT vy,
+                             int                        nr,
+                             int                        nc) {
+    constexpr int qk = QK_K;
+    const int     nb = n / qk;
+
+    constexpr int ncols_interleaved = 8;
+    constexpr int blocklen          = 4;
+
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    constexpr int    col_groups = ncols_interleaved / 4;
+    const uint8x16_t m4b        = vdupq_n_u8(0x0f);
+    const uint8x16_t mask_lo    = vdupq_n_u8(0x03);
+    const uint8x16_t mask_hi    = vdupq_n_u8(0x30);
+
+    // 1x8 tile = 2 x 4
+    float32x4_t acc_f32[2];
+
+    const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy;
+
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+        for (int i = 0; i < col_groups; i++) {
+            acc_f32[i] = vdupq_n_f32(0);
+        }
+
+        for (int b = 0; b < nb; b++) {
+            float32x4_t q6_d_0     = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d));      // d0 d1 d2 d3
+            float32x4_t q6_d_1     = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4));  // d4 d5 d6 d7
+            float32x4_t q8_d       = vdupq_n_f32(q8_ptr[b].d);
+            float32x4_t sb_scale_0 = vmulq_f32(q6_d_0, q8_d);
+            float32x4_t sb_scale_1 = vmulq_f32(q6_d_1, q8_d);
+
+            int32x4_t acc[col_groups];
+            for (int i = 0; i < col_groups; i++) {
+                acc[i] = vdupq_n_s32(0);
+            }
+
+            // Load all 16 scales once and widen to int16 (Q6_K has 16 scales per block)
+            // Reused for bias and dequantization later
+            int16_t q6_scales[16 * 8];
+            for (int i = 0; i < 16; i++) {
+                int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8));
+                vst1q_s16(q6_scales + i * 8, scales);
+            }
+
+            // Compute bias per column using q8 bsums and preloaded scales to skip the -32 shift
+            int32x4_t bias_lo = vdupq_n_s32(0);
+            int32x4_t bias_hi = vdupq_n_s32(0);
+
+            // Load bsums in chunks of 4 to process with vectorized operations
+            for (int i = 0; i < 16; i += 4) {
+                int16x4_t bsums_vec   = vld1_s16(q8_ptr[b].bsums + i);
+                int16x4_t scales_lo_0 = vld1_s16(q6_scales + (i + 0) * 8);
+                int16x4_t scales_hi_0 = vld1_s16(q6_scales + (i + 0) * 8 + 4);
+                int16x4_t scales_lo_1 = vld1_s16(q6_scales + (i + 1) * 8);
+                int16x4_t scales_hi_1 = vld1_s16(q6_scales + (i + 1) * 8 + 4);
+                int16x4_t scales_lo_2 = vld1_s16(q6_scales + (i + 2) * 8);
+                int16x4_t scales_hi_2 = vld1_s16(q6_scales + (i + 2) * 8 + 4);
+                int16x4_t scales_lo_3 = vld1_s16(q6_scales + (i + 3) * 8);
+                int16x4_t scales_hi_3 = vld1_s16(q6_scales + (i + 3) * 8 + 4);
+
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_0, bsums_vec, 0);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_0, bsums_vec, 0);
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_1, bsums_vec, 1);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_1, bsums_vec, 1);
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_2, bsums_vec, 2);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_2, bsums_vec, 2);
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_3, bsums_vec, 3);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_3, bsums_vec, 3);
+            }
+            bias_lo = vshlq_n_s32(bias_lo, 5);
+            bias_hi = vshlq_n_s32(bias_hi, 5);
+
+            // Process two 128-value halves per superblock
+            for (int half = 0; half < 2; half++) {
+                const uint8_t * ql_base = q6_ptr[b].ql + half * 512;
+                const uint8_t * qh_base = q6_ptr[b].qh + half * 256;
+
+                // A subblock (sb) is a set of weights that share the scale
+                // Since q6_K scales are per 16 elements
+                // num sbs -> 256 elements / (16 elements/scale * 2 elements/byte * 2 halves)
+                for (int sb = 0; sb < QK_K / 64; sb++) {
+                    const int8_t * q8_base_l = q8_ptr[b].qs + half * 128 + sb * 16;
+                    const int8_t * q8_base_h = q8_base_l + 64;
+
+                    // Load and duplicate q8 values (each register covers four interleaved columns of q6)
+                    int8x16_t q8_l[4];
+                    int8x16_t q8_h[4];
+                    for (int i = 0; i < 4; i++) {
+                        q8_l[i] = (int8x16_t) vld1q_dup_s32((const int32_t *) (q8_base_l + i * 4));
+                        q8_h[i] = (int8x16_t) vld1q_dup_s32((const int32_t *) (q8_base_h + i * 4));
+                    }
+
+                    const int ql_off_base = sb * QK_K / 2;
+                    const int qh_off_base = ql_off_base & 255;  // wraps after 256 bytes
+
+                    // Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1)
+                    uint8x16x4_t q6_ql_0 = vld1q_u8_x4(ql_base + ql_off_base);
+                    uint8x16x4_t q6_ql_1 = vld1q_u8_x4(ql_base + ql_off_base + 64);
+                    uint8x16x4_t q6_qh_0 = vld1q_u8_x4(qh_base + qh_off_base);
+                    uint8x16x4_t q6_qh_1 = vld1q_u8_x4(qh_base + qh_off_base + 64);
+
+                    // Adjust qh for subblocks 2 and 3 (shift right by 2)
+                    if (sb > 1) {
+                        q6_qh_0.val[0] = vshrq_n_u8(q6_qh_0.val[0], 2);
+                        q6_qh_0.val[1] = vshrq_n_u8(q6_qh_0.val[1], 2);
+                        q6_qh_0.val[2] = vshrq_n_u8(q6_qh_0.val[2], 2);
+                        q6_qh_0.val[3] = vshrq_n_u8(q6_qh_0.val[3], 2);
+                        q6_qh_1.val[0] = vshrq_n_u8(q6_qh_1.val[0], 2);
+                        q6_qh_1.val[1] = vshrq_n_u8(q6_qh_1.val[1], 2);
+                        q6_qh_1.val[2] = vshrq_n_u8(q6_qh_1.val[2], 2);
+                        q6_qh_1.val[3] = vshrq_n_u8(q6_qh_1.val[3], 2);
+                    }
+
+                    const uint8x16_t q6_ql[8] = { q6_ql_0.val[0], q6_ql_0.val[1], q6_ql_0.val[2], q6_ql_0.val[3],
+                                                  q6_ql_1.val[0], q6_ql_1.val[1], q6_ql_1.val[2], q6_ql_1.val[3] };
+                    const uint8x16_t q6_qh[8] = { q6_qh_0.val[0], q6_qh_0.val[1], q6_qh_0.val[2], q6_qh_0.val[3],
+                                                  q6_qh_1.val[0], q6_qh_1.val[1], q6_qh_1.val[2], q6_qh_1.val[3] };
+
+                    // Process column groups (0-3, 4-7)
+                    for (int g = 0; g < col_groups; g++) {
+                        int32x4_t sb_acc_l = vdupq_n_s32(0);
+                        int32x4_t sb_acc_h = vdupq_n_s32(0);
+
+                        for (int chunk = 0; chunk < 4; chunk++) {
+                            const int idx = chunk * 2 + g;
+
+                            const uint8x16_t q6_qs_l = q6_ql[idx];
+                            const uint8x16_t q6_qs_h = q6_qh[idx];
+
+                            // Extract high 2 bits for upper nibble reconstruction
+                            const uint8x16_t q6_qs_hh = vandq_u8(q6_qs_h, mask_hi);
+
+                            // q6 = (low4 | high2<<4), without -32 bias (handled via bsums)
+                            const int8x16_t q6_l =
+                                vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_qs_l, m4b), vandq_u8(q6_qs_h, mask_lo), 4));
+                            const int8x16_t q6_h = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_l, 4), q6_qs_hh));
+
+                            sb_acc_l = vdotq_s32(sb_acc_l, q6_l, q8_l[chunk]);
+                            sb_acc_h = vdotq_s32(sb_acc_h, q6_h, q8_h[chunk]);
+                        }
+
+                        const int scale_idx_l = half * 8 + sb;
+                        const int scale_idx_h = half * 8 + sb + 4;
+
+                        const int32x4_t scale_vec_l = vmovl_s16(vld1_s16(q6_scales + scale_idx_l * 8 + g * 4));
+                        const int32x4_t scale_vec_h = vmovl_s16(vld1_s16(q6_scales + scale_idx_h * 8 + g * 4));
+
+                        acc[g] = vmlaq_s32(acc[g], sb_acc_l, scale_vec_l);
+                        acc[g] = vmlaq_s32(acc[g], sb_acc_h, scale_vec_h);
+                    }
+                }
+            }  // for half
+
+            // Bias correction
+            acc[0] = vsubq_s32(acc[0], bias_lo);
+            acc[1] = vsubq_s32(acc[1], bias_hi);
+
+            // Apply superblock scale (no mins for q6_K)
+            // acc[g] has [c0, c1, c2, c3]
+            float32x4_t w_0123 = vmulq_f32(vcvtq_f32_s32(acc[0]), sb_scale_0);
+            float32x4_t w_4567 = vmulq_f32(vcvtq_f32_s32(acc[1]), sb_scale_1);
+
+            acc_f32[0] = vaddq_f32(acc_f32[0], w_0123);
+            acc_f32[1] = vaddq_f32(acc_f32[1], w_4567);
+        }  // for b
+
+        int base = x * ncols_interleaved;
+        vst1q_f32(s + base, acc_f32[0]);
+        vst1q_f32(s + base + 4, acc_f32[1]);
+    }  // for x
+    return;
+#endif  // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    ggml_gemv_q6_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q6_K_8x8_q8_K(int                        n,
                              float * GGML_RESTRICT      s,
                              size_t                     bs,
@@ -1177,15 +1366,14 @@ void ggml_gemv_q6_K_8x8_q8_K(int                        n,
                         q8_h[i] = (int8x16_t) vld1q_dup_s64((const int64_t *) (q8_base_h + i * 8));
                     }
 
-                    // TODO: Test other qh repack patterns to reduce loads
                     const int ql_off_base = sb * QK_K / 2;
                     const int qh_off_base = ql_off_base & 255;  // wraps after 256 bytes
 
                     // Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1)
-                    ggml_uint8x16x4_t q6_ql_0 = ggml_vld1q_u8_x4(ql_base + ql_off_base);
-                    ggml_uint8x16x4_t q6_ql_1 = ggml_vld1q_u8_x4(ql_base + ql_off_base + 64);
-                    ggml_uint8x16x4_t q6_qh_0 = ggml_vld1q_u8_x4(qh_base + qh_off_base);
-                    ggml_uint8x16x4_t q6_qh_1 = ggml_vld1q_u8_x4(qh_base + qh_off_base + 64);
+                    uint8x16x4_t q6_ql_0 = vld1q_u8_x4(ql_base + ql_off_base);
+                    uint8x16x4_t q6_ql_1 = vld1q_u8_x4(ql_base + ql_off_base + 64);
+                    uint8x16x4_t q6_qh_0 = vld1q_u8_x4(qh_base + qh_off_base);
+                    uint8x16x4_t q6_qh_1 = vld1q_u8_x4(qh_base + qh_off_base + 64);
 
                     // Adjust qh for subblocks 2 and 3 (shift right by 2)
                     if (sb > 1) {
@@ -3474,6 +3662,208 @@ void ggml_gemm_q5_K_8x8_q8_K(int                        n,
     ggml_gemm_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
 }
 
+void ggml_gemm_q6_K_8x4_q8_K(int                        n,
+                             float * GGML_RESTRICT      s,
+                             size_t                     bs,
+                             const void * GGML_RESTRICT vx,
+                             const void * GGML_RESTRICT vy,
+                             int                        nr,
+                             int                        nc) {
+    constexpr int qk = QK_K;
+    const int     nb = n / qk;
+
+    constexpr int ncols_interleaved = 8;
+    constexpr int blocklen          = 4;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    constexpr int    q8_k_blocklen = 4;
+    constexpr int    col_groups    = ncols_interleaved / 4;
+    constexpr int    acc_size      = q8_k_blocklen * col_groups;  // 4 rows, 2 column groups
+    const uint8x16_t m4b           = vdupq_n_u8(0x0f);
+    const uint8x16_t mask_lo       = vdupq_n_u8(0x03);
+    const uint8x16_t mask_hi       = vdupq_n_u8(0x30);
+    const int8x16_t  m32s          = vdupq_n_s8(32);
+
+    float32x4_t acc_f32[acc_size];
+
+    for (int y = 0; y < nr / q8_k_blocklen; y++) {
+        const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+            for (int i = 0; i < acc_size; i++) {
+                acc_f32[i] = vdupq_n_f32(0);
+            }
+
+            for (int b = 0; b < nb; b++) {
+                float32x4_t q6_d_0123 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d));
+                float32x4_t q6_d_4567 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4));
+                float32x4_t q8_d_0123 = vld1q_f32(q8_ptr[b].d);
+
+                float32x4_t sbd_scale_0123[q8_k_blocklen];
+                float32x4_t sbd_scale_4567[q8_k_blocklen];
+
+                sbd_scale_0123[0] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 0);
+                sbd_scale_4567[0] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 0);
+                sbd_scale_0123[1] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 1);
+                sbd_scale_4567[1] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 1);
+                sbd_scale_0123[2] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 2);
+                sbd_scale_4567[2] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 2);
+                sbd_scale_0123[3] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 3);
+                sbd_scale_4567[3] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 3);
+
+                int32x4_t acc_s32[acc_size];
+                for (int i = 0; i < acc_size; i++) {
+                    acc_s32[i] = vdupq_n_s32(0);
+                }
+
+                int16_t q6_scales[8 * 16];
+                for (int i = 0; i < 16; i++) {
+                    int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8));
+                    vst1q_s16(q6_scales + i * 8, scales);
+                }
+
+                for (int half = 0; half < 2; half++) {
+                    const uint8_t * ql_base = q6_ptr[b].ql + half * 512;
+                    const uint8_t * qh_base = q6_ptr[b].qh + half * 256;
+
+                    for (int sb = 0; sb < QK_K / 64; sb++) {
+                        int32x4_t acc_lo[acc_size];
+                        int32x4_t acc_hi[acc_size];
+                        for (int i = 0; i < acc_size; i++) {
+                            acc_lo[i] = vdupq_n_s32(0);
+                            acc_hi[i] = vdupq_n_s32(0);
+                        }
+
+                        const int8_t * q8_base_l = q8_ptr[b].qs + half * 512 + sb * 64;
+                        const int8_t * q8_base_h = q8_ptr[b].qs + half * 512 + 256 + sb * 64;
+
+                        // 4 rows * 16 elements per scale
+                        // 4 reads of 16 bytes each
+                        constexpr int reads_per_sb = 4;
+                        int8x16_t     q8_l[reads_per_sb];
+                        int8x16_t     q8_h[reads_per_sb];
+                        for (int k = 0; k < reads_per_sb; k++) {
+                            q8_l[k] = vld1q_s8(q8_base_l + 16 * k);
+                            q8_h[k] = vld1q_s8(q8_base_h + 16 * k);
+                        }
+
+                        const int ql_off_base = sb * QK_K / 2;
+                        const int qh_off_base = ql_off_base & 255;
+
+                        uint8x16_t q6_ql_0123[reads_per_sb];
+                        uint8x16_t q6_ql_4567[reads_per_sb];
+                        uint8x16_t q6_qh_0123[reads_per_sb];
+                        uint8x16_t q6_qh_4567[reads_per_sb];
+
+                        for (int k = 0; k < reads_per_sb; k++) {
+                            q6_ql_0123[k] = vld1q_u8(ql_base + ql_off_base + k * 32);
+                            q6_ql_4567[k] = vld1q_u8(ql_base + ql_off_base + k * 32 + 16);
+                            q6_qh_0123[k] = vld1q_u8(qh_base + qh_off_base + k * 32);
+                            q6_qh_4567[k] = vld1q_u8(qh_base + qh_off_base + k * 32 + 16);
+                        }
+
+                        if (sb > 1) {
+                            for (int k = 0; k < reads_per_sb; k++) {
+                                q6_qh_0123[k] = vshrq_n_u8(q6_qh_0123[k], 2);
+                                q6_qh_4567[k] = vshrq_n_u8(q6_qh_4567[k], 2);
+                            }
+                        }
+
+                        for (int k = 0; k < reads_per_sb; k++) {
+                            // q = (ql | qh) - 32
+                            const uint8x16_t hbit_lo_0123 = vandq_u8(q6_qh_0123[k], mask_lo);
+                            const uint8x16_t hbit_hi_0123 = vandq_u8(q6_qh_0123[k], mask_hi);
+                            const uint8x16_t hbit_lo_4567 = vandq_u8(q6_qh_4567[k], mask_lo);
+                            const uint8x16_t hbit_hi_4567 = vandq_u8(q6_qh_4567[k], mask_hi);
+
+                            const int8x16_t q6_0123_lo = vsubq_s8(
+                                vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_ql_0123[k], m4b), hbit_lo_0123, 4)), m32s);
+                            const int8x16_t q6_0123_hi = vsubq_s8(
+                                vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_ql_0123[k], 4), hbit_hi_0123)), m32s);
+
+                            acc_lo[0] = vdotq_laneq_s32(acc_lo[0], q6_0123_lo, q8_l[k], 0);  //  0..3  r0 c0123
+                            acc_lo[1] = vdotq_laneq_s32(acc_lo[1], q6_0123_lo, q8_l[k], 1);  //  0..3  r1 c0123
+                            acc_lo[2] = vdotq_laneq_s32(acc_lo[2], q6_0123_lo, q8_l[k], 2);  //  0..3  r2 c0123
+                            acc_lo[3] = vdotq_laneq_s32(acc_lo[3], q6_0123_lo, q8_l[k], 3);  //  0..3  r3 c0123
+
+                            acc_hi[0] = vdotq_laneq_s32(acc_hi[0], q6_0123_hi, q8_h[k], 0);  // 64..67 r0 c0123
+                            acc_hi[1] = vdotq_laneq_s32(acc_hi[1], q6_0123_hi, q8_h[k], 1);  // 64..67 r1 c0123
+                            acc_hi[2] = vdotq_laneq_s32(acc_hi[2], q6_0123_hi, q8_h[k], 2);  // 64..67 r2 c0123
+                            acc_hi[3] = vdotq_laneq_s32(acc_hi[3], q6_0123_hi, q8_h[k], 3);  // 64..67 r3 c0123
+
+                            const int8x16_t q6_4567_lo = vsubq_s8(
+                                vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_ql_4567[k], m4b), hbit_lo_4567, 4)), m32s);
+                            const int8x16_t q6_4567_hi = vsubq_s8(
+                                vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_ql_4567[k], 4), hbit_hi_4567)), m32s);
+
+                            acc_lo[4] = vdotq_laneq_s32(acc_lo[4], q6_4567_lo, q8_l[k], 0);  //  0..3  r0 c4567
+                            acc_lo[5] = vdotq_laneq_s32(acc_lo[5], q6_4567_lo, q8_l[k], 1);  //  0..3  r1 c4567
+                            acc_lo[6] = vdotq_laneq_s32(acc_lo[6], q6_4567_lo, q8_l[k], 2);  //  0..3  r2 c4567
+                            acc_lo[7] = vdotq_laneq_s32(acc_lo[7], q6_4567_lo, q8_l[k], 3);  //  0..3  r3 c4567
+
+                            acc_hi[4] = vdotq_laneq_s32(acc_hi[4], q6_4567_hi, q8_h[k], 0);  // 64..67 r0 c4567
+                            acc_hi[5] = vdotq_laneq_s32(acc_hi[5], q6_4567_hi, q8_h[k], 1);  // 64..67 r1 c4567
+                            acc_hi[6] = vdotq_laneq_s32(acc_hi[6], q6_4567_hi, q8_h[k], 2);  // 64..67 r2 c4567
+                            acc_hi[7] = vdotq_laneq_s32(acc_hi[7], q6_4567_hi, q8_h[k], 3);  // 64..67 r3 c4567
+                        }
+
+                        // Scale and bias
+                        const int scale_idx_l = half * 8 + sb;
+                        const int scale_idx_h = half * 8 + sb + 4;
+
+                        for (int g = 0; g < col_groups; g++) {
+                            const int16x4_t scales_l16  = vld1_s16(q6_scales + scale_idx_l * 8 + g * 4);
+                            const int16x4_t scales_h16  = vld1_s16(q6_scales + scale_idx_h * 8 + g * 4);
+                            const int32x4_t scale_vec_l = vmovl_s16(scales_l16);
+                            const int32x4_t scale_vec_h = vmovl_s16(scales_h16);
+                            const int       acc_offset  = g * q8_k_blocklen;
+
+                            for (int row = 0; row < q8_k_blocklen; row++) {
+                                const int idx = row * 2 + g;
+                                acc_s32[idx]  = vmlaq_s32(acc_s32[idx], acc_lo[acc_offset + row], scale_vec_l);
+                                acc_s32[idx]  = vmlaq_s32(acc_s32[idx], acc_hi[acc_offset + row], scale_vec_h);
+                            }
+                        }
+                    }
+                }
+
+                // Finally we apply the superblock scales
+                for (int row = 0; row < q8_k_blocklen; row++) {
+                    const int       idx0     = 2 * row;
+                    const int       idx1     = 2 * row + 1;
+                    const int32x4_t acc_0123 = acc_s32[idx0];
+                    const int32x4_t acc_4567 = acc_s32[idx1];
+
+                    acc_f32[idx0] = vmlaq_f32(acc_f32[idx0], vcvtq_f32_s32(acc_0123), sbd_scale_0123[row]);
+                    acc_f32[idx1] = vmlaq_f32(acc_f32[idx1], vcvtq_f32_s32(acc_4567), sbd_scale_4567[row]);
+                }
+            }  // for b
+
+            for (int i = 0; i < q8_k_blocklen; i++) {
+                int row = y * q8_k_blocklen + i;
+                for (int j = 0; j < 2; j++) {
+                    int col    = x * ncols_interleaved + j * 4;
+                    int offset = row * bs + col;
+                    vst1q_f32(s + offset, acc_f32[2 * i + j]);
+                }
+            }
+        }  // for x
+    }  // for y
+    return;
+#endif  // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    ggml_gemm_q6_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemm_q6_K_8x8_q8_K(int                        n,
                              float * GGML_RESTRICT      s,
                              size_t                     bs,

ggml/src/ggml-cpu/binary-ops.cpp

@@ -59,11 +59,7 @@ static void apply_binary_op(const ggml_compute_params * params, ggml_tensor * ds
     GGML_ASSERT(nb00 == sizeof(src0_t));
 
     const auto [ir0, ir1] = get_thread_range(params, src0);
-    const bool is_src1_contiguous = (nb10 == sizeof(src1_t));
-
-    if (!is_src1_contiguous) { // broadcast not implemented yet for non-contiguous
-        GGML_ASSERT(ggml_are_same_shape(src0, src1));
-    }
+    const bool is_src1_contiguous_rows = ggml_is_contiguous_rows(src1);
 
 #ifdef GGML_USE_ACCELERATE
     vDSP_fn_t vDSP_op = nullptr;
@@ -94,7 +90,7 @@ static void apply_binary_op(const ggml_compute_params * params, ggml_tensor * ds
         const src0_t * src0_ptr = (const src0_t *) ((const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
         const src1_t * src1_ptr = (const src1_t *) ((const char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
 
-        if (is_src1_contiguous) {
+        if (is_src1_contiguous_rows) {
             // src1 is broadcastable across src0 and dst in i1, i2, i3
             const int64_t nr0 = ne00 / ne10;
 

ggml/src/ggml-cpu/ops.cpp

@@ -2096,10 +2096,14 @@ static void ggml_compute_forward_gelu_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2113,10 +2117,14 @@ static void ggml_compute_forward_gelu_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2135,10 +2143,14 @@ static void ggml_compute_forward_gelu_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2152,10 +2164,14 @@ static void ggml_compute_forward_gelu_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2276,10 +2292,14 @@ static void ggml_compute_forward_gelu_erf_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2293,10 +2313,14 @@ static void ggml_compute_forward_gelu_erf_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_erf_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2315,10 +2339,14 @@ static void ggml_compute_forward_gelu_erf_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2332,10 +2360,14 @@ static void ggml_compute_forward_gelu_erf_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_erf_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2379,10 +2411,14 @@ static void ggml_compute_forward_gelu_quick_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2396,10 +2432,14 @@ static void ggml_compute_forward_gelu_quick_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_quick_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2418,10 +2458,14 @@ static void ggml_compute_forward_gelu_quick_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2435,10 +2479,14 @@ static void ggml_compute_forward_gelu_quick_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_quick_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2482,10 +2530,14 @@ static void ggml_compute_forward_silu_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2499,10 +2551,14 @@ static void ggml_compute_forward_silu_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_silu_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2521,10 +2577,14 @@ static void ggml_compute_forward_silu_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2538,10 +2598,14 @@ static void ggml_compute_forward_silu_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_silu_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -7629,8 +7693,7 @@ static void ggml_compute_forward_pad_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-    GGML_ASSERT( dst->nb[0] == sizeof(float));
+    assert(dst->nb[0] == sizeof(float));
 
     const int ith = params->ith;
     const int nth = params->nth;

ggml/src/ggml-cpu/repack.cpp

@@ -256,6 +256,200 @@ template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTR
     ggml_quantize_mat_q8_K_4x8(x, vy, n_per_row);
 }
 
+template <int M, int N>
+static void ggml_gemv_q6_K_NxM_q8_K_generic_impl(int                        n,
+                                                 float * GGML_RESTRICT      s,
+                                                 size_t                     bs,
+                                                 const void * GGML_RESTRICT vx,
+                                                 const void * GGML_RESTRICT vy,
+                                                 int                        nr,
+                                                 int                        nc) {
+    constexpr int blocklen          = M;
+    constexpr int ncols_interleaved = N;
+    const int     qk                = QK_K;
+    const int     nb                = n / qk;
+    const int     blocks_per_half   = 64 / blocklen;
+
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+    UNUSED(nr);
+
+    float sumf[8];
+
+    const block_q8_K * a_ptr = (const block_q8_K *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) {
+            sumf[j] = 0.0f;
+        }
+
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                const int base_l = (k / blocks_per_half) * 128 + (k % blocks_per_half) * blocklen;
+                const int base_h = base_l + 64;
+
+                const int scale_idx_l = base_l / 16;
+                const int scale_idx_h = base_h / 16;
+
+                const int qh_shift_l = ((base_l % 128) / 32) * 2;
+                const int qh_shift_h = ((base_h % 128) / 32) * 2;
+
+                const int qh_half_l = (base_l / 128) * 32;
+                const int qh_half_h = (base_h / 128) * 32;
+
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    const int8_t scale_l = b_ptr[l].scales[scale_idx_l * ncols_interleaved + j];
+                    const int8_t scale_h = b_ptr[l].scales[scale_idx_h * ncols_interleaved + j];
+
+                    int sumi_l = 0;
+                    int sumi_h = 0;
+
+                    for (int i = 0; i < blocklen; i++) {
+                        const int ql_pos = k * ncols_interleaved * blocklen + j * blocklen + i;
+                        const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
+                        const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
+
+                        const int qh_idx_l    = qh_half_l + ((base_l + i) % 32);
+                        const int qh_chunk_l  = qh_idx_l / blocklen;
+                        const int qh_pos_l    = qh_idx_l % blocklen;
+                        const int qh_offset_l = qh_chunk_l * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_l;
+                        const int hi_2_l      = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
+
+                        const int qh_idx_h    = qh_half_h + ((base_h + i) % 32);
+                        const int qh_chunk_h  = qh_idx_h / blocklen;
+                        const int qh_pos_h    = qh_idx_h % blocklen;
+                        const int qh_offset_h = qh_chunk_h * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_h;
+                        const int hi_2_h      = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
+
+                        const int q_l = ((hi_2_l << 4) | l_4) - 32;
+                        const int q_h = ((hi_2_h << 4) | hi_4) - 32;
+
+                        const int8_t a_l = a_ptr[l].qs[base_l + i];
+                        const int8_t a_h = a_ptr[l].qs[base_h + i];
+
+                        sumi_l += q_l * a_l;
+                        sumi_h += q_h * a_h;
+                    }
+
+                    sumf[j] +=
+                        (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
+                }
+            }
+        }
+
+        for (int j = 0; j < ncols_interleaved; j++) {
+            s[x * ncols_interleaved + j] = sumf[j];
+        }
+    }
+}
+
+template <int M, int N>
+static void ggml_gemm_q6_K_NxM_q8_K_generic_impl(int                        n,
+                                                 float * GGML_RESTRICT      s,
+                                                 size_t                     bs,
+                                                 const void * GGML_RESTRICT vx,
+                                                 const void * GGML_RESTRICT vy,
+                                                 int                        nr,
+                                                 int                        nc) {
+    constexpr int blocklen          = M;
+    constexpr int ncols_interleaved = N;
+    const int     qk                = QK_K;
+    const int     nb                = n / qk;
+    const int     blocks_per_half   = 64 / blocklen;
+    const int     q8_half_stride    = 512;
+    const int     q8_low_high_step  = 256;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+
+    float sumf[4][8];
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumf[m][j] = 0.0f;
+                }
+            }
+
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    const int base_l = (k / blocks_per_half) * 128 + (k % blocks_per_half) * blocklen;
+                    const int base_h = base_l + 64;
+
+                    const int scale_idx_l = base_l / 16;
+                    const int scale_idx_h = base_h / 16;
+
+                    const int qh_shift_l = ((base_l % 128) / 32) * 2;
+                    const int qh_shift_h = ((base_h % 128) / 32) * 2;
+
+                    const int qh_half_l = (base_l / 128) * 32;
+                    const int qh_half_h = (base_h / 128) * 32;
+
+                    const int q8_base = (k / blocks_per_half) * q8_half_stride + (k % blocks_per_half) * (blocklen * 4);
+
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            const int8_t scale_l = b_ptr[l].scales[scale_idx_l * ncols_interleaved + j];
+                            const int8_t scale_h = b_ptr[l].scales[scale_idx_h * ncols_interleaved + j];
+
+                            int sumi_l = 0;
+                            int sumi_h = 0;
+
+                            for (int i = 0; i < blocklen; i++) {
+                                const int ql_pos = k * ncols_interleaved * blocklen + j * blocklen + i;
+                                const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
+                                const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
+
+                                const int qh_idx_l   = qh_half_l + ((base_l + i) % 32);
+                                const int qh_chunk_l = qh_idx_l / blocklen;
+                                const int qh_pos_l   = qh_idx_l % blocklen;
+                                const int qh_offset_l =
+                                    qh_chunk_l * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_l;
+                                const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
+
+                                const int qh_idx_h   = qh_half_h + ((base_h + i) % 32);
+                                const int qh_chunk_h = qh_idx_h / blocklen;
+                                const int qh_pos_h   = qh_idx_h % blocklen;
+                                const int qh_offset_h =
+                                    qh_chunk_h * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_h;
+                                const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
+
+                                const int q_l = ((hi_2_l << 4) | l_4) - 32;
+                                const int q_h = ((hi_2_h << 4) | hi_4) - 32;
+
+                                const int8_t q8_l = a_ptr[l].qs[q8_base + m * blocklen + i];
+                                const int8_t q8_h = a_ptr[l].qs[q8_base + m * blocklen + i + q8_low_high_step];
+
+                                sumi_l += q_l * q8_l;
+                                sumi_h += q_h * q8_h;
+                            }
+
+                            sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) *
+                                          a_ptr[l].d[m];
+                        }
+                    }
+                }
+            }
+
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+                }
+            }
+        }
+    }
+}
+
 extern "C" {
 
 void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -704,94 +898,12 @@ void ggml_gemv_q5_K_8x8_q8_K_generic(int                        n,
 }
 
 
+void ggml_gemv_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    ggml_gemv_q6_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
-    constexpr int qk = QK_K;
-    const int nb = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen = 8;
-
-    assert(n % qk == 0);
-    assert(nc % ncols_interleaved == 0);
-
-    UNUSED(bs);
-    UNUSED(nr);
-
-    float sumf[8];
-
-    const block_q8_K * a_ptr = (const block_q8_K *) vy;
-    for (int x = 0; x < nc / ncols_interleaved; x++) {
-        const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
-
-        for (int j = 0; j < ncols_interleaved; j++) {
-            sumf[j] = 0.0f;
-        }
-
-        for (int l = 0; l < nb; l++) {
-
-
-            for (int k = 0; k < 16; k++) {
-                // k = 0.. 7 weights 0-63 low, 64-127 high
-                // k = 8..15 weights 128-191 low, 192-255 high
-                const int base_l = (k / 8) * 128 + (k % 8) * 8;
-                const int base_h = base_l + 64;
-
-                const int scale_idx_l = base_l / 16;
-                const int scale_idx_h = base_h / 16;
-
-                // Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half
-                const int qh_shift_l = ((base_l % 128) / 32) * 2;
-                const int qh_shift_h = ((base_h % 128) / 32) * 2;
-
-                // qh_half: offset to the correct 32-byte half (0 or 32)
-                const int qh_half_l = (base_l / 128) * 32;
-                const int qh_half_h = (base_h / 128) * 32;
-
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    // Interleaved scales
-                    const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j];
-                    const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j];
-
-                    int sumi_l = 0;
-                    int sumi_h = 0;
-
-                    for (int i = 0; i < blocklen; i++) {
-                        const int ql_pos = k * 64 + j * 8 + i;
-                        const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
-                        const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
-
-                        // qh indexing with 8-byte interleaving (like q5_K)
-                        const int qh_byte_l   = qh_half_l + ((base_l + i) % 32);
-                        const int qh_chunk_l  = qh_byte_l / 8;
-                        const int qh_pos_l    = qh_byte_l % 8;
-                        const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l;
-                        const int hi_2_l      = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
-
-                        const int qh_byte_h   = qh_half_h + ((base_h + i) % 32);
-                        const int qh_chunk_h  = qh_byte_h / 8;
-                        const int qh_pos_h    = qh_byte_h % 8;
-                        const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h;
-                        const int hi_2_h      = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
-
-                        const int q_l = ((hi_2_l << 4) | l_4) - 32;
-                        const int q_h = ((hi_2_h << 4) | hi_4) - 32;
-
-                        const int8_t a_l = a_ptr[l].qs[base_l + i];
-                        const int8_t a_h = a_ptr[l].qs[base_h + i];
-
-                        sumi_l += q_l * a_l;
-                        sumi_h += q_h * a_h;
-                    }
-
-                    sumf[j] +=
-                        (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
-                }
-            }
-        }
-
-        for (int j = 0; j < ncols_interleaved; j++) {
-            s[x * ncols_interleaved + j] = sumf[j];
-        }
-    }
+    ggml_gemv_q6_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -1485,109 +1597,12 @@ void ggml_gemm_q5_K_8x8_q8_K_generic(int                        n,
     }
 }
 
-void ggml_gemm_q6_K_8x8_q8_K_generic(int                        n,
-                                     float * GGML_RESTRICT      s,
-                                     size_t                     bs,
-                                     const void * GGML_RESTRICT vx,
-                                     const void * GGML_RESTRICT vy,
-                                     int                        nr,
-                                     int                        nc) {
-    const int qk                = QK_K;
-    const int nb                = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen          = 8;
+void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    ggml_gemm_q6_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc);
+}
 
-    assert(n % qk == 0);
-    assert(nr % 4 == 0);
-    assert(nc % ncols_interleaved == 0);
-
-    UNUSED(bs);
-
-    float sumf[4][8];
-
-    for (int y = 0; y < nr / 4; y++) {
-        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
-
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    sumf[m][j] = 0.0f;
-                }
-            }
-
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < 16; k++) {
-                    // k = 0.. 7 weights 0-63 low, 64-127 high
-                    // k = 8..15 weights 128-191 low, 192-255 high
-                    const int base_l = (k / 8) * 128 + (k % 8) * 8;
-                    const int base_h = base_l + 64;
-
-                    const int scale_idx_l = base_l / 16;
-                    const int scale_idx_h = base_h / 16;
-
-                    // Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half
-                    const int qh_shift_l = ((base_l % 128) / 32) * 2;
-                    const int qh_shift_h = ((base_h % 128) / 32) * 2;
-
-                    // qh_half: offset to the correct 32-byte half (0 or 32)
-                    const int qh_half_l = (base_l / 128) * 32;
-                    const int qh_half_h = (base_h / 128) * 32;
-
-                    // Activation base indices for q8_Kx4 interleaved format
-                    // Layout: 128-value halves (k/8), then 8-value sub-blocks (k%8) with stride 32
-                    const int q8_base = (k / 8) * 512 + (k % 8) * 32;
-
-                    for (int m = 0; m < 4; m++) {
-                        for (int j = 0; j < ncols_interleaved; j++) {
-                            // Interleaved scales
-                            const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j];
-                            const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j];
-
-                            int sumi_l = 0;
-                            int sumi_h = 0;
-
-                            for (int i = 0; i < blocklen; i++) {
-                                const int ql_pos = k * 64 + j * 8 + i;
-                                const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
-                                const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
-
-                                const int qh_idx_l    = qh_half_l + ((base_l + i) % 32);
-                                const int qh_chunk_l  = qh_idx_l / 8;
-                                const int qh_pos_l    = qh_idx_l % 8;
-                                const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l;
-                                const int hi_2_l      = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
-
-                                const int qh_idx_h    = qh_half_h + ((base_h + i) % 32);
-                                const int qh_chunk_h  = qh_idx_h / 8;
-                                const int qh_pos_h    = qh_idx_h % 8;
-                                const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h;
-                                const int hi_2_h      = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
-
-                                const int q_l = ((hi_2_l << 4) | l_4) - 32;
-                                const int q_h = ((hi_2_h << 4) | hi_4) - 32;
-
-                                const int8_t q8_l = a_ptr[l].qs[q8_base + m * 8 + i];
-                                const int8_t q8_h = a_ptr[l].qs[q8_base + m * 8 + i + 256];
-
-                                sumi_l += q_l * q8_l;
-                                sumi_h += q_h * q8_h;
-                            }
-
-                            sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) *
-                                          a_ptr[l].d[m];
-                        }
-                    }
-                }
-            }
-
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
-                }
-            }
-        }
-    }
+void ggml_gemm_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+   ggml_gemm_q6_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -2097,18 +2112,18 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in
     }
 
     const int end_ls = QK_K * 4 / blck_size_interleave;
-    // Interleave Q6_K quants by taking 8 bytes at a time
+    // Interleave Q6_K quants by taking blck_size_interleave bytes at a time
     for (int i = 0; i < end_ls; ++i) {
         int src_id     = i % n_blocks;
         int src_offset = (i / n_blocks) * blck_size_interleave;
         int dst_offset = i * blck_size_interleave;
 
         uint64_t elem_ls;
-        memcpy(&elem_ls, &in[src_id].ql[src_offset], sizeof(uint64_t));
-        memcpy(&out.ql[dst_offset], &elem_ls, sizeof(uint64_t));
+        memcpy(&elem_ls, &in[src_id].ql[src_offset], blck_size_interleave);
+        memcpy(&out.ql[dst_offset], &elem_ls, blck_size_interleave);
     }
 
-    // Interleave high bits using same 8-byte pattern as low bits
+    // Interleave high bits using same chunk size as low bits
     const int end_hs = end_ls / 2;
     for (int i = 0; i < end_hs; ++i) {
         int src_id     = i % n_blocks;
@@ -2116,8 +2131,8 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in
         int dst_offset = i * blck_size_interleave;
 
         uint64_t elem_hs;
-        memcpy(&elem_hs, &in[src_id].qh[src_offset], sizeof(uint64_t));
-        memcpy(&out.qh[dst_offset], &elem_hs, sizeof(uint64_t));
+        memcpy(&elem_hs, &in[src_id].qh[src_offset], blck_size_interleave);
+        memcpy(&out.qh[dst_offset], &elem_hs, blck_size_interleave);
     }
 
     // The below logic is designed so as to unpack and rearrange scales in Q6_K
@@ -2262,7 +2277,7 @@ static int repack_q5_K_to_q5_K_8_bl(struct ggml_tensor *       t,
 
 static int repack_q6_K_to_q6_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q6_K);
-    GGML_ASSERT(interleave_block == 8);
+    GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
     constexpr int nrows_interleaved = 8;
 
     block_q6_Kx8 * dst = (block_q6_Kx8 *)t->data;
@@ -2511,6 +2526,10 @@ template <> int repack<block_q5_K, 8, 8>(struct ggml_tensor * t, const void * da
     return repack_q5_K_to_q5_K_8_bl(t, 8, data, data_size);
 }
 
+template <> int repack<block_q6_K, 4, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q6_K_to_q6_K_8_bl(t, 4, data, data_size);
+}
+
 template <> int repack<block_q6_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
     return repack_q6_K_to_q6_K_8_bl(t, 8, data, data_size);
 }
@@ -2575,6 +2594,10 @@ template <> void gemv<block_q5_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
     ggml_gemv_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemv<block_q6_K, 4, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q6_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemv<block_q6_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
@@ -2634,6 +2657,10 @@ template <> void gemm<block_q5_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
     ggml_gemm_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemm<block_q6_K, 4, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q6_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemm<block_q6_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
@@ -3043,6 +3070,7 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
     static const ggml::cpu::repack::tensor_traits<block_q5_K, 8, 8, GGML_TYPE_Q8_K> q5_K_8x8_q8_K;
 
     // instance for Q6_K
+    static const ggml::cpu::repack::tensor_traits<block_q6_K, 4, 8, GGML_TYPE_Q8_K> q6_K_8x4_q8_K;
     static const ggml::cpu::repack::tensor_traits<block_q6_K, 8, 8, GGML_TYPE_Q8_K> q6_K_8x8_q8_K;
 
     // instance for Q2
@@ -3107,6 +3135,11 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
                 return &q6_K_8x8_q8_K;
             }
         }
+        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
+            if (cur->ne[1] % 8 == 0) {
+                return &q6_K_8x4_q8_K;
+            }
+        }
     } else if (cur->type == GGML_TYPE_IQ4_NL) {
         if (ggml_cpu_has_avx2()) {
             if (cur->ne[1] % 8 == 0) {

ggml/src/ggml-cpu/repack.h

@@ -112,6 +112,7 @@ void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -122,6 +123,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -142,6 +144,7 @@ void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemv_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -152,6 +155,7 @@ void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemm_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);

ggml/src/ggml-cpu/unary-ops.cpp

@@ -111,7 +111,7 @@ template <float (*op)(float), typename src0_t, typename dst_t>
 static void apply_unary_op(const ggml_compute_params * params, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_1(src0) && ggml_is_contiguous_1(dst) && ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_contiguous_rows(src0) && ggml_is_contiguous_rows(dst) && ggml_are_same_shape(src0, dst));
 
     GGML_TENSOR_UNARY_OP_LOCALS
 

ggml/src/ggml-cuda/CMakeLists.txt

@@ -64,7 +64,7 @@ if (CUDAToolkit_FOUND)
         FetchContent_Declare(
             CCCL
             GIT_REPOSITORY https://github.com/nvidia/cccl.git
-            GIT_TAG        v3.2.0-rc2
+            GIT_TAG        v3.2.0
             GIT_SHALLOW    TRUE
         )
 

ggml/src/ggml-cuda/binbcast.cu

@@ -39,13 +39,16 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
                                    const uint3            ne11,
                                    const uint3            ne12,
                                    const uint3            ne13,
-                                   /*int s0, */ const int s1,
+                                 /*const int              s0,*/
+                                   const int              s1,
                                    const int              s2,
                                    const int              s3,
-                                   /*int s00,*/ const int s01,
+                                   const int              s00,
+                                   const int              s01,
                                    const int              s02,
                                    const int              s03,
-                                   /*int s10,*/ const int s11,
+                                   const int              s10,
+                                   const int              s11,
                                    const int              s12,
                                    const int              s13,
                                    src1_ptrs... src1s) {
@@ -72,11 +75,11 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     for (int i0 = i0s; i0 < ne0; i0 += blockDim.x * gridDim.x) {
         const uint32_t i10 = fastmodulo(i0, ne10);
 
-        float result = src0_row ? (float) src0_row[i0] : 0.0f;
+        float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
         if constexpr (sizeof...(src1_ptrs) > 0) {
-            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
         } else {
-            result = bin_op(result, (float)src1[i_src1 + i10]);
+            result = bin_op(result, (float)src1[i_src1 + i10*s10]);
         }
 
         dst_row[i0] = (dst_t) result;
@@ -101,13 +104,16 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
                                            const uint3            ne11,
                                            const uint3            ne12,
                                            const uint3            ne13,
-                                           /*int s0, */ const int s1,
+                                         /*const int              s0,*/
+                                           const int              s1,
                                            const int              s2,
                                            const int              s3,
-                                           /*int s00,*/ const int s01,
+                                           const int              s00,
+                                           const int              s01,
                                            const int              s02,
                                            const int              s03,
-                                           /*int s10,*/ const int s11,
+                                           const int              s10,
+                                           const int              s11,
                                            const int              s12,
                                            const int              s13,
                                            src1_ptrs... src1s) {
@@ -135,11 +141,11 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
 
     const int i10 = fastmodulo(i0, ne10);
 
-    float result = src0_row ? (float) src0_row[i0] : 0.0f;
+    float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
     if constexpr (sizeof...(src1_ptrs) > 0) {
-        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
     } else {
-        result = bin_op(result, (float)src1[i_src1 + i10]);
+        result = bin_op(result, (float)src1[i_src1 + i10*s10]);
     }
 
     dst_row[i0] = (dst_t) result;
@@ -179,7 +185,7 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         cnb[3] *= cne[3];
     };
 
-    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) {
         for (int i = 0; i < 4; i++) {
             if (nr[i] != 1) {
                 break;
@@ -221,7 +227,7 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         size_t nb12 = cnb1[2];
         size_t nb13 = cnb1[3];
 
-        size_t s0 = nb0 / sizeof(dst_t);
+      //size_t s0 = nb0 / sizeof(dst_t);
         size_t s1 = nb1 / sizeof(dst_t);
         size_t s2 = nb2 / sizeof(dst_t);
         size_t s3 = nb3 / sizeof(dst_t);
@@ -251,10 +257,6 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
         GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
 
-        GGML_ASSERT(s0 == 1);
-        GGML_ASSERT(s00 == 1);
-        GGML_ASSERT(s10 == 1);
-
         const int block_size = 128;
 
         int64_t hne0 = std::max(ne0 / 2LL, 1LL);
@@ -284,31 +286,31 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
                 k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t><<<block_num, block_size, 0, stream>>>(
                     src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv, ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11,
                     ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
+                  /*s0,*/ s1,  s2,  s3,
+                    s00, s01, s02, s03,
+                    s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
             } else {
                 k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
                     <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv,
                                                            ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11, ne12, ne13,
-                                                           /* s0, */ s1, s2, s3,
-                                                           /* s00,*/ s01, s02, s03,
-                                                           /* s10,*/ s11, s12, s13);
+                                                         /*s0,*/ s1,  s2,  s3,
+                                                           s00, s01, s02, s03,
+                                                           s10, s11, s12, s13);
             }
         } else {
             const uint3 ne3_fastdiv = init_fastdiv_values((uint32_t) ne3);
             if constexpr (sizeof...(I) > 0) {
                 k_bin_bcast<bin_op, src0_t, src1_t, dst_t><<<block_nums, block_dims, 0, stream>>>(
                     src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
+                  /*s0,*/ s1, s2,  s3,
+                    s00 ,s01, s02, s03,
+                    s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
             } else {
                 k_bin_bcast<bin_op, src0_t, src1_t, dst_t><<<block_nums, block_dims, 0, stream>>>(
                     src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13);
+                  /*s0,*/ s1,  s2,  s3,
+                    s00, s01, s02, s03,
+                    s10, s11, s12, s13);
             }
         }
     }

ggml/src/ggml-cuda/fattn-wmma-f16.cu

@@ -63,11 +63,19 @@ static __global__ void flash_attn_ext_f16(
     constexpr int frag_m = ncols == 8 ? 32 : 16;
     constexpr int frag_n = ncols == 8 ?  8 : 16;
     static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
+#if defined(GGML_USE_HIP)
+    typedef wmma::fragment<wmma::matrix_a,    frag_m, frag_n, 16, _Float16, wmma::row_major> frag_a_K;
+    typedef wmma::fragment<wmma::matrix_a,    frag_m, frag_n, 16, _Float16, wmma::col_major> frag_a_V;
+    typedef wmma::fragment<wmma::matrix_b,    frag_m, frag_n, 16, _Float16, wmma::col_major> frag_b;
+    typedef wmma::fragment<wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t>                      frag_c_KQ;
+    typedef wmma::fragment<wmma::accumulator, frag_m, frag_n, 16, _Float16>                          frag_c_VKQ;
+#else
     typedef wmma::fragment<wmma::matrix_a,    frag_m, frag_n, 16, half, wmma::row_major> frag_a_K;
     typedef wmma::fragment<wmma::matrix_a,    frag_m, frag_n, 16, half, wmma::col_major> frag_a_V;
     typedef wmma::fragment<wmma::matrix_b,    frag_m, frag_n, 16, half, wmma::col_major> frag_b;
     typedef wmma::fragment<wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t>                      frag_c_KQ;
     typedef wmma::fragment<wmma::accumulator, frag_m, frag_n, 16, half>                          frag_c_VKQ;
+#endif
 
     constexpr int KQ_stride_tc  = nwarps*frag_m; // Number of KQ rows calculated in parallel.
     constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
@@ -126,6 +134,19 @@ static __global__ void flash_attn_ext_f16(
 
     __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
     half2 * VKQ2 = (half2 *) VKQ;
+
+#if defined(GGML_USE_HIP)
+    const _Float16 * K_h_f16  = reinterpret_cast<const _Float16 *>(K_h);
+    const _Float16 * V_h_f16  = reinterpret_cast<const _Float16 *>(V_h);
+    _Float16       * KQ_f16   = reinterpret_cast<_Float16 *>(KQ);
+    _Float16       * VKQ_f16  = reinterpret_cast<_Float16 *>(VKQ);
+#else
+    const half * K_h_f16  = K_h;
+    const half * V_h_f16  = V_h;
+    half       * KQ_f16   = KQ;
+    half       * VKQ_f16  = VKQ;
+#endif
+
 #pragma unroll
     for (int j0 = 0; j0 < ncols; j0 += nwarps) {
         const int j = j0 + threadIdx.y;
@@ -160,7 +181,7 @@ static __global__ void flash_attn_ext_f16(
     for (int i0 = 0; i0 < D; i0 += 16) {
 #pragma unroll
         for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-            wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
+            wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ_f16 + j0*D_padded + i0, D_padded);
         }
     }
 
@@ -180,7 +201,7 @@ static __global__ void flash_attn_ext_f16(
 #pragma unroll
             for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
                 frag_a_K K_a;
-                wmma::load_matrix_sync(K_a, K_h + int64_t(k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
+                wmma::load_matrix_sync(K_a, K_h_f16 + int64_t(k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
 #pragma unroll
                 for (int j = 0; j < ncols/frag_n; ++j) {
                     wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
@@ -310,7 +331,7 @@ static __global__ void flash_attn_ext_f16(
                 const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
                 wmma::load_matrix_sync(
                     KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
-                    KQ + j0*(kqar*kqs_padded) + k,
+                    KQ_f16 + j0*(kqar*kqs_padded) + k,
                     kqar*kqs_padded);
             }
         }
@@ -328,7 +349,7 @@ static __global__ void flash_attn_ext_f16(
                 const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
 
                 frag_a_V v_a;
-                wmma::load_matrix_sync(v_a, V_h + int64_t(k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
+                wmma::load_matrix_sync(v_a, V_h_f16 + int64_t(k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
 #pragma unroll
                 for (int j = 0; j < ncols/frag_n; ++j) {
                     wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
@@ -344,7 +365,7 @@ static __global__ void flash_attn_ext_f16(
 #pragma unroll
             for (int j0 = 0; j0 < ncols; j0 += frag_n) {
                 wmma::store_matrix_sync(
-                    KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
+                    KQ_f16 + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
                     VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
                     D_padded, wmma::mem_col_major);
             }

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

@@ -4834,8 +4834,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SUM_ROWS:
         case GGML_OP_MEAN:
         case GGML_OP_GROUP_NORM:
-        case GGML_OP_PAD:
             return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_PAD:
+            return true;
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_ARANGE:

ggml/src/ggml-cuda/pad.cu

@@ -7,7 +7,7 @@ __device__ __forceinline__ int64_t wrap_around(int64_t coord, int64_t size) {
     return (coord + size) % size;
 }
 
-static __global__ void pad_f32(const float * src, float * dst,
+static __global__ void pad_f32(const float * src, size_t s00, size_t s01, size_t s02, size_t s03, float * dst,
                                const int lp0, const int rp0, const int lp1, const int rp1,
                                const int lp2, const int rp2, const int lp3, const int rp3,
                                const int ne0, const int ne1, const int ne2, const int ne3,
@@ -34,11 +34,8 @@ static __global__ void pad_f32(const float * src, float * dst,
             const int64_t i01  = i1 - lp1;
             const int64_t i02  = i2 - lp2;
             const int64_t i03  = i3 - lp3;
-            const int64_t ne02 = ne2 - lp2 - rp2;
-            const int64_t ne01 = ne1 - lp1 - rp1;
-            const int64_t ne00 = ne0 - lp0 - rp0;
 
-            const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
+            const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
 
             dst[dst_idx] = src[src_idx];
         } else {
@@ -57,21 +54,21 @@ static __global__ void pad_f32(const float * src, float * dst,
         const int64_t i02 = wrap_around(i2 - lp2, ne02);
         const int64_t i03 = wrap_around(i3 - lp3, ne03);
 
-        const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
+        const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
 
         dst[dst_idx] = src[src_idx];
     }
 }
 
 
-static void pad_f32_cuda(const float * src, float * dst,
+static void pad_f32_cuda(const float * src, size_t s00, size_t s01, size_t s02, size_t s03, float * dst,
     const int lp0, const int rp0, const int lp1, const int rp1,
     const int lp2, const int rp2, const int lp3, const int rp3,
     const int ne0, const int ne1, const int ne2, const int ne3,
     const bool circular, cudaStream_t stream) {
     int  num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
     dim3 gridDim(num_blocks, ne1, ne2 * ne3);
-    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, dst,
+    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, s00, s01, s02, s03, dst,
                                                          lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
                                                          ne0, ne1, ne2, ne3, circular);
 }
@@ -82,9 +79,10 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float *             dst_d  = (float *) dst->data;
     cudaStream_t        stream = ctx.stream();
 
+    GGML_TENSOR_UNARY_OP_LOCALS;
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous(src0));
 
     const int32_t lp0      = ((const int32_t *) (dst->op_params))[0];
     const int32_t rp0      = ((const int32_t *) (dst->op_params))[1];
@@ -96,7 +94,12 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int32_t rp3      = ((const int32_t *) (dst->op_params))[7];
     const int32_t circular = ((const int32_t *) (dst->op_params))[8];
 
-    pad_f32_cuda(src0_d, dst_d,
+    const size_t s00 = nb00 / ggml_type_size(src0->type);
+    const size_t s01 = nb01 / ggml_type_size(src0->type);
+    const size_t s02 = nb02 / ggml_type_size(src0->type);
+    const size_t s03 = nb03 / ggml_type_size(src0->type);
+
+    pad_f32_cuda(src0_d, s00, s01, s02, s03, dst_d,
                  lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
                  dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
                  (bool) circular, stream);

ggml/src/ggml-hexagon/ggml-hexagon.cpp

@@ -1935,11 +1935,6 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se
         return false;
     }
 
-    // TODO: add support for non-contigiuos tensors
-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
-        return false;
-    }
-
     return true;
 }
 
@@ -1991,6 +1986,25 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses
     return true;
 }
 
+static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0];
+    const struct ggml_tensor * dst  = op;
+
+    if (!hex_supported_src0_type(src0->type)) {
+        return false;
+    }
+    if (!hex_supported_dst_type(dst->type)) {
+        return false;
+    }
+
+    // TODO: add support for non-contigiuos tensors
+    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+        return false;
+    }
+
+    return true;
+}
+
 static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess,
                                                const struct ggml_tensor *          op) {
     const struct ggml_tensor * src0 = op->src[0];
@@ -2111,6 +2125,26 @@ static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session *
     return true;
 }
 
+static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0]; // values
+    const struct ggml_tensor * dst  = op;         // indices
+
+    if (src0->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    if (dst->type != GGML_TYPE_I32) {
+        return false;
+    }
+
+    if (src0->ne[0] > (16*1024)) {
+        // reject tensors with huge rows for now
+        return false;
+    }
+
+    return true;
+}
+
 static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
     const int32_t * op_params = &op->op_params[0];
 
@@ -2278,6 +2312,9 @@ static inline size_t init_binary_req(htp_general_req * req, dspqueue_buffer * bu
         case GGML_OP_SUB:
             req->op = HTP_OP_SUB;
             break;
+        case GGML_OP_DIV:
+            req->op = HTP_OP_DIV;
+            break;
         default:
             GGML_ABORT("ggml-hex: binary : unsupported op: %d\n", t->op);
             break;
@@ -2316,6 +2353,17 @@ static inline size_t init_get_rows_req(htp_general_req * req, dspqueue_buffer *
     return n_bufs;
 }
 
+static inline size_t init_argsort_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
+    req->op = HTP_OP_ARGSORT;
+    memcpy(&req->op_params, &t->op_params, sizeof(t->op_params));
+
+    size_t n_bufs = 0;
+    n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ);
+    n_bufs += htp_req_buff_init(&req->dst,  &bufs[n_bufs], t,         DSPQBUF_TYPE_DSP_WRITE_CPU_READ);
+
+    return n_bufs;
+}
+
 template <bool _is_src0_constant>
 static inline size_t init_binary_id_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
     switch (t->op) {
@@ -2370,6 +2418,16 @@ static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * buf
             supported = true;
             break;
 
+        case GGML_OP_SQR:
+            req->op   = HTP_OP_SQR;
+            supported = true;
+            break;
+
+        case GGML_OP_SQRT:
+            req->op   = HTP_OP_SQRT;
+            supported = true;
+            break;
+
         case GGML_OP_UNARY:
             if (ggml_get_unary_op(t) == GGML_UNARY_OP_SILU) {
                 req->op   = HTP_OP_UNARY_SILU;
@@ -2387,6 +2445,9 @@ static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * buf
             } else if (ggml_get_glu_op(t) == GGML_GLU_OP_SWIGLU_OAI) {
                 req->op   = HTP_OP_GLU_SWIGLU_OAI;
                 supported = true;
+            } else if (ggml_get_glu_op(t) == GGML_GLU_OP_GEGLU) {
+                req->op   = HTP_OP_GLU_GEGLU;
+                supported = true;
             }
             break;
 
@@ -2411,6 +2472,17 @@ static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * buf
     return n_bufs;
 }
 
+static inline size_t init_sum_rows_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
+    memcpy(&req->op_params, &t->op_params, sizeof(t->op_params));
+    req->op = HTP_OP_SUM_ROWS;
+
+    size_t n_bufs = 0;
+    n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ);
+    n_bufs += htp_req_buff_init(&req->dst,  &bufs[n_bufs], t,         DSPQBUF_TYPE_DSP_WRITE_CPU_READ);
+
+    return n_bufs;
+}
+
 static inline size_t init_rope_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
     memcpy(&req->op_params, &t->op_params, sizeof(t->op_params));
     req->op = HTP_OP_ROPE;
@@ -2519,6 +2591,7 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
             case GGML_OP_MUL:
             case GGML_OP_ADD:
             case GGML_OP_SUB:
+            case GGML_OP_DIV:
                 ggml_hexagon_dispatch_op<init_binary_req<false>>(sess, node, flags);
                 break;
             case GGML_OP_ADD_ID:
@@ -2528,6 +2601,13 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
             case GGML_OP_SCALE:
                 ggml_hexagon_dispatch_op<init_unary_req>(sess, node, flags);
                 break;
+            case GGML_OP_SQR:
+            case GGML_OP_SQRT:
+                ggml_hexagon_dispatch_op<init_unary_req>(sess, node, flags);
+                break;
+            case GGML_OP_SUM_ROWS:
+                ggml_hexagon_dispatch_op<init_sum_rows_req>(sess, node, flags);
+                break;
             case GGML_OP_UNARY:
                 if ((ggml_get_unary_op(node) == GGML_UNARY_OP_SILU) ||
                         (ggml_get_unary_op(node) == GGML_UNARY_OP_GELU)) {
@@ -2536,7 +2616,8 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 break;
             case GGML_OP_GLU:
                 if ((ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU) ||
-                        (ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU_OAI)) {
+                        (ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU_OAI) ||
+                        (ggml_get_glu_op(node) == GGML_GLU_OP_GEGLU)) {
                     ggml_hexagon_dispatch_op<init_unary_req>(sess, node, flags);
                 }
                 break;
@@ -2564,6 +2645,10 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 ggml_hexagon_dispatch_op<init_cpy_req>(sess, node, flags);
                 break;
 
+            case GGML_OP_ARGSORT:
+                ggml_hexagon_dispatch_op<init_argsort_req>(sess, node, flags);
+                break;
+
             default:
                 GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node));
         }
@@ -2916,6 +3001,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_MUL:
         case GGML_OP_ADD:
         case GGML_OP_SUB:
+        case GGML_OP_DIV:
             supp = ggml_hexagon_supported_binary(sess, op);
             break;
 
@@ -2928,6 +3014,15 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_unary(sess, op);
             break;
 
+        case GGML_OP_SQR:
+        case GGML_OP_SQRT:
+            supp = ggml_hexagon_supported_unary(sess, op);
+            break;
+
+        case GGML_OP_SUM_ROWS:
+            supp = ggml_hexagon_supported_sum_rows(sess, op);
+            break;
+
         case GGML_OP_SOFT_MAX:
             supp = ggml_hexagon_supported_softmax(sess, op);
             break;
@@ -2943,7 +3038,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_GLU:
             {
                 const auto glu_op = ggml_get_glu_op(op);
-                if ((glu_op == GGML_GLU_OP_SWIGLU) || (glu_op == GGML_GLU_OP_SWIGLU_OAI)) {
+                if ((glu_op == GGML_GLU_OP_SWIGLU) || (glu_op == GGML_GLU_OP_SWIGLU_OAI) || (glu_op == GGML_GLU_OP_GEGLU)) {
                     supp = ggml_hexagon_supported_activations(sess, op);
                 }
                 break;
@@ -2968,6 +3063,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_cpy(sess, op);
             break;
 
+        case GGML_OP_ARGSORT:
+            supp = ggml_hexagon_supported_argsort(sess, op);
+            break;
+
         default:
             break;
     }

ggml/src/ggml-hexagon/htp/CMakeLists.txt

@@ -6,6 +6,7 @@ include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake)
 include_directories(
     ${HEXAGON_SDK_ROOT}/incs
     ${HEXAGON_SDK_ROOT}/incs/stddef
+    ${CMAKE_CURRENT_SOURCE_DIR}/../../../include
     ${CMAKE_CURRENT_SOURCE_DIR}/../..
     ${CMAKE_CURRENT_SOURCE_DIR}/..
     ${CMAKE_CURRENT_SOURCE_DIR}
@@ -21,6 +22,7 @@ add_library(${HTP_LIB} SHARED
     matmul-ops.c
     binary-ops.c
     unary-ops.c
+    sum-rows-ops.c
     softmax-ops.c
     act-ops.c
     rope-ops.c
@@ -28,6 +30,7 @@ add_library(${HTP_LIB} SHARED
     set-rows-ops.c
     get-rows-ops.c
     cpy-ops.c
+    argsort-ops.c
 )
 
 target_compile_definitions(${HTP_LIB} PRIVATE

ggml/src/ggml-hexagon/htp/act-ops.c

@@ -410,7 +410,7 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0,
             // gelu = x * sigmoid(1.702 * x) // current implementation
             hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0);
             hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
-            hvx_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue,
@@ -516,7 +516,7 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
 
             // silu = x * sigmoid(x)
             hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0);
-            hvx_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue,
@@ -541,6 +541,143 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
          ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
+static const float GELU_COEF_A     = 0.044715f;
+static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+
+static void glu_geglu_f32_per_thread(const struct htp_tensor * src0,
+                                       const struct htp_tensor * src1,
+                                       struct htp_tensor *       dst,
+                                       const int32_t *           op_params,
+                                       struct htp_spad *         src0_spad,
+                                       struct htp_spad *         src1_spad,
+                                       struct htp_spad *         dst_spad,
+                                       uint32_t                  nth,
+                                       uint32_t                  ith,
+                                       uint32_t                  src0_nrows_per_thread,
+                                       dma_queue *               dma_queue) {
+    htp_act_preamble3;
+
+    size_t src0_row_size = nb01;
+    size_t src1_row_size = nb11;
+    size_t dst_row_size  = nb1;
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
+
+    const uint32_t src0_start_row = src0_nrows_per_thread * ith;
+    const uint32_t src0_end_row   = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+
+    // no work for this thread
+    if (src0_start_row >= src0_end_row) {
+        return;
+    }
+
+    const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
+    const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
+    uint8_t * restrict data_dst        = (uint8_t *) dst->data;
+
+    const bool src1_valid = src1->ne[0];
+    const int  nc         = (src1_valid) ? ne00 : ne00 / 2;
+    if (!src1_valid) {
+        const int32_t swapped = op_params[1];
+        data_src1             = data_src0;
+        src1_row_size         = src0_row_size;
+
+        const size_t nc_in_bytes = nc * SIZEOF_FP32;
+        data_src0 += swapped ? nc_in_bytes : 0;
+        data_src1 += swapped ? 0 : nc_in_bytes;
+    }
+
+    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
+    const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
+    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+
+    uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
+    uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread);
+    uint8_t * restrict dst_spad_data  = dst_spad->data + (ith * dst_spad->size_per_thread);
+
+    // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0
+    size_t src0_spad_half_size = src0_spad->size_per_thread / 2;
+    size_t src1_spad_half_size = src1_spad->size_per_thread / 2;
+    size_t dst_spad_half_size  = dst_spad->size_per_thread / 2;
+
+    const int BLOCK = src0_spad_half_size / src0_row_size_aligned;  // How many rows can we process in one block
+    if (BLOCK == 0) {
+        FARF(ERROR,
+             "geglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n",
+             src0_spad->size_per_thread, src0_row_size_aligned);
+        return;
+    }
+
+    // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379
+    for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) {
+        const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
+
+        // Dummy DMA transation for sequencing (interleaving dst,src,dst,...)
+        dma_queue_push_vtcm_to_ddr(dma_queue,
+            dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)),
+            dst_row_size, dst_row_size_aligned, 0);
+
+        dma_queue_push_ddr_to_vtcm(dma_queue,
+            dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)),
+            src0_row_size_aligned, src0_row_size, block_size);
+        dma_queue_push_ddr_to_vtcm(dma_queue,
+            dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)),
+            src1_row_size_aligned, src1_row_size, block_size);
+    }
+
+    for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
+        const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
+
+        float * dst_spad  = (float *) dma_queue_pop(dma_queue).src;
+        float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
+        float * src1_spad = (float *) dma_queue_pop(dma_queue).dst;
+
+        for (uint32_t ib = 0; ib < block_size; ib++) {
+            const uint8_t * src0_spad_ptr = (const uint8_t *)(src0_spad + ib * (src0_row_size_aligned / sizeof(float)));
+            const uint8_t * src1_spad_ptr = (const uint8_t *)(src1_spad + ib * (src1_row_size_aligned / sizeof(float)));
+            uint8_t *       dst_spad_ptr  = (uint8_t *)(dst_spad + ib * (dst_row_size_aligned / sizeof(float)));
+
+            // geglu tanh implementation
+            // geglu(x, g) = gelu(x) * g
+            // gelu(x) = 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)))
+            hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, src0_spad_ptr, nc);                       // res = x*x
+            hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, GELU_COEF_A, nc);   // res = res * GELU_COEF_A
+            hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 1.0f, nc);          // res = res + 1.0f
+            hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc);       // res = res * x
+            hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, SQRT_2_OVER_PI, nc); // res = result * SQRT_2_OVER_PI
+            hvx_tanh_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc);         // res = tanh(res)
+            hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, 1.0f, nc);           // res = res + 1.0f
+            hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc);       // res = res * x
+            hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 0.5f, nc);          // res = res + 0.5f
+            hvx_mul_f32_aaa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, src1_spad_ptr, nc);       // res = res * g
+        }
+
+        dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size,
+                                   dst_row_size_aligned, block_size);
+
+        // prefetch N+2 loop iteration if any
+        const uint32_t pref_block = (ir + BLOCK * 2);
+        if (pref_block < src0_end_row) {
+            const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block);
+            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)),
+                                       src0_row_size_aligned, src0_row_size, pref_block_size);
+            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)),
+                                       src1_row_size_aligned, src1_row_size, pref_block_size);
+        }
+    }
+
+    dma_queue_flush(dma_queue);
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "geglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth,
+         ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3,
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
 static void unary_silu_f32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = (struct htp_ops_context *) data;
     unary_silu_f32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
@@ -559,6 +696,12 @@ static void glu_swiglu_oai_f32(unsigned int n, unsigned int i, void * data) {
                                    &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
 }
 
+static void glu_geglu_f32(unsigned int n, unsigned int i, void * data) {
+    struct htp_ops_context * octx = (struct htp_ops_context *) data;
+    glu_geglu_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
+                               &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
+}
+
 static int execute_op_activations_f32(struct htp_ops_context * octx) {
     int err = HTP_STATUS_OK;
 
@@ -593,6 +736,11 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
             act_op_func = unary_gelu_f32;
             op_type     = "gelu-f32";
             break;
+
+        case HTP_OP_GLU_GEGLU:
+            act_op_func = glu_geglu_f32;
+            op_type     = "geglu-f32";
+            break;
         default:
             FARF(ERROR, "Unsupported activations Op %u\n", octx->op);
             return HTP_STATUS_NO_SUPPORT;

ggml/src/ggml-hexagon/htp/argsort-ops.c

@@ -0,0 +1,281 @@
+#include <string.h>
+#include <stdlib.h>
+#include <math.h>
+#include <HAP_farf.h>
+#include <HAP_perf.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "ggml.h"
+
+#include "hvx-utils.h"
+#include "hex-dma.h"
+
+#include "htp-ctx.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+
+#ifndef MIN
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#endif
+
+struct htp_argsort_context {
+    struct htp_ops_context * octx;
+    uint32_t                 nrows_per_thread;
+};
+
+static inline bool all_greater_f32(HVX_Vector x, HVX_Vector y)
+{
+    const HVX_Vector one  = Q6_V_vsplat_R(1);
+    const HVX_Vector zero = Q6_V_vzero();
+
+    HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(x, y);
+    HVX_Vector matches = Q6_V_vmux_QVV(pred, one, zero);
+    HVX_Vector sum = hvx_vec_reduce_sum_i32(matches);
+    return hvx_vec_get_i32(sum) == 32;
+}
+
+// Sorts values and mirrors swaps to indices.
+static void quicksort_values_indices_asc(float * values, int32_t * indices, int left, int right) {
+    if (left >= right) return;
+
+    int pivot_idx = (left + right) / 2;
+    float pivot = values[pivot_idx];
+    int i = left;
+    int j = right;
+
+    HVX_Vector pivot_vec = hvx_vec_splat_f32(pivot);
+    while (i <= j) {
+        // Vectorized scan for i
+        while (i <= j) {
+            // Check if we have at least one full vector
+            if (i + 32 <= j) {
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + i);
+                if (all_greater_f32(pivot_vec, vals_vec)) {
+                    // If all elements are < pivot, we can skip this whole block
+                    i += 32;
+                    continue;
+                }
+            }
+
+            // Scalar fallback / cleanup
+            if (values[i] < pivot) {
+                i++;
+            } else {
+                break;
+            }
+        }
+
+        // Vectorized scan for j
+        while (i <= j) {
+            if (j - 32 >= i) {
+                // Load 32 elements ending at j.
+                // Since we want `values[j] > pivot`, let's load from j-31 to j.
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + j - 31);
+                if (all_greater_f32(vals_vec, pivot_vec)) {
+                    j -= 32;
+                    continue;
+                }
+            }
+
+            if (values[j] > pivot) {
+                j--;
+            } else {
+                break;
+            }
+        }
+
+        if (i <= j) {
+            float tmp_val = values[i];
+            values[i] = values[j];
+            values[j] = tmp_val;
+
+            int32_t tmp_idx = indices[i];
+            indices[i] = indices[j];
+            indices[j] = tmp_idx;
+            i++;
+            j--;
+        }
+    }
+
+    if (left < j) quicksort_values_indices_asc(values, indices, left, j);
+    if (i < right) quicksort_values_indices_asc(values, indices, i, right);
+}
+
+static void quicksort_values_indices_desc(float * values, int32_t * indices, int left, int right) {
+    if (left >= right) return;
+
+    int pivot_idx = (left + right) / 2;
+    float pivot = values[pivot_idx];
+    int i = left;
+    int j = right;
+
+    HVX_Vector pivot_vec = hvx_vec_splat_f32(pivot);
+
+    while (i <= j) {
+        // Vectorized scan for i (values[i] > pivot)
+        while (i <= j) {
+            if (i + 32 <= j) {
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + i);
+                if (all_greater_f32(vals_vec, pivot_vec)) {
+                    i += 32;
+                    continue;
+                }
+            }
+
+            if (values[i] > pivot) {
+                i++;
+            } else {
+                break;
+            }
+        }
+
+        // Vectorized scan for j (values[j] < pivot)
+        while (i <= j) {
+            if (j - 32 >= i) {
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + j - 31);
+                if (all_greater_f32(pivot_vec, vals_vec)) {
+                    j -= 32;
+                    continue;
+                }
+            }
+
+            if (values[j] < pivot) {
+                j--;
+            } else {
+                break;
+            }
+        }
+
+        if (i <= j) {
+            float tmp_val = values[i];
+            values[i] = values[j];
+            values[j] = tmp_val;
+
+            int32_t tmp_idx = indices[i];
+            indices[i] = indices[j];
+            indices[j] = tmp_idx;
+            i++;
+            j--;
+        }
+    }
+
+    if (left < j) quicksort_values_indices_desc(values, indices, left, j);
+    if (i < right) quicksort_values_indices_desc(values, indices, i, right);
+}
+
+static void htp_argsort_f32(unsigned int n, unsigned int i, void * data) {
+    struct htp_argsort_context * actx = (struct htp_argsort_context *)data;
+    struct htp_ops_context * octx = actx->octx;
+
+    // Unpack context
+    const struct htp_tensor * src0 = &octx->src0;
+    const struct htp_tensor * dst = &octx->dst;
+
+    // Scratchpad memory
+    uint8_t * spad = octx->src0_spad.data + octx->src0_spad.size_per_thread * i;
+
+    // Dimensions
+    uint32_t ne00 = src0->ne[0];
+    uint32_t ne01 = src0->ne[1];
+    uint32_t ne02 = src0->ne[2];
+    uint32_t ne03 = src0->ne[3];
+
+    uint32_t nb01 = src0->nb[1];
+    //uint32_t nb02 = src0->nb[2];
+    //uint32_t nb03 = src0->nb[3];
+
+    uint32_t nb1 = dst->nb[1];
+    //uint32_t nb2 = dst->nb[2];
+    //uint32_t nb3 = dst->nb[3];
+
+    // Sort order
+    enum ggml_sort_order order = (enum ggml_sort_order) octx->op_params[0];
+
+    // Rows to process
+    uint32_t total_rows = ne01 * ne02 * ne03;
+    uint32_t rows_per_thread = actx->nrows_per_thread;
+    uint32_t start_row = rows_per_thread * i;
+    uint32_t end_row = MIN(start_row + rows_per_thread, total_rows);
+
+    // Scratchpad layout:
+    // We need space for one row of float data (values) and one row of int32 indices.
+    // values: ne00 * sizeof(float)
+    // indices: ne00 * sizeof(int32_t)
+    // Padded to 128 bytes.
+
+    size_t values_size = hex_round_up(ne00 * sizeof(float), 128);
+    float * values_buf = (float *) spad;
+    int32_t * indices_buf = (int32_t *) (spad + values_size);
+
+    for (uint32_t r = start_row; r < end_row; r++) {
+        uint32_t src_offset = r * nb01;
+        uint32_t dst_offset = r * nb1;
+
+        uint8_t * src_ptr = (uint8_t *) src0->data + src_offset;
+        uint8_t * dst_ptr = (uint8_t *) dst->data  + dst_offset;
+
+        hex_l2fetch(src_ptr, ne00 * sizeof(float), ne00 * sizeof(float), 1);
+        hvx_copy_f32_au((uint8_t*)values_buf, src_ptr, ne00);
+
+        // Initialize indices
+        for (uint32_t j = 0; j < ne00; j++) {
+            indices_buf[j] = j;
+        }
+
+        // Sort values and mirror swaps to indices
+        if (order == GGML_SORT_ORDER_ASC) {
+            quicksort_values_indices_asc(values_buf, indices_buf, 0, ne00 - 1);
+        } else {
+            quicksort_values_indices_desc(values_buf, indices_buf, 0, ne00 - 1);
+        }
+
+        // Copy indices back to DDR
+        hvx_copy_f32_ua(dst_ptr, (const uint8_t *) indices_buf, ne00);
+    }
+}
+
+int op_argsort(struct htp_ops_context * octx) {
+    // Check supported types
+    if (octx->src0.type != HTP_TYPE_F32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    // Allocate scratchpad
+    // We need 1 row of float + 1 row of int32 per thread.
+    uint32_t ne00 = octx->src0.ne[0];
+    size_t values_size  = hex_round_up(ne00 * sizeof(float), 128);
+    size_t indices_size = hex_round_up(ne00 * sizeof(int32_t), 128);
+    size_t spad_per_thread = values_size + indices_size;
+
+    // Make sure we round up to 256 for alignment requirements
+    spad_per_thread = hex_round_up(spad_per_thread, 256);
+
+    size_t total_spad_size = spad_per_thread * octx->n_threads;
+
+    if (octx->ctx->vtcm_size < total_spad_size) {
+        FARF(ERROR, "argsort: VTCM size too small. Needed %zu, have %zu", total_spad_size, octx->ctx->vtcm_size);
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    octx->src0_spad.data = octx->ctx->vtcm_base;
+    octx->src0_spad.size = total_spad_size;
+    octx->src0_spad.size_per_thread = spad_per_thread;
+
+    FARF(HIGH, "argsort: %ux%ux%ux%u -> %ux%ux%ux%u (0x%x, 0x%x)",
+         octx->src0.ne[0], octx->src0.ne[1], octx->src0.ne[2], octx->src0.ne[3],
+         octx->dst.ne[0], octx->dst.ne[1], octx->dst.ne[2], octx->dst.ne[3],
+         octx->src0.data, octx->dst.data);
+
+    uint32_t total_rows = octx->src0.ne[1] * octx->src0.ne[2] * octx->src0.ne[3];
+    uint32_t n_jobs = MIN(total_rows, octx->n_threads);
+
+    struct htp_argsort_context actx;
+    actx.octx = octx;
+    actx.nrows_per_thread = (total_rows + n_jobs - 1) / n_jobs;
+
+    // Run jobs
+    worker_pool_run_func(octx->ctx->worker_pool, htp_argsort_f32, &actx, n_jobs);
+
+    return HTP_STATUS_OK;
+}

ggml/src/ggml-hexagon/htp/htp-msg.h

@@ -42,32 +42,36 @@ enum htp_data_type {
     HTP_TYPE_COUNT
 };
 
-// These values are manually translated over to HTP
-// !!!! DO NOT ALTER THE ORDER OF THE FIRST FOUR ENUMS !!!!
+// Do not reorder first 4 (used as an index)
 enum htp_op {
-    HTP_OP_MUL            = 0,
-    HTP_OP_ADD            = 1,
-    HTP_OP_SUB            = 2,
-    HTP_OP_DIV            = 3,
-    HTP_OP_MUL_MAT        = 4,
-    HTP_OP_MUL_MAT_ID     = 5,
-    HTP_OP_RMS_NORM       = 6,
-    HTP_OP_UNARY_SILU     = 7,
-    HTP_OP_UNARY_GELU     = 8,
-    HTP_OP_GLU_SWIGLU     = 9,
-    HTP_OP_GLU_SWIGLU_OAI = 10,
-    HTP_OP_SOFTMAX        = 11,
-    HTP_OP_ADD_ID         = 12,
-    HTP_OP_ROPE           = 13,
-    HTP_OP_FLASH_ATTN_EXT = 14,
-    HTP_OP_SET_ROWS       = 15,
-    HTP_OP_SCALE          = 16,
-    HTP_OP_GET_ROWS       = 17,
-    HTP_OP_CPY            = 18,
+    HTP_OP_MUL = 0,
+    HTP_OP_ADD = 1,
+    HTP_OP_SUB = 2,
+    HTP_OP_DIV = 3,
+    HTP_OP_MUL_MAT,
+    HTP_OP_MUL_MAT_ID,
+    HTP_OP_RMS_NORM,
+    HTP_OP_UNARY_SILU,
+    HTP_OP_UNARY_GELU,
+    HTP_OP_GLU_SWIGLU,
+    HTP_OP_GLU_SWIGLU_OAI,
+    HTP_OP_GLU_GEGLU,
+    HTP_OP_SOFTMAX,
+    HTP_OP_ADD_ID,
+    HTP_OP_ROPE,
+    HTP_OP_FLASH_ATTN_EXT,
+    HTP_OP_SET_ROWS,
+    HTP_OP_GET_ROWS,
+    HTP_OP_SCALE,
+    HTP_OP_CPY,
+    HTP_OP_ARGSORT,
+    HTP_OP_SQR,
+    HTP_OP_SQRT,
+    HTP_OP_SUM_ROWS,
     INVALID
 };
 
-static inline size_t htp_type_block_size(uint32_t t) {
+static inline size_t htp_t_block_size(uint32_t t) {
     switch (t) {
         case HTP_TYPE_F32:
             return 1;
@@ -103,22 +107,6 @@ static inline size_t htp_type_nbytes(uint32_t t) {
     return 0;
 }
 
-static const char * htp_type_name(uint32_t t) {
-    switch (t) {
-        case HTP_TYPE_F32:
-            return "fp32";
-        case HTP_TYPE_F16:
-            return "fp16";
-        case HTP_TYPE_Q4_0:
-            return "q4_0";
-        case HTP_TYPE_Q8_0:
-            return "q8_0";
-        case HTP_TYPE_MXFP4:
-            return "mxfp4";
-    }
-    return 0;
-}
-
 // Internal types
 #define QK_Q4_0x4x2  256  // 4x Q4_0 blocks packed with next 4x Q4_0 blocks (size in bytes 128)
 #define QK_Q8_0x4x2  256  // 4x Q8_0 blocks concat with next 4x Q8_0 blocks

ggml/src/ggml-hexagon/htp/htp-ops.h

@@ -64,25 +64,12 @@ struct htp_ops_context {
     struct fastdiv_values broadcast_rv2;
     struct fastdiv_values broadcast_rv3;
 
-    struct fastdiv_values mm_div_ne12_ne1; // fastdiv values for ne12 * ne1
-    struct fastdiv_values mm_div_ne1;      // fastdiv values for ne1
-    struct fastdiv_values mm_div_r2;       // fastdiv values for ne12 / ne02
-    struct fastdiv_values mm_div_r3;       // fastdiv values for ne13 / ne03
-
     struct fastdiv_values set_rows_div_ne12; // fastdiv values for ne12
     struct fastdiv_values set_rows_div_ne11; // fastdiv values for ne11
 
     struct fastdiv_values get_rows_div_ne10;      // fastdiv values for ne10
     struct fastdiv_values get_rows_div_ne10_ne11; // fastdiv values for ne10 * ne11
 
-    struct fastdiv_values cpy_div_ne01; // fastdiv values for ne01
-    struct fastdiv_values cpy_div_ne02; // fastdiv values for ne02
-    struct fastdiv_values cpy_div_ne03; // fastdiv values for ne03
-
-    struct fastdiv_values cpy_rshp_div_n0;       // fastdiv values for ne00
-    struct fastdiv_values cpy_rshp_div_n1n0;     // fastdiv values for ne00*ne01
-    struct fastdiv_values cpy_rshp_div_n2n1n0;   // fastdiv values for ne00*ne01*ne02
-
     uint32_t flags;
 };
 
@@ -90,6 +77,7 @@ int op_matmul(struct htp_ops_context * octx);
 int op_matmul_id(struct htp_ops_context * octx);
 int op_binary(struct htp_ops_context * octx);
 int op_unary(struct htp_ops_context * octx);
+int op_sum_rows(struct htp_ops_context * octx);
 int op_activations(struct htp_ops_context * octx);
 int op_softmax(struct htp_ops_context * octx);
 int op_add_id(struct htp_ops_context * octx);
@@ -98,5 +86,6 @@ int op_flash_attn_ext(struct htp_ops_context * octx);
 int op_set_rows(struct htp_ops_context * octx);
 int op_get_rows(struct htp_ops_context * octx);
 int op_cpy(struct htp_ops_context * octx);
+int op_argsort(struct htp_ops_context * octx);
 
 #endif /* HTP_OPS_H */

ggml/src/ggml-hexagon/htp/hvx-arith.h

@@ -46,127 +46,76 @@
 #define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
 #endif
 
-// ADD variants
+// Generic macro to define alignment permutations for an op
+#define DEFINE_HVX_BINARY_OP_VARIANTS(OP_NAME, OP_MACRO) \
+static inline void OP_NAME##_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    assert((uintptr_t) src0 % 128 == 0); \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    assert((uintptr_t) src0 % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) src0 % 128 == 0); \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, OP_MACRO); \
+} \
+static inline void OP_NAME##_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) src0 % 128 == 0); \
+    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u, OP_MACRO); \
+} \
+static inline void OP_NAME##_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u, OP_MACRO); \
+} \
+static inline void OP_NAME##_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, OP_MACRO); \
+} \
 
-static inline void hvx_add_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_ADD);
+DEFINE_HVX_BINARY_OP_VARIANTS(hvx_add_f32, HVX_OP_ADD)
+DEFINE_HVX_BINARY_OP_VARIANTS(hvx_sub_f32, HVX_OP_SUB)
+DEFINE_HVX_BINARY_OP_VARIANTS(hvx_mul_f32, HVX_OP_MUL)
+
+// Dispatcher logic
+#define HVX_BINARY_DISPATCHER(OP_NAME) \
+static inline void OP_NAME(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) { \
+    if (hex_is_aligned((void *) dst, 128)) { \
+        if (hex_is_aligned((void *) src0, 128)) { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_aaa(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_aau(dst, src0, src1, num_elems); \
+        } else { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_aua(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_auu(dst, src0, src1, num_elems); \
+        } \
+    } else { \
+        if (hex_is_aligned((void *) src0, 128)) { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_uaa(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_uau(dst, src0, src1, num_elems); \
+        } else { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_uua(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_uuu(dst, src0, src1, num_elems); \
+        } \
+    } \
 }
 
-static inline void hvx_add_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_ADD);
-}
-
-// SUB variants
-
-static inline void hvx_sub_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_SUB);
-}
-
-// MUL variants
-
-static inline void hvx_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MUL);
-}
-
-// Dispatchers
-
-static inline void hvx_add_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_add_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_add_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_add_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_add_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-static inline void hvx_sub_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_sub_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_sub_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_sub_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_sub_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-static inline void hvx_mul_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_mul_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_mul_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_mul_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_mul_f32_uu(dst, src0, src1, num_elems);
-    }
-}
+HVX_BINARY_DISPATCHER(hvx_add_f32)
+HVX_BINARY_DISPATCHER(hvx_sub_f32)
+HVX_BINARY_DISPATCHER(hvx_mul_f32)
 
 // Mul-Mul Optimized
-
 static inline void hvx_mul_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint8_t * restrict src2, const uint32_t num_elems) {
     assert((unsigned long) dst % 128 == 0);
     assert((unsigned long) src0 % 128 == 0);
@@ -443,6 +392,68 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t *
     }
 }
 
+//
+// Square
+//
+
+#define hvx_sqr_loop_body(dst_type, src_type, vec_store)           \
+    do {                                                                   \
+        dst_type * restrict vdst  = (dst_type *) dst;                      \
+        src_type * restrict vsrc = (src_type *) src;                       \
+                                                                           \
+        const uint32_t elem_size = sizeof(float);                          \
+        const uint32_t epv  = 128 / elem_size;                             \
+        const uint32_t nvec = n / epv;                                     \
+        const uint32_t nloe = n % epv;                                     \
+                                                                           \
+        uint32_t i = 0;                                                    \
+                                                                           \
+        _Pragma("unroll(4)")                                               \
+        for (; i < nvec; i++) {                                            \
+            vdst[i] = HVX_OP_MUL(vsrc[i], vsrc[i]);                        \
+        }                                                                  \
+        if (nloe) {                                                        \
+            HVX_Vector v = HVX_OP_MUL(vsrc[i], vsrc[i]);                   \
+            vec_store((void *) &vdst[i], nloe * elem_size, v);             \
+        }                                                                  \
+    } while(0)
+
+static inline void hvx_sqr_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqr_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqr_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    hvx_sqr_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqr_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqr_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqr_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    hvx_sqr_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqr_f32(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t num_elems) {
+    if (hex_is_aligned((void *) dst, 128)) {
+        if (hex_is_aligned((void *) src, 128)) {
+            hvx_sqr_f32_aa(dst, src, num_elems);
+        } else {
+            hvx_sqr_f32_au(dst, src, num_elems);
+        }
+    } else {
+        if (hex_is_aligned((void *) src, 128)) {
+            hvx_sqr_f32_ua(dst, src, num_elems);
+        } else {
+            hvx_sqr_f32_uu(dst, src, num_elems);
+        }
+    }
+}
+
 #undef HVX_OP_ADD
 #undef HVX_OP_SUB
 #undef HVX_OP_MUL
@@ -453,5 +464,7 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t *
 #undef hvx_scalar_loop_body
 #undef HVX_OP_MIN_SCALAR
 #undef HVX_OP_CLAMP_SCALAR
+#undef DEFINE_HVX_BINARY_OP_VARIANTS
+#undef HVX_BINARY_DISPATCHER
 
 #endif // HVX_ARITH_H

ggml/src/ggml-hexagon/htp/hvx-base.h

@@ -66,6 +66,12 @@ static inline float hvx_vec_get_f32(HVX_Vector v) {
     return x;
 }
 
+static inline int32_t hvx_vec_get_i32(HVX_Vector v) {
+    int32_t __attribute__((aligned(128))) x;
+    hvx_vec_store_a(&x, 4, v);
+    return x;
+}
+
 static inline HVX_Vector hvx_vec_abs_f16(HVX_Vector v) {
     // abs by clearing the fp16 sign bit
     HVX_Vector mask = Q6_Vh_vsplat_R(0x7fff);

ggml/src/ggml-hexagon/htp/hvx-copy.h

@@ -136,8 +136,6 @@ static inline void hvx_copy_f32_uu(uint8_t * restrict dst, const uint8_t * restr
         dst_type * restrict vdst = (dst_type *) dst;                                \
         src_type * restrict vsrc = (src_type *) src;                                \
                                                                                     \
-        const HVX_Vector zero = Q6_V_vsplat_R(0);                                   \
-                                                                                    \
         const uint32_t elem_size = sizeof(__fp16);                                  \
         const uint32_t epv  = 128 / elem_size;                                      \
         const uint32_t nvec = n / epv;                                              \

ggml/src/ggml-hexagon/htp/hvx-div.h

@@ -0,0 +1,116 @@
+#ifndef HVX_DIV_H
+#define HVX_DIV_H
+
+#include <HAP_farf.h>
+
+#include <math.h>
+#include <string.h>
+#include <assert.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include "hvx-base.h"
+#include "hex-utils.h"
+#include "hvx-inverse.h"
+#include "hvx-arith.h"
+
+#if __HVX_ARCH__ < 79
+#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
+#define hvx_div_f32_loop_body(dst_type, src0_type, src1_type, vec_store)             \
+    do {                                                                             \
+        dst_type * restrict vdst = (dst_type *) dst;                                 \
+        src0_type * restrict vsrc0 = (src0_type *) src0;                             \
+        src1_type * restrict vsrc1 = (src1_type *) src1;                             \
+                                                                                     \
+        const HVX_Vector nan_inf_mask = Q6_V_vsplat_R(0x7f800000);                   \
+                                                                                     \
+        const uint32_t nvec = n / VLEN_FP32;                                         \
+        const uint32_t nloe = n % VLEN_FP32;                                         \
+                                                                                     \
+        uint32_t i = 0;                                                              \
+                                                                                     \
+        _Pragma("unroll(4)")                                                         \
+        for (; i < nvec; i++) {                                                      \
+            HVX_Vector inv_src1 = hvx_vec_inverse_f32_guard(vsrc1[i], nan_inf_mask); \
+            HVX_Vector res = HVX_OP_MUL(vsrc0[i], inv_src1);                         \
+            vdst[i] = res;                                                           \
+        }                                                                            \
+        if (nloe) {                                                                  \
+            HVX_Vector inv_src1 = hvx_vec_inverse_f32_guard(vsrc1[i], nan_inf_mask); \
+            HVX_Vector res = HVX_OP_MUL(vsrc0[i], inv_src1);                         \
+            vec_store((void *) &vdst[i], nloe * SIZEOF_FP32, res);                   \
+        }                                                                            \
+    } while(0)
+
+// 3-letter suffix variants
+static inline void hvx_div_f32_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    assert((uintptr_t) src0 % 128 == 0);
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    assert((uintptr_t) src0 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) src0 % 128 == 0);
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) src0 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    hvx_div_f32_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
+    if (hex_is_aligned((void *) dst, 128)) {
+        if (hex_is_aligned((void *) src0, 128)) {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_aaa(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_aau(dst, src0, src1, num_elems);
+        } else {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_aua(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_auu(dst, src0, src1, num_elems);
+        }
+    } else {
+        if (hex_is_aligned((void *) src0, 128)) {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_uaa(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_uau(dst, src0, src1, num_elems);
+        } else {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_uua(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_uuu(dst, src0, src1, num_elems);
+        }
+    }
+}
+
+#undef HVX_OP_MUL
+
+#endif // HVX_DIV_H

ggml/src/ggml-hexagon/htp/hvx-sigmoid.h

@@ -91,6 +91,27 @@ static inline HVX_Vector hvx_vec_tanh_f32(HVX_Vector x) {
         }                                                       \
     } while(0)
 
+#define hvx_tanh_loop_body(dst_type, src_type, vec_store)       \
+    do {                                                        \
+        dst_type * restrict vdst = (dst_type *) dst;            \
+        src_type * restrict vsrc = (src_type *) src;            \
+                                                                \
+        const uint32_t epv  = 128 / sizeof(float);              \
+        const uint32_t nvec = n / epv;                          \
+        const uint32_t nloe = n % epv;                          \
+                                                                \
+        uint32_t i = 0;                                         \
+                                                                \
+        _Pragma("unroll(4)")                                    \
+        for (; i < nvec; i++) {                                 \
+             vdst[i] = hvx_vec_tanh_f32(vsrc[i]);               \
+        }                                                       \
+        if (nloe) {                                             \
+             HVX_Vector tmp = hvx_vec_tanh_f32(vsrc[i]);        \
+             vec_store((void *) &vdst[i], nloe * sizeof(float), tmp); \
+        }                                                       \
+    } while(0)
+
 static inline void hvx_sigmoid_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
     assert((unsigned long) dst % 128 == 0);
     assert((unsigned long) src % 128 == 0);
@@ -111,4 +132,10 @@ static inline void hvx_sigmoid_f32_uu(uint8_t * restrict dst, const uint8_t * re
     hvx_sigmoid_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
 }
 
+static inline void hvx_tanh_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+    hvx_tanh_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
 #endif /* HVX_SIGMOID_H */

ggml/src/ggml-hexagon/htp/hvx-sqrt.h

@@ -12,11 +12,17 @@
 #define RSQRT_ONE_HALF     0x3f000000  // 0.5
 #define RSQRT_THREE_HALVES 0x3fc00000  // 1.5
 
+#if __HVX_ARCH__ < 79
+#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
 static inline HVX_Vector hvx_vec_rsqrt_f32(HVX_Vector in_vec) {
     //Algorithm :
     //  x2 = input*0.5
     //  y  = * (long *) &input
-    //  y  = 0x5f3759df - (y>>2)
+    //  y  = 0x5f3759df - (y>>1)
     //  y  = y*(threehalfs - x2*y*y)
 
     HVX_Vector rsqrtconst = Q6_V_vsplat_R(RSQRT_CONST);
@@ -57,4 +63,64 @@ static inline HVX_Vector hvx_vec_rsqrt_f32(HVX_Vector in_vec) {
     return Q6_Vsf_equals_Vqf32(temp);
 }
 
+// Compute sqrt(x) as x*inv_sqrt(x)
+#define hvx_sqrt_f32_loop_body(dst_type, src_type, vec_store)                \
+    do {                                                                     \
+        dst_type * restrict vdst = (dst_type *) dst;                         \
+        src_type * restrict vsrc = (src_type *) src;                         \
+                                                                             \
+        const uint32_t nvec = n / VLEN_FP32;                                 \
+        const uint32_t nloe = n % VLEN_FP32;                                 \
+                                                                             \
+        uint32_t i = 0;                                                      \
+                                                                             \
+        _Pragma("unroll(4)")                                                 \
+        for (; i < nvec; i++) {                                              \
+            HVX_Vector inv_sqrt = hvx_vec_rsqrt_f32(vsrc[i]);                \
+            HVX_Vector sqrt_res = HVX_OP_MUL(inv_sqrt, vsrc[i]);             \
+            vdst[i] = sqrt_res;                                              \
+        }                                                                    \
+        if (nloe) {                                                          \
+            HVX_Vector inv_sqrt = hvx_vec_rsqrt_f32(vsrc[i]);                \
+            HVX_Vector sqrt_res = HVX_OP_MUL(inv_sqrt, vsrc[i]);             \
+            vec_store((void *) &vdst[i], nloe * SIZEOF_FP32, sqrt_res);      \
+        }                                                                    \
+    } while(0)
+
+static inline void hvx_sqrt_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqrt_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqrt_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    hvx_sqrt_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqrt_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqrt_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqrt_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    hvx_sqrt_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqrt_f32(uint8_t * restrict dst, const uint8_t * restrict src, const int num_elems) {
+    if ((unsigned long) dst % 128 == 0) {
+        if ((unsigned long) src % 128 == 0) {
+            hvx_sqrt_f32_aa(dst, src, num_elems);
+        } else {
+            hvx_sqrt_f32_au(dst, src, num_elems);
+        }
+    } else {
+        if ((unsigned long) src % 128 == 0) {
+            hvx_sqrt_f32_ua(dst, src, num_elems);
+        } else {
+            hvx_sqrt_f32_uu(dst, src, num_elems);
+        }
+    }
+}
+
 #endif /* HVX_SQRT_H */

ggml/src/ggml-hexagon/htp/hvx-utils.h

@@ -12,6 +12,7 @@
 #include "hvx-sigmoid.h"
 #include "hvx-sqrt.h"
 #include "hvx-arith.h"
+#include "hvx-div.h"
 #include "hvx-base.h"
 
 #endif /* HVX_UTILS_H */

ggml/src/ggml-hexagon/htp/main.c

@@ -189,7 +189,7 @@ static int vtcm_release_callback(unsigned int rctx, void * state) {
     // otherwise we'll release it once we're done with the current Op.
 
     if (ctx->vtcm_inuse) {
-        ctx->vtcm_needs_release = false;
+        ctx->vtcm_needs_release = true;
         return 0;
     }
 
@@ -440,6 +440,45 @@ static void proc_matmul_req(struct htp_context *     ctx,
     send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
 }
 
+static void proc_argsort_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
+    struct dspqueue_buffer rsp_bufs[1];
+
+    // We had written to the output buffer, we'd also need to flush it
+    rsp_bufs[0].fd     = bufs[1].fd;
+    rsp_bufs[0].ptr    = bufs[1].ptr;
+    rsp_bufs[0].offset = bufs[1].offset;
+    rsp_bufs[0].size   = bufs[1].size;
+    rsp_bufs[0].flags  = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER |         // Flush HTP
+                         DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT);  // Invalidate CPU
+
+    // Setup Op context
+    struct htp_ops_context octx = { 0 };
+    octx.ctx                    = ctx;
+    octx.src0                   = req->src0;
+    octx.dst                    = req->dst;
+    octx.flags                  = req->flags;
+    octx.op                     = req->op;
+
+    memcpy(octx.op_params, req->op_params, sizeof(octx.op_params));
+
+    // Update data pointers
+    octx.src0.data = (uint32_t) bufs[0].ptr;
+    octx.dst.data  = (uint32_t) bufs[1].ptr;
+    octx.n_threads = ctx->n_threads;
+
+    struct profile_data prof;
+    profile_start(&prof);
+
+    uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR;
+    if (vtcm_acquire(ctx) == AEE_SUCCESS) {
+        rsp_status = op_argsort(&octx);
+        vtcm_release(ctx);
+    }
+
+    profile_stop(&prof);
+    send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
+}
+
 static void proc_cpy_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
     struct dspqueue_buffer rsp_bufs[1];
 
@@ -679,6 +718,45 @@ static void proc_unary_req(struct htp_context * ctx, struct htp_general_req * re
     send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
 }
 
+static void proc_sum_rows_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
+    struct dspqueue_buffer rsp_bufs[HTP_MAX_PACKET_BUFFERS];
+
+    // We had written to the output buffer, we'd also need to flush it
+    rsp_bufs[0].fd     = bufs[1].fd;
+    rsp_bufs[0].ptr    = bufs[1].ptr;
+    rsp_bufs[0].offset = bufs[1].offset;
+    rsp_bufs[0].size   = bufs[1].size;
+    rsp_bufs[0].flags  = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER |         // Flush HTP
+                         DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT);  // Invalidate CPU
+
+    // Setup Op context
+    struct htp_ops_context octx = { 0 };
+    octx.ctx                    = ctx;
+    octx.src0                   = req->src0;
+    octx.dst                    = req->dst;
+    octx.flags                  = req->flags;
+    octx.op                     = req->op;
+
+    memcpy(octx.op_params, req->op_params, sizeof(octx.op_params));
+
+    // Update data pointers
+    octx.src0.data = (uint32_t) bufs[0].ptr;
+    octx.dst.data  = (uint32_t) bufs[1].ptr;
+    octx.n_threads = ctx->n_threads;
+
+    struct profile_data prof;
+    profile_start(&prof);
+
+    uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR;
+    if (vtcm_acquire(ctx) == AEE_SUCCESS) {
+        rsp_status = op_sum_rows(&octx);
+        vtcm_release(ctx);
+    }
+
+    profile_stop(&prof);
+    send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
+}
+
 static void proc_activations_req(struct htp_context *     ctx,
                                  struct htp_general_req * req,
                                  struct dspqueue_buffer * bufs,
@@ -951,6 +1029,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             case HTP_OP_MUL:
             case HTP_OP_ADD:
             case HTP_OP_SUB:
+            case HTP_OP_DIV:
                 if (n_bufs != 3) {
                     FARF(ERROR, "Bad binary-req buffer list");
                     continue;
@@ -968,6 +1047,25 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
                 proc_unary_req(ctx, &req, bufs);
                 break;
 
+            case HTP_OP_SQR:
+            case HTP_OP_SQRT:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad unary-req buffer list");
+                    continue;
+                }
+
+                proc_unary_req(ctx, &req, bufs);
+                break;
+
+            case HTP_OP_SUM_ROWS:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad unary-req buffer list");
+                    continue;
+                }
+
+                proc_sum_rows_req(ctx, &req, bufs);
+                break;
+
             case HTP_OP_UNARY_SILU:
             case HTP_OP_UNARY_GELU:
                 if (n_bufs != 2) {
@@ -980,6 +1078,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             case HTP_OP_GLU_SWIGLU:
             case HTP_OP_GLU_SWIGLU_OAI:
             case HTP_OP_SOFTMAX:
+            case HTP_OP_GLU_GEGLU:
                 if ((n_bufs != 2) && (n_bufs != 3)) {
                     FARF(ERROR, "Bad act-req buffer list");
                     continue;
@@ -1035,6 +1134,14 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
                 proc_cpy_req(ctx, &req, bufs);
                 break;
 
+            case HTP_OP_ARGSORT:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad argsort-req buffer list");
+                    continue;
+                }
+                proc_argsort_req(ctx, &req, bufs);
+                break;
+
             default:
                 FARF(ERROR, "Unknown Op %u", req.op);
                 break;

ggml/src/ggml-hexagon/htp/sum-rows-ops.c

@@ -0,0 +1,115 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <HAP_farf.h>
+#include <HAP_perf.h>
+
+#include <string.h>
+#include <math.h>
+
+#include "hex-dma.h"
+#include "hvx-utils.h"
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+
+
+#define sum_rows_preamble                       \
+    struct htp_tensor *src0 =  &octx->src0;\
+    struct htp_tensor *dst  = &octx->dst;  \
+                                           \
+    const uint32_t ne00 = src0->ne[0];     \
+    const uint32_t ne01 = src0->ne[1];     \
+    const uint32_t ne02 = src0->ne[2];     \
+    const uint32_t ne03 = src0->ne[3];     \
+                                           \
+    const uint32_t nb00 = src0->nb[0];     \
+    const uint32_t nb01 = src0->nb[1];     \
+    const uint32_t nb02 = src0->nb[2];     \
+    const uint32_t nb03 = src0->nb[3];     \
+                                           \
+    const uint32_t  ne0 = dst->ne[0];      \
+    const uint32_t  ne1 = dst->ne[1];      \
+    const uint32_t  ne2 = dst->ne[2];      \
+    const uint32_t  ne3 = dst->ne[3];      \
+                                           \
+    const uint32_t  nb0 = dst->nb[0];      \
+    const uint32_t  nb1 = dst->nb[1];      \
+    const uint32_t  nb2 = dst->nb[2];      \
+    const uint32_t  nb3 = dst->nb[3];      \
+
+static int sum_rows_thread_f32(struct htp_ops_context * octx, const int nth, const int ith) {
+    sum_rows_preamble;
+
+    const uint32_t src0_nrows_per_thread  = octx->src0_nrows_per_thread;
+    const size_t src0_row_size = nb01;
+    const size_t dst_row_size  = nb1;
+
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
+
+    const uint32_t src0_start_row = src0_nrows_per_thread * ith;
+    const uint32_t src0_end_row   = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+
+    // no work for this thread
+    if (src0_start_row >= src0_end_row) {
+        return HTP_STATUS_OK;
+    }
+
+    int opt_path   = 0;
+    if ((0 == hex_is_aligned((void *) src0->data, VLEN)) && !(nb01 & (VLEN - 1))) {
+        opt_path = 1;
+    }
+
+    const uint8_t * restrict data_src = (const uint8_t *) src0->data;
+    uint8_t * restrict data_dst       = (uint8_t *) dst->data;
+
+    const float * restrict src_th = (float *) (data_src + (src0_start_row * src0_row_size));
+    float * restrict dst_th       = (float *) (data_dst + (src0_start_row * dst_row_size));
+
+    for (uint32_t ir = 0; ir < src0_nrows_per_thread; ir++) {
+        const float * restrict src_local = src_th + (ir * ne00);
+
+        if (ir + 1 < src0_nrows_per_thread) {
+            hex_l2fetch(src_local + ne00, src0_row_size, src0_row_size, 1);
+        }
+
+        if (1 == opt_path) {
+            dst_th[ir] = hvx_reduce_sum_f32_a((const uint8_t *) src_local, ne00);
+        } else {
+            dst_th[ir] = hvx_reduce_sum_f32((const uint8_t *) src_local, ne00);
+        }
+    }
+
+    return HTP_STATUS_OK;
+}
+
+static void sum_rows_work_f32(unsigned int n, unsigned int i, void *data) {
+    sum_rows_thread_f32((struct htp_ops_context *) data, n, i);
+}
+
+int op_sum_rows(struct htp_ops_context * octx) {
+    sum_rows_preamble;
+
+    if (octx->src0.type != HTP_TYPE_F32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    const int      n_threads  = octx->n_threads;
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;
+
+    uint32_t n_jobs = MIN(n_threads, src0_nrows);
+    octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs;
+
+    worker_pool_run_func(octx->ctx->worker_pool, sum_rows_work_f32, octx, n_jobs);
+
+    return HTP_STATUS_OK;
+}
+

ggml/src/ggml-hexagon/htp/unary-ops.c

@@ -132,6 +132,56 @@ static void rms_norm_htp_f32(const float * restrict src,
     }
 }
 
+static void sqr_htp_f32(const float * restrict src,
+                          float * restrict dst,
+                          uint8_t * restrict spad,
+                          const uint32_t num_rows,
+                          const uint32_t row_elems,
+                          const size_t   row_size,
+                          int32_t *      op_params,
+                          int            opt_path) {
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const float * restrict src_local = src + (ir * row_elems);
+        float * restrict dst_local       = dst + (ir * row_elems);
+
+        if (ir + 1 < num_rows) {
+            hex_l2fetch(src_local + row_elems, row_size, row_size, 1);
+        }
+
+        if (1 == opt_path) {
+            hvx_sqr_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        } else {
+            hvx_sqr_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        }
+    }
+}
+
+static void sqrt_htp_f32(const float * restrict src,
+                          float * restrict dst,
+                          uint8_t * restrict spad,
+                          const uint32_t num_rows,
+                          const uint32_t row_elems,
+                          const size_t   row_size,
+                          int32_t *      op_params,
+                          int            opt_path) {
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const float * restrict src_local = src + (ir * row_elems);
+        float * restrict dst_local       = dst + (ir * row_elems);
+
+        if (ir + 1 < num_rows) {
+            hex_l2fetch(src_local + row_elems, row_size, row_size, 1);
+        }
+
+        if (1 == opt_path) {
+            hvx_sqrt_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        } else {
+            hvx_sqrt_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        }
+    }
+}
+
 static void unary_job_f32_per_thread(const struct htp_tensor * src,
                                      struct htp_tensor *       dst,
                                      uint8_t *                 spad,
@@ -181,6 +231,12 @@ static void unary_job_f32_per_thread(const struct htp_tensor * src,
         case HTP_OP_SCALE:
             scale_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path);
             break;
+        case HTP_OP_SQR:
+            sqr_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path);
+            break;
+        case HTP_OP_SQRT:
+            sqrt_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path);
+            break;
 
         default:
             break;
@@ -218,6 +274,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
             unary_op_func = unary_job_dispatcher_f32;
             op_type       = "scale-f32";
             break;
+        case HTP_OP_SQR:
+            unary_op_func = unary_job_dispatcher_f32;
+            op_type       = "sqr-f32";
+            break;
+        case HTP_OP_SQRT:
+            unary_op_func = unary_job_dispatcher_f32;
+            op_type       = "sqrt-f32";
+            break;
 
         default:
             FARF(ERROR, "Unsupported unary Op %u\n", octx->op);

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

@@ -212,61 +212,69 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat(ggml_meta
 }
 
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary(ggml_metal_library_t lib, const ggml_tensor * op) {
-    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
-
     char base[256];
     char name[256];
 
-    const int64_t n = ggml_nelements(op);
+    int op_num = -1;
 
-    const char * op_str = "undefined";
     switch (op->op) {
-        case GGML_OP_SCALE:      op_str = "scale";      break;
-        case GGML_OP_FILL:       op_str = "fill";       break;
-        case GGML_OP_CLAMP:      op_str = "clamp";      break;
-        case GGML_OP_SQR:        op_str = "sqr";        break;
-        case GGML_OP_SQRT:       op_str = "sqrt";       break;
-        case GGML_OP_SIN:        op_str = "sin";        break;
-        case GGML_OP_COS:        op_str = "cos";        break;
-        case GGML_OP_LOG:        op_str = "log";        break;
-        case GGML_OP_LEAKY_RELU: op_str = "leaky_relu"; break;
+        case GGML_OP_SCALE:      op_num = OP_UNARY_NUM_SCALE;      break;
+        case GGML_OP_FILL:       op_num = OP_UNARY_NUM_FILL;       break;
+        case GGML_OP_CLAMP:      op_num = OP_UNARY_NUM_CLAMP;      break;
+        case GGML_OP_SQR:        op_num = OP_UNARY_NUM_SQR;        break;
+        case GGML_OP_SQRT:       op_num = OP_UNARY_NUM_SQRT;       break;
+        case GGML_OP_SIN:        op_num = OP_UNARY_NUM_SIN;        break;
+        case GGML_OP_COS:        op_num = OP_UNARY_NUM_COS;        break;
+        case GGML_OP_LOG:        op_num = OP_UNARY_NUM_LOG;        break;
+        case GGML_OP_LEAKY_RELU: op_num = OP_UNARY_NUM_LEAKY_RELU; break;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
-                case GGML_UNARY_OP_TANH:        op_str = "tanh";        break;
-                case GGML_UNARY_OP_RELU:        op_str = "relu";        break;
-                case GGML_UNARY_OP_SIGMOID:     op_str = "sigmoid";     break;
-                case GGML_UNARY_OP_GELU:        op_str = "gelu";        break;
-                case GGML_UNARY_OP_GELU_ERF:    op_str = "gelu_erf";    break;
-                case GGML_UNARY_OP_GELU_QUICK:  op_str = "gelu_quick";  break;
-                case GGML_UNARY_OP_SILU:        op_str = "silu";        break;
-                case GGML_UNARY_OP_ELU:         op_str = "elu";         break;
-                case GGML_UNARY_OP_NEG:         op_str = "neg";         break;
-                case GGML_UNARY_OP_ABS:         op_str = "abs";         break;
-                case GGML_UNARY_OP_SGN:         op_str = "sgn";         break;
-                case GGML_UNARY_OP_STEP:        op_str = "step";        break;
-                case GGML_UNARY_OP_HARDSWISH:   op_str = "hardswish";   break;
-                case GGML_UNARY_OP_HARDSIGMOID: op_str = "hardsigmoid"; break;
-                case GGML_UNARY_OP_EXP:         op_str = "exp";         break;
-                case GGML_UNARY_OP_SOFTPLUS:    op_str = "softplus";    break;
-                case GGML_UNARY_OP_EXPM1:       op_str = "expm1";       break;
+                case GGML_UNARY_OP_TANH:        op_num = OP_UNARY_NUM_TANH;        break;
+                case GGML_UNARY_OP_RELU:        op_num = OP_UNARY_NUM_RELU;        break;
+                case GGML_UNARY_OP_SIGMOID:     op_num = OP_UNARY_NUM_SIGMOID;     break;
+                case GGML_UNARY_OP_GELU:        op_num = OP_UNARY_NUM_GELU;        break;
+                case GGML_UNARY_OP_GELU_ERF:    op_num = OP_UNARY_NUM_GELU_ERF;    break;
+                case GGML_UNARY_OP_GELU_QUICK:  op_num = OP_UNARY_NUM_GELU_QUICK;  break;
+                case GGML_UNARY_OP_SILU:        op_num = OP_UNARY_NUM_SILU;        break;
+                case GGML_UNARY_OP_ELU:         op_num = OP_UNARY_NUM_ELU;         break;
+                case GGML_UNARY_OP_NEG:         op_num = OP_UNARY_NUM_NEG;         break;
+                case GGML_UNARY_OP_ABS:         op_num = OP_UNARY_NUM_ABS;         break;
+                case GGML_UNARY_OP_SGN:         op_num = OP_UNARY_NUM_SGN;         break;
+                case GGML_UNARY_OP_STEP:        op_num = OP_UNARY_NUM_STEP;        break;
+                case GGML_UNARY_OP_HARDSWISH:   op_num = OP_UNARY_NUM_HARDSWISH;   break;
+                case GGML_UNARY_OP_HARDSIGMOID: op_num = OP_UNARY_NUM_HARDSIGMOID; break;
+                case GGML_UNARY_OP_EXP:         op_num = OP_UNARY_NUM_EXP;         break;
+                case GGML_UNARY_OP_SOFTPLUS:    op_num = OP_UNARY_NUM_SOFTPLUS;    break;
+                case GGML_UNARY_OP_EXPM1:       op_num = OP_UNARY_NUM_EXPM1;       break;
                 default: GGML_ABORT("fatal error");
             } break;
         default: GGML_ABORT("fatal error");
     };
 
-    const char * suffix = "";
-    if (n % 4 == 0) {
-        suffix = "_4";
-    }
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t_str  = ggml_type_name(op->type);
 
-    snprintf(base, 256, "kernel_%s_%s%s", op_str, ggml_type_name(op->src[0]->type), suffix);
-    snprintf(name, 256, "%s", base);
+    const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
+    const bool is_cnt = ggml_is_contiguous(op->src[0]) && ggml_nelements(op) < 32768;
+
+    snprintf(base, 256, "kernel_unary_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
+    snprintf(name, 256, "%s_op=%d_cnt=%d", base, op_num, is_cnt);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_UNARY + 0);
+        ggml_metal_cv_set_bool (cv, is_cnt, FC_UNARY + 1);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
+    res.c4  = is_c4;
+    res.cnt = is_cnt;
+
     return res;
 }
 
@@ -320,31 +328,46 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum(ggml_metal_l
 }
 
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum_rows(ggml_metal_library_t lib, const ggml_tensor * op) {
-    GGML_ASSERT(op->src[0]->nb[0] == ggml_type_size(op->src[0]->type));
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
 
     char base[256];
     char name[256];
 
-    const char * op_str = "undefined";
+    int op_num = -1;
+
     switch (op->op) {
-        case GGML_OP_SUM_ROWS:
-            op_str = "sum_rows"; break;
-        case GGML_OP_MEAN:
-            op_str = "mean"; break;
+        case GGML_OP_SUM_ROWS: op_num = OP_SUM_ROWS_NUM_SUM_ROWS; break;
+        case GGML_OP_MEAN:     op_num = OP_SUM_ROWS_NUM_MEAN;     break;
         default: GGML_ABORT("fatal error");
     };
 
-    snprintf(base, 256, "kernel_%s_%s", op_str, ggml_type_name(op->src[0]->type));
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t_str  = ggml_type_name(op->type);
 
-    snprintf(name, 256, "%s", base);
+    const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
+
+    snprintf(base, 256, "kernel_sum_rows_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
+    snprintf(name, 256, "%s_op=%d", base, op_num);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_SUM_ROWS + 0);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
     res.smem = 32*sizeof(float);
 
+    if (is_c4) {
+        res.smem *= 4;
+    }
+
+    res.c4  = is_c4;
+
     return res;
 }
 
@@ -1472,13 +1495,15 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one(ggml_met
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
     assert(op->op == GGML_OP_L2_NORM);
 
-    GGML_ASSERT(op->src[0]->ne[0] % 4 == 0);
-    GGML_ASSERT(ggml_is_contiguous_1(op->src[0]));
-
     char base[256];
     char name[256];
 
-    snprintf(base, 256, "kernel_l2_norm_f32");
+    const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
+
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t_str  = ggml_type_name(op->type);
+
+    snprintf(base, 256, "kernel_l2_norm_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
     snprintf(name, 256, "%s", base);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
@@ -1486,6 +1511,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_met
         res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
     }
 
+    res.c4   = is_c4;
     res.smem = 32*sizeof(float);
 
     return res;

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

@@ -1011,6 +1011,15 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
     }
 
     switch (op->op) {
+        case GGML_OP_SCALE:
+        case GGML_OP_FILL:
+        case GGML_OP_CLAMP:
+        case GGML_OP_SQR:
+        case GGML_OP_SQRT:
+        case GGML_OP_SIN:
+        case GGML_OP_COS:
+        case GGML_OP_LOG:
+            return ggml_is_contiguous_rows(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_TANH:
@@ -1030,7 +1039,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_EXPM1:
-                    return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+                    return ggml_is_contiguous_rows(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
                 default:
                     return false;
             }
@@ -1061,8 +1070,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ACC:
         case GGML_OP_REPEAT:
-        case GGML_OP_SCALE:
-        case GGML_OP_FILL:
         case GGML_OP_CONV_TRANSPOSE_1D:
             return true;
         case GGML_OP_CONV_TRANSPOSE_2D:
@@ -1070,14 +1077,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                 (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32) &&
                 op->src[1]->type == GGML_TYPE_F32 &&
                 op->type == GGML_TYPE_F32;
-        case GGML_OP_CLAMP:
-            return op->src[0]->type == GGML_TYPE_F32;
-        case GGML_OP_SQR:
-        case GGML_OP_SQRT:
-        case GGML_OP_SIN:
-        case GGML_OP_COS:
-        case GGML_OP_LOG:
-            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SUM:
             return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
         case GGML_OP_TRI:
@@ -1087,9 +1086,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_MEAN:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_GROUP_NORM:
-            return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_L2_NORM:
-            return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
+            return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_COUNT_EQUAL:
             return has_simdgroup_reduction &&
                 op->src[0]->type == GGML_TYPE_I32 &&

ggml/src/ggml-metal/ggml-metal-impl.h

@@ -80,7 +80,9 @@
 #define FC_SSM_CONV                    900
 #define FC_SOLVE_TRI                   1000
 #define FC_COUNT_EQUAL                 1100
-#define FC_BIN                         1200
+#define FC_UNARY                       1200
+#define FC_BIN                         1300
+#define FC_SUM_ROWS                    1400
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8
@@ -89,6 +91,37 @@
 #define OP_FLASH_ATTN_EXT_VEC_NQPSG 1
 #define OP_FLASH_ATTN_EXT_VEC_NCPSG 32
 
+#define OP_UNARY_NUM_SCALE      10
+#define OP_UNARY_NUM_FILL       11
+#define OP_UNARY_NUM_CLAMP      12
+#define OP_UNARY_NUM_SQR        13
+#define OP_UNARY_NUM_SQRT       14
+#define OP_UNARY_NUM_SIN        15
+#define OP_UNARY_NUM_COS        16
+#define OP_UNARY_NUM_LOG        17
+#define OP_UNARY_NUM_LEAKY_RELU 18
+
+#define OP_UNARY_NUM_TANH        100
+#define OP_UNARY_NUM_RELU        101
+#define OP_UNARY_NUM_SIGMOID     102
+#define OP_UNARY_NUM_GELU        103
+#define OP_UNARY_NUM_GELU_ERF    104
+#define OP_UNARY_NUM_GELU_QUICK  105
+#define OP_UNARY_NUM_SILU        106
+#define OP_UNARY_NUM_ELU         107
+#define OP_UNARY_NUM_NEG         108
+#define OP_UNARY_NUM_ABS         109
+#define OP_UNARY_NUM_SGN         110
+#define OP_UNARY_NUM_STEP        111
+#define OP_UNARY_NUM_HARDSWISH   112
+#define OP_UNARY_NUM_HARDSIGMOID 113
+#define OP_UNARY_NUM_EXP         114
+#define OP_UNARY_NUM_SOFTPLUS    115
+#define OP_UNARY_NUM_EXPM1       116
+
+#define OP_SUM_ROWS_NUM_SUM_ROWS 10
+#define OP_SUM_ROWS_NUM_MEAN     11
+
 // kernel argument structs
 //
 // - element counters (e.g. ne00) typically use int32_t to reduce register usage
@@ -124,6 +157,31 @@ typedef struct {
     int32_t  dim;
 } ggml_metal_kargs_concat;
 
+typedef struct {
+    int32_t  ne00;
+    int32_t  ne01;
+    int32_t  ne02;
+    int32_t  ne03;
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  ne0;
+    int32_t  ne1;
+    int32_t  ne2;
+    int32_t  ne3;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
+    float    slope;
+    float    scale;
+    float    bias;
+    float    val;
+    float    min;
+    float    max;
+} ggml_metal_kargs_unary;
+
 typedef struct {
     int32_t  ne00;
     int32_t  ne01;
@@ -181,20 +239,6 @@ typedef struct {
     uint64_t nb3;
 } ggml_metal_kargs_repeat;
 
-typedef struct {
-    float scale;
-    float bias;
-} ggml_metal_kargs_scale;
-
-typedef struct {
-    float val;
-} ggml_metal_kargs_fill;
-
-typedef struct {
-    float min;
-    float max;
-} ggml_metal_kargs_clamp;
-
 typedef struct {
     int64_t  nk0;
     int64_t  ne00;
@@ -498,8 +542,21 @@ typedef struct {
 
 typedef struct {
     int32_t  ne00;
-    int32_t  ne00_4;
+    int32_t  ne01;
+    int32_t  ne02;
+    int32_t  ne03;
+    uint64_t nb00;
     uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  ne0;
+    int32_t  ne1;
+    int32_t  ne2;
+    int32_t  ne3;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
     float    eps;
 } ggml_metal_kargs_l2_norm;
 
@@ -881,10 +938,6 @@ typedef struct {
     int      max_period;
 } ggml_metal_kargs_timestep_embedding;
 
-typedef struct {
-    float    slope;
-} ggml_metal_kargs_leaky_relu;
-
 typedef struct {
     int32_t  ne00;
     int32_t  ne01;

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

@@ -287,17 +287,9 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
                 n_fuse = ggml_metal_op_acc(ctx, idx);
             } break;
         case GGML_OP_SCALE:
-            {
-                n_fuse = ggml_metal_op_scale(ctx, idx);
-            } break;
         case GGML_OP_FILL:
-            {
-                n_fuse = ggml_metal_op_fill(ctx, idx);
-            } break;
         case GGML_OP_CLAMP:
-            {
-                n_fuse = ggml_metal_op_clamp(ctx, idx);
-            } break;
+        case GGML_OP_LEAKY_RELU:
         case GGML_OP_SQR:
         case GGML_OP_SQRT:
         case GGML_OP_SIN:
@@ -426,10 +418,6 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_top_k(ctx, idx);
             } break;
-        case GGML_OP_LEAKY_RELU:
-            {
-                n_fuse = ggml_metal_op_leaky_relu(ctx, idx);
-            } break;
         case GGML_OP_TRI:
             {
                 n_fuse = ggml_metal_op_tri(ctx, idx);
@@ -722,119 +710,6 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
-int ggml_metal_op_scale(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    float scale;
-    float bias;
-    memcpy(&scale, ((const int32_t *) op->op_params) + 0, sizeof(float));
-    memcpy(&bias,  ((const int32_t *) op->op_params) + 1, sizeof(float));
-
-    ggml_metal_kargs_scale args = {
-        /*.scale =*/ scale,
-        /*.bias  =*/ bias,
-    };
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
-int ggml_metal_op_fill(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    const float val = ggml_get_op_params_f32(op, 0);
-
-    ggml_metal_kargs_fill args = {
-        /*.val =*/ val
-    };
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
-int ggml_metal_op_clamp(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    float min;
-    float max;
-    memcpy(&min, ((const int32_t *) op->op_params) + 0, sizeof(float));
-    memcpy(&max, ((const int32_t *) op->op_params) + 1, sizeof(float));
-
-    ggml_metal_kargs_clamp args = {
-        /*.min =*/ min,
-        /*.max =*/ max,
-    };
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
 int ggml_metal_op_unary(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
@@ -846,19 +721,79 @@ int ggml_metal_op_unary(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
-    int64_t n = ggml_nelements(op);
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
 
-    if (n % 4 == 0) {
-        n /= 4;
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
+    ggml_metal_kargs_unary args = {
+        /*.ne00  =*/ ne00,
+        /*.ne01  =*/ ne01,
+        /*.ne02  =*/ ne02,
+        /*.ne03  =*/ ne03,
+        /*.nb00  =*/ nb00,
+        /*.nb01  =*/ nb01,
+        /*.nb02  =*/ nb02,
+        /*.nb03  =*/ nb03,
+        /*.ne0   =*/ ne0,
+        /*.ne1   =*/ ne1,
+        /*.ne2   =*/ ne2,
+        /*.ne3   =*/ ne3,
+        /*.nb0   =*/ nb0,
+        /*.nb1   =*/ nb1,
+        /*.nb2   =*/ nb2,
+        /*.nb3   =*/ nb3,
+        /*.slope =*/ 0.0,
+        /*.scale =*/ 0.0,
+        /*.bias  =*/ 0.0,
+        /*.val   =*/ 0.0,
+        /*.min   =*/ 0.0,
+        /*.max   =*/ 0.0,
+    };
+
+    if (op->op == GGML_OP_LEAKY_RELU) {
+        args.slope = ggml_get_op_params_f32(op, 0);
+    }
+
+    if (op->op == GGML_OP_SCALE) {
+        args.scale = ggml_get_op_params_f32(op, 0);
+        args.bias  = ggml_get_op_params_f32(op, 1);
+    }
+
+    if (op->op == GGML_OP_FILL) {
+        args.val = ggml_get_op_params_f32(op, 0);
+    }
+
+    if (op->op == GGML_OP_CLAMP) {
+        args.min = ggml_get_op_params_f32(op, 0);
+        args.max = ggml_get_op_params_f32(op, 1);
     }
 
     auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
 
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         1);
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne0  = ne0/4;
+    }
 
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
+
+    if (pipeline.cnt) {
+        const int n = pipeline.c4 ? ggml_nelements(op)/4 : ggml_nelements(op);
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
+    } else {
+        const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
+
+        const int nth = MIN(args.ne00, nth_max);
+
+        const int nk0 = (args.ne00 + nth - 1)/nth;
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, nk0*ne01, ne02, ne03, nth, 1, 1);
+    }
 
     return 1;
 }
@@ -969,6 +904,11 @@ int ggml_metal_op_sum_rows(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
+
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
     ggml_metal_kargs_sum_rows args = {
         /*.ne00 =*/ ne00,
         /*.ne01 =*/ ne01,
@@ -990,21 +930,26 @@ int ggml_metal_op_sum_rows(ggml_metal_op_t ctx, int idx) {
 
     auto pipeline = ggml_metal_library_get_pipeline_sum_rows(lib, op);
 
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne0  = ne0/4;
+    }
+
     int nth = 32; // SIMD width
 
-    while (nth < ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+    while (nth < args.ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
         nth *= 2;
     }
 
     nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-    nth = std::min(nth, ne00);
+    nth = std::min(nth, (int) args.ne00);
 
     const size_t smem = pipeline.smem;
 
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
 
     ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
 
@@ -3044,39 +2989,59 @@ int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
+
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
     float eps;
     memcpy(&eps, op->op_params, sizeof(float));
 
-    int nth = 32; // SIMD width
-
     ggml_metal_kargs_l2_norm args = {
-        /*.ne00   =*/ ne00,
-        /*.ne00_4 =*/ ne00/4,
-        /*.nb01   =*/ nb01,
-        /*.eps    =*/ eps,
+        /*.ne00  =*/ ne00,
+        /*.ne01  =*/ ne01,
+        /*.ne02  =*/ ne02,
+        /*.ne03  =*/ ne03,
+        /*.nb00  =*/ nb00,
+        /*.nb01  =*/ nb01,
+        /*.nb02  =*/ nb02,
+        /*.nb03  =*/ nb03,
+        /*.ne0   =*/ ne0,
+        /*.ne1   =*/ ne1,
+        /*.ne2   =*/ ne2,
+        /*.ne3   =*/ ne3,
+        /*.nb0   =*/ nb0,
+        /*.nb1   =*/ nb1,
+        /*.nb2   =*/ nb2,
+        /*.nb3   =*/ nb3,
+        /*.eps   =*/ eps,
     };
 
     auto pipeline = ggml_metal_library_get_pipeline_l2_norm(lib, op);
 
-    while (nth < ne00/4 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne0  = ne0/4;
+    }
+
+    int nth = 32; // SIMD width
+
+    while (nth < ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
         nth *= 2;
     }
 
     nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-    nth = std::min(nth, ne00/4);
 
     const size_t smem = pipeline.smem;
 
-    const int64_t nrows = ggml_nrows(op->src[0]);
-
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
 
     ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
 
-    ggml_metal_encoder_dispatch_threadgroups(enc, nrows, 1, 1, nth, 1, 1);
+    ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
 
     return 1;
 }
@@ -4084,42 +4049,6 @@ int ggml_metal_op_top_k(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
-int ggml_metal_op_leaky_relu(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    float slope;
-    memcpy(&slope, op->op_params, sizeof(float));
-
-    ggml_metal_kargs_leaky_relu args = {
-        /*.slope =*/ slope
-    };
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
 int ggml_metal_op_tri(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 

ggml/src/ggml-metal/ggml-metal-ops.h

@@ -46,9 +46,6 @@ size_t ggml_metal_op_flash_attn_ext_extra_tmp(const struct ggml_tensor * op);
 int ggml_metal_op_concat            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_repeat            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_acc               (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_scale             (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_fill              (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_clamp             (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_unary             (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_glu               (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_sum               (ggml_metal_op_t ctx, int idx);
@@ -86,7 +83,6 @@ int ggml_metal_op_timestep_embedding(ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_argmax            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_argsort           (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_top_k             (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_leaky_relu        (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_tri               (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_opt_step_adamw    (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_opt_step_sgd      (ggml_metal_op_t ctx, int idx);

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

@@ -77,6 +77,14 @@ static inline float dot(float x, float y) {
     return x*y;
 }
 
+static inline float sum(float x) {
+    return x;
+}
+
+static inline float sum(float4 x) {
+    return x[0] + x[1] + x[2] + x[3];
+}
+
 // NOTE: this is not dequantizing - we are simply fitting the template
 template <typename type4x4>
 void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
@@ -895,6 +903,210 @@ enum ggml_sort_order {
     GGML_SORT_ORDER_DESC,
 };
 
+constant float GELU_COEF_A     = 0.044715f;
+constant float GELU_QUICK_COEF = -1.702f;
+constant float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+constant float SQRT_2_INV      = 0.70710678118654752440084436210484f;
+
+// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
+// ref: https://www.johndcook.com/blog/python_erf/
+constant float p_erf  = 0.3275911f;
+constant float a1_erf = 0.254829592f;
+constant float a2_erf = -0.284496736f;
+constant float a3_erf = 1.421413741f;
+constant float a4_erf = -1.453152027f;
+constant float a5_erf = 1.061405429f;
+
+template<typename T>
+inline T erf_approx(T x) {
+    T sign_x = sign(x);
+    x = fabs(x);
+    T t = 1.0f / (1.0f + p_erf * x);
+    T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
+    return sign_x * y;
+}
+
+template<typename T> T elu_approx(T x);
+
+template<> inline float elu_approx<float>(float x) {
+    return (x > 0.f) ? x : (exp(x) - 1);
+}
+
+template<> inline float4 elu_approx<float4>(float4 x) {
+    float4 res;
+
+    res[0] = (x[0] > 0.0f) ? x[0] : (exp(x[0]) - 1.0f);
+    res[1] = (x[1] > 0.0f) ? x[1] : (exp(x[1]) - 1.0f);
+    res[2] = (x[2] > 0.0f) ? x[2] : (exp(x[2]) - 1.0f);
+    res[3] = (x[3] > 0.0f) ? x[3] : (exp(x[3]) - 1.0f);
+
+    return res;
+}
+
+constant short FC_unary_op [[function_constant(FC_UNARY + 0)]];
+constant bool  FC_unary_cnt[[function_constant(FC_UNARY + 1)]];
+
+template <typename T0, typename T, typename TC>
+kernel void kernel_unary_impl(
+        constant ggml_metal_kargs_unary & args,
+        device const char * src0,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+#define FC_OP  FC_unary_op
+#define FC_CNT FC_unary_cnt
+
+    device const T0 * src0_ptr;
+    device       T  * dst_ptr;
+
+    int i0;
+
+    if (FC_CNT) {
+        i0 = tgpig.x;
+
+        src0_ptr = (device const T0 *) (src0);
+        dst_ptr  = (device       T  *) (dst);
+    } else {
+        const int i03 = tgpig.z;
+        const int i02 = tgpig.y;
+        const int k0  = tgpig.x/args.ne01;
+        const int i01 = tgpig.x - k0*args.ne01;
+
+        i0 = k0*ntg.x + tpitg.x;
+
+        src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
+        dst_ptr  = (device       T  *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1 );
+    }
+
+    {
+        //threadgroup_barrier(mem_flags::mem_none);
+
+        if (!FC_CNT) {
+            if (i0 >= args.ne0) {
+                return;
+            }
+        }
+
+        const TC x = (TC) src0_ptr[i0];
+
+        if (FC_OP == OP_UNARY_NUM_SCALE) {
+            dst_ptr[i0] = (T) (args.scale * x + args.bias);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_FILL) {
+            dst_ptr[i0] = (T) args.val;
+        }
+
+        if (FC_OP == OP_UNARY_NUM_CLAMP) {
+            dst_ptr[i0] = (T) clamp(x, args.min, args.max);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SQR) {
+            dst_ptr[i0] = (T) (x * x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SQRT) {
+            dst_ptr[i0] = (T) sqrt(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SIN) {
+            dst_ptr[i0] = (T) sin(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_COS) {
+            dst_ptr[i0] = (T) cos(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_LOG) {
+            dst_ptr[i0] = (T) log(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_LEAKY_RELU) {
+            dst_ptr[i0] = (T) (TC(x > 0)*x + TC(x <= 0)*(x * args.slope));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_TANH) {
+            dst_ptr[i0] = (T) precise::tanh(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_RELU) {
+            dst_ptr[i0] = (T) fmax(0, x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SIGMOID) {
+            dst_ptr[i0] = (T) (1 / (1 + exp(-x)));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_GELU) {
+            dst_ptr[i0] = (T) (0.5*x*(1 + precise::tanh(SQRT_2_OVER_PI*x*(1 + GELU_COEF_A*x*x))));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_GELU_ERF) {
+            dst_ptr[i0] = (T) (0.5*x*(1 + erf_approx(SQRT_2_INV*x)));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_GELU_QUICK) {
+            dst_ptr[i0] = (T) (x * (1/(1 + exp(GELU_QUICK_COEF*x))));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SILU) {
+            dst_ptr[i0] = (T) (x / (1 + exp(-x)));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_ELU) {
+            dst_ptr[i0] = (T) elu_approx(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_NEG) {
+            dst_ptr[i0] = (T) -x;
+        }
+
+        if (FC_OP == OP_UNARY_NUM_ABS) {
+            dst_ptr[i0] = (T) fabs(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SGN) {
+            dst_ptr[i0] = T(x > 0) - T(x < 0);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_STEP) {
+            dst_ptr[i0] = T(x > 0);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_HARDSWISH) {
+            dst_ptr[i0] = (T) (x * fmax(0, fmin(1, x/6 + 0.5)));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_HARDSIGMOID) {
+            dst_ptr[i0] = (T) fmax(0, fmin(1, x/6 + 0.5));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_EXP) {
+            dst_ptr[i0] = (T) exp(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SOFTPLUS) {
+            dst_ptr[i0] = (T) select(log(1 + exp(x)), x, x > 20);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_EXPM1) {
+            // TODO: precise implementation
+            dst_ptr[i0] = (T) (exp(x) - 1);
+        }
+    }
+
+#undef FC_OP
+#undef FC_CNT
+}
+
+typedef decltype(kernel_unary_impl<float, float, float>) kernel_unary_t;
+
+template [[host_name("kernel_unary_f32_f32")]]   kernel kernel_unary_t kernel_unary_impl<float,  float,  float>;
+template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_unary_impl<float4, float4, float4>;
+template [[host_name("kernel_unary_f16_f16")]]   kernel kernel_unary_t kernel_unary_impl<half,   half,   float>;
+template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_unary_impl<half4,  half4,  float4>;
+
 // OP: 0 - add, 1 - sub, 2 - mul, 3 - div
 constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
 constant short FC_bin_f  [[function_constant(FC_BIN + 1)]];
@@ -1114,414 +1326,6 @@ template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat
 template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
 template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
 
-kernel void kernel_scale_f32(
-        constant ggml_metal_kargs_scale & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * args.scale + args.bias;
-}
-
-kernel void kernel_scale_f32_4(
-        constant ggml_metal_kargs_scale & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * args.scale + args.bias;
-}
-
-kernel void kernel_fill_f32(
-        constant ggml_metal_kargs_fill & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = args.val;
-}
-
-kernel void kernel_fill_f32_4(
-        constant ggml_metal_kargs_fill & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = args.val;
-}
-
-kernel void kernel_clamp_f32(
-        constant ggml_metal_kargs_clamp & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = clamp(src0[tpig], args.min, args.max);
-}
-
-kernel void kernel_clamp_f32_4(
-        constant ggml_metal_kargs_clamp & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = clamp(src0[tpig], args.min, args.max);
-}
-
-kernel void kernel_relu_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = max(0.0f, src0[tpig]);
-}
-
-kernel void kernel_relu_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = max(0.0f, src0[tpig]);
-}
-
-kernel void kernel_sigmoid_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig]));
-}
-
-kernel void kernel_sigmoid_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig]));
-}
-
-kernel void kernel_tanh_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = precise::tanh(src0[tpig]);
-}
-
-kernel void kernel_tanh_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = precise::tanh(src0[tpig]);
-}
-
-constant float GELU_COEF_A     = 0.044715f;
-constant float GELU_QUICK_COEF = -1.702f;
-constant float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
-constant float SQRT_2_INV      = 0.70710678118654752440084436210484f;
-
-kernel void kernel_gelu_f32(
-    device const float * src0,
-    device       float * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-
-    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
-}
-
-kernel void kernel_gelu_f32_4(
-    device const float4 * src0,
-    device       float4 * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-
-    // BEWARE !!!
-    // Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs!
-    // This was observed with Falcon 7B and 40B models
-    //
-    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
-}
-
-kernel void kernel_gelu_quick_f32(
-    device const float * src0,
-    device       float * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-
-    dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
-}
-
-kernel void kernel_gelu_quick_f32_4(
-    device const float4 * src0,
-    device       float4 * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-
-    dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
-}
-
-// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
-// ref: https://www.johndcook.com/blog/python_erf/
-constant float p_erf  = 0.3275911f;
-constant float a1_erf = 0.254829592f;
-constant float a2_erf = -0.284496736f;
-constant float a3_erf = 1.421413741f;
-constant float a4_erf = -1.453152027f;
-constant float a5_erf = 1.061405429f;
-
-template<typename T>
-T erf_approx(T x) {
-    T sign_x = sign(x);
-    x = fabs(x);
-    T t = 1.0f / (1.0f + p_erf * x);
-    T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
-    return sign_x * y;
-}
-
-kernel void kernel_gelu_erf_f32(
-    device const float * src0,
-    device       float * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-
-    dst[tpig] = 0.5f*x*(1.0f+erf_approx<float>(x*SQRT_2_INV));
-}
-
-kernel void kernel_gelu_erf_f32_4(
-    device const float4 * src0,
-    device       float4 * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-
-    dst[tpig] = 0.5f*x*(1.0f+erf_approx<float4>(x*SQRT_2_INV));
-}
-
-kernel void kernel_silu_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-    dst[tpig] = x / (1.0f + exp(-x));
-}
-
-kernel void kernel_silu_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-    dst[tpig] = x / (1.0f + exp(-x));
-}
-
-kernel void kernel_elu_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = (x > 0.0f) ? x : (exp(x) - 1.0f);
-}
-
-kernel void kernel_elu_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig][0] = (x[0] > 0.0f) ? x[0] : (exp(x[0]) - 1.0f);
-    dst[tpig][1] = (x[1] > 0.0f) ? x[1] : (exp(x[1]) - 1.0f);
-    dst[tpig][2] = (x[2] > 0.0f) ? x[2] : (exp(x[2]) - 1.0f);
-    dst[tpig][3] = (x[3] > 0.0f) ? x[3] : (exp(x[3]) - 1.0f);
-}
-
-kernel void kernel_sqr_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * src0[tpig];
-}
-
-kernel void kernel_sqr_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * src0[tpig];
-}
-
-kernel void kernel_sqrt_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sqrt(src0[tpig]);
-}
-
-kernel void kernel_sqrt_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sqrt(src0[tpig]);
-}
-
-kernel void kernel_sin_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sin(src0[tpig]);
-}
-
-kernel void kernel_sin_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sin(src0[tpig]);
-}
-
-kernel void kernel_cos_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = cos(src0[tpig]);
-}
-
-kernel void kernel_cos_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = cos(src0[tpig]);
-}
-
-kernel void kernel_log_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = log(src0[tpig]);
-}
-
-kernel void kernel_log_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = log(src0[tpig]);
-}
-
-kernel void kernel_neg_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = -src0[tpig];
-}
-
-kernel void kernel_neg_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = -src0[tpig];
-}
-
-kernel void kernel_abs_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = fabs(src0[tpig]);
-}
-
-kernel void kernel_abs_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = fabs(src0[tpig]);
-}
-
-kernel void kernel_sgn_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sign(src0[tpig]);
-}
-
-kernel void kernel_sgn_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sign(src0[tpig]);
-}
-
-kernel void kernel_step_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = step(0.0f, src0[tpig]);
-}
-
-kernel void kernel_step_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = step(0.0f, src0[tpig]);
-}
-
-kernel void kernel_hardswish_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = x * fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_hardswish_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig] = x * fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_hardsigmoid_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_hardsigmoid_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig] = fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_exp_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]);
-}
-
-kernel void kernel_exp_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]);
-}
-
-kernel void kernel_softplus_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-    dst[tpig] = select(log(1.0f + exp(x)), x, x > 20.0f);
-}
-
-kernel void kernel_softplus_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-    dst[tpig] = select(log(1.0f + exp(x)), x, x > 20.0f);
-}
-
-kernel void kernel_expm1_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]) - 1.0f;
-}
-
-kernel void kernel_expm1_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]) - 1.0f;
-}
-
 kernel void kernel_reglu_f32(
         constant ggml_metal_kargs_glu & args,
         device const char * src0,
@@ -1705,33 +1509,35 @@ kernel void kernel_op_sum_f32(
     }
 }
 
-template <bool norm>
-kernel void kernel_sum_rows(
+constant short FC_sum_rows_op [[function_constant(FC_SUM_ROWS + 0)]];
+
+template <typename T0, typename T>
+kernel void kernel_sum_rows_impl(
         constant ggml_metal_kargs_sum_rows & args,
-        device const float * src0,
-        device       float * dst,
-        threadgroup  float * shmem_f32 [[threadgroup(0)]],
+        device const char * src0,
+        device       char * dst,
+        threadgroup  char * shmem [[threadgroup(0)]],
         uint3   tgpig[[threadgroup_position_in_grid]],
         ushort3 tpitg[[thread_position_in_threadgroup]],
         ushort  sgitg[[simdgroup_index_in_threadgroup]],
         ushort  tiisg[[thread_index_in_simdgroup]],
         ushort3   ntg[[threads_per_threadgroup]]) {
-    int64_t i3 = tgpig.z;
-    int64_t i2 = tgpig.y;
-    int64_t i1 = tgpig.x;
+#define FC_OP  FC_sum_rows_op
 
-    if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
-        return;
-    }
+    const int i3 = tgpig.z;
+    const int i2 = tgpig.y;
+    const int i1 = tgpig.x;
+
+    threadgroup T0 * shmem_t = (threadgroup T0 *) shmem;
 
     if (sgitg == 0) {
-        shmem_f32[tiisg] = 0.0f;
+        shmem_t[tiisg] = 0.0f;
     }
 
-    device const float * src_row = (device const float *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
-    device       float * dst_row = (device       float *) ((device       char *) dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
+    device const T0 * src_row = (device const T0 *) (src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
+    device       T  * dst_row = (device       T  *) (dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
 
-    float sumf = 0;
+    T0 sumf = T0(0.0f);
 
     for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
         sumf += src_row[i0];
@@ -1742,23 +1548,33 @@ kernel void kernel_sum_rows(
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     if (tiisg == 0) {
-        shmem_f32[sgitg] = sumf;
+        shmem_t[sgitg] = sumf;
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
-    sumf = shmem_f32[tiisg];
+    sumf = shmem_t[tiisg];
     sumf = simd_sum(sumf);
 
     if (tpitg.x == 0) {
-        dst_row[0] = norm ? sumf / args.ne00 : sumf;
+        if (FC_OP == OP_SUM_ROWS_NUM_MEAN) {
+            if (is_same<float4, T0>::value) {
+                dst_row[0] = sum(sumf) / (4*args.ne00);
+            } else {
+                dst_row[0] = sum(sumf) / args.ne00;
+            }
+        } else {
+            dst_row[0] = sum(sumf);
+        }
     }
+
+#undef FC_OP
 }
 
-typedef decltype(kernel_sum_rows<false>) kernel_sum_rows_t;
+typedef decltype(kernel_sum_rows_impl<float, float>) kernel_sum_rows_t;
 
-template [[host_name("kernel_sum_rows_f32")]] kernel kernel_sum_rows_t kernel_sum_rows<false>;
-template [[host_name("kernel_mean_f32")]]     kernel kernel_sum_rows_t kernel_sum_rows<true>;
+template [[host_name("kernel_sum_rows_f32_f32")]]   kernel kernel_sum_rows_t kernel_sum_rows_impl<float,  float>;
+template [[host_name("kernel_sum_rows_f32_f32_4")]] kernel kernel_sum_rows_t kernel_sum_rows_impl<float4, float>;
 
 template<typename T>
 kernel void kernel_cumsum_blk(
@@ -2639,9 +2455,6 @@ kernel void kernel_solve_tri_f32(
     const short K   = FC_solve_tri_k;
     const short NP  = PAD2(N, NW);
 
-    const int32_t ne02 = args.ne02;
-    const int32_t ne03 = args.ne03;
-
     const int32_t i03 = tgpig.z;
     const int32_t i02 = tgpig.y;
     const int32_t i01 = tgpig.x*NSG + sgitg;
@@ -2928,26 +2741,32 @@ template [[host_name("kernel_rms_norm_f32_4")]]         kernel kernel_rms_norm_f
 template [[host_name("kernel_rms_norm_mul_f32_4")]]     kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 2>;
 template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 3>;
 
-kernel void kernel_l2_norm_f32(
+template <typename T0, typename T>
+kernel void kernel_l2_norm_impl(
         constant ggml_metal_kargs_l2_norm & args,
         device const char * src0,
         device       char * dst,
         threadgroup float * shmem_f32 [[threadgroup(0)]],
-        uint   tgpig[[threadgroup_position_in_grid]],
-        ushort tpitg[[thread_position_in_threadgroup]],
-        ushort sgitg[[simdgroup_index_in_threadgroup]],
-        ushort tiisg[[thread_index_in_simdgroup]],
-        ushort   ntg[[threads_per_threadgroup]]) {
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
+        ushort  tiisg[[thread_index_in_simdgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    const int i03 = tgpig.z;
+    const int i02 = tgpig.y;
+    const int i01 = tgpig.x;
+
     if (sgitg == 0) {
         shmem_f32[tiisg] = 0.0f;
     }
 
-    device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01);
+    device const T0 * x = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
+    device       T  * y = (device       T  *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1);
 
     float sumf = 0.0f;
 
     // parallel sum
-    for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
+    for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
         sumf += dot(x[i00], x[i00]);
     }
     sumf = simd_sum(sumf);
@@ -2965,12 +2784,16 @@ kernel void kernel_l2_norm_f32(
 
     const float scale = 1.0f/sqrt(max(sumf, args.eps));
 
-    device float4 * y = (device float4 *) dst + tgpig*args.ne00_4;
-    for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
+    for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
         y[i00] = x[i00] * scale;
     }
 }
 
+typedef decltype(kernel_l2_norm_impl<float, float>) kernel_l2_norm_t;
+
+template [[host_name("kernel_l2_norm_f32_f32")]]   kernel kernel_l2_norm_t kernel_l2_norm_impl<float,  float>;
+template [[host_name("kernel_l2_norm_f32_f32_4")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float4, float4>;
+
 kernel void kernel_group_norm_f32(
         constant ggml_metal_kargs_group_norm & args,
         device const float * src0,
@@ -5072,24 +4895,6 @@ kernel void kernel_argsort_merge_f32_i32(
 template [[host_name("kernel_argsort_merge_f32_i32_asc")]]  kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_ASC>;
 template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_DESC>;
 
-kernel void kernel_leaky_relu_f32(
-        constant     ggml_metal_kargs_leaky_relu & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = x > 0.0f ? x : x * args.slope;
-}
-
-kernel void kernel_leaky_relu_f32_4(
-        constant     ggml_metal_kargs_leaky_relu & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig] = float4(x > 0.0f)*x + float4(x <= 0.0f)*(x * args.slope);
-}
-
 constant bool FC_flash_attn_ext_pad_has_mask [[function_constant(FC_FLASH_ATTN_EXT_PAD + 0)]];
 
 constant int32_t FC_flash_attn_ext_pad_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_PAD + 25)]];
@@ -6161,7 +5966,7 @@ kernel void kernel_flash_attn_ext_vec(
     static_assert(DK4 % NL == 0, "DK4 must be divisible by NL");
     static_assert(DV4 % NL == 0, "DV4 must be divisible by NL");
 
-    const short T = PK + NSG*SH; // shared memory size per query in (half)
+  //const short T = PK + NSG*SH; // shared memory size per query in (half)
 
   //threadgroup q_t   * sq  = (threadgroup q_t   *) (shmem_f16 +                      0*PK); // holds the query data
     threadgroup q4_t  * sq4 = (threadgroup q4_t  *) (shmem_f16 +                      0*PK); // same as above but in q4_t
@@ -8749,7 +8554,9 @@ kernel void kernel_mul_mm(
     threadgroup S0 * sa = (threadgroup S0 *)(shmem);
     threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
 
+#ifdef GGML_METAL_HAS_TENSOR
     threadgroup float * sc = (threadgroup float *)(shmem);
+#endif
 
     constexpr int NR0 = 64;
     constexpr int NR1 = 32;
@@ -8872,8 +8679,8 @@ kernel void kernel_mul_mm(
             const short sx = (tiitg%NL1);
             const short sy = (tiitg/NL1)/8;
 
-            const short dx = sx;
-            const short dy = sy;
+          //const short dx = sx;
+          //const short dy = sy;
 
             const short ly = (tiitg/NL1)%8;
 
@@ -9122,7 +8929,9 @@ kernel void kernel_mul_mm_id(
     threadgroup S0 * sa = (threadgroup S0 *)(shmem);
     threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
 
+#ifdef GGML_METAL_HAS_TENSOR
     threadgroup float * sc = (threadgroup float *)(shmem);
+#endif
 
     constexpr int NR0 = 64;
     constexpr int NR1 = 32;
@@ -9257,8 +9066,8 @@ kernel void kernel_mul_mm_id(
             const short sx = (tiitg%NL1);
             const short sy = (tiitg/NL1)/8;
 
-            const short dx = sx;
-            const short dy = sy;
+          //const short dx = sx;
+          //const short dy = sy;
 
             const short ly = (tiitg/NL1)%8;
 
@@ -9939,7 +9748,7 @@ kernel void kernel_opt_step_sgd_f32(
 
 template<typename T>
 kernel void kernel_memset(
-        constant ggml_metal_kargs_fill & args,
+        constant ggml_metal_kargs_memset & args,
         device T * dst,
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = args.val;

ggml/src/ggml-opencl/CMakeLists.txt

@@ -85,6 +85,9 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q4_0_f32_8x_flat
     mul_mv_q4_0_f32_1d_8x_flat
     mul_mv_q4_0_f32_1d_16x_flat
+    mul_mv_q4_1_f32
+    mul_mv_q4_1_f32_flat
+    mul_mv_q4_k_f32
     mul_mv_q6_k_f32
     mul_mv_q6_k_f32_flat
     mul_mv_q8_0_f32
@@ -100,7 +103,10 @@ set(GGML_OPENCL_KERNELS
     gemv_moe_mxfp4_f32
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
+    mul_mm_q4_0_f32_l4_lm
+    mul_mm_q4_1_f32_l4_lm
     mul_mm_q8_0_f32_l4_lm
+    mul_mm_q6_k_f32_l4_lm
     mul_mm_q8_0_f32_8x4
     gemv_noshuffle_general_q8_0_f32
     mul

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

@@ -525,6 +525,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mm_f16_f32_kq;
     cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
     cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
+    cl_kernel kernel_convert_block_q4_1, kernel_restore_block_q4_1;
     cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans;
     cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans;
     cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
@@ -532,6 +533,9 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_restore_block_q4_0_noshuffle;
     cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
     cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
+    cl_kernel kernel_mul_mv_q4_1_f32;
+    cl_kernel kernel_mul_mv_q4_1_f32_flat;
+    cl_kernel kernel_mul_mv_q4_K_f32;
     cl_kernel kernel_mul_mv_q6_K_f32;
     cl_kernel kernel_mul_mv_q6_K_f32_flat;
     cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
@@ -563,7 +567,10 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mv_id_mxfp4_f32_flat;
     cl_kernel kernel_mul_mm_f32_f32_l4_lm;
     cl_kernel kernel_mul_mm_f16_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q4_0_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q4_1_f32_l4_lm;
     cl_kernel kernel_mul_mm_q8_0_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
 
     std::vector<ProfilingInfo> profiling_info;
 
@@ -886,6 +893,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q4_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q4_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4_trans", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4_trans", &err), err));
@@ -1117,6 +1126,57 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_q4_1_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q4_1_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q4_1_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mv_q4_1_f32_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q4_1_f32_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q4_1_f32_flat.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32_flat", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mv_q4_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q4_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q4_k_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_q6_k_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1342,6 +1402,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mm_q4_0_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q4_0_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q4_0_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_0_f32_l4_lm", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mm_q4_1_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q4_1_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q4_1_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_1_f32_l4_lm", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_q8_0_f32_l4_lm
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1358,6 +1450,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mm_q6_k_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q6_k_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q6_k_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q6_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q6_k_f32_l4_lm", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_f16_f32_kq_kqv
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2887,6 +2996,59 @@ struct ggml_tensor_extra_cl_q4_0 {
     }
 };
 
+struct ggml_tensor_extra_cl_q4_1 {
+    // Quantized values.
+    cl_mem q = nullptr;
+    // Quantized values in image1d_buffer_t.
+    cl_mem q_img = nullptr;
+    // Scales.
+    cl_mem d = nullptr;
+    // Scales in image1d_buffer_t.
+    cl_mem d_img = nullptr;
+    // Min
+    cl_mem m = nullptr;
+    // Min in image1d_buffer_t.
+    cl_mem m_img = nullptr;
+    // Size of quantized values.
+    size_t size_q = 0;
+    // Size of scales.
+    size_t size_d = 0;
+    // Size of min values.
+    size_t size_m = 0;
+
+    ~ggml_tensor_extra_cl_q4_1() {
+        reset();
+    }
+
+    void reset() {
+        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
+        // They must be properly released so that the original buffer can be
+        // properly released to avoid memory leak.
+        if (q != nullptr) {
+            CL_CHECK(clReleaseMemObject(q));
+            q = nullptr;
+        }
+        if (d != nullptr) {
+            CL_CHECK(clReleaseMemObject(d));
+            d = nullptr;
+        }
+        if (m != nullptr) {
+            CL_CHECK(clReleaseMemObject(m));
+            m = nullptr;
+        }
+        // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
+        // enabled. They point to the images in ggml_backend_opencl_buffer_context.
+        // So, there is no need to release them here.
+        // TODO: initialize them for non SMALL_PATH path, or remove them.
+        q_img = nullptr;
+        d_img = nullptr;
+        m_img = nullptr;
+        size_q = 0;
+        size_d = 0;
+        size_m = 0;
+    }
+};
+
 struct ggml_tensor_extra_cl_mxfp4 {
     // Quantized values.
     cl_mem q = nullptr;
@@ -3363,7 +3525,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 return true;
             } else if (op->src[0]->type == GGML_TYPE_F32) {
                 return op->src[1]->type == GGML_TYPE_F32;
-            } else if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_MXFP4 ||
+            } else if (op->src[0]->type == GGML_TYPE_Q4_0  || op->src[0]->type == GGML_TYPE_Q4_1 ||
+                       op->src[0]->type == GGML_TYPE_MXFP4 ||
+                       op->src[0]->type == GGML_TYPE_Q4_K  ||
                        op->src[0]->type == GGML_TYPE_Q6_K) {
                 return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
             } else if (op->src[0]->type == GGML_TYPE_Q8_0) {
@@ -3592,6 +3756,21 @@ struct ggml_backend_opencl_buffer_context {
         return extra;
     }
 
+    ggml_tensor_extra_cl_q4_1 * ggml_opencl_alloc_temp_tensor_extra_q4_1() {
+        ggml_tensor_extra_cl_q4_1 * extra;
+        if (temp_tensor_extras_q4_1.empty()) {
+            extra = new ggml_tensor_extra_cl_q4_1();
+        } else {
+            extra = temp_tensor_extras_q4_1.back();
+            temp_tensor_extras_q4_1.pop_back();
+        }
+
+        temp_tensor_extras_q4_1_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
     ggml_tensor_extra_cl_mxfp4 * ggml_opencl_alloc_temp_tensor_extra_mxfp4() {
         ggml_tensor_extra_cl_mxfp4 * extra;
         if (temp_tensor_extras_mxfp4.empty()) {
@@ -3648,6 +3827,11 @@ struct ggml_backend_opencl_buffer_context {
         }
         temp_tensor_extras_q4_0_in_use.clear();
 
+        for (ggml_tensor_extra_cl_q4_1 * e : temp_tensor_extras_q4_1_in_use) {
+            temp_tensor_extras_q4_1.push_back(e);
+        }
+        temp_tensor_extras_q4_1_in_use.clear();
+
         for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
             temp_tensor_extras_mxfp4.push_back(e);
         }
@@ -3673,6 +3857,8 @@ struct ggml_backend_opencl_buffer_context {
     std::vector<ggml_tensor_extra_cl *> temp_tensor_extras_in_use;
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0;
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
+    std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1;
+    std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1_in_use;
     std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4;
     std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4_in_use;
     std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0;
@@ -4042,6 +4228,75 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         return;
 
     }
+    if (tensor->type == GGML_TYPE_Q4_1) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_q4_1 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q4_1();
+
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_m = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        GGML_ASSERT(size_d + size_m + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        cl_buffer_region region;
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, mins, then quants.
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for mins.
+        region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
+        region.size = size_m;
+        extra->m = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        previous_origin = region.origin;
+
+        // Create subbuffer for quants.
+        region.origin = align_to(previous_origin + size_m, backend_ctx->alignment);
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->m));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        tensor->extra = extra;
+
+        return;
+    }
     if (tensor->type == GGML_TYPE_MXFP4) {
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
         GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
@@ -4544,7 +4799,35 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             size, data, 0, NULL, NULL));
         CL_CHECK(clReleaseMemObject(data_device));
         return;
-    } else if (tensor->type == GGML_TYPE_MXFP4) {
+    }
+    if (tensor->type == GGML_TYPE_Q4_1) {
+        ggml_tensor_extra_cl_q4_1 * extra = (ggml_tensor_extra_cl_q4_1 *)tensor->extra;
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_restore_block_q4_1;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->m));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &data_device));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {1, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+            global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clEnqueueReadBuffer(
+            queue, data_device, CL_TRUE, offset,
+            size, data, 0, NULL, NULL));
+        CL_CHECK(clReleaseMemObject(data_device));
+        return;
+    }
+    if (tensor->type == GGML_TYPE_MXFP4) {
         ggml_tensor_extra_cl_mxfp4 * extra = (ggml_tensor_extra_cl_mxfp4 *)tensor->extra;
 
         cl_int err;
@@ -8372,6 +8655,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
 #ifdef GGML_OPENCL_SOA_Q
     ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
     ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
     ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
     ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
@@ -8885,6 +9169,91 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
                 return;
             }
+            case GGML_TYPE_Q4_0: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
+            case GGML_TYPE_Q4_1: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_1->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_1->d));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q4_1->m));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
             case GGML_TYPE_Q8_0: {
                 if (ne11 < 32) {
                     break;
@@ -8927,6 +9296,50 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
                 return;
             }
+            case GGML_TYPE_Q6_K: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q6_k_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q6_K->ql));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q6_K->qh));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q6_K->s));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra0_q6_K->d));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
             default:
                 break;
         }
@@ -9181,7 +9594,71 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
 #endif // GGML_OPENCL_SOA_Q
             break;
-        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q4_1: {
+#ifdef GGML_OPENCL_SOA_Q
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            kernel = backend_ctx->kernel_mul_mv_q4_1_f32_flat;
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_1->q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_1->d));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q4_1->m));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &r3));
+#else
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            kernel = backend_ctx->kernel_mul_mv_q4_1_f32;
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+#endif // GGML_OPENCL_SOA_Q
+            break;
+        }
         case GGML_TYPE_Q8_0: {
 #ifdef GGML_OPENCL_SOA_Q
             kernel = backend_ctx->kernel_mul_mv_q8_0_f32_flat;
@@ -9262,7 +9739,42 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
         }
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q4_K: {
+            kernel = backend_ctx->kernel_mul_mv_q4_K_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),     &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(int),        &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),     &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(int),        &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),     &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),        &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),        &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),        &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong),   &nb01));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong),   &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),   &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),        &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong),   &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong),   &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong),   &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),        &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),        &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),        &r2));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),        &r3));
+            break;
+        }
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
 #ifdef GGML_OPENCL_SOA_Q
@@ -9424,7 +9936,10 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
         backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
     } else if (src0t == GGML_TYPE_Q4_K) {
-        GGML_ASSERT(false && "not implemented");
+        size_t global_work_size[] = {(size_t)(ne01+ndst*nth1-1)/(ndst*nth1)*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
+        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
     } else if (src0t == GGML_TYPE_Q3_K) {
         GGML_ASSERT(false && "not implemented");
     } else if (src0t == GGML_TYPE_Q5_K) {

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

@@ -46,6 +46,15 @@ struct block_q4_0
     uint8_t qs[QK4_0 / 2];
 };
 
+//------------------------------------------------------------------------------
+// block_q4_1
+//------------------------------------------------------------------------------
+struct block_q4_1 {
+    half d; // delta
+    half m; // min
+    uchar qs[QK4_1 / 2]; // nibbles / quants
+};
+
 //------------------------------------------------------------------------------
 // block_q6_K
 //------------------------------------------------------------------------------
@@ -148,6 +157,48 @@ kernel void kernel_restore_block_q4_0_noshuffle(
     }
 }
 
+//------------------------------------------------------------------------------
+// kernel_convert_block_q4_1
+// Convert the block_q4_1 format to 2 separate arrays (AOS -> SOA).
+// This kernel does not deshuffle the bits.
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_q4_1(
+    global struct block_q4_1 * src0,
+    global uchar * dst_q,
+    global half  * dst_d,
+    global half  * dst_m
+) {
+    global struct block_q4_1 * b = (global struct block_q4_1 *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_1/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+    global half  * m = (global half *) dst_m + get_global_id(0);
+
+    *d = b->d;
+    *m = b->m;
+
+    for (int i = 0; i < QK4_1/2; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_q4_1(
+    global uchar * src_q,
+    global half  * src_d,
+    global half  * src_m,
+    global struct block_q4_1 * dst
+) {
+    global struct block_q4_1 * b = (global struct block_q4_1 *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK4_1/2*get_global_id(0);
+    global half  * d = (global half *) src_d + get_global_id(0);
+    global half  * m = (global half *) src_m + get_global_id(0);
+
+    b->d = *d;
+    b->m = *m;
+    for (int i = 0; i < QK4_1/2; ++i) {
+        b->qs[i] = q[i];
+    }
+}
+
 //------------------------------------------------------------------------------
 // block_mxfp4
 //------------------------------------------------------------------------------

ggml/src/ggml-opencl/kernels/mul_mm_q4_0_f32_l4_lm.cl

@@ -0,0 +1,163 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 8
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q4_0_f32_l4_lm(
+    global uchar4 * src0_q,
+    global half   * src0_d,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+                int ib  = idx / 4;
+                int iqs = idx % 4;
+
+                float d = (float)src0_d[ib];
+                global uchar4 * qs = src0_q + ib*4 + iqs;
+                uchar4 q = *qs;
+                float4 v1 = (convert_float4((uchar4)((q.s0   )&0x0F, (q.s1   )&0x0F, (q.s2   )&0x0F, (q.s3   )&0x0F)) - 8.0f)*d;
+                float4 v2 = (convert_float4((uchar4)((q.s0>>4)&0x0F, (q.s1>>4)&0x0F, (q.s2>>4)&0x0F, (q.s3>>4)&0x0F)) - 8.0f)*d;
+
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = v1.s0;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = v1.s1;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = v1.s2;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = v1.s3;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
+            } else {
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mm_q4_1_f32_l4_lm.cl

@@ -0,0 +1,165 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 8
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q4_1_f32_l4_lm(
+    global uchar4 * src0_q,
+    global half   * src0_d,
+    global half   * src0_m,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+                int ib  = idx / 4;
+                int iqs = idx % 4;
+
+                float d = (float)src0_d[ib];
+                float m = (float)src0_m[ib];
+                global uchar4 * qs = src0_q + ib*4 + iqs;
+                uchar4 q = *qs;
+                float4 v1 = (convert_float4((uchar4)((q.s0   )&0x0F, (q.s1   )&0x0F, (q.s2   )&0x0F, (q.s3   )&0x0F)))*d + m;
+                float4 v2 = (convert_float4((uchar4)((q.s0>>4)&0x0F, (q.s1>>4)&0x0F, (q.s2>>4)&0x0F, (q.s3>>4)&0x0F)))*d + m;
+
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = v1.s0;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = v1.s1;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = v1.s2;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = v1.s3;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
+            } else {
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mm_q6_k_f32_l4_lm.cl

@@ -0,0 +1,158 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 2
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q6_k_f32_l4_lm(
+    global uchar * src0_ql,
+    global uchar * src0_qh,
+    global char  * src0_s,
+    global half  * src0_d,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+
+                int ib = idx / 128;                  // 2 values per idx
+                int iqs = idx % 128;                 // 0..127
+
+                int n = iqs / 64;                    // 0,1
+                int b = (iqs % 64) / 32;             // 0,1
+                int is_b = (iqs % 16) / 8;           // 0,1
+                int qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+                int is = 8 * n + qhshift + is_b;     // 0..15
+                int qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+                int qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+                float dscale = (float)src0_d[ib] * (float)src0_s[ib*16 + is];
+
+                buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = dscale * convert_float(convert_char(((src0_ql[128*ib + qsi + 0] >> (b * 4)) & 0xF) | (((src0_qh[64*ib + qhi + 0] >> qhshift) & 3) << 4)) - 32);
+                buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = dscale * convert_float(convert_char(((src0_ql[128*ib + qsi + 1] >> (b * 4)) & 0xF) | (((src0_qh[64*ib + qhi + 1] >> qhshift) & 3) << 4)) - 32);
+            } else {
+                buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32.cl

@@ -0,0 +1,219 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_1                   32
+
+struct block_q4_1 {
+    half d; // delta
+    half m; // min
+    uchar qs[QK4_1 / 2]; // nibbles / quants
+};
+
+inline float block_q4_1_dot_y(
+    global const struct block_q4_1 * qb_curr,
+    float sumy,
+    float16 yl,
+    int il
+) {
+    float d = qb_curr->d;
+    float m = qb_curr->m;
+
+    float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
+
+    global const ushort * qs = ((global const ushort *) qb_curr + 2 + il/2);
+
+    acc.s0 += yl.s0 * (qs[0] & 0x000F);
+    acc.s0 += yl.s1 * (qs[0] & 0x0F00);
+    acc.s0 += yl.s8 * (qs[0] & 0x00F0);
+    acc.s3 += yl.s9 * (qs[0] & 0xF000);
+
+    acc.s0 += yl.s2 * (qs[1] & 0x000F);
+    acc.s1 += yl.s3 * (qs[1] & 0x0F00);
+    acc.s2 += yl.sa * (qs[1] & 0x00F0);
+    acc.s3 += yl.sb * (qs[1] & 0xF000);
+
+    acc.s0 += yl.s4 * (qs[2] & 0x000F);
+    acc.s1 += yl.s5 * (qs[2] & 0x0F00);
+    acc.s2 += yl.sc * (qs[2] & 0x00F0);
+    acc.s3 += yl.sd * (qs[2] & 0xF000);
+
+    acc.s0 += yl.s6 * (qs[3] & 0x000F);
+    acc.s1 += yl.s7 * (qs[3] & 0x0F00);
+    acc.s2 += yl.se * (qs[3] & 0x00F0);
+    acc.s3 += yl.sf * (qs[3] & 0xF000);
+
+    return d * (acc.s0 + acc.s1 + acc.s2 + acc.s3) + sumy * m;
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each subgroup works on 4 rows
+#define N_SIMDGROUP 1 // number of subgroups in a thread group
+#define N_SIMDWIDTH 16 // assuming subgroup size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_1;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_q4_1 * x = (global struct block_q4_1 *) src0 + offset0;
+    global float             * y = (global float             *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_1 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q4_1_dot_y(x+ib+0*nb, sumy, yl, il);
+        sumf.s1 += block_q4_1_dot_y(x+ib+1*nb, sumy, yl, il);
+        sumf.s2 += block_q4_1_dot_y(x+ib+2*nb, sumy, yl, il);
+        sumf.s3 += block_q4_1_dot_y(x+ib+3*nb, sumy, yl, il);
+
+        yb += QK4_1 * (N_SIMDWIDTH/2);
+    }
+
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q4_1_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32_flat.cl

@@ -0,0 +1,229 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_1                   32
+
+struct block_q4_1 {
+    half d; // delta
+    half m; // min
+    uchar qs[QK4_1 / 2]; // nibbles / quants
+};
+
+inline float block_q4_1_dot_y_flat(
+    global const uchar * x,
+    global const half  * dh,
+    global const half  * mh,
+    float sumy,
+    float16 yl,
+    int il
+) {
+    float                 d   = *dh;
+    float                 m   = *mh;
+    global const ushort * qs = ((global const ushort *) x + il/2);
+
+    float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
+
+    acc.s0 += yl.s0 * (qs[0] & 0x000F);
+    acc.s0 += yl.s1 * (qs[0] & 0x0F00);
+    acc.s0 += yl.s8 * (qs[0] & 0x00F0);
+    acc.s3 += yl.s9 * (qs[0] & 0xF000);
+
+    acc.s0 += yl.s2 * (qs[1] & 0x000F);
+    acc.s1 += yl.s3 * (qs[1] & 0x0F00);
+    acc.s2 += yl.sa * (qs[1] & 0x00F0);
+    acc.s3 += yl.sb * (qs[1] & 0xF000);
+
+    acc.s0 += yl.s4 * (qs[2] & 0x000F);
+    acc.s1 += yl.s5 * (qs[2] & 0x0F00);
+    acc.s2 += yl.sc * (qs[2] & 0x00F0);
+    acc.s3 += yl.sd * (qs[2] & 0xF000);
+
+    acc.s0 += yl.s6 * (qs[3] & 0x000F);
+    acc.s1 += yl.s7 * (qs[3] & 0x0F00);
+    acc.s2 += yl.se * (qs[3] & 0x00F0);
+    acc.s3 += yl.sf * (qs[3] & 0xF000);
+
+    return d * (acc.s0 + acc.s1 + acc.s2 + acc.s3) + sumy * m;
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each subgroup works on 4 rows
+#define N_SIMDGROUP 1 // number of subgroups in a thread group
+#define N_SIMDWIDTH 16 // assuming subgroup size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_flat(
+        global void * src0_q,
+        global void * src0_d,
+        global void * src0_m,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_1;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    // The number of scales/mins is the same as the number of blocks.
+    ulong offset0_dm = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02));
+    // Each block contains QK4_1/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q  = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_1/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_dm;
+    global half  * m = (global half  *) src0_m + offset0_dm;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_1 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 0*nb*QK4_1/2, d + ib + 0*nb, m + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 1*nb*QK4_1/2, d + ib + 1*nb, m + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 2*nb*QK4_1/2, d + ib + 2*nb, m + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 3*nb*QK4_1/2, d + ib + 3*nb, m + ib + 3*nb, sumy, yl, il);
+
+        yb += QK4_1 * (N_SIMDWIDTH/2);
+    }
+
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q4_1_f32_flat(
+        global void * src0_q,
+        global void * src0_d,
+        global void * src0_m,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_flat(src0_q, src0_d, src0_m, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl

@@ -0,0 +1,180 @@
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+//------------------------------------------------------------------------------
+// block_q4_K
+//------------------------------------------------------------------------------
+#define QK_K            256
+#define K_SCALE_SIZE    12
+
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+typedef struct {
+    half d;    // super-block scale for quantized scales
+    half dmin; // super-block scale for quantized mins
+
+    uchar scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uchar qs[QK_K/2];           // 4-bit quants
+} block_q4_K;
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // number of rows each SIMD group works on
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // SIMD group size
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+#undef  BLOCK_STRIDE
+// number of (super) blocks each subgroup processes
+// each thread in a subgroup processes a block (32 weights)
+#define BLOCK_STRIDE (N_SIMDWIDTH/8)
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q4_K_f32(
+        global char * src0,
+        int offset0,
+        global char * src1,
+        int offset1,
+        global char * dst,
+        int offsetd,
+        int ne00,
+        int ne01,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne12,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst  + offsetd;
+
+    ushort kmask1 = 0x3f3f;
+    ushort kmask2 = 0x0f0f;
+    ushort kmask3 = 0xc0c0;
+
+    int ix = get_sub_group_local_id()/8;  // super block index
+    int it = get_sub_group_local_id()%8;  // block index (inside super block)
+    int iq = it/4;     // 0 or 1 - first or second half of the super block
+    int ir = it%4;     // 0...3 - block index in the half super block
+
+    int nb = ne00/QK_K;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    int offset_src0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    int offset_src1 =        r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+    global block_q4_K * x = (global block_q4_K *) (src0 + offset_src0);
+    global float      * y = (global float      *) (src1 + offset_src1);
+
+    float yl[16];
+    float yh[16];
+    float sumf[N_DST] = {0.f};
+    float all_sum;
+
+    global float * y4 = y + ix * QK_K + 64 * iq + 8 * ir;
+
+    ushort  sc16[4];
+    uchar * sc8 = (uchar *)sc16;
+
+    for (int ib = ix; ib < nb; ib += BLOCK_STRIDE) {
+        float4 sumy = {0.f, 0.f, 0.f, 0.f};
+        for (int i = 0; i < 8; ++i) {
+            yl[i+0] = y4[i+0];
+            sumy.s0 += yl[i+0];
+
+            yl[i+8] = y4[i+32];
+            sumy.s1 += yl[i+8];
+
+            yh[i+0] = y4[i+128];
+            sumy.s2 += yh[i+0];
+
+            yh[i+8] = y4[i+160];
+            sumy.s3 += yh[i+8];
+        }
+
+        global ushort * sc = (global ushort *)x[ib].scales + iq;
+        global ushort * q1 = (global ushort *)x[ib].qs + 16 * iq + 4 * ir;
+        global half     * dh = &x[ib].d;
+
+        for (int row = 0; row < N_DST; row++) {
+            sc16[0] = sc[0] & kmask1;
+            sc16[1] = sc[2] & kmask1;
+            sc16[2] = ((sc[4] >> 0) & kmask2) | ((sc[0] & kmask3) >> 2);
+            sc16[3] = ((sc[4] >> 4) & kmask2) | ((sc[2] & kmask3) >> 2);
+
+            global ushort * q2 = q1 + 32;
+
+            float4 acc1 = {0.f, 0.f, 0.f, 0.f};
+            float4 acc2 = {0.f, 0.f, 0.f, 0.f};
+            for (int i = 0; i < 8; i += 2) {
+                acc1.s0 += yl[i+0] * (q1[i/2] & 0x000F);
+                acc1.s1 += yl[i+1] * (q1[i/2] & 0x0F00);
+                acc1.s2 += yl[i+8] * (q1[i/2] & 0x00F0);
+                acc1.s3 += yl[i+9] * (q1[i/2] & 0xF000);
+                acc2.s0 += yh[i+0] * (q2[i/2] & 0x000F);
+                acc2.s1 += yh[i+1] * (q2[i/2] & 0x0F00);
+                acc2.s2 += yh[i+8] * (q2[i/2] & 0x00F0);
+                acc2.s3 += yh[i+9] * (q2[i/2] & 0xF000);
+            }
+
+            float dall = dh[0];
+            float dmin = dh[1];
+            sumf[row] += dall * ((acc1.s0 + 1.f/256.f * acc1.s1) * sc8[0] +
+                                 (acc1.s2 + 1.f/256.f * acc1.s3) * sc8[1] * 1.f/16.f +
+                                 (acc2.s0 + 1.f/256.f * acc2.s1) * sc8[4] +
+                                 (acc2.s2 + 1.f/256.f * acc2.s3) * sc8[5] * 1.f/16.f) -
+                         dmin * (sumy.s0 * sc8[2] + sumy.s1 * sc8[3] + sumy.s2 * sc8[6] + sumy.s3 * sc8[7]);
+
+            q1 += nb01/2;
+            sc += nb01/2;
+            dh += nb01/2;
+        }
+
+        y4 += BLOCK_STRIDE * QK_K;
+    }
+
+    global float * dst_f32 = (global float *) dst + im*ne0*ne1 + r1*ne0;
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = sub_group_reduce_add(sumf[row]);
+        if (first_row + row < ne01) {
+            if (get_sub_group_local_id() == 0) {
+                dst_f32[first_row + row] = all_sum;
+            }
+        }
+    }
+}

ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

@@ -4,6 +4,7 @@
 #include "ggml.h"
 #include "pre_wgsl.hpp"
 
+#include <memory>
 #include <string>
 #include <vector>
 
@@ -18,9 +19,9 @@
 #define GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE 512u
 
 struct ggml_webgpu_processed_shader {
-    std::string wgsl;
-    std::string variant;
-    void *      decisions;
+    std::string           wgsl;
+    std::string           variant;
+    std::shared_ptr<void> decisions;
 };
 
 // Same hash combine function as in boost
@@ -192,13 +193,13 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_flash_attn_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                         = preprocessor.preprocess(shader_src, defines);
-    result.variant                                      = variant;
-    ggml_webgpu_flash_attn_shader_decisions * decisions = new ggml_webgpu_flash_attn_shader_decisions();
-    decisions->q_tile                                   = q_tile;
-    decisions->kv_tile                                  = kv_tile;
-    decisions->wg_size                                  = wg_size;
-    result.decisions                                    = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_flash_attn_shader_decisions>();
+    decisions->q_tile  = q_tile;
+    decisions->kv_tile = kv_tile;
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -270,11 +271,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_pad_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -305,11 +306,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_argsort_shader(
     }
     defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_argsort_shader_decisions * decisions = new ggml_webgpu_argsort_shader_decisions();
-    decisions->wg_size                               = wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_argsort_shader_decisions>();
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -324,11 +325,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_argsort_merge_shader(
     uint32_t wg_size = std::min(GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE, context.max_wg_size);
     defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_argsort_shader_decisions * decisions = new ggml_webgpu_argsort_shader_decisions();
-    decisions->wg_size                               = wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_argsort_shader_decisions>();
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -391,11 +392,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_set_rows_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -457,11 +458,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -527,11 +528,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
 
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 #endif  // GGML_WEBGPU_SHADER_LIB_HPP

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -186,11 +186,17 @@ struct webgpu_buf_pool {
     void cleanup() {
         std::lock_guard<std::mutex> lock(mutex);
         for (auto & bufs : free) {
-            bufs.host_buf.Destroy();
-            bufs.dev_buf.Destroy();
+            if (bufs.host_buf) {
+                bufs.host_buf.Destroy();
+            }
+            if (bufs.dev_buf) {
+                bufs.dev_buf.Destroy();
+            }
         }
         free.clear();
     }
+
+    ~webgpu_buf_pool() { this->cleanup(); }
 };
 
 #ifdef GGML_WEBGPU_GPU_PROFILE
@@ -252,13 +258,15 @@ struct webgpu_gpu_profile_buf_pool {
         }
         free.clear();
     }
+
+    ~webgpu_gpu_profile_buf_pool() { this->cleanup(); }
 };
 #endif
 
 struct webgpu_pipeline {
     wgpu::ComputePipeline pipeline;
     std::string           name;
-    void *                context = nullptr;
+    std::shared_ptr<void> context = nullptr;
 };
 
 struct webgpu_command {
@@ -319,6 +327,23 @@ struct webgpu_global_context_struct {
     wgpu::Buffer debug_host_buf;
     wgpu::Buffer debug_dev_buf;
 #endif
+
+    ~webgpu_global_context_struct() {
+        if (this->get_tensor_staging_buf) {
+            this->get_tensor_staging_buf.Destroy();
+            this->get_tensor_staging_buf = nullptr;
+        }
+#ifdef GGML_WEBGPU_DEBUG
+        if (this->debug_host_buf) {
+            this->debug_host_buf.Destroy();
+            this->debug_host_buf = nullptr;
+        }
+        if (this->debug_dev_buf) {
+            this->debug_dev_buf.Destroy();
+            this->debug_dev_buf = nullptr;
+        }
+#endif
+    }
 };
 
 typedef std::shared_ptr<webgpu_global_context_struct> webgpu_global_context;
@@ -744,7 +769,6 @@ static const char * ggml_backend_webgpu_name(ggml_backend_t backend) {
     return ctx->name.c_str();
 }
 
-// TODO: implement proper cleanup
 static void ggml_backend_webgpu_free(ggml_backend_t backend) {
     ggml_backend_webgpu_context * ctx = (ggml_backend_webgpu_context *) backend->context;
     WEBGPU_LOG_DEBUG("ggml_backend_webgpu_free(" << ctx->name << ")");
@@ -788,9 +812,8 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) {
     std::cout << "ggml_webgpu: gpu/cpu ratio: " << (total_cpu > 0.0 ? total_gpu / total_cpu : 0.0) << "\n";
 #endif
 
-#if !defined(GGML_WEBGPU_CPU_PROFILE) && !defined(GGML_WEBGPU_GPU_PROFILE)
-    GGML_UNUSED(ctx);
-#endif
+    delete ctx;
+    delete backend;
 }
 
 static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) {
@@ -896,8 +919,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g
         ctx->pad_pipelines.emplace(pipeline_key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
 
     const uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -941,7 +963,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g
          .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
     };
 
-    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -975,8 +997,7 @@ static std::optional<webgpu_command> ggml_webgpu_set_rows(webgpu_context & ctx,
         ctx->set_rows_pipelines.emplace(key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
 
     std::optional<webgpu_pool_bufs> error_bufs = std::nullopt;
     if (key.i64_idx) {
@@ -1028,7 +1049,7 @@ static std::optional<webgpu_command> ggml_webgpu_set_rows(webgpu_context & ctx,
     } else {
         threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3];
     }
-    uint32_t wg_x = CEIL_DIV(threads, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(threads, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, 1,
                                      error_bufs);
 }
@@ -1297,10 +1318,9 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx,
         ctx->flash_attn_pipelines.emplace(key, pipeline);
     }
 
-    ggml_webgpu_flash_attn_shader_decisions decisions =
-        *static_cast<ggml_webgpu_flash_attn_shader_decisions *>(pipeline.context);
+    auto * decisions = static_cast<ggml_webgpu_flash_attn_shader_decisions *>(pipeline.context.get());
 
-    uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions.q_tile);
+    uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions->q_tile);
     uint32_t wg_x        = wg_per_head * Q->ne[2] * Q->ne[3];  // wg per head * number of heads * number of batches
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
@@ -1331,8 +1351,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
         ctx->unary_pipelines.emplace(pipeline_key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
 
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1392,7 +1411,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
                             .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
     }
 
-    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -1425,8 +1444,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
         ctx->binary_pipelines.emplace(pipeline_key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
+    auto * decisions = static_cast<ggml_webgpu_argsort_shader_decisions *>(pipeline.context.get());
 
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1471,7 +1489,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
                             .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
     }
 
-    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -1821,8 +1839,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr
         argsort_pipeline.context = processed.decisions;
         ctx->argsort_pipelines.emplace(order, argsort_pipeline);
     }
-    ggml_webgpu_argsort_shader_decisions argsort_decisions =
-        *static_cast<ggml_webgpu_argsort_shader_decisions *>(argsort_pipeline.context);
+    auto * argsort_decisions = static_cast<ggml_webgpu_argsort_shader_decisions *>(argsort_pipeline.context.get());
 
     webgpu_pipeline argsort_merge_pipeline;
     it = ctx->argsort_merge_pipelines.find(order);
@@ -1839,13 +1856,13 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr
 
     const uint32_t src_ne0 = (uint32_t) src->ne[0];
     const uint32_t nrows   = (uint32_t) ggml_nrows(src);
-    const uint32_t npr     = CEIL_DIV(src_ne0, argsort_decisions.wg_size);
+    const uint32_t npr     = CEIL_DIV(src_ne0, argsort_decisions->wg_size);
     const uint32_t block_size =
-        is_top_k ? std::min(argsort_decisions.wg_size, (uint32_t) dst->ne[0]) : argsort_decisions.wg_size;
+        is_top_k ? std::min(argsort_decisions->wg_size, (uint32_t) dst->ne[0]) : argsort_decisions->wg_size;
     uint32_t out_ne0 = src_ne0;
     if (is_top_k) {
         if (npr > 1) {
-            const uint32_t last_tile = src_ne0 - (npr - 1) * argsort_decisions.wg_size;
+            const uint32_t last_tile = src_ne0 - (npr - 1) * argsort_decisions->wg_size;
             out_ne0                  = (npr - 1) * block_size + std::min(last_tile, block_size);
         } else {
             out_ne0 = block_size;
@@ -2198,7 +2215,10 @@ static ggml_backend_i ggml_backend_webgpu_i = {
 
 static void ggml_backend_webgpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     ggml_backend_webgpu_buffer_context * ctx = static_cast<ggml_backend_webgpu_buffer_context *>(buffer->context);
-    ctx->buffer.Destroy();
+    if (ctx != nullptr && ctx->buffer != nullptr) {
+        ctx->buffer.Destroy();
+        delete ctx;
+    }
 }
 
 // Returns the "fake" base pointer.
@@ -2926,12 +2946,12 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
     dev_desc.SetDeviceLostCallback(
         wgpu::CallbackMode::AllowSpontaneous,
         [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) {
+            if (reason == wgpu::DeviceLostReason::Destroyed) {
+                return;
+            }
             GGML_UNUSED(device);
-            GGML_UNUSED(reason);
-            GGML_UNUSED(message);
-            //TODO: uncomment once proper free logic is in place
-            //GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast<int>(reason),
-            //std::string(message).c_str());
+            GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast<int>(reason),
+                           std::string(message).c_str());
         });
     dev_desc.SetUncapturedErrorCallback(
         [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) {
@@ -3365,10 +3385,7 @@ static size_t ggml_backend_webgpu_reg_get_device_count(ggml_backend_reg_t reg) {
     return ctx->device_count;
 }
 
-// TODO: Does this need to be thread safe? Is it only called once?
-// TODO: move most logic to device_init function so backend can be freed/initialized properly
 // Only one device is supported for now
-
 static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t reg, size_t index) {
     GGML_ASSERT(index == 0);
     WEBGPU_LOG_DEBUG("ggml_backend_reg_get_device()");

ggml/src/ggml.c

@@ -5749,7 +5749,7 @@ static struct ggml_tensor * ggml_unary_impl(
         struct ggml_tensor  * a,
         enum ggml_unary_op    op,
         bool                  inplace) {
-    GGML_ASSERT(ggml_is_contiguous_1(a));
+    GGML_ASSERT(ggml_is_contiguous_rows(a));
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 

gguf-py/gguf/constants.py

@@ -142,6 +142,7 @@ class Keys:
         EMBEDDING_SCALE                   = "{arch}.embedding_scale"
         TOKEN_SHIFT_COUNT                 = "{arch}.token_shift_count"
         INTERLEAVE_MOE_LAYER_STEP         = "{arch}.interleave_moe_layer_step"
+        FULL_ATTENTION_INTERVAL           = "{arch}.full_attention_interval"
         ACTIVATION_SPARSITY_SCALE         = "{arch}.activation_sparsity_scale"
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
@@ -382,10 +383,10 @@ class MODEL_ARCH(IntEnum):
     QWEN3            = auto()
     QWEN3MOE         = auto()
     QWEN3NEXT        = auto()
-    QWEN3_5          = auto()
-    QWEN3_5_MOE      = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
+    QWEN35           = auto()
+    QWEN35MOE        = auto()
     PHI2             = auto()
     PHI3             = auto()
     PHIMOE           = auto()
@@ -559,13 +560,14 @@ class MODEL_TENSOR(IntEnum):
     SSM_D                = auto()
     SSM_NORM             = auto()
     SSM_OUT              = auto()
+    SSM_ALPHA            = auto() # qwen3.5
     SSM_BETA_ALPHA       = auto() # qwen3next
     SSM_CONV1D_Q         = auto() # Kimi Linear
     SSM_CONV1D_K         = auto() # Kimi Linear
     SSM_CONV1D_V         = auto() # Kimi Linear
     SSM_F_A              = auto() # Kimi Linear
     SSM_F_B              = auto() # Kimi Linear
-    SSM_BETA             = auto() # Kimi Linear
+    SSM_BETA             = auto() # Kimi Linear qwen3.5
     SSM_G_A              = auto() # Kimi Linear
     SSM_G_B              = auto() # Kimi Linear
     TIME_MIX_W0          = auto()
@@ -814,10 +816,10 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN3:            "qwen3",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
-    MODEL_ARCH.QWEN3_5:          "qwen3_5",
-    MODEL_ARCH.QWEN3_5_MOE:      "qwen3_5moe",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
+    MODEL_ARCH.QWEN35:           "qwen35",
+    MODEL_ARCH.QWEN35MOE:        "qwen35moe",
     MODEL_ARCH.PHI2:             "phi2",
     MODEL_ARCH.PHI3:             "phi3",
     MODEL_ARCH.PHIMOE:           "phimoe",
@@ -989,13 +991,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.SSM_D:                     "blk.{bid}.ssm_d",
     MODEL_TENSOR.SSM_NORM:                  "blk.{bid}.ssm_norm",
     MODEL_TENSOR.SSM_OUT:                   "blk.{bid}.ssm_out",
+    MODEL_TENSOR.SSM_ALPHA:                 "blk.{bid}.ssm_alpha",            # qwen3.5
     MODEL_TENSOR.SSM_BETA_ALPHA:            "blk.{bid}.ssm_ba",
     MODEL_TENSOR.SSM_CONV1D_Q:              "blk.{bid}.ssm_conv1d_q",         # Kimi Linear
     MODEL_TENSOR.SSM_CONV1D_K:              "blk.{bid}.ssm_conv1d_k",         # Kimi Linear
     MODEL_TENSOR.SSM_CONV1D_V:              "blk.{bid}.ssm_conv1d_v",         # Kimi Linear
     MODEL_TENSOR.SSM_F_A:                   "blk.{bid}.ssm_f_a",              # Kimi Linear
     MODEL_TENSOR.SSM_F_B:                   "blk.{bid}.ssm_f_b",              # Kimi Linear
-    MODEL_TENSOR.SSM_BETA:                  "blk.{bid}.ssm_beta",             # Kimi Linear
+    MODEL_TENSOR.SSM_BETA:                  "blk.{bid}.ssm_beta",             # Kimi Linear qwen3.5
     MODEL_TENSOR.SSM_G_A:                   "blk.{bid}.ssm_g_a",              # Kimi Linear
     MODEL_TENSOR.SSM_G_B:                   "blk.{bid}.ssm_g_b",              # Kimi Linear
     MODEL_TENSOR.TIME_MIX_W0:               "blk.{bid}.time_mix_w0",
@@ -1788,61 +1791,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.SSM_BETA_ALPHA,
         MODEL_TENSOR.SSM_OUT
     ],
-    MODEL_ARCH.QWEN3_5: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_Q_NORM,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_K_NORM,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.ATTN_POST_NORM,
-        MODEL_TENSOR.ATTN_GATE,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.FFN_GATE,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-        MODEL_TENSOR.SSM_A,
-        MODEL_TENSOR.SSM_CONV1D,
-        MODEL_TENSOR.SSM_DT,
-        MODEL_TENSOR.SSM_NORM,
-        MODEL_TENSOR.SSM_IN,
-        MODEL_TENSOR.SSM_BETA_ALPHA,
-        MODEL_TENSOR.SSM_OUT,
-    ],
-    MODEL_ARCH.QWEN3_5_MOE: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_Q_NORM,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_K_NORM,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.ATTN_POST_NORM,
-        MODEL_TENSOR.ATTN_GATE,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.FFN_GATE_INP,
-        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
-        MODEL_TENSOR.FFN_UP_SHEXP,
-        MODEL_TENSOR.FFN_DOWN_SHEXP,
-        MODEL_TENSOR.FFN_GATE_SHEXP,
-        MODEL_TENSOR.FFN_DOWN_EXP,
-        MODEL_TENSOR.FFN_UP_EXP,
-        MODEL_TENSOR.FFN_GATE_EXP,
-        MODEL_TENSOR.SSM_A,
-        MODEL_TENSOR.SSM_CONV1D,
-        MODEL_TENSOR.SSM_DT,
-        MODEL_TENSOR.SSM_NORM,
-        MODEL_TENSOR.SSM_IN,
-        MODEL_TENSOR.SSM_BETA_ALPHA,
-        MODEL_TENSOR.SSM_OUT,
-    ],
     MODEL_ARCH.QWEN3VL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -1877,6 +1825,61 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.QWEN35: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_BETA,
+        MODEL_TENSOR.SSM_ALPHA,
+        MODEL_TENSOR.SSM_OUT
+    ],
+    MODEL_ARCH.QWEN35MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_BETA,
+        MODEL_TENSOR.SSM_ALPHA,
+        MODEL_TENSOR.SSM_OUT
+    ],
     MODEL_ARCH.PLAMO: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3763,6 +3766,7 @@ class VisionProjectorType:
     VOXTRAL = "voxtral"
     LFM2 = "lfm2"
     KIMIVL = "kimivl"
+    KIMIK25 = "kimik25"
     LIGHTONOCR = "lightonocr"
     COGVLM = "cogvlm"
     JANUS_PRO = "janus_pro"

gguf-py/gguf/gguf_writer.py

@@ -708,6 +708,9 @@ class GGUFWriter:
     def add_leading_dense_block_count(self, length: int) -> None:
         self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
 
+    def add_full_attention_interval(self, interval: int) -> None:
+        self.add_uint32(Keys.LLM.FULL_ATTENTION_INTERVAL.format(arch=self.arch), interval)
+
     def add_feed_forward_length(self, length: int | Sequence[int]) -> None:
         if isinstance(length, int):
             self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)

gguf-py/gguf/tensor_mapping.py

@@ -359,9 +359,9 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_GATE: (
-            "model.layers.{bid}.self_attn.gate_proj",   # afmoe
-            "model.layers.{bid}.self_attn.g_proj",      # step3.5 head-wise attention gate
-            "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
+            "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.linear_attn.in_proj_z",  # qwen3.5
+            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
         ),
 
         # Feed-forward norm
@@ -825,6 +825,10 @@ class TensorNameMap:
             "model.layers.layers.{bid}.mixer.out_proj",  # plamo2
         ),
 
+        MODEL_TENSOR.SSM_ALPHA: (
+            "model.layers.{bid}.linear_attn.in_proj_a",  # qwen3.5
+        ),
+
         MODEL_TENSOR.SSM_BETA_ALPHA: (
             "model.layers.{bid}.linear_attn.in_proj_ba",  # qwen3next
         ),
@@ -846,7 +850,8 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.f_b_proj",
         ),
         MODEL_TENSOR.SSM_BETA: (
-            "model.layers.{bid}.self_attn.b_proj",
+            "model.layers.{bid}.linear_attn.in_proj_b",  # qwen3.5
+            "model.layers.{bid}.self_attn.b_proj",       # Kimi Linear
         ),
         MODEL_TENSOR.SSM_G_A: (
             "model.layers.{bid}.self_attn.g_a_proj",
@@ -1298,6 +1303,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MMPROJ: (
             "multi_modal_projector.linear_{bid}",
+            "mm_projector.proj.linear_{bid}", # Kimi-K2.5
             "visual.merger.mlp.{bid}", # qwen2vl
             "merger.mlp.{bid}",
         ),
@@ -1359,6 +1365,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_ATTN_QKV: (
             "visual.blocks.{bid}.attn.qkv", # qwen3vl
             "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm
+            "vision_tower.encoder.blocks.{bid}.wqkv" # Kimi-K2.5
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
@@ -1533,6 +1540,7 @@ class TensorNameMap:
             "multi_modal_projector.norm",
             "multi_modal_projector.layer_norm",
             "multi_modal_projector.pre_norm",
+            "mm_projector.pre_norm", # Kimi-K2.5
             "pre_mm_projector_norm",
             "model.vision.linear_proj.norm1", # cogvlm
             "merger.ln_q",

include/llama.h

@@ -482,7 +482,7 @@ extern "C" {
     enum llama_params_fit_status {
         LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
         LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
-        LLAMA_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occured, e.g. because no model could be found at the specified path
+        LLAMA_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occurred, e.g. because no model could be found at the specified path
     };
 
     // fits mparams and cparams to free device memory (assumes system memory is unlimited)
@@ -1150,9 +1150,9 @@ extern "C" {
     //
 
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
-    /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
+    ///
     /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
-    /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
+    /// @param tmpl A Jinja template to use for this chat.
     /// @param chat Pointer to a list of multiple llama_chat_message
     /// @param n_msg Number of llama_chat_message in this chat
     /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.

scripts/pr2wt.sh

@@ -30,12 +30,18 @@ fi
 PR=$1
 [[ "$PR" =~ ^[0-9]+$ ]] || { echo "error: PR number must be numeric"; exit 1; }
 
+url_origin=$(git config --get remote.upstream.url 2>/dev/null) || \
 url_origin=$(git config --get remote.origin.url) || {
-    echo "error: no remote named 'origin' in this repository"
+    echo "error: no remote named 'upstream' or 'origin' in this repository"
     exit 1
 }
 
-org_repo=$(echo $url_origin | cut -d/ -f4-)
+# Extract org/repo from either https or ssh format.
+if [[ $url_origin =~ ^git@ ]]; then
+    org_repo=$(echo $url_origin | cut -d: -f2)
+else
+    org_repo=$(echo $url_origin | cut -d/ -f4-)
+fi
 org_repo=${org_repo%.git}
 
 echo "org/repo: $org_repo"

scripts/sync_vendor.py

@@ -2,6 +2,8 @@
 
 import urllib.request
 
+HTTPLIB_VERSION = "f80864ca031932351abef49b74097c67f14719c6"
+
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",
     "https://github.com/nlohmann/json/releases/latest/download/json_fwd.hpp": "vendor/nlohmann/json_fwd.hpp",
@@ -12,8 +14,8 @@ vendor = {
     # "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.23/miniaudio.h": "vendor/miniaudio/miniaudio.h",
     "https://github.com/mackron/miniaudio/raw/669ed3e844524fcd883231b13095baee9f6de304/miniaudio.h": "vendor/miniaudio/miniaudio.h",
 
-    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.2/httplib.h": "vendor/cpp-httplib/httplib.h",
-    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.2/LICENSE":   "vendor/cpp-httplib/LICENSE",
+    f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/httplib.h": "vendor/cpp-httplib/httplib.h",
+    f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/LICENSE":   "vendor/cpp-httplib/LICENSE",
 
     "https://raw.githubusercontent.com/sheredom/subprocess.h/b49c56e9fe214488493021017bf3954b91c7c1f5/subprocess.h": "vendor/sheredom/subprocess.h",
 }

src/CMakeLists.txt

@@ -57,7 +57,6 @@ add_library(llama
             models/deci.cpp
             models/deepseek.cpp
             models/deepseek2.cpp
-            models/delta.cpp
             models/dots1.cpp
             models/dream.cpp
             models/ernie4-5-moe.cpp
@@ -123,8 +122,8 @@ add_library(llama
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
             models/qwen3next.cpp
-            models/qwen3-5.cpp
-            models/qwen3-5moe.cpp
+            models/qwen35.cpp
+            models/qwen35moe.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp

src/llama-arch.cpp

@@ -35,10 +35,10 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN3,            "qwen3"            },
     { LLM_ARCH_QWEN3MOE,         "qwen3moe"         },
     { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
-    { LLM_ARCH_QWEN3_5,          "qwen3_5"          },
-    { LLM_ARCH_QWEN3_5_MOE,      "qwen3_5moe"       },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
+    { LLM_ARCH_QWEN35,           "qwen35"           },
+    { LLM_ARCH_QWEN35MOE,        "qwen35moe"        },
     { LLM_ARCH_PHI2,             "phi2"             },
     { LLM_ARCH_PHI3,             "phi3"             },
     { LLM_ARCH_PHIMOE,           "phimoe"           },
@@ -197,6 +197,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EMBEDDING_SCALE,                   "%s.embedding_scale"                   },
     { LLM_KV_TOKEN_SHIFT_COUNT,                 "%s.token_shift_count"                 },
     { LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         "%s.interleave_moe_layer_step"         },
+    { LLM_KV_FULL_ATTENTION_INTERVAL,           "%s.full_attention_interval"           },
 
     { LLM_KV_ATTENTION_HEAD_COUNT,                   "%s.attention.head_count"                   },
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,                "%s.attention.head_count_kv"                },
@@ -368,6 +369,7 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_SSM_CONV1D,                             "blk.%d.ssm_conv1d" },
     { LLM_TENSOR_SSM_DT,                                 "blk.%d.ssm_dt" },
     { LLM_TENSOR_SSM_BETA_ALPHA,                         "blk.%d.ssm_ba" },
+    { LLM_TENSOR_SSM_ALPHA,                              "blk.%d.ssm_alpha" },
     { LLM_TENSOR_SSM_IN,                                 "blk.%d.ssm_in" },
     { LLM_TENSOR_SSM_NORM,                               "blk.%d.ssm_norm" },
     { LLM_TENSOR_SSM_OUT,                                "blk.%d.ssm_out" },
@@ -970,7 +972,6 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_ATTN_OUT,
                 LLM_TENSOR_ATTN_QKV,
                 LLM_TENSOR_ATTN_GATE,
-                LLM_TENSOR_FFN_NORM,
                 LLM_TENSOR_FFN_GATE_INP,
                 LLM_TENSOR_FFN_GATE_EXPS,
                 LLM_TENSOR_FFN_DOWN_EXPS,
@@ -987,7 +988,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
-        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN35:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
                 LLM_TENSOR_OUTPUT_NORM,
@@ -1008,12 +1009,12 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_A_NOSCAN,
                 LLM_TENSOR_SSM_CONV1D,
                 LLM_TENSOR_SSM_DT,
-                LLM_TENSOR_SSM_BETA_ALPHA,
-                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_BETA,
+                LLM_TENSOR_SSM_ALPHA,
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
-        case LLM_ARCH_QWEN3_5_MOE:
+        case LLM_ARCH_QWEN35MOE:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
                 LLM_TENSOR_OUTPUT_NORM,
@@ -1039,8 +1040,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_A_NOSCAN,
                 LLM_TENSOR_SSM_CONV1D,
                 LLM_TENSOR_SSM_DT,
-                LLM_TENSOR_SSM_BETA_ALPHA,
-                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_BETA,
+                LLM_TENSOR_SSM_ALPHA,
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
@@ -2515,6 +2516,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_SSM_X,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_DT,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_OUT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_ALPHA,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_BETA_ALPHA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_W1,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_W2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -2733,9 +2735,9 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_QWEN35:
+        case LLM_ARCH_QWEN35MOE:
             return true;
         default:
             return false;

src/llama-arch.h

@@ -39,10 +39,10 @@ enum llm_arch {
     LLM_ARCH_QWEN3,
     LLM_ARCH_QWEN3MOE,
     LLM_ARCH_QWEN3NEXT,
-    LLM_ARCH_QWEN3_5,
-    LLM_ARCH_QWEN3_5_MOE,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
+    LLM_ARCH_QWEN35,
+    LLM_ARCH_QWEN35MOE,
     LLM_ARCH_PHI2,
     LLM_ARCH_PHI3,
     LLM_ARCH_PHIMOE,
@@ -201,6 +201,7 @@ enum llm_kv {
     LLM_KV_EMBEDDING_SCALE,
     LLM_KV_TOKEN_SHIFT_COUNT,
     LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
+    LLM_KV_FULL_ATTENTION_INTERVAL,
 
     LLM_KV_ATTENTION_HEAD_COUNT,
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -406,13 +407,14 @@ enum llm_tensor {
     LLM_TENSOR_SSM_NORM,
     LLM_TENSOR_SSM_OUT,
     LLM_TENSOR_SSM_BETA_ALPHA,      // qwen3next
+    LLM_TENSOR_SSM_ALPHA,           // qwen3.5
     // Kimi Linear KDA (using SSM_ prefix for consistency)
     LLM_TENSOR_SSM_CONV1D_Q,        // kimi: Q conv1d weight
     LLM_TENSOR_SSM_CONV1D_K,        // kimi: K conv1d weight
     LLM_TENSOR_SSM_CONV1D_V,        // kimi: V conv1d weight
     LLM_TENSOR_SSM_F_A,             // kimi: forget gate projection A
     LLM_TENSOR_SSM_F_B,             // kimi: forget gate projection B
-    LLM_TENSOR_SSM_BETA,            // kimi: beta mixing coefficient
+    LLM_TENSOR_SSM_BETA,            // kimi: beta mixing coefficient and qwen3.5
     LLM_TENSOR_SSM_G_A,             // kimi: output gate projection A
     LLM_TENSOR_SSM_G_B,             // kimi: output gate projection B
     LLM_TENSOR_TIME_MIX_W0,

src/llama-context.cpp

@@ -677,7 +677,7 @@ enum llama_pooling_type llama_context::pooling_type() const {
 float * llama_context::get_logits() {
     output_reorder();
 
-    return logits;
+    return logits.data;
 }
 
 int64_t llama_context::output_resolve_row(int32_t i) const {
@@ -715,7 +715,7 @@ float * llama_context::get_logits_ith(int32_t i) {
     output_reorder();
 
     try {
-        if (logits == nullptr) {
+        if (logits.data == nullptr) {
             throw std::runtime_error("no logits");
         }
 
@@ -739,7 +739,7 @@ float * llama_context::get_logits_ith(int32_t i) {
             throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
         }
 
-        return logits + j*model.vocab.n_tokens();
+        return logits.data + j*model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
@@ -753,11 +753,11 @@ float * llama_context::get_logits_ith(int32_t i) {
 float * llama_context::get_embeddings() {
     output_reorder();
 
-    return embd;
+    return embd.data;
 }
 
 llama_token * llama_context::get_sampled_tokens()  const{
-    return sampling.sampled;
+    return sampling.sampled.data;
 }
 
 float * llama_context::get_embeddings_ith(int32_t i) {
@@ -766,7 +766,7 @@ float * llama_context::get_embeddings_ith(int32_t i) {
     output_reorder();
 
     try {
-        if (embd == nullptr) {
+        if (embd.data == nullptr) {
             throw std::runtime_error("no embeddings");
         }
 
@@ -791,7 +791,7 @@ float * llama_context::get_embeddings_ith(int32_t i) {
         }
 
         const uint32_t n_embd_out = model.hparams.n_embd_out();
-        return embd + j*n_embd_out;
+        return embd.data + j*n_embd_out;
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
@@ -814,14 +814,14 @@ float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
 llama_token llama_context::get_sampled_token_ith(int32_t idx) {
     output_reorder();
 
-    if (sampling.sampled == nullptr) {
+    if (!sampling.sampled.has_data()) {
         return LLAMA_TOKEN_NULL;
     }
 
     try {
         const int64_t row = output_resolve_row(idx);
-        GGML_ASSERT(row < (int64_t) sampling.sampled_size);
-        return sampling.sampled[row];
+        GGML_ASSERT(row < (int64_t) sampling.sampled.size);
+        return sampling.sampled.data[row];
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid backend sampled token id %d, reason: %s\n", __func__, idx, err.what());
         return LLAMA_TOKEN_NULL;
@@ -831,7 +831,7 @@ llama_token llama_context::get_sampled_token_ith(int32_t idx) {
 float * llama_context::get_sampled_probs_ith(int32_t idx) {
     output_reorder();
 
-    if (sampling.probs == nullptr) {
+    if (!sampling.probs.has_data()) {
         return nullptr;
     }
 
@@ -840,7 +840,7 @@ float * llama_context::get_sampled_probs_ith(int32_t idx) {
         if ((size_t) row >= sampling.probs_count.size() || sampling.probs_count[row] == 0) {
             return nullptr;
         }
-        return sampling.probs + row*model.vocab.n_tokens();
+        return sampling.probs.data + row*model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid backend sampled probs id %d, reason: %s\n", __func__, idx, err.what());
         return nullptr;
@@ -850,7 +850,7 @@ float * llama_context::get_sampled_probs_ith(int32_t idx) {
 float * llama_context::get_sampled_logits_ith(int32_t idx) {
     output_reorder();
 
-    if (sampling.logits == nullptr) {
+    if (!sampling.logits.has_data()) {
         return nullptr;
     }
 
@@ -859,7 +859,7 @@ float * llama_context::get_sampled_logits_ith(int32_t idx) {
         if ((size_t) row >= sampling.logits_count.size() || sampling.logits_count[row] == 0) {
             return nullptr;
         }
-        return sampling.logits + row*model.vocab.n_tokens();
+        return sampling.logits.data + row*model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid backend sampled logits id %d, reason: %s\n", __func__, idx, err.what());
         return nullptr;
@@ -871,10 +871,10 @@ const llama_token * llama_context::get_sampled_candidates_ith(int32_t idx) {
 
     try {
         const int64_t row = output_resolve_row(idx);
-        if (sampling.candidates != nullptr &&
+        if (sampling.candidates.has_data() &&
             (size_t) row < sampling.candidates_count.size() &&
             sampling.candidates_count[row] > 0) {
-            return sampling.candidates + row*model.vocab.n_tokens();
+            return sampling.candidates.data + row*model.vocab.n_tokens();
         }
     } catch (const std::exception & err) {
         // fallback to full vocab list
@@ -886,7 +886,7 @@ const llama_token * llama_context::get_sampled_candidates_ith(int32_t idx) {
 size_t llama_context::get_sampled_candidates_count(int32_t idx) {
     output_reorder();
 
-    if (sampling.candidates == nullptr) {
+    if (!sampling.candidates.has_data()) {
         return 0;
     }
 
@@ -905,7 +905,7 @@ size_t llama_context::get_sampled_candidates_count(int32_t idx) {
 size_t llama_context::get_sampled_logits_count(int32_t idx) {
     output_reorder();
 
-    if (sampling.logits == nullptr) {
+    if (!sampling.logits.has_data()) {
         return model.vocab.n_tokens();
     }
 
@@ -924,7 +924,7 @@ size_t llama_context::get_sampled_logits_count(int32_t idx) {
 size_t llama_context::get_sampled_probs_count(int32_t idx) {
     output_reorder();
 
-    if (sampling.probs == nullptr) {
+    if (!sampling.probs.has_data()) {
         return 0;
     }
 
@@ -1254,16 +1254,16 @@ int llama_context::encode(const llama_batch & batch_inp) {
     auto * t_embd = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
 
     // extract logits
-    if (logits && t_logits) {
+    if (logits.data && t_logits) {
         ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
         GGML_ASSERT(backend_res != nullptr);
-        GGML_ASSERT(logits != nullptr);
+        GGML_ASSERT(logits.data != nullptr);
 
-        ggml_backend_tensor_get_async(backend_res, t_logits, logits, 0, n_tokens*n_vocab*sizeof(float));
+        ggml_backend_tensor_get_async(backend_res, t_logits, logits.data, 0, n_tokens*n_vocab*sizeof(float));
     }
 
     // extract embeddings
-    if (embd && t_embd) {
+    if (embd.data && t_embd) {
         ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
         GGML_ASSERT(backend_embd != nullptr);
 
@@ -1271,11 +1271,11 @@ int llama_context::encode(const llama_batch & batch_inp) {
             case LLAMA_POOLING_TYPE_NONE:
                 {
                     // extract token embeddings
-                    GGML_ASSERT(embd != nullptr);
+                    GGML_ASSERT(embd.data != nullptr);
                     const uint32_t n_embd_out = hparams.n_embd_out();
 
-                    GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd_size);
-                    ggml_backend_tensor_get_async(backend_embd, t_embd, embd, 0, n_tokens*n_embd_out*sizeof(float));
+                    GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd.size);
+                    ggml_backend_tensor_get_async(backend_embd, t_embd, embd.data, 0, n_tokens*n_embd_out*sizeof(float));
                 } break;
             case LLAMA_POOLING_TYPE_MEAN:
             case LLAMA_POOLING_TYPE_CLS:
@@ -1323,7 +1323,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
         cross.n_embd = t_embd->ne[0];
         cross.n_enc  = t_embd->ne[1];
         cross.v_embd.resize(cross.n_embd*cross.n_enc);
-        memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd));
+        memcpy(cross.v_embd.data(), embd.data, ggml_nbytes(t_embd));
 
         const auto & batch = balloc->get_batch();
 
@@ -1363,11 +1363,10 @@ static std::map<llama_seq_id, uint32_t> build_seq_to_output_row(const llama_ubat
 
 static void copy_tensor_async_ints(
     const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
-    llama_token * sampled,
-    size_t sampled_size,
+    const buffer_view<llama_token> & sampled,
     const std::map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (sampled == nullptr) {
+    if (!sampled.has_data()) {
         return;
     }
 
@@ -1378,23 +1377,23 @@ static void copy_tensor_async_ints(
         }
 
         const uint32_t row = it->second;
-        GGML_ASSERT(row < sampled_size);
+        GGML_ASSERT(row < sampled.size);
 
         GGML_ASSERT(ggml_is_contiguous(tensor) && "sampled tokens tensor must be contiguous for async copy");
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        ggml_backend_tensor_get_async(backend, tensor, sampled + row, 0, sizeof(sampled[row]));
+        ggml_backend_tensor_get_async(backend, tensor, sampled.data + row, 0, sizeof(sampled.data[row]));
     }
 }
 
 static void copy_tensor_async_floats(
     const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
-    float * dst,
+    const buffer_view<float> & dst,
     size_t stride,
     std::vector<uint32_t> & counts,
     const std::map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (dst == nullptr) {
+    if (!dst.has_data()) {
         return;
     }
 
@@ -1410,7 +1409,7 @@ static void copy_tensor_async_floats(
         GGML_ASSERT(ggml_is_contiguous(tensor) && "logits/probs tensor must be contiguous for async copy");
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        float * row_ptr = dst + (size_t) row * stride;
+        float * row_ptr = dst.data + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
 
         // Update the actual number of logits/probabilities that were written for this row.
@@ -1420,12 +1419,12 @@ static void copy_tensor_async_floats(
 
 static void copy_tensor_async_candidates(
     const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
-    llama_token * dst,
+    const buffer_view<llama_token> & dst,
     size_t stride,
     std::vector<uint32_t> & counts,
     const std::map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (dst == nullptr) {
+    if (!dst.has_data()) {
         return;
     }
 
@@ -1441,7 +1440,7 @@ static void copy_tensor_async_candidates(
         GGML_ASSERT(ggml_is_contiguous(tensor) && "candidates tensor must be contiguous for async copy");
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        llama_token * row_ptr = dst + (size_t) row * stride;
+        llama_token * row_ptr = dst.data + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
 
         // Update the actual number of candidates that were written.
@@ -1671,22 +1670,22 @@ int llama_context::decode(const llama_batch & batch_inp) {
         }
 
         // extract logits
-        if (logits && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
+        if (logits.data && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
             ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
             GGML_ASSERT(backend_res != nullptr);
-            GGML_ASSERT(logits != nullptr);
+            GGML_ASSERT(logits.data != nullptr);
 
-            float * logits_out = logits + n_outputs_prev*n_vocab;
+            float * logits_out = logits.data + n_outputs_prev*n_vocab;
 
             if (n_outputs) {
                 GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits.size);
                 ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
             }
         }
 
         // extract embeddings
-        if (embd && t_embd && n_outputs > 0) {
+        if (embd.data && t_embd && n_outputs > 0) {
             ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
             GGML_ASSERT(backend_embd != nullptr);
 
@@ -1694,13 +1693,13 @@ int llama_context::decode(const llama_batch & batch_inp) {
                 case LLAMA_POOLING_TYPE_NONE:
                     {
                         // extract token embeddings
-                        GGML_ASSERT(embd != nullptr);
+                        GGML_ASSERT(embd.data != nullptr);
                         const uint32_t n_embd_out = hparams.n_embd_out();
-                        float * embd_out = embd + n_outputs_prev*n_embd_out;
+                        float * embd_out = embd.data + n_outputs_prev*n_embd_out;
 
                         if (n_outputs) {
                             GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd_out <= (int64_t) embd_size);
+                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd_out <= (int64_t) embd.size);
                             ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd_out*sizeof(float));
                         }
                     } break;
@@ -1747,7 +1746,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
             const auto stride = n_vocab;
 
             // async copy the sampling data from the backend to the host
-            copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
+            copy_tensor_async_ints(res->t_sampled, sampling.sampled, seq_to_output_row, sched.get());
 
             copy_tensor_async_floats    (res->t_sampled_logits, sampling.logits,     stride, sampling.logits_count,     seq_to_output_row, sched.get());
             copy_tensor_async_floats    (res->t_sampled_probs,  sampling.probs,      stride, sampling.probs_count,      seq_to_output_row, sched.get());
@@ -1841,19 +1840,14 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
 
-    logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-    embd_size   = has_embd ? n_embd_out*n_outputs_max : 0;
+    logits.size = has_logits ? n_vocab*n_outputs_max : 0;
+    embd.size   = has_embd ? n_embd_out*n_outputs_max : 0;
 
     // Allocate backend sampling output buffers if there are backend samplers configured.
     const bool has_sampling = !sampling.samplers.empty();
     if (has_sampling) {
-        sampling.logits_size     = n_vocab*n_outputs_max;
-        sampling.probs_size      = n_vocab*n_outputs_max;
-        sampling.sampled_size    =         n_outputs_max;
-        sampling.candidates_size = n_vocab*n_outputs_max;
-
-        backend_float_count = sampling.logits_size  + sampling.probs_size;
-        backend_token_count = sampling.sampled_size + sampling.candidates_size;
+        backend_float_count = 2 * n_vocab * n_outputs_max;      // logits + probs
+        backend_token_count = (1 + n_vocab) * n_outputs_max;    // sampled + candidates
     }
 
     if (output_ids.empty()) {
@@ -1863,7 +1857,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
     const size_t new_size  =
-        (logits_size + embd_size + backend_float_count) * sizeof(float) +
+        (logits.size + embd.size + backend_float_count) * sizeof(float) +
         (                          backend_token_count) * sizeof(llama_token);
 
     // alloc only when more than the current capacity is required
@@ -1878,8 +1872,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
             // TODO: not needed?
             buf_output = nullptr;
-            logits = nullptr;
-            embd = nullptr;
+            logits.data = nullptr;
+            embd.data = nullptr;
         }
 
         auto * buft = ggml_backend_cpu_buffer_type();
@@ -1898,35 +1892,32 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
 
-    logits = nullptr;
-    embd   = nullptr;
-
     size_t offset = 0;
     uint8_t * base = (uint8_t *) output_base;
 
-    logits = has_logits ? output_base : nullptr;
-    offset += logits_size * sizeof(float);
+    logits = has_logits ? buffer_view<float>{output_base, logits.size} : buffer_view<float>{nullptr, 0};
+    offset += logits.size * sizeof(float);
 
-    embd = has_embd ? (float *) (base + offset) : nullptr;
-    offset += embd_size * sizeof(float);
+    embd = has_embd ? buffer_view<float>{(float *) (base + offset), embd.size} : buffer_view<float>{nullptr, 0};
+    offset += embd.size * sizeof(float);
 
-    sampling.logits     = nullptr;
-    sampling.probs      = nullptr;
-    sampling.sampled    = nullptr;
-    sampling.candidates = nullptr;
+    sampling.logits     = {nullptr, 0};
+    sampling.probs      = {nullptr, 0};
+    sampling.sampled    = {nullptr, 0};
+    sampling.candidates = {nullptr, 0};
 
     if (has_sampling) {
-        sampling.logits = (float *) (base + offset);
-        offset += sampling.logits_size * sizeof(float);
+        sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+        offset += sampling.logits.size * sizeof(float);
 
-        sampling.probs = (float *) (base + offset);
-        offset += sampling.probs_size * sizeof(float);
+        sampling.probs = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+        offset += sampling.probs.size * sizeof(float);
 
-        sampling.sampled = (llama_token *) (base + offset);
-        offset += sampling.sampled_size * sizeof(llama_token);
+        sampling.sampled = {(llama_token *) (base + offset), (size_t)n_outputs_max};
+        offset += sampling.sampled.size * sizeof(llama_token);
 
-        sampling.candidates = (llama_token *) (base + offset);
-        offset += sampling.candidates_size * sizeof(llama_token);
+        sampling.candidates = {(llama_token *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+        offset += sampling.candidates.size * sizeof(llama_token);
 
         // The count vectors keep track of the actual number of logits/probs/candidates
         // copied from the backend for each output row.
@@ -1939,7 +1930,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         std::fill(sampling.probs_count.begin(),      sampling.probs_count.end(),      0);
         std::fill(sampling.candidates_count.begin(), sampling.candidates_count.end(), 0);
 
-        std::fill_n(sampling.sampled, sampling.sampled_size, LLAMA_TOKEN_NULL);
+        std::fill_n(sampling.sampled.data, sampling.sampled.size, LLAMA_TOKEN_NULL);
     }
 
     // set all ids as invalid (negative)
@@ -1958,38 +1949,38 @@ void llama_context::output_reorder() {
         const uint64_t i0 = output_swaps[s].i0;
         const uint64_t i1 = output_swaps[s].i1;
 
-        if (logits_size > 0) {
+        if (logits.size > 0) {
             for (uint64_t k = 0; k < n_vocab; k++) {
-                std::swap(logits[i0*n_vocab + k], logits[i1*n_vocab + k]);
+                std::swap(logits.data[i0*n_vocab + k], logits.data[i1*n_vocab + k]);
             }
         }
 
-        if (embd_size > 0) {
+        if (embd.size > 0) {
             for (uint64_t k = 0; k < n_embd; k++) {
-                std::swap(embd[i0*n_embd + k], embd[i1*n_embd + k]);
+                std::swap(embd.data[i0*n_embd + k], embd.data[i1*n_embd + k]);
             }
         }
 
-        if (sampling.logits && sampling.logits_size > 0) {
+        if (sampling.logits.has_data()) {
             for (uint64_t k = 0; k < n_vocab; ++k) {
-                std::swap(sampling.logits[i0*n_vocab + k], sampling.logits[i1*n_vocab + k]);
+                std::swap(sampling.logits.data[i0*n_vocab + k], sampling.logits.data[i1*n_vocab + k]);
             }
         }
 
-        if (sampling.probs && sampling.probs_size > 0) {
+        if (sampling.probs.has_data()) {
             for (uint64_t k = 0; k < n_vocab; ++k) {
-                std::swap(sampling.probs[i0*n_vocab + k], sampling.probs[i1*n_vocab + k]);
+                std::swap(sampling.probs.data[i0*n_vocab + k], sampling.probs.data[i1*n_vocab + k]);
             }
         }
 
-        if (sampling.candidates && sampling.candidates_size > 0) {
+        if (sampling.candidates.has_data()) {
             for (uint64_t k = 0; k < n_vocab; ++k) {
-                std::swap(sampling.candidates[i0*n_vocab + k], sampling.candidates[i1*n_vocab + k]);
+                std::swap(sampling.candidates.data[i0*n_vocab + k], sampling.candidates.data[i1*n_vocab + k]);
             }
         }
 
-        if (sampling.sampled && sampling.sampled_size > 0) {
-            std::swap(sampling.sampled[i0], sampling.sampled[i1]);
+        if (sampling.sampled.has_data()) {
+            std::swap(sampling.sampled.data[i0], sampling.sampled.data[i1]);
         }
 
         if (!sampling.logits_count.empty()) {
@@ -2013,7 +2004,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
         return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
@@ -2533,12 +2524,12 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
     {
         LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__);
 
-        const uint64_t logits_size = std::min((uint64_t) this->logits_size, (uint64_t) n_outputs * model.vocab.n_tokens());
+        const uint64_t logits_size = std::min((uint64_t) this->logits.size, (uint64_t) n_outputs * model.vocab.n_tokens());
 
         io.write(&logits_size, sizeof(logits_size));
 
         if (logits_size) {
-            io.write(logits, logits_size * sizeof(float));
+            io.write(logits.data, logits_size * sizeof(float));
         }
     }
 
@@ -2546,12 +2537,12 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
     {
         LLAMA_LOG_DEBUG("%s: - writing embeddings\n", __func__);
 
-        const uint64_t embd_size = std::min((uint64_t) this->embd_size, (uint64_t) n_outputs * model.hparams.n_embd);
+        const uint64_t embd_size = std::min((uint64_t) this->embd.size, (uint64_t) n_outputs * model.hparams.n_embd);
 
         io.write(&embd_size, sizeof(embd_size));
 
         if (embd_size) {
-            io.write(embd, embd_size * sizeof(float));
+            io.write(embd.data, embd_size * sizeof(float));
         }
     }
 
@@ -2619,12 +2610,12 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
         uint64_t logits_size;
         io.read_to(&logits_size, sizeof(logits_size));
 
-        if (this->logits_size < logits_size) {
+        if (this->logits.size < logits_size) {
             throw std::runtime_error("logits buffer too small");
         }
 
         if (logits_size) {
-            io.read_to(this->logits, logits_size * sizeof(float));
+            io.read_to(this->logits.data, logits_size * sizeof(float));
         }
     }
 
@@ -2635,12 +2626,12 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
         uint64_t embd_size;
         io.read_to(&embd_size, sizeof(embd_size));
 
-        if (this->embd_size < embd_size) {
+        if (this->embd.size < embd_size) {
             throw std::runtime_error("embeddings buffer too small");
         }
 
         if (embd_size) {
-            io.read_to(this->embd, embd_size * sizeof(float));
+            io.read_to(this->embd.data, embd_size * sizeof(float));
         }
     }
 

src/llama-context.h

@@ -4,6 +4,7 @@
 #include "llama-cparams.h"
 #include "llama-graph.h"
 #include "llama-adapter.h"
+#include "llama-impl.h"
 
 #include "ggml-cpp.h"
 #include "ggml-opt.h"
@@ -269,29 +270,19 @@ private:
     std::unique_ptr<llama_memory_i> memory;
 
     // decode output (2-dimensional array: [n_outputs][n_vocab])
-    size_t  logits_size = 0; // capacity (of floats) for logits
-    float * logits      = nullptr;
+    struct buffer_view<float>  logits = {nullptr, 0};
 
     // embeddings output (2-dimensional array: [n_outputs][n_embd])
     // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
-    size_t  embd_size = 0; // capacity (of floats) for embeddings
-    float * embd      = nullptr;
+    struct buffer_view<float>  embd = {nullptr, 0};
 
-    // TODO: simplify
     struct sampling_info {
         std::map<llama_seq_id, llama_sampler *> samplers;
 
-        float       * logits      = nullptr;
-        size_t        logits_size = 0;
-
-        llama_token * sampled      = nullptr;
-        size_t        sampled_size = 0;
-
-        float       * probs        = nullptr;
-        size_t        probs_size   = 0;
-
-        llama_token * candidates   = nullptr;
-        size_t        candidates_size = 0;
+        struct buffer_view<float>       logits     = {nullptr, 0};
+        struct buffer_view<llama_token> sampled    = {nullptr, 0};
+        struct buffer_view<float>       probs      = {nullptr, 0};
+        struct buffer_view<llama_token> candidates = {nullptr, 0};
 
         std::vector<uint32_t> logits_count;
         std::vector<uint32_t> probs_count;

src/llama-hparams.h

@@ -42,7 +42,6 @@ struct llama_hparams {
 
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
-    uint32_t n_embd_features = 0;
     uint32_t n_layer;
     int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
     uint32_t n_rot;

src/llama-impl.h

@@ -49,6 +49,16 @@ struct time_meas {
     int64_t & t_acc;
 };
 
+template <typename T>
+struct buffer_view {
+    T * data;
+    size_t size = 0;
+
+    bool has_data() const {
+        return data && size > 0;
+    }
+};
+
 void replace_all(std::string & s, const std::string & search, const std::string & replace);
 
 // TODO: rename to llama_format ?

src/llama-model.cpp

@@ -125,6 +125,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_21B_A3B:       return "21B.A3B";
         case LLM_TYPE_30B_A3B:       return "30B.A3B";
         case LLM_TYPE_31B_A3_5B:     return "31B.A3.5B";
+        case LLM_TYPE_35B_A3B:       return "35B.A3B";
         case LLM_TYPE_48B_A3B:       return "48B.A3B";
         case LLM_TYPE_80B_A3B:       return "80B.A3B";
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
@@ -522,7 +523,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used,    false);
 
     if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
-        ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
+        ml.get_key(LLM_KV_FEATURES_LENGTH,  hparams.n_embd);
+        ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd_out_impl);
 
         ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
         ml.get_key(LLM_KV_POSNET_BLOCK_COUNT,      hparams.posnet.n_layer);
@@ -2403,8 +2405,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
                 // Mark recurrent layers (linear attention layers)
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0); // TODO: extract the magic 4 from "full_attention_interval"
+                {
+                    uint32_t full_attn_interval = 4;
+                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+                    }
                 }
 
                 switch (hparams.n_layer) {
@@ -2412,12 +2418,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
+        case LLM_ARCH_QWEN35:
             {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
 
                 // Load linear attention (gated delta net) parameters
                 ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
@@ -2427,8 +2431,47 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
                 // Mark recurrent layers (linear attention layers)
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
+                {
+                    uint32_t full_attn_interval = 4;
+                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+                    }
+                }
+
+                switch (hparams.n_layer) {
+                    case 24: type = LLM_TYPE_2B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_QWEN35MOE:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
+
+                // Load linear attention (gated delta net) parameters
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+                // Mark recurrent layers (linear attention layers)
+                {
+                    uint32_t full_attn_interval = 4;
+                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+                    }
+                }
+
+                switch (hparams.n_layer) {
+                    case 28: type = LLM_TYPE_35B_A3B; break;
+                    case 48: type = LLM_TYPE_80B_A3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_MISTRAL3:
@@ -6004,9 +6047,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_WAVTOKENIZER_DEC:
                 {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd, n_vocab}, 0);
 
-                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
+                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
                     conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);
 
                     // posnet
@@ -6102,8 +6145,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                     }
 
-                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
-                    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
+                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, hparams.n_embd_out()}, 0);
+                    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {hparams.n_embd_out()}, 0);
                 } break;
             case LLM_ARCH_BAILINGMOE:
                 {
@@ -7120,64 +7163,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
                     }
                 } break;
-            case LLM_ARCH_QWEN3_5:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    const int64_t ba_dim = n_v_heads * 2;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Full attention layers
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        // Dense FFN for all layers
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3_5_MOE:
+            case LLM_ARCH_QWEN35MOE:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
 
@@ -7185,6 +7171,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
                     output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
 
+                    // if output is NULL, init from the input tok embed
                     if (output == NULL) {
                         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
                     }
@@ -7200,7 +7187,71 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t value_dim  = head_v_dim * n_v_heads;
                     const int64_t conv_dim   = key_dim * 2 + value_dim;
 
-                    const int64_t ba_dim = n_v_heads * 2;
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            // Q/K normalization for attention layers
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            // Create tensors with calculated dimensions
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+
+                        // Shared experts
+                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
+                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, n_ff_shexp }, 0);
+                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, n_ff_shexp }, 0);
+                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { n_ff_shexp, n_embd }, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN35:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+                    }
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
 
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
@@ -7209,38 +7260,32 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
 
                         if (!hparams.is_recurrent(i)) {
-                            // Full attention layers
+                            // Attention layers
                             layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
                             layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
                             layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
                             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
 
+                            // Q/K normalization for attention layers
                             layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
                             layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
                         } else {
                             // Linear attention (gated delta net) specific tensors
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
+                            // Create tensors with calculated dimensions
                             layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
                             layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
                             layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
                             layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
                             layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+                            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", i), { n_embd, n_v_heads }, 0);
                             layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
                             layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
                         }
 
-                        // MoE FFN
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-
-                        // Shared experts
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
-                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
-                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
-                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
                 } break;
             case LLM_ARCH_MIMO2:
@@ -7687,8 +7732,8 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
         arch == LLM_ARCH_QWEN3NEXT ||
-        arch == LLM_ARCH_QWEN3_5 ||
-        arch == LLM_ARCH_QWEN3_5_MOE ||
+        arch == LLM_ARCH_QWEN35 ||
+        arch == LLM_ARCH_QWEN35MOE ||
         arch == LLM_ARCH_NEMOTRON_H ||
         arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
@@ -8487,13 +8532,13 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_qwen3next>(*this, params);
             } break;
-        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN35:
             {
-                llm = std::make_unique<llm_build_qwen3_5>(*this, params);
+                llm = std::make_unique<llm_build_qwen35>(*this, params);
             } break;
-        case LLM_ARCH_QWEN3_5_MOE:
+        case LLM_ARCH_QWEN35MOE:
             {
-                llm = std::make_unique<llm_build_qwen3_5_moe>(*this, params);
+                llm = std::make_unique<llm_build_qwen35moe>(*this, params);
             } break;
         case LLM_ARCH_MISTRAL3:
             {
@@ -8755,8 +8800,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_PANGU_EMBED:
         case LLM_ARCH_AFMOE:
         case LLM_ARCH_QWEN3NEXT:
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
             return LLAMA_ROPE_TYPE_NEOX;
@@ -8765,6 +8808,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
             return LLAMA_ROPE_TYPE_MROPE;
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_QWEN3VLMOE:
+        case LLM_ARCH_QWEN35:
+        case LLM_ARCH_QWEN35MOE:
             return LLAMA_ROPE_TYPE_IMROPE;
 
         case LLM_ARCH_GLM4:

src/llama-model.h

@@ -118,6 +118,7 @@ enum llm_type {
     LLM_TYPE_21B_A3B, // Ernie MoE small
     LLM_TYPE_30B_A3B,
     LLM_TYPE_31B_A3_5B,
+    LLM_TYPE_35B_A3B, // Qwen3.5
     LLM_TYPE_48B_A3B, // Kimi Linear
     LLM_TYPE_80B_A3B, // Qwen3 Next
     LLM_TYPE_100B_A6B,
@@ -322,6 +323,9 @@ struct llama_layer {
     // qwen3next
     struct ggml_tensor * ssm_beta_alpha = nullptr;
 
+    // qwen3.5
+    struct ggml_tensor * ssm_alpha = nullptr;
+
     // rwkv
     struct ggml_tensor * time_mix_w1         = nullptr;
     struct ggml_tensor * time_mix_w2         = nullptr;

src/llama-vocab.cpp

@@ -368,6 +368,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_QWEN35:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             case LLAMA_VOCAB_PRE_TYPE_PORO:
             case LLAMA_VOCAB_PRE_TYPE_BLOOM:
             case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH:
@@ -1926,6 +1933,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "kormo") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
                 clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "qwen35") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN35;
+                clean_spaces = false;
             } else if (
                 tokenizer_pre == "stablelm2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_STABLELM2;

src/llama-vocab.h

@@ -54,6 +54,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN      = 43,
     LLAMA_VOCAB_PRE_TYPE_YOUTU           = 44,
     LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE      = 45,
+    LLAMA_VOCAB_PRE_TYPE_QWEN35          = 46,
 };
 
 struct LLM_KV;

src/models/delta.cpp

@@ -1,618 +0,0 @@
-#include "models.h"
-#include "ggml.h"
-#include <cmath>
-#include <utility>
-#include <cassert>
-
-llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
-
-/**
- * Unified Delta Net implementation supporting both GDA and KDA modes.
- *
- * GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
- *   - Per-head gating, broadcasts over K dimension
- *
- * KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
- *   - Per-key gating
- *
- * The mode is auto-detected based on g's dimensionality.
- *
- * Tensor dimension convention:
- *   GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
- *   PyTorch: dim 0 is outermost, dim -1 is innermost
- *   So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
- */
-
-// Helper to get a slice along dimension 2 (n_chunks dimension)
-static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
-    return ggml_view_4d(ctx, t,
-        t->ne[0], t->ne[1], 1, t->ne[3],
-        t->nb[1], t->nb[2], t->nb[3],
-        chunk * t->nb[2]);
-}
-
-/**
- * Unified chunked Delta Net implementation.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g         Gate tensor:
- *                    GDA: [H_v, n_tokens, n_seqs]
- *                    KDA: [S_k, H_v, n_tokens, n_seqs]
- * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param causal_mask   Lower triangular mask [chunk_size, chunk_size]
- * @param identity      Identity matrix [chunk_size, chunk_size]
- * @param diag_mask     Diagonal mask [chunk_size, chunk_size]
- * @param il            Layer index (for debugging callbacks)
- * @param chunk_size    Chunk size for chunked processing
- * @param eps_norm      Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_chunking(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state_reshaped,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    // Detect KDA vs GDA based on g's shape
-    // GDA: g has shape [H_v, n_tokens, n_seqs]
-    // KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
-    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
-    // Validate tensor shapes
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    if (is_kda) {
-        // KDA: g shape [S_k, H_v, n_tokens, n_seqs]
-        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
-    } else {
-        // GDA: g shape [H_v, n_tokens, n_seqs]
-        GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    }
-
-    // L2 normalize q and k
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf((float)S_v);
-    q = ggml_scale(ctx0, q, scale);
-
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    // Permute tensors to working format [S, n_tokens, H, n_seqs]
-    // Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    if (is_kda) {
-        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    } else {
-        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-    }
-    beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
-
-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state_reshaped, "state_in", il);
-
-    // Padding for chunk processing
-    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
-    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
-    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
-    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
-    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
-    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
-    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
-
-
-    cb(q, "q_pad", il);
-    cb(k, "k_pad", il);
-    cb(v, "v_pad", il);
-    cb(beta, "beta_pad", il);
-    cb(g, "g_pad", il);
-
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
-    cb(v_beta, "v_beta", il);
-    cb(k_beta, "k_beta", il);
-
-    // Reshape to chunks
-    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-    beta   = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
-
-    // Reshape g for chunks
-    ggml_tensor * g_cumsum;
-    ggml_tensor * g_cumsum_t;
-    if (is_kda) {
-        // KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
-        g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
-        // Cumsum along chunk_size dimension (ne[1])
-        // GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
-        g = ggml_cont(ctx0, ggml_transpose(ctx0, g));  // [chunk_size, S_k, n_chunks, H_k * n_seqs]
-        g_cumsum_t = ggml_cumsum(ctx0, g);
-        g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t));  // [S_k, chunk_size, n_chunks, H_k * n_seqs]
-    } else {
-        // GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
-        g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-        g_cumsum = ggml_cumsum(ctx0, g);
-        g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
-    }
-
-    cb(g_cumsum, "g_cumsum", il);
-
-    // Build attention matrix A for the WY representation solve
-    // For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
-    // For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-    // KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
-
-    ggml_tensor * k_decay;
-    ggml_tensor * decay_mask = nullptr;
-    ggml_tensor * g_exp_pos = nullptr;
-
-    if (is_kda) {
-        // KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
-        // A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-        // By putting S_k in dim 0, mul_mat implicitly sums over it
-
-        const int64_t CHB = n_chunks * H_k * n_seqs;
-
-        // g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
-        // Reshape to [chunk_size, S_k, CHB] then build decay mask
-        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
-        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-
-        // Build decay mask: [chunk_size, chunk_size, S_k, CHB]
-        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
-        decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
-        cb(decay_mask, "decay_mask_kda", il);
-
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-        decay_mask = ggml_exp(ctx0, decay_mask);
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
-        decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
-        // Reshape k and k_beta for broadcasting with decay_mask
-        // k_i: indexed at position i (dim 2 of decay_mask)
-        // k_beta_j: indexed at position j (dim 1 of decay_mask)
-        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-        ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
-
-        // decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
-        ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
-
-        // mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
-        k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
-        k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-
-        // g_exp_pos is still needed for later (kbeta_gexp, etc.)
-        g_exp_pos = ggml_exp(ctx0, g_cumsum);
-    } else {
-        // GDA: Use decay mask approach (g broadcasts over K dimension)
-        // g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
-        ggml_tensor * gcs_i = g_cumsum;
-        ggml_tensor * gcs_j = g_cumsum_t;
-        g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
-        ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-        decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
-        cb(decay_mask, "decay_mask", il);
-
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-        decay_mask = ggml_exp(ctx0, decay_mask);
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-        ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-        k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    }
-
-    ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
-    cb(attn, "attn_pre_solve", il);
-
-    // Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-    ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn = ggml_add(ctx0, attn, identity);
-
-    cb(attn, "attn_solved", il);
-
-    // Compute u = A @ v and w = A @ (g.exp() * k)
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
-    cb(kbeta_gexp, "kbeta_gexp", il);
-
-    ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
-        ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-    cb(k_cumdecay, "k_cumdecay", il);
-
-    // Attention scores q @ k^T with decay
-    // For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
-    // For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-    ggml_tensor * attn_kq;
-    if (is_kda) {
-        // KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
-        const int64_t CHB = n_chunks * H_k * n_seqs;
-
-        // Rebuild decay mask (same structure as k_decay)
-        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
-        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
-        ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
-        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-        decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
-        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-
-        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
-        decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
-        // q_j: indexed at position j, k_i: indexed at position i
-        ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
-        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-
-        // decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
-        ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
-
-        // mul_mat sums over S_k
-        attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
-        attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-    } else {
-        // GDA: Use decay mask
-        attn_kq = ggml_mul_mat(ctx0, k, q);
-        attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
-        attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
-    }
-    cb(attn_kq, "attn_kq", il);
-
-    // Compute g_last and g_diff for state updates
-    ggml_tensor * g_last;
-    ggml_tensor * g_diff_exp;
-    ggml_tensor * g_last_exp;
-
-    if (is_kda) {
-        // KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
-        // Get last element along chunk_size dimension (ne[1])
-        g_last = ggml_view_4d(ctx0, g_cumsum,
-            g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
-        g_last = ggml_cont(ctx0, g_last);
-        g_last_exp = ggml_exp(ctx0, g_last);
-
-        // g_diff = g_last - g_cumsum
-        ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
-            g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
-        ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
-        g_diff_exp = ggml_exp(ctx0, g_diff);
-    } else {
-        // GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
-        g_last = ggml_view_4d(ctx0, g_cumsum,
-            1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
-        g_last = ggml_cont(ctx0, g_last);
-        g_last_exp = ggml_exp(ctx0, g_last);
-
-        ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
-        g_diff_exp = ggml_exp(ctx0, g_diff);
-    }
-
-    cb(g_last, "g_last", il);
-    cb(g_last_exp, "g_last_exp", il);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
-    cb(key_gdiff, "key_gdiff", il);
-
-    // Process chunks
-    ggml_tensor * new_state = state_reshaped;
-    ggml_tensor * core_attn_out = nullptr;
-
-    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
-        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
-        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
-        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
-        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
-
-        cb(attn_chunk, "attn_chunk", il);
-
-        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
-            S_v, S_v, 1, H_v * n_seqs);
-
-        // v_prime = k_cumdecay @ state
-        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
-        cb(v_prime, "v_prime_chunk", il);
-
-        // v_new = v - v_prime
-        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
-        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-        cb(v_new, "v_new_chunk", il);
-
-        // attn_inter = (q * g.exp()) @ state
-        ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
-        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-        cb(attn_inter, "attn_inter_chunk", il);
-
-        // output = attn_inter + attn @ v_new
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
-        cb(v_attn, "v_attn_chunk", il);
-
-        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
-        cb(core_attn_out_chunk, "core_attn_out_chunk", il);
-
-        core_attn_out = core_attn_out == nullptr
-            ? core_attn_out_chunk
-            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
-        // State update: state = state * g_last_exp + key_gdiff^T @ v_new
-        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
-
-        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
-
-        if (is_kda) {
-            // KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
-            // State: [S_v, S_v, H_v, n_seqs]
-            // Need to reshape g_last_exp to broadcast correctly over V dimension only
-            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
-                1, gexp_last_chunk->ne[0], H_v, n_seqs);  // [1, S_k, H_v, n_seqs]
-            // Transpose to [S_k, 1, H_v, n_seqs] then broadcast
-            gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
-        } else {
-            // GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
-            // Broadcasts over both K and V dimensions
-            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
-                gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
-        }
-
-        new_state = ggml_add(ctx0,
-            ggml_mul(ctx0, new_state, gexp_last_chunk),
-            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
-    }
-
-    // Truncate padding and permute back
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
-        S_v, n_tokens, H_v, n_seqs,
-        ggml_row_size(core_attn_out->type, S_v),
-        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
-        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    cb(output_tokens, "output_tokens", il);
-
-    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    return {output_tokens, new_state};
-}
-
-
-/**
- * Unified autoregressive Delta Net implementation (single token processing).
- *
- * This implementation uses matrix multiplication instead of elementwise operations + summation,
- * which is more efficient and mathematically equivalent. See inline comments for equivalences.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, 1, n_seqs]
- * @param k         Key tensor [S_k, H_k, 1, n_seqs]
- * @param v         Value tensor [S_v, H_v, 1, n_seqs]
- * @param g         Gate tensor:
- *                    GDA: [H_v, 1, n_seqs]
- *                    KDA: [S_k, H_v, 1, n_seqs]
- * @param beta      Beta tensor [H_v, 1, 1, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param il        Layer index (for debugging callbacks)
- * @param eps_norm  Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_autoregressive(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_tokens == 1);  // Autoregressive mode is for single token
-
-    // Detect KDA vs GDA based on g's shape
-    // GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
-    // KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
-    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
-    // Validate shapes
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    if (is_kda) {
-        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
-    } else {
-        GGML_ASSERT(g->ne[0] == H_v);
-    }
-
-    // L2 normalize q and k
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf((float)S_v);
-    q = ggml_scale(ctx0, q, scale);
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    // Reshape g and beta for broadcasting
-    ggml_tensor * g_t;
-    ggml_tensor * beta_t;
-
-    if (is_kda) {
-        // KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
-        // For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
-        g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
-    } else {
-        // GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
-        // For state multiplication, broadcasts over both K and V
-        g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
-    }
-
-    beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
-    // Apply exponential to g_t
-    g_t = ggml_exp(ctx0, g_t);
-
-    // State decay: state = state * exp(g)
-    if (is_kda) {
-        // KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
-        // Need to broadcast g_t over V dimension (ne[0] of state)
-        // Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
-        ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
-        state = ggml_mul(ctx0, state, g_broadcast);
-    } else {
-        // GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
-        state = ggml_mul(ctx0, state, g_t);
-    }
-
-    // Equivalence to previous version:
-    // Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
-    // Current:  k_state = state_t @ k_t using matrix multiplication
-    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
-    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-    ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
-    ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
-
-    // v_diff = v - k_state (equivalent to v - kv_mem in previous version)
-    ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
-    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
-
-    // Equivalence to previous version:
-    // Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
-    // Current:  state += v_diff^T @ k_beta^T using matrix multiplication
-    // These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
-    state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
-
-    // Equivalence to previous version:
-    // Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
-    // Current:  core_attn_out = state_t @ q using matrix multiplication
-    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
-    q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
-    state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-    ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
-    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
-    cb(core_attn_out, "output_tokens", il);
-    cb(state, "new_state", il);
-
-    return {core_attn_out, state};
-}
-
-
-/**
- * Main entry point that dispatches to chunked or autoregressive based on n_tokens.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g         Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
- * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t n_tokens = q->ne[2];
-
-    if (n_tokens == 1) {
-        return build_delta_net_unified_autoregressive(
-            ctx0, q, k, v, g, beta, state, il, eps_norm);
-    }
-    return build_delta_net_unified_chunking(
-        ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
-        il, chunk_size, eps_norm);
-}

src/models/kimi-linear.cpp

@@ -1,4 +1,5 @@
 #include "models.h"
+#include "ggml.h"
 
 #define CHUNK_SIZE 64
 

src/models/models.h

@@ -17,53 +17,6 @@ struct llm_graph_context_mamba : public llm_graph_context {
 
 };
 
-struct llm_graph_context_delta : public llm_graph_context_mamba {
-    llm_graph_context_delta(const llm_graph_params & params);
-
-    virtual ~llm_graph_context_delta() = default;
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_chunking(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm);
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_autoregressive(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il,
-        float         eps_norm);
-
-    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm);
-};
-
 // Base class for RWKV-related models
 struct llm_build_rwkv6_base : public llm_graph_context {
     const llama_model & model;
@@ -523,7 +476,8 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
     llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
-struct llm_build_qwen3next : public llm_graph_context_delta {
+
+struct llm_build_qwen3next : public llm_graph_context_mamba {
     llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
 private:
     ggml_tensor * build_layer_attn(
@@ -581,29 +535,14 @@ private:
     const llama_model & model;
 };
 
-struct llm_build_qwen3_5 : public llm_graph_context_delta {
-    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params);
-
-protected:
-    // Tag type for subclass constructors that need to call build_graph() themselves
-    // (to ensure virtual dispatch works correctly)
-    struct defer_graph_build_t {};
-
-    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t);
-
-    void build_graph();
-
-    virtual ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il);
-
-    const llama_model & model;
-
+struct llm_build_qwen35 : public llm_graph_context_mamba {
+    llm_build_qwen35(const llama_model & model, const llm_graph_params & params);
 private:
     ggml_tensor * build_layer_attn(
     llm_graph_input_attn_kv * inp_attn,
                 ggml_tensor * cur,
                 ggml_tensor * inp_pos,
+                        int * sections,
                         int   il);
 
     ggml_tensor * build_layer_attn_linear(
@@ -614,24 +553,104 @@ private:
                 ggml_tensor * diag_mask,
                         int   il);
 
+    ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                int           il);
+
     ggml_tensor * build_norm_gated(
                 ggml_tensor * input,
                 ggml_tensor * weights,
                 ggml_tensor * gate,
                         int   layer);
 
+    // returns pair of qkv, z
     std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
                 ggml_tensor * input,
                         int   il);
+
+    const llama_model & model;
 };
 
-struct llm_build_qwen3_5_moe : public llm_build_qwen3_5 {
-    llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params);
+struct llm_build_qwen35moe : public llm_graph_context_mamba {
+    llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params);
+private:
+    ggml_tensor * build_layer_attn(
+    llm_graph_input_attn_kv * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                        int * sections,
+                        int   il);
+
+    ggml_tensor * build_layer_attn_linear(
+         llm_graph_input_rs * inp,
+                ggml_tensor * cur,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
 
-protected:
     ggml_tensor * build_layer_ffn(
                 ggml_tensor * cur,
-                        int   il) override;
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                int           il);
+
+    ggml_tensor * build_norm_gated(
+                ggml_tensor * input,
+                ggml_tensor * weights,
+                ggml_tensor * gate,
+                        int   layer);
+
+    // returns pair of qkv, z
+    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+
+    const llama_model & model;
 };
 
 struct llm_build_qwen : public llm_graph_context {

src/models/qwen3-5.cpp

@@ -1,421 +0,0 @@
-#include "models.h"
-
-#define CHUNK_SIZE 64
-
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_delta(params), model(model) {
-    build_graph();
-}
-
-// virtual call in constructor fix
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
-    llm_graph_context_delta(params), model(model) {
-}
-
-void llm_build_qwen3_5::build_graph() {
-    ggml_tensor * cur;
-    ggml_tensor * inpL;
-
-    inpL = build_inp_embd(model.tok_embd);
-    cb(inpL, "model.embed_tokens", -1);
-
-    auto * inp = build_inp_mem_hybrid();
-
-    ggml_tensor * inp_pos     = build_inp_pos();
-    ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-    ggml_tensor * causal_mask =
-        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
-                    GGML_TRI_TYPE_LOWER);
-
-    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
-    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
-
-    ggml_build_forward_expand(gf, causal_mask);
-    ggml_build_forward_expand(gf, identity);
-    ggml_build_forward_expand(gf, diag_mask);
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
-        cb(cur, "attn_norm", il);
-
-        if (hparams.is_recurrent(il)) {
-            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
-        } else {
-            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
-        }
-
-        if (il == n_layer - 1 && inp_out_ids) {
-            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-        }
-
-        cur = ggml_add(ctx0, cur, inpSA);
-        cb(cur, "attn_residual", il);
-
-        ggml_tensor * ffn_residual = cur;
-
-        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
-        cb(attn_post_norm, "attn_post_norm", il);
-
-        cur = build_layer_ffn(attn_post_norm, il);
-        cb(cur, "ffn_out", il);
-
-        cur = ggml_add(ctx0, cur, ffn_residual);
-        cb(cur, "post_ffn", il);
-
-        inpL = cur;
-    }
-    cur = inpL;
-
-    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
-
-    cb(cur, "result_norm", -1);
-    res->t_embd = cur;
-
-    cur = build_lora_mm(model.output, cur);
-
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
-
-    ggml_build_forward_expand(gf, cur);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_norm_gated(
-        ggml_tensor * input,
-        ggml_tensor * weights,
-        ggml_tensor * gate,
-        int           layer) {
-    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
-    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
-
-    return ggml_mul(ctx0, normalized, gated_silu);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn(
-        llm_graph_input_attn_kv * inp,
-        ggml_tensor *             cur,
-        ggml_tensor *             inp_pos,
-        int                       il) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
-    cb(Qcur_full, "Qcur_full", il);
-
-    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
-        ggml_element_size(Qcur_full) * n_embd_head * 2,
-        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
-    cb(Qcur, "Qcur_reshaped", il);
-
-    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
-    cb(Qcur, "Qcur_normed", il);
-
-    ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-    cb(Kcur, "Kcur", il);
-
-    ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-    cb(Vcur, "Vcur", il);
-
-    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
-    cb(Kcur, "Kcur_normed", il);
-
-    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
-        ggml_element_size(Qcur_full) * n_embd_head * 2,
-        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
-        ggml_element_size(Qcur_full) * n_embd_head);
-    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
-    cb(gate, "gate_reshaped", il);
-
-    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-    Qcur = ggml_rope_ext(
-            ctx0, Qcur, inp_pos, nullptr,
-            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-            ext_factor, attn_factor, beta_fast, beta_slow);
-
-    Kcur = ggml_rope_ext(
-            ctx0, Kcur, inp_pos, nullptr,
-            n_rot, rope_type, n_ctx_orig, freq_base,
-            freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
-    cb(Qcur, "Qcur", il);
-    cb(Kcur, "Kcur", il);
-    cb(Vcur, "Vcur", il);
-
-    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-    cur = build_attn(inp,
-                nullptr, nullptr,
-                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-    cb(cur, "attn_pregate", il);
-
-    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
-    cb(gate_sigmoid, "gate_sigmoid", il);
-
-    cur = ggml_mul(ctx0, cur, gate_sigmoid);
-    cb(cur, "attn_gated", il);
-
-    cur = build_lora_mm(model.layers[il].wo, cur);
-    cb(cur, "attn_output", il);
-
-    return cur;
-}
-
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3_5::build_qkvz(
-                ggml_tensor * input,
-                        int   il) {
-    const int64_t d_inner      = hparams.ssm_d_inner;
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t head_k_dim   = hparams.ssm_d_state;
-    const int64_t num_k_heads  = hparams.ssm_n_group;
-    const int64_t num_v_heads  = hparams.ssm_dt_rank;
-    const int64_t head_v_dim   = d_inner / num_v_heads;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-    if (model.layers[il].wqkv) {
-        ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
-        qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
-        cb(qkv_mixed, "linear_attn_qkv_mixed", il);
-
-        ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
-        cb(z, "z", il);
-
-        return { qkv_mixed, z };
-
-    }
-    // legacy path for combined in_proj_qkvz
-    ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
-    cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
-
-    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
-    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
-    int64_t split_sizes_qkvz[4] = {
-        head_k_dim,
-        head_k_dim,
-        head_v_dim * num_v_heads / num_k_heads,
-        head_v_dim * num_v_heads / num_k_heads
-    };
-
-    ggml_tensor * query =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
-                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
-    cb(query, "q", il);
-
-    ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
-                                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                    split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
-    cb(key, "k", il);
-
-    ggml_tensor * value =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
-                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                    (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
-    cb(value, "v", il);
-
-    ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
-                                mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
-    z = ggml_cont(ctx0, z);
-    cb(z, "z", il);
-
-    ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(query_flat, "query_flat", il);
-
-    ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(key_flat, "key_flat", il);
-
-    ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(value_flat, "value_flat", il);
-
-    ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
-    qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
-    cb(qkv_mixed, "qkv_mixed", il);
-
-    return { qkv_mixed, z };
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
-        llm_graph_input_rs * inp,
-        ggml_tensor *        cur,
-        ggml_tensor *        causal_mask,
-        ggml_tensor *        identity,
-        ggml_tensor *        diag_mask,
-        int                  il) {
-    const auto * mctx_cur = inp->mctx;
-
-    const int64_t d_inner      = hparams.ssm_d_inner;
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t head_k_dim   = hparams.ssm_d_state;
-    const int64_t num_k_heads  = hparams.ssm_n_group;
-    const int64_t num_v_heads  = hparams.ssm_dt_rank;
-    const int64_t head_v_dim   = d_inner / num_v_heads;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-    const auto kv_head = mctx_cur->get_head();
-
-    GGML_ASSERT(n_seqs != 0);
-    GGML_ASSERT(ubatch.equal_seqs());
-    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-    auto qkvz = build_qkvz(cur, il);
-    ggml_tensor * qkv_mixed = qkvz.first;
-    ggml_tensor * z         = qkvz.second;
-
-    ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
-    cb(mixed_ba, "linear_attn_mixed_ba", il);
-
-    int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
-    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
-    int64_t split_sizes_ba[2] = {
-        num_v_heads / num_k_heads,
-        num_v_heads / num_k_heads
-    };
-
-    ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
-    cb(b, "b", il);
-
-    ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
-                                   split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
-    cb(a, "a", il);
-
-    ggml_tensor * beta  = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
-
-    ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
-
-    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
-    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
-    cb(alpha_softplus, "a_softplus", il);
-    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);
-    cb(gate, "gate", il);
-
-    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-    cb(conv_states, "conv_states", il);
-
-    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
-    const int64_t conv_kernel_size = conv_kernel->ne[0];
-    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
-    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
-    cb(conv_states, "conv_states_reshaped", il);
-
-    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
-    cb(qkv_mixed, "qkv_mixed_permuted", il);
-
-    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
-    cb(conv_input, "conv_input", il);
-
-    ggml_tensor * last_conv_states =
-        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-    cb(last_conv_states, "last_conv_states", il);
-
-    ggml_tensor * state_update_target =
-        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
-                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-    cb(state_update_target, "state_update_target", il);
-
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
-    cb(conv_states_all, "conv_states_updated", il);
-
-    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
-    cb(conv_output_proper, "conv_output_raw", il);
-
-    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
-    cb(conv_output_silu, "conv_output_silu", il);
-
-    ggml_tensor * conv_qkv_mix = conv_output_silu;
-
-    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
-    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
-
-    ggml_tensor * q_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
-    cb(q_conv, "q_conv", il);
-    ggml_tensor * k_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
-                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
-    cb(k_conv, "k_conv", il);
-    ggml_tensor * v_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
-                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
-    cb(v_conv, "v_conv", il);
-
-    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
-    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
-    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
-
-    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
-    cb(state, "state_predelta", il);
-
-    if (num_k_heads != num_v_heads) {
-        GGML_ASSERT(num_v_heads % num_k_heads == 0);
-        int64_t repeat_factor = num_v_heads / num_k_heads;
-
-        ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-        ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-
-        ggml_tensor * q_repeated =
-            ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-        ggml_tensor * k_repeated =
-            ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-
-        q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
-        k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
-    }
-
-    cb(q_conv, "q_conv_predelta", il);
-    cb(k_conv, "k_conv_predelta", il);
-    cb(v_conv, "v_conv_predelta", il);
-
-    std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
-            gate, beta, state, causal_mask, identity, diag_mask,
-            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
-    cb(output, "attn_output", il);
-    cb(new_state, "new_state", il);
-
-    ggml_build_forward_expand(gf,
-                              ggml_cpy(ctx0, new_state,
-                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
-                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
-
-    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
-    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
-    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
-
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(final_output, "final_output", il);
-
-    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
-    cb(cur, "linear_attn_out", il);
-
-    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-    return cur;
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
-    // Qwen3.5 Dense always uses dense FFN
-    cur = build_ffn(cur,
-        model.layers[il].ffn_up, NULL, NULL,
-        model.layers[il].ffn_gate, NULL, NULL,
-        model.layers[il].ffn_down, NULL, NULL,
-        NULL,
-        LLM_FFN_SILU, LLM_FFN_PAR, il);
-    cb(cur, "ffn_out", il);
-    return cur;
-}

src/models/qwen3-5moe.cpp

@@ -1,52 +0,0 @@
-#include "models.h"
-
-llm_build_qwen3_5_moe::llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params) :
-    llm_build_qwen3_5(model, params, defer_graph_build_t{}) {
-    build_graph();
-}
-
-ggml_tensor * llm_build_qwen3_5_moe::build_layer_ffn(ggml_tensor * cur, const int il) {
-    // Check if this is an MoE layer
-    if (model.layers[il].ffn_gate_inp != nullptr) {
-        // MoE branch
-        ggml_tensor * moe_out =
-            build_moe_ffn(cur,
-                model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
-                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
-                nullptr,
-                n_expert, n_expert_used, LLM_FFN_SILU,
-                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
-        cb(moe_out, "ffn_moe_out", il);
-
-        // Add shared experts if present
-        if (model.layers[il].ffn_up_shexp != nullptr) {
-            ggml_tensor * ffn_shexp =
-                build_ffn(cur,
-                    model.layers[il].ffn_up_shexp, NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(ffn_shexp, "ffn_shexp", il);
-
-            // Apply shared expert gating (sigmoid)
-            ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
-            cb(shared_gate, "shared_expert_gate", il);
-
-            shared_gate = ggml_sigmoid(ctx0, shared_gate);
-            cb(shared_gate, "shared_expert_gate_sigmoid", il);
-
-            ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
-            cb(ffn_shexp, "ffn_shexp_gated", il);
-
-            cur = ggml_add(ctx0, moe_out, ffn_shexp);
-            cb(cur, "ffn_out", il);
-        } else {
-            cur = moe_out;
-        }
-    } else {
-        // Dense FFN branch (fallback)
-        cur = llm_build_qwen3_5::build_layer_ffn(cur, il);
-    }
-    return cur;
-}

src/models/qwen35.cpp

@@ -0,0 +1,740 @@
+#include "ggml.h"
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params), model(model) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    cb(inpL, "model.input_embed", -1);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * causal_mask =
+        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+    ggml_build_forward_expand(gf, causal_mask);
+    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // Determine layer type and build appropriate attention mechanism
+        if (hparams.is_recurrent(il)) {
+            // Linear attention layer (gated delta net)
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+        } else {
+            // Full attention layer
+            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        // Residual connection
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "attn_residual", il);
+
+        // Save the tensor before post-attention norm for residual connection
+        ggml_tensor * ffn_residual = cur;
+
+        // Post-attention norm
+        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+        cb(attn_post_norm, "attn_post_norm", il);
+
+        // Dense FFN layer - without residual connection
+        cur = build_layer_ffn(attn_post_norm, il);
+        cb(cur, "ffn_out", il);
+
+        // Residual connection for FFN - add to the tensor from before post_attention_layernorm
+        cur = ggml_add(ctx0, cur, ffn_residual);
+        cb(cur, "post_ffn", il);
+
+        // Input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    // Final norm
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // LM head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+    qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+    cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+    ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+    cb(z, "z", il);
+
+    return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen35::build_norm_gated(
+        ggml_tensor * input,
+        ggml_tensor * weights,
+        ggml_tensor * gate,
+        int           layer) {
+    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+    return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_attn(
+        llm_graph_input_attn_kv * inp,
+        ggml_tensor *             cur,
+        ggml_tensor *             inp_pos,
+        int *                     sections,
+        int                       il) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
+
+    // Qwen3Next uses a single Q projection that outputs query + gate
+    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
+    cb(Qcur_full, "Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
+    cb(Qcur, "Qcur_reshaped", il);
+
+    // Apply Q normalization
+    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "Qcur_normed", il);
+
+    ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+    cb(Kcur, "Kcur", il);
+
+    ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+    cb(Vcur, "Vcur", il);
+
+    // Apply K normalization
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "Kcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+        ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "gate_reshaped", il);
+
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+    // Apply MRoPE
+    Qcur = ggml_rope_multi(
+            ctx0, Qcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow
+            );
+
+    Kcur = ggml_rope_multi(
+            ctx0, Kcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow
+            );
+
+    cb(Qcur, "Qcur", il);
+    cb(Kcur, "Kcur", il);
+    cb(Vcur, "Vcur", il);
+
+    // Attention computation
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    cur = build_attn(inp,
+                nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_pregate", il);
+
+    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+    cb(gate_sigmoid, "gate_sigmoid", il);
+
+    cur = ggml_mul(ctx0, cur, gate_sigmoid);
+    cb(cur, "attn_gated", il);
+
+    cur = build_lora_mm(model.layers[il].wo, cur);
+    cb(cur, "attn_output", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        cur,
+        ggml_tensor *        causal_mask,
+        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
+        int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    // Input projections
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
+
+    ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
+    beta  = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
+    cb(beta, "beta", il);
+    ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
+    alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
+    cb(alpha, "alpha", il);
+
+    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+    cb(alpha_softplus, "a_softplus", il);
+    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);  // -A_log.exp() * softplus
+    cb(gate, "gate", il);
+
+    // Get convolution states from cache
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
+
+    // Build the convolution states tensor
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    // Calculate convolution kernel size
+    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
+    const int64_t conv_kernel_size = conv_kernel->ne[0];
+    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    // Update convolution state cache
+    // Extract the last (conv_kernel_size - 1) states from conv_input
+    ggml_tensor * last_conv_states =
+        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+    cb(last_conv_states, "last_conv_states", il);
+
+    ggml_tensor * state_update_target =
+        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+    cb(state_update_target, "state_update_target", il);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    cb(conv_states_all, "conv_states_updated", il);
+
+    // Apply SSM convolution
+    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+    cb(conv_output_proper, "conv_output_raw", il);
+
+    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+    cb(conv_output_silu, "conv_output_silu", il);
+
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    // Calculate the total conv dimension
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+    // Extract the convolved Q, K, V from conv_output
+    ggml_tensor * q_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+    cb(q_conv, "q_conv", il);
+    ggml_tensor * k_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(k_conv, "k_conv", il);
+    ggml_tensor * v_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(v_conv, "v_conv", il);
+
+    // Unsqueeze them
+    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+    cb(state, "state_predelta", il);
+
+    // if head keys and value keys are different, repeat Q/K to match V's head count
+    // V heads are in tiled order (from conversion), so simple tiled repeat works
+    if (num_k_heads != num_v_heads) {
+        GGML_ASSERT(num_v_heads % num_k_heads == 0);
+        q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+        k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+    }
+
+    cb(q_conv, "q_conv_predelta", il);
+    cb(k_conv, "k_conv_predelta", il);
+    cb(v_conv, "v_conv_predelta", il);
+
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
+
+    // Update the recurrent states
+    ggml_build_forward_expand(gf,
+                              ggml_cpy(ctx0, new_state,
+                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    // Reshape both attn_out_final and z to 2D tensors for normalization
+    // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // Apply gated normalization: self.norm(core_attn_out, z)
+    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
+    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(final_output, "final_output", il);
+
+    // Output projection
+    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+    cb(cur, "linear_attn_out", il);
+
+    // Reshape back to original dimensions
+    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Qwen3.5 does not use MoE FFN
+    GGML_ASSERT(model.layers[il].ffn_gate_inp == nullptr);
+
+    cur = build_ffn(cur,
+        model.layers[il].ffn_up, NULL, NULL,
+        model.layers[il].ffn_gate, NULL, NULL,
+        model.layers[il].ffn_down, NULL, NULL,
+        NULL,
+        LLM_FFN_SILU, LLM_FFN_PAR, il);
+    cb(cur, "ffn_out", il);
+
+    return cur;
+}

src/models/qwen35moe.cpp

@@ -0,0 +1,774 @@
+#include "ggml.h"
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params), model(model) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    cb(inpL, "model.input_embed", -1);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * causal_mask =
+        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+    ggml_build_forward_expand(gf, causal_mask);
+    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // Determine layer type and build appropriate attention mechanism
+        if (hparams.is_recurrent(il)) {
+            // Linear attention layer (gated delta net)
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+        } else {
+            // Full attention layer
+            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        // Residual connection
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "attn_residual", il);
+
+        // Save the tensor before post-attention norm for residual connection
+        ggml_tensor * ffn_residual = cur;
+
+        // Post-attention norm
+        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+        cb(attn_post_norm, "attn_post_norm", il);
+
+        // MOE FFN layer
+        cur = build_layer_ffn(attn_post_norm, il);
+        cb(cur, "ffn_out", il);
+
+        // Residual connection for FFN - add to the tensor from before post_attention_layernorm
+        cur = ggml_add(ctx0, cur, ffn_residual);
+        cb(cur, "post_moe", il);
+
+        // Input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    // Final norm
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // LM head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+    qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+    cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+    ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+    cb(z, "z", il);
+
+    return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen35moe::build_norm_gated(
+        ggml_tensor * input,
+        ggml_tensor * weights,
+        ggml_tensor * gate,
+        int           layer) {
+    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+    return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_attn(
+        llm_graph_input_attn_kv * inp,
+        ggml_tensor *             cur,
+        ggml_tensor *             inp_pos,
+        int *                     sections,
+        int                       il) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
+
+    // Qwen3Next uses a single Q projection that outputs query + gate
+    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
+    cb(Qcur_full, "Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
+    cb(Qcur, "Qcur_reshaped", il);
+
+    // Apply Q normalization
+    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "Qcur_normed", il);
+
+    ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+    cb(Kcur, "Kcur", il);
+
+    ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+    cb(Vcur, "Vcur", il);
+
+    // Apply K normalization
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "Kcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+        ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "gate_reshaped", il);
+
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+    // Apply IMRoPE
+    Qcur = ggml_rope_multi(
+            ctx0, Qcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow
+            );
+
+    Kcur = ggml_rope_multi(
+            ctx0, Kcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow
+            );
+
+    cb(Qcur, "Qcur", il);
+    cb(Kcur, "Kcur", il);
+    cb(Vcur, "Vcur", il);
+
+    // Attention computation
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    cur = build_attn(inp,
+                nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_pregate", il);
+
+    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+    cb(gate_sigmoid, "gate_sigmoid", il);
+
+    cur = ggml_mul(ctx0, cur, gate_sigmoid);
+    cb(cur, "attn_gated", il);
+
+    cur = build_lora_mm(model.layers[il].wo, cur);
+    cb(cur, "attn_output", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        cur,
+        ggml_tensor *        causal_mask,
+        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
+        int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    // Input projections
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
+
+    ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
+    beta  = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
+    cb(beta, "beta", il);
+    ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
+    alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
+    cb(alpha, "alpha", il);
+
+    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+    cb(alpha_softplus, "a_softplus", il);
+    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);  // -A_log.exp() * softplus
+    cb(gate, "gate", il);
+
+    // Get convolution states from cache
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
+
+    // Build the convolution states tensor
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    // Calculate convolution kernel size
+    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
+    const int64_t conv_kernel_size = conv_kernel->ne[0];
+    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    // Update convolution state cache
+    // Extract the last (conv_kernel_size - 1) states from conv_input
+    ggml_tensor * last_conv_states =
+        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+    cb(last_conv_states, "last_conv_states", il);
+
+    ggml_tensor * state_update_target =
+        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+    cb(state_update_target, "state_update_target", il);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    cb(conv_states_all, "conv_states_updated", il);
+
+    // Apply SSM convolution
+    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+    cb(conv_output_proper, "conv_output_raw", il);
+
+    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+    cb(conv_output_silu, "conv_output_silu", il);
+
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    // Calculate the total conv dimension
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+    // Extract the convolved Q, K, V from conv_output
+    ggml_tensor * q_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+    cb(q_conv, "q_conv", il);
+    ggml_tensor * k_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(k_conv, "k_conv", il);
+    ggml_tensor * v_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(v_conv, "v_conv", il);
+
+    // Unsqueeze them
+    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+    cb(state, "state_predelta", il);
+
+    // if head keys and value keys are different, repeat Q/K to match V's head count
+    // V heads are in tiled order (from conversion), so simple tiled repeat works
+    if (num_k_heads != num_v_heads) {
+        GGML_ASSERT(num_v_heads % num_k_heads == 0);
+        q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+        k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+    }
+
+    cb(q_conv, "q_conv_predelta", il);
+    cb(k_conv, "k_conv_predelta", il);
+    cb(v_conv, "v_conv_predelta", il);
+
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
+
+    // Update the recurrent states
+    ggml_build_forward_expand(gf,
+                              ggml_cpy(ctx0, new_state,
+                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    // Reshape both attn_out_final and z to 2D tensors for normalization
+    // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // Apply gated normalization: self.norm(core_attn_out, z)
+    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
+    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(final_output, "final_output", il);
+
+    // Output projection
+    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+    cb(cur, "linear_attn_out", il);
+
+    // Reshape back to original dimensions
+    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Check if this is an MoE layer
+    GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);
+
+    ggml_tensor * moe_out =
+        build_moe_ffn(cur,
+            model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
+            model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+            nullptr,
+            n_expert, n_expert_used, LLM_FFN_SILU,
+            true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+    cb(moe_out, "ffn_moe_out", il);
+
+    // Add shared experts if present - following Qwen3Next reference implementation
+    if (model.layers[il].ffn_up_shexp != nullptr) {
+        ggml_tensor * ffn_shexp =
+            build_ffn(cur,
+                model.layers[il].ffn_up_shexp, NULL, NULL,
+                model.layers[il].ffn_gate_shexp, NULL, NULL,
+                model.layers[il].ffn_down_shexp, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "ffn_shexp", il);
+
+        // Apply shared expert gating as in the reference implementation
+        // The shared expert has its own gate that is sigmoided
+        // Note: ffn_gate_inp_shexp is the shared expert gate (outputs 1 value per token)
+        ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
+        cb(shared_gate, "shared_expert_gate", il);
+
+        // Apply sigmoid to the gate
+        shared_gate = ggml_sigmoid(ctx0, shared_gate);
+        cb(shared_gate, "shared_expert_gate_sigmoid", il);
+
+
+        // Apply the gate to the shared expert output
+        ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
+        cb(ffn_shexp, "ffn_shexp_gated", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        cb(cur, "ffn_out", il);
+    } else {
+        cur = moe_out;
+    }
+
+    return cur;
+}

src/models/qwen3next.cpp

@@ -1,9 +1,10 @@
+#include "ggml.h"
 #include "models.h"
 
 #define CHUNK_SIZE 64
 
 llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_delta(params), model(model) {
+    llm_graph_context_mamba(params), model(model) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -85,6 +86,362 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
 ggml_tensor * llm_build_qwen3next::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
@@ -395,7 +752,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
     cb(state, "state_predelta", il);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
@@ -424,10 +781,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
-            gate, beta, state, causal_mask, identity, diag_mask,
-            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
     ggml_tensor * output    = attn_out.first;
     ggml_tensor * new_state = attn_out.second;
     cb(output, "attn_output", il);

tests/test-backend-ops.cpp

@@ -1943,7 +1943,11 @@ struct test_unary : public test_case {
 
         ggml_tensor * a;
         if (v & 1) {
-            auto ne = ne_a; ne[0] *= 3;
+            auto ne = ne_a;
+            ne[0] *= 3;
+            ne[1] *= 2;
+            ne[2] *= 5;
+            ne[3] *= 4;
             a = ggml_new_tensor(ctx, type, 4, ne.data());
             if (grad_supported) {
                 ggml_set_param(a);
@@ -2964,11 +2968,12 @@ struct test_bin_bcast : public test_case {
     const std::array<int64_t, 4> ne;
     const std::array<int, 4> nr;
     int nf; // number of fused ops, nf == 1 -> single op (no fusion)
+    bool perm1; // permute src1?
 
     bool run_whole_graph() override { return nf > 1; }
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne, nr, nf);
+        return VARS_TO_STR5(type, ne, nr, nf, perm1);
     }
 
     size_t op_size(ggml_tensor * t) override {
@@ -2978,8 +2983,9 @@ struct test_bin_bcast : public test_case {
     test_bin_bcast(op_t op, ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {10, 10, 1, 1},
             std::array<int, 4> nr = {1, 2, 1, 1},
-            int nf = 1)
-        : op(op), type(type), ne(ne), nr(nr), nf(nf) {}
+            int nf = 1,
+            bool perm1 = false)
+        : op(op), type(type), ne(ne), nr(nr), nf(nf), perm1(perm1) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         GGML_ASSERT(nf <= 16);
@@ -2989,12 +2995,19 @@ struct test_bin_bcast : public test_case {
 
         ggml_tensor * b[16];
         for (int i = 0; i < nf; ++i) {
-            b[i] = ggml_new_tensor(ctx, type, 4, ne.data());
+            if (perm1) {
+                const int p[4] = { 1, 2, 0, 3 }; // hardcoded for now
+
+                b[i] = ggml_new_tensor_4d(ctx, type, ne[p[0]], ne[p[1]], ne[p[2]], ne[p[3]]);
+                b[i] = ggml_permute(ctx, b[i], p[0], p[1], p[2], p[3]);
+            } else {
+                b[i] = ggml_new_tensor(ctx, type, 4, ne.data());
+            }
             ggml_set_name(b[i], (std::string("b") + std::to_string(i)).c_str());
         }
 
         // The backward pass supports broadcasting only for GGML_ADD:
-        const bool grad_supported = op == ggml_add && ggml_are_same_shape(a, b[0]) && nf == 1;
+        const bool grad_supported = op == ggml_add && ggml_are_same_shape(a, b[0]) && nf == 1 && !perm1;
         if (grad_supported) {
             ggml_set_param(a);
             ggml_set_param(b[0]);
@@ -5894,33 +5907,36 @@ struct test_pad_ext : public test_case {
     const int rp2;
     const int lp3;
     const int rp3;
-    const bool v;
+    const int tfrm; // 0 - none, 1 - non-cont, 2 - perm
     const bool circular;
 
     std::string vars() override {
-        return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v, circular);
+        return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, tfrm, circular);
     }
 
     test_pad_ext(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne_a = {512, 512, 3, 1},
             int lp0 = 1, int rp0 = 1, int lp1 = 1, int rp1 = 1,
             int lp2 = 1, int rp2 = 1, int lp3 = 1, int rp3 = 1,
-            bool v = false, bool circular = false)
+            int tfrm = 0, bool circular = false)
         : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3),
-          v(v), circular(circular) {}
+          tfrm(tfrm), circular(circular) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
         ggml_set_name(a, "a");
 
-        if (v) {
+        if (tfrm == 1) {
             a = ggml_view_4d(ctx, a, (a->ne[0] + 1) / 2, (a->ne[1] + 1) / 2, (a->ne[2] + 1) / 2, (a->ne[3] + 1) / 2, a->nb[1], a->nb[2], a->nb[3], 0);
             ggml_set_name(a, "view of a");
+        } else if (tfrm == 2) {
+            a = ggml_permute(ctx, a, 2, 1, 0, 3);
+            ggml_set_name(a, "permuted a");
         }
 
         ggml_tensor * out = circular
             ? ggml_pad_ext_circular(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3)
-            : ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+            : ggml_pad_ext         (ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
         ggml_set_name(out, "out");
 
         return out;
@@ -7474,25 +7490,27 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
-    auto add_test_bin_bcast = [&](ggml_type type, std::array<int64_t, 4> ne, std::array<int, 4> nr) {
+    auto add_test_bin_bcast = [&](ggml_type type, std::array<int64_t, 4> ne, std::array<int, 4> nr, bool perm1 = false) {
         for (auto op : {ggml_add, ggml_sub, ggml_mul, ggml_div}) {
-            test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr));
+            test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr, 1, perm1));
         }
     };
     for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
-        add_test_bin_bcast(type, {1, 1, 8, 1}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {1, 1, 1, 1}, {32, 1, 1, 1});
-        add_test_bin_bcast(type, {1, 1, 320, 320}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 1, 1}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 1}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 2});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 2});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 2, 2});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 2, 2, 2});
+        for (bool perm1 : {false, true}) {
+            add_test_bin_bcast(type, {1,  1,   8,   1}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {1,  1,   1,   1}, {32, 1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {1,  1, 320, 320}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   1,   1}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   1}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {2,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  2, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 2, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 1, 2}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 2, 2}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  2, 2, 2}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {2,  2, 2, 2}, perm1);
+        }
 
         // test case for k_bin_bcast_unravel in CUDA backend
         add_test_bin_bcast(type, {1, 1, 65536, 1}, {256, 1, 1, 1});
@@ -7879,20 +7897,27 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_round     (type));
         test_cases.emplace_back(new test_trunc     (type));
         test_cases.emplace_back(new test_sqr       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_sqr       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_sqrt      (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_sqrt      (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_log       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_log       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_sin       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_sin       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_cos       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_cos       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_clamp     (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_clamp     (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_leaky_relu(type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_leaky_relu(type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_floor     (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_floor     (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_floor     (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_ceil      (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_ceil      (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_ceil      (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_round     (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_round     (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_round     (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_trunc     (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_trunc     (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_trunc     (type, {1024, 1024, 1, 1}));
     }
 
     test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
@@ -8107,24 +8132,30 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     }
 
     test_cases.emplace_back(new test_sum());
-    test_cases.emplace_back(new test_sum_rows());
     test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 2, 1, 3}));  // row-contiguous but non-contiguous
     test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 3, 2, 1}));
     test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 1, 3, 2}));
+    test_cases.emplace_back(new test_mean());
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 1, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 256, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32769, 1, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32, 1, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32, 256, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32768, 1, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1024, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }, { 1, 0, 2, 3 })); // sum dst not-contiguous
+    test_cases.emplace_back(new test_sum_rows());
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, true, false));
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, false, true));
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, true, true));
-    test_cases.emplace_back(new test_mean());
-    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1, 1, 1 }));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 16, 5, 6, 3 }, true, false));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 16, 5, 6, 3 }, false, true));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 16, 5, 6, 3 }, true, true));
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 1, 1, 1 }));
-    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 1, 1, 1 }));
-    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1024, 1, 1 }));
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 1024, 1, 1 }));
-    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }));
-    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }, { 1, 0, 2, 3 })); // sum dst not-contiguous
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 256, 1, 1 }));
-    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 256, 1, 1 }));
-    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32769, 1, 1, 1 }));
     test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {64, 64, 320, 1}));
     test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1}));
     test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {64, 64, 320, 1}));
@@ -8198,10 +8229,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 200, 64, 4, 4 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 384, 64, 4, 4 }));
 
-    for (bool v : {false, true}) {
+    for (int tfrm : {0, 1, 2}) {
         for (bool circular : {false, true}) {
-            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v, circular));
-            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v, circular));
+            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, tfrm, circular));
+            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, tfrm, circular));
         }
     }
 
@@ -8520,7 +8551,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
                     test_cases.emplace_back(new test_rope(type, { 80,  32, 512, 1},  20, GGML_ROPE_TYPE_NEOX, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // neox (stablelm)
                     test_cases.emplace_back(new test_rope(type, { 64,   8, 512, 1},  64, GGML_ROPE_TYPE_NEOX, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // neox (falcon 40B)
                     test_cases.emplace_back(new test_rope(type, {128,  12, 512, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,m-rope (qwen2vl 2B)
-                    test_cases.emplace_back(new test_rope(type, {128,  12, 2, 1}, 128, GGML_ROPE_TYPE_IMROPE,  512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,imrope (qwen3vl 2B)
+                    test_cases.emplace_back(new test_rope(type, {128,  12, 512, 1}, 128, GGML_ROPE_TYPE_IMROPE,  512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,imrope (qwen3vl 2B)
                     test_cases.emplace_back(new test_rope(type, { 80,  16, 2, 1},  80, GGML_ROPE_TYPE_VISION, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,m-rope (qwen2vl ViT)
                 }
             }