llama : fix device state save/load (#22805)

* tests : add long-seq + tail cases for gated_delta_net

* tests : realistic input ranges for gated_delta_net

.github/ISSUE_TEMPLATE/010-bug-compilation.yml

@@ -12,6 +12,8 @@ body:
         after recreating the CMake build directory and with `-DGGML_CCACHE=OFF`.
         If the compilation succeeds with ccache disabled you should be able to permanently fix the issue
         by clearing `~/.cache/ccache` (on Linux).
+
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
   - type: textarea
     id: commit
     attributes:

.github/ISSUE_TEMPLATE/011-bug-results.yml

@@ -1,5 +1,5 @@
 name: Bug (model use)
-description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module).
+description: Something goes wrong when running a model (crashes, garbled outputs, etc.).
 title: "Eval bug: "
 labels: ["bug-unconfirmed", "model evaluation"]
 body:
@@ -12,6 +12,8 @@ body:
         If you encountered the issue while using an external UI (e.g. ollama),
         please reproduce your issue using one of the examples/binaries in this repository.
         The `llama-completion` binary can be used for simple and reproducible model inference.
+
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
   - type: textarea
     id: version
     attributes:

.github/ISSUE_TEMPLATE/019-bug-misc.yml

@@ -10,6 +10,8 @@ body:
         This issue template is intended for miscellaneous bugs that don't fit into any other category.
         If you encountered the issue while using an external UI (e.g. ollama),
         please reproduce your issue using one of the examples/binaries in this repository.
+
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
   - type: textarea
     id: version
     attributes:

.github/ISSUE_TEMPLATE/020-enhancement.yml

@@ -8,6 +8,8 @@ body:
       value: |
         [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggml-org/llama.cpp/discussions/categories/ideas)
 
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
+
   - type: checkboxes
     id: prerequisites
     attributes:

.github/ISSUE_TEMPLATE/030-research.yml

@@ -8,6 +8,8 @@ body:
       value: |
         Don't forget to check for any [duplicate research issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
 
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
+
   - type: checkboxes
     id: research-stage
     attributes:

.github/ISSUE_TEMPLATE/040-refactor.yml

@@ -9,6 +9,8 @@ body:
         Don't forget to [check for existing refactor issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
         Also you may want to check [Pull request refactor label as well](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
 
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
+
   - type: textarea
     id: background-description
     attributes:

.github/workflows/gguf-publish.yml

@@ -29,10 +29,10 @@ jobs:
       uses: actions/setup-python@v6
       with:
         python-version: '3.11'
+        pip-install: poetry==2.4.0
     - name: Install dependencies
       run: |
         cd gguf-py
-        python -m pip install poetry==2.3.2
         poetry install
 
     - name: Build package

.github/workflows/python-type-check.yml

@@ -31,7 +31,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: "3.11"
-          pip-install: -r requirements/requirements-all.txt ty==0.0.26
+          pip-install: -r requirements/requirements-all.txt ty==0.0.33
       # - name: Type-check with Pyright
       #   uses: jakebailey/pyright-action@v2
       #   with:

.gitignore

@@ -105,6 +105,8 @@
 __pycache__/
 */poetry.lock
 poetry.toml
+poetry.lock
+uv.lock
 
 # Nix
 

.pi/gg/SYSTEM.md

@@ -4,6 +4,7 @@ General:
 - By very precise and concise when writing code, comments, explanations, etc.
 - PR and commit titles format: `<module> : <title>`. Lookup recents for examples
 - Don't try to build or run the code unless you are explicitly asked to do so
+- Use the `gh` CLI tool when querying PRs, issues, or other GitHub resources
 
 Coding:
 - When in doubt, always refer to the CONTRIBUTING.md file of the project

CODEOWNERS

@@ -76,6 +76,7 @@
 /ggml/src/ggml-vulkan/                  @ggml-org/ggml-vulkan
 /ggml/src/ggml-webgpu/                  @ggml-org/ggml-webgpu
 /ggml/src/ggml-zdnn/                    @ggml-org/ggml-zdnn @Andreas-Krebbel @AlekseiNikiforovIBM
+/ggml/src/ggml-zendnn/                  @avinashcpandey @Jiten1parmar @z-vishal
 /ggml/src/ggml.c                        @ggerganov
 /ggml/src/ggml.cpp                      @ggerganov
 /ggml/src/gguf.cpp                      @JohannesGaessler @Green-Sky

README.md

@@ -529,6 +529,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
 - [How to build](docs/build.md)
 - [Running on Docker](docs/docker.md)
 - [Build on Android](docs/android.md)
+- [Multi-GPU usage](docs/multi-gpu.md)
 - [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks)
 

common/arg.h

@@ -25,7 +25,8 @@ struct common_arg {
     const char * value_hint_2 = nullptr; // for second arg value
     const char * env          = nullptr;
     std::string help;
-    bool is_sparam = false; // is current arg a sampling param?
+    bool is_sampling = false; // is current arg a sampling param?
+    bool is_spec = false; // is current arg a speculative decoding param?
     bool is_preset_only = false; // is current arg preset-only (not treated as CLI arg)
     void (*handler_void)   (common_params & params) = nullptr;
     void (*handler_string) (common_params & params, const std::string &) = nullptr;
@@ -74,7 +75,8 @@ struct common_arg {
     common_arg & set_examples(std::initializer_list<enum llama_example> examples);
     common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
     common_arg & set_env(const char * env);
-    common_arg & set_sparam();
+    common_arg & set_sampling();
+    common_arg & set_spec();
     common_arg & set_preset_only();
     bool in_example(enum llama_example ex);
     bool is_exclude(enum llama_example ex);

common/chat-auto-parser-generator.cpp

@@ -136,10 +136,10 @@ common_peg_parser analyze_reasoning::build_parser(parser_build_context & ctx) co
         if (!end.empty()) {
             if (!start.empty()) {
                 // Standard tag-based: optional(<think>reasoning</think>)
-                return p.optional(start + p.reasoning(p.until(end)) + end + p.space());
+                return p.optional(p.optspace(start) + p.reasoning(p.until(trim_whitespace(end))) + p.optspace(end));
             }
             // Delimiter-style (empty start)
-            return p.optional(p.reasoning(p.until(end)) + end + p.space());
+            return p.optional(p.reasoning(p.until(trim_whitespace(end))) + p.optspace(end));
         }
     }
 
@@ -186,7 +186,6 @@ common_peg_parser analyze_tools::build_parser(parser_build_context & ctx) const
 common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
-    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
     // Build effective field names with dot notation if function_field is set
     std::string name_field = format.name_field;
@@ -225,8 +224,7 @@ common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_cont
         tool_start = format.per_call_start;
     }
 
-    return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(p.until(tool_start)))) + tools_parser +
-           p.end();
+    return ctx.reasoning_parser + p.optional(p.content(p.until(tool_start))) + tools_parser + p.end();
 }
 
 common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p, const std::string & name,
@@ -270,7 +268,6 @@ common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p,
 common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
-    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
     common_peg_parser tool_choice = p.choice();
 
@@ -336,14 +333,12 @@ common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context
 
     std::string trigger_marker       = !format.section_start.empty() ? format.section_start : format.per_call_start;
     auto        content_before_tools = trigger_marker.empty() ? p.eps() : p.until(trigger_marker);
-    return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) + tool_calls +
-           p.end();
+    return ctx.reasoning_parser + p.optional(p.content(content_before_tools)) + tool_calls + p.end();
 }
 
 common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
-    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
     auto until_suffix = p.rule("until-suffix", p.until(arguments.value_suffix));
 
@@ -471,8 +466,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
 
     std::string trigger_marker       = !format.section_start.empty() ? format.section_start : format.per_call_start;
     auto        content_before_tools = trigger_marker.empty() ? p.eps() : p.until(trigger_marker);
-    return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) + tool_calls +
-           p.end();
+    return ctx.reasoning_parser + p.optional(p.content(content_before_tools)) + tool_calls + p.end();
 }
 
 }  // namespace autoparser

common/chat-diff-analyzer.cpp

@@ -342,7 +342,7 @@ void analyze_reasoning::compare_thinking_enabled() {
     if (left_trimmed.empty() && !diff.right.empty()) {
         if (!right_trimmed.empty() && string_ends_with(comparison->output_B, right_trimmed)) {
             if (start.empty()) {
-                start = trim_leading_whitespace(diff.right);
+                start = diff.right;
                 mode  = reasoning_mode::TAG_BASED;
             }
         }
@@ -353,7 +353,7 @@ void analyze_reasoning::compare_thinking_enabled() {
                 if (seg.size() >= 2 && seg[seg.size() - 1].value == left_trimmed && seg[seg.size() - 2].type == segment_type::MARKER) {
                     start = seg[seg.size() - 2].value;
                 }
-                end = trim_trailing_whitespace(diff.left);
+                end = diff.left;
                 mode = reasoning_mode::TAG_BASED;
             }
         }
@@ -445,14 +445,14 @@ void analyze_reasoning::compare_reasoning_scope() {
         auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B);
         if (result.result.success()) {
             start = result.tags["pre"];
-            end = trim_trailing_whitespace(result.tags["post"]);
+            end = result.tags["post"];
         } else {
             auto parser_delimiter = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
                 return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space())));
             });
             result = parser_delimiter.parse_anywhere_and_extract(comparison->output_B);
             if (result.result.success()) {
-                end = trim_trailing_whitespace(result.tags["post"]);
+                end = result.tags["post"];
             } else {
                 LOG_DBG(ANSI_ORANGE "%s: Unable to extract reasoning markers, falling back to reasoning = NONE\n" ANSI_RESET, __func__);
                 mode = reasoning_mode::NONE;

common/chat-peg-parser.cpp

@@ -816,6 +816,32 @@ common_peg_parser common_chat_peg_builder::prefix(const std::string & s, const s
     return literal(s.substr(0, s.rfind(delimiter)));
 }
 
+common_peg_parser common_chat_peg_builder::optspace(const std::string & tag) {
+    auto parser = eps();
+    size_t end_of_prefix_space = tag.size();
+    size_t start_of_suffix_space = tag.size();
+    for (size_t i = 0; i < tag.size(); i++) {
+        if (!std::isspace(tag[i])) {
+            end_of_prefix_space = i;
+            break;
+        }
+    }
+    for (size_t i = tag.size(); i > 0; i--) {
+        if (!std::isspace(tag[i - 1])) {
+            start_of_suffix_space = i;
+            break;
+        }
+    }
+    for (size_t i = 0; i < end_of_prefix_space; i++) {
+        parser += optional(literal(std::string(1, tag[i])));
+    }
+    parser += literal(tag.substr(end_of_prefix_space, start_of_suffix_space - end_of_prefix_space));
+    for (size_t i = start_of_suffix_space; i < tag.size(); i++) {
+        parser += optional(literal(std::string(1, tag[i])));
+    }
+    return parser;
+}
+
 common_peg_parser common_chat_peg_builder::standard_json_tools(
                                                        const std::string &              section_start,
                                                        const std::string &              section_end,

common/chat-peg-parser.h

@@ -96,6 +96,9 @@ class common_chat_peg_builder : public common_peg_parser_builder {
     // Return a parser that parses the prefix of a string, up to a given delimiter.
     common_peg_parser prefix(const std::string & s, const std::string & delimiter = {});
 
+    // Return a parser that parses all elements of tag, but leading and trailing spaces are optional
+    common_peg_parser optspace(const std::string & tag);
+
     // Legacy-compatible helper for building standard JSON tool calls
     // Used by tests and manual parsers
     // name_key/args_key: JSON key names for function name and arguments

common/chat.cpp

@@ -80,7 +80,7 @@ json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const {
     if (!content.empty()) {
         jmsg["content"] = content;
     } else if (!content_parts.empty()) {
-        if (concat_typed_text) {
+        if (concat_typed_text || contains_media()) {
             std::string text;
             bool last_was_media_marker = false;
             // join parts with newline, do not add newline before or after media markers
@@ -2116,22 +2116,38 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
     return std::nullopt;
 }
 
+static std::string common_chat_templates_generation_prompt(const common_chat_template & tmpl, const autoparser::generation_params & inputs) {
+    autoparser::generation_params params = inputs;
+    params.add_generation_prompt = false;
+    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params);
+    params.add_generation_prompt = true;
+    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
+
+    size_t prefix_len = 0;
+    size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
+    while (prefix_len < min_size && no_gen_prompt[prefix_len] == gen_prompt[prefix_len]) {
+        prefix_len++;
+    }
+    return gen_prompt.substr(prefix_len);
+}
+
 static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates *        tmpls,
                                                             const struct common_chat_templates_inputs & inputs) {
     autoparser::generation_params params;
     params.tools = common_chat_tools_to_json_oaicompat(inputs.tools);
     const auto & tmpl =
         params.tools.is_array() && tmpls->template_tool_use ? *tmpls->template_tool_use : *tmpls->template_default;
-    const auto & src        = tmpl.source();
-    const auto & caps       = tmpl.original_caps();
-    params.messages         = render_message_to_json(inputs.messages, tmpl.original_caps());
-    params.tool_choice      = inputs.tool_choice;
-    params.reasoning_format = inputs.reasoning_format;
-    params.enable_thinking  = inputs.enable_thinking;
-    params.grammar          = inputs.grammar;
-    params.now              = inputs.now;
-    params.add_bos          = tmpls->add_bos;
-    params.add_eos          = tmpls->add_eos;
+    const auto & src             = tmpl.source();
+    const auto & caps            = tmpl.original_caps();
+    params.messages              = render_message_to_json(inputs.messages, tmpl.original_caps());
+    params.tool_choice           = inputs.tool_choice;
+    params.reasoning_format      = inputs.reasoning_format;
+    params.enable_thinking       = inputs.enable_thinking;
+    params.grammar               = inputs.grammar;
+    params.now                   = inputs.now;
+    params.add_generation_prompt = inputs.add_generation_prompt;
+    params.add_bos               = tmpls->add_bos;
+    params.add_eos               = tmpls->add_eos;
 
     if (src.find("<|channel|>") == std::string::npos) {
         // map developer to system for all models except for GPT-OSS
@@ -2153,14 +2169,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         workaround::func_args_not_string(params.messages);
     }
 
-    params.add_generation_prompt = false;
-    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params);
-    params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
-    auto        diff             = calculate_diff_split(no_gen_prompt, gen_prompt);
-    params.generation_prompt     = diff.right + diff.suffix;
-
-    params.add_generation_prompt = inputs.add_generation_prompt;
+    params.generation_prompt = common_chat_templates_generation_prompt(tmpl, params);
 
     params.extra_context = common_chat_extra_context();
     for (auto el : inputs.chat_template_kwargs) {
@@ -2212,8 +2221,8 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
         auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
         if (auto_params.supports_thinking) {
-            auto_params.thinking_start_tag = autoparser.reasoning.start;
-            auto_params.thinking_end_tag   = autoparser.reasoning.end;
+            auto_params.thinking_start_tag = trim_whitespace(autoparser.reasoning.start);
+            auto_params.thinking_end_tag   = trim_whitespace(autoparser.reasoning.end);
         }
         auto_params.generation_prompt = params.generation_prompt;
         common_peg_arena arena;

common/chat.h

@@ -94,6 +94,15 @@ struct common_chat_msg {
                tool_name.empty() && tool_call_id.empty();
     }
 
+    bool contains_media() const {
+        for (const auto & part : content_parts) {
+            if (part.type == "media_marker") {
+                return true;
+            }
+        }
+        return false;
+    }
+
     void set_tool_call_ids(std::vector<std::string> &           ids_cache,
                            const std::function<std::string()> & gen_tool_call_id) {
         for (auto i = 0u; i < tool_calls.size(); i++) {

common/common.cpp

@@ -70,7 +70,7 @@ common_time_meas::~common_time_meas() {
 // CPU utils
 //
 
-int32_t cpu_get_num_physical_cores() {
+int32_t common_cpu_get_num_physical_cores() {
 #ifdef __linux__
     // enumerate the set of thread siblings, num entries is num cores
     std::unordered_set<std::string> siblings;
@@ -185,11 +185,11 @@ static int cpu_count_math_cpus(int n_cpu) {
 /**
  * Returns number of CPUs on system that are useful for math.
  */
-int32_t cpu_get_num_math() {
+int32_t common_cpu_get_num_math() {
 #if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
     int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
     if (n_cpu < 1) {
-        return cpu_get_num_physical_cores();
+        return common_cpu_get_num_physical_cores();
     }
     if (is_hybrid_cpu()) {
         cpu_set_t affinity;
@@ -202,7 +202,7 @@ int32_t cpu_get_num_math() {
         }
     }
 #endif
-    return cpu_get_num_physical_cores();
+    return common_cpu_get_num_physical_cores();
 }
 
 // Helper for setting process priority
@@ -263,7 +263,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
 //
 
 
-void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
+void postprocess_cpu_params(common_cpu_params & cpuparams, const common_cpu_params * role_model) {
     int32_t n_set = 0;
 
     if (cpuparams.n_threads < 0) {
@@ -271,7 +271,7 @@ void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model)
         if (role_model != nullptr) {
             cpuparams = *role_model;
         } else {
-            cpuparams.n_threads = cpu_get_num_math();
+            cpuparams.n_threads = common_cpu_get_num_math();
         }
     }
 
@@ -1521,7 +1521,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     return cparams;
 }
 
-struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
+struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params) {
     struct ggml_threadpool_params tpp;
 
     ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults

common/common.h

@@ -54,7 +54,7 @@ struct common_control_vector_load_info;
 // CPU utils
 //
 
-struct cpu_params {
+struct common_cpu_params {
     int      n_threads                   = -1;
     bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
     bool     mask_valid                  = false;   // Default: any CPU
@@ -63,8 +63,8 @@ struct cpu_params {
     uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
 };
 
-int32_t cpu_get_num_physical_cores();
-int32_t cpu_get_num_math();
+int32_t common_cpu_get_num_physical_cores();
+int32_t common_cpu_get_num_math();
 
 //
 // Common params
@@ -297,34 +297,19 @@ struct common_params_model {
 
 struct common_ngram_mod;
 
-struct common_params_speculative {
-    common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding
+// draft-model-based speculative decoding parameters
+struct common_params_speculative_draft {
+    int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
+    int32_t n_min = 0;  // minimum number of draft tokens to use for speculative decoding
 
-    // general-purpose speculative decoding parameters
+    float p_split = 0.1f;  // speculative decoding split probability
+    float p_min   = 0.75f; // minimum speculative decoding probability (greedy)
 
-    int32_t n_max   = 16; // maximum number of tokens to draft during speculative decoding
-    int32_t n_min   = 0;  // minimum number of draft tokens to use for speculative decoding
-    float   p_split = 0.1f; // speculative decoding split probability
-    float   p_min   = 0.75f; // minimum speculative decoding probability (greedy)
+    common_params_model mparams;
 
-    // ngram-based speculative decoding
+    llama_model * model = nullptr; // a llama_model that can be shared by multiple speculative contexts
 
-    uint16_t ngram_size_n   = 12; // ngram size for lookup
-    uint16_t ngram_size_m   = 48; // mgram size for speculative tokens
-    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;
-
-    std::string lookup_cache_static;  // path of static ngram cache file for lookup decoding           // NOLINT
-    std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding          // NOLINT
-
-    // draft-model speculative decoding
-
-    struct common_params_model mparams_dft;
-
-    llama_model * model_dft = nullptr; // a llama_model that can be shared by multiple speculative contexts
-
-    llama_context_params cparams_dft; // these are the parameters for the draft llama_context
+    llama_context_params cparams; // these are the parameters for the draft llama_context
 
     int32_t n_ctx        = 0;  // draft context size
     int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
@@ -332,25 +317,60 @@ struct common_params_speculative {
     ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
     ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
 
-    struct cpu_params cpuparams;
-    struct cpu_params cpuparams_batch;
+    common_cpu_params cpuparams;
+    common_cpu_params cpuparams_batch;
 
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
 
     std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
     std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+};
+
+struct common_params_speculative_ngram_mod {
+    int32_t n_match = 24;
+
+    int32_t n_max = 64;
+    int32_t n_min = 48;
+
+    // shared instance of the ngram container for all speculative decoding contexts
+    std::shared_ptr<common_ngram_mod> obj;
+};
+
+struct common_params_speculative_ngram_map {
+    uint16_t size_n   = 12; // ngram size for lookup
+    uint16_t size_m   = 48; // mgram size for speculative tokens
+    uint16_t min_hits = 1;  // minimum hits at ngram/mgram lookup for mgram to be proposed
+};
+
+struct common_params_speculative_ngram_cache {
+    std::string lookup_cache_static;  // path of static ngram cache file for lookup decoding
+    std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding
+};
+
+struct common_params_speculative {
+    // TODO: become a vector in order to support "chains of speculators"
+    common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE;
+
+    common_params_speculative_draft draft;
+
+    common_params_speculative_ngram_mod ngram_mod;
+    common_params_speculative_ngram_map ngram_simple;
+    common_params_speculative_ngram_map ngram_map_k;
+    common_params_speculative_ngram_map ngram_map_k4v;
+
+    common_params_speculative_ngram_cache ngram_cache;
 
     bool has_dft() const {
-        return !mparams_dft.path.empty() || !mparams_dft.hf_repo.empty();
+        return !draft.mparams.path.empty() || !draft.mparams.hf_repo.empty();
     }
 };
 
 struct common_params_vocoder {
     struct common_params_model model;
 
-    std::string speaker_file = ""; // speaker file path                                      // NOLINT
+    std::string speaker_file; // speaker file path
 
-    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy            // NOLINT
+    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy
 };
 
 struct common_params_diffusion {
@@ -433,8 +453,8 @@ struct common_params {
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
-    struct cpu_params cpuparams;
-    struct cpu_params cpuparams_batch;
+    common_cpu_params cpuparams;
+    common_cpu_params cpuparams_batch;
 
     ggml_backend_sched_eval_callback cb_eval = nullptr;
     void * cb_eval_user_data                 = nullptr;
@@ -678,7 +698,7 @@ std::string common_params_get_system_info(const common_params & params);
 
 bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
 bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
-void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
+void postprocess_cpu_params(common_cpu_params & cpuparams, const common_cpu_params * role_model = nullptr);
 bool set_process_priority(enum ggml_sched_priority prio);
 
 //
@@ -846,7 +866,7 @@ common_init_result_ptr common_init_from_params(common_params & params);
 
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
-struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
+struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params);
 
 // clear LoRA adapters from context, then apply new list of adapters
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);

common/fit.cpp

@@ -109,16 +109,24 @@ static std::vector<llama_device_memory_data> common_get_device_memory_data(
         ret.back().total = total;
     }
     for (size_t i = 0; i < nd; i++) {
+        ggml_backend_dev_t dev = llama_model_get_device(model, i);
+
         size_t free;
         size_t total;
-        ggml_backend_dev_memory(llama_model_get_device(model, i), &free, &total);
+        ggml_backend_dev_memory(dev, &free, &total);
 
-        // devices can return 0 bytes for free and total memory if they do not
-        // have any to report. in this case, we will use the host memory as a fallback
-        // fixes: https://github.com/ggml-org/llama.cpp/issues/18577
+        // Some non-GPU accelerator backends, such as BLAS, report 0/0 and rely on
+        // the host-memory fallback. For GPU-like backends, keep 0/0 so --fit does
+        // not assign anything to a device with an unknown memory budget.
         if (free == 0 && total == 0) {
-            free  = ret.back().free;
-            total = ret.back().total;
+            const enum ggml_backend_dev_type type = ggml_backend_dev_type(dev);
+            if (type == GGML_BACKEND_DEVICE_TYPE_GPU || type == GGML_BACKEND_DEVICE_TYPE_IGPU) {
+                LOG_WRN("%s: device %s did not report memory; --fit will not use it\n",
+                        __func__, ggml_backend_dev_name(dev));
+            } else {
+                free  = ret.back().free;
+                total = ret.back().total;
+            }
         }
         ret[i].free  = free;
         ret[i].total = total;

common/hf-cache.cpp

@@ -57,7 +57,7 @@ static fs::path get_cache_directory() {
 #ifndef _WIN32
         const struct passwd * pw = getpwuid(getuid());
 
-        if (pw->pw_dir && *pw->pw_dir) {
+        if (pw && pw->pw_dir && *pw->pw_dir) {
             return fs::path(pw->pw_dir) / ".cache" / "huggingface" / "hub";
         }
 #endif

common/log.cpp

@@ -49,7 +49,7 @@ enum common_log_col : int {
 };
 
 // disable colors by default
-static std::vector<const char *> g_col = {
+static const char* g_col[] = {
     "",
     "",
     "",
@@ -247,7 +247,6 @@ public:
 
             entries = std::move(new_entries);
         }
-
         cv.notify_one();
     }
 
@@ -265,7 +264,6 @@ public:
                 {
                     std::unique_lock<std::mutex> lock(mtx);
                     cv.wait(lock, [this]() { return head != tail; });
-
                     cur = entries[head];
 
                     head = (head + 1) % entries.size();
@@ -301,7 +299,6 @@ public:
 
                 tail = (tail + 1) % entries.size();
             }
-
             cv.notify_one();
         }
 
@@ -338,7 +335,7 @@ public:
             g_col[COMMON_LOG_COL_CYAN]    = LOG_COL_CYAN;
             g_col[COMMON_LOG_COL_WHITE]   = LOG_COL_WHITE;
         } else {
-            for (size_t i = 0; i < g_col.size(); i++) {
+            for (size_t i = 0; i < std::size(g_col); i++) {
                 g_col[i] = "";
             }
         }
@@ -368,14 +365,20 @@ struct common_log * common_log_init() {
 }
 
 struct common_log * common_log_main() {
-    static struct common_log log;
+    // We intentionally leak (i.e. do not delete) the logger singleton because
+    // common_log destructor called at DLL teardown phase will cause hanging on Windows.
+    // OS will release resources anyway so it should not be a significant issue,
+    // though this design may cause logs to be lost if not flushed before the program exits.
+    // Refer to https://github.com/ggml-org/llama.cpp/issues/22142 for details.
+    static struct common_log * log;
     static std::once_flag    init_flag;
     std::call_once(init_flag, [&]() {
+        log = new common_log;
         // Set default to auto-detect colors
-        log.set_colors(tty_can_use_colors());
+        log->set_colors(tty_can_use_colors());
     });
 
-    return &log;
+    return log;
 }
 
 void common_log_pause(struct common_log * log) {

common/log.h

@@ -49,7 +49,11 @@ void common_log_default_callback(enum ggml_log_level level, const char * text, v
 struct common_log;
 
 struct common_log * common_log_init();
-struct common_log * common_log_main(); // singleton, automatically destroys itself on exit
+
+// Singleton, intentionally leaked to avoid Windows teardown hangs.
+// Call common_log_flush() before exit if you want to ensure all logs are flushed.
+struct common_log * common_log_main();
+
 void                common_log_pause (struct common_log * log); // pause  the worker thread, not thread-safe
 void                common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe
 void                common_log_free  (struct common_log * log);

common/preset.cpp

@@ -43,7 +43,7 @@ static std::set<std::string> get_remote_preset_whitelist(const std::map<std::str
     for (const auto & it : key_to_opt) {
         const std::string & key = it.first;
         const common_arg & opt = it.second;
-        if (allowed_options.find(key) != allowed_options.end() || opt.is_sparam) {
+        if (allowed_options.find(key) != allowed_options.end() || opt.is_sampling) {
             allowed_keys.insert(key);
             // also add variant keys (args without leading dashes and env vars)
             for (const auto & arg : opt.get_args()) {

common/reasoning-budget.cpp

@@ -122,6 +122,20 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
             }
             break;
         case REASONING_BUDGET_DONE:
+            // Re-arm on a new start tag: some models emit multiple <think> blocks
+            // per response, and each should get a fresh budget window.
+            if (ctx->start_matcher.advance(token)) {
+                ctx->state = REASONING_BUDGET_COUNTING;
+                ctx->remaining = ctx->budget;
+                ctx->end_matcher.reset();
+                LOG_INF("reasoning-budget: re-activated on new start tag, budget=%d tokens\n", ctx->budget);
+
+                if (ctx->remaining <= 0) {
+                    ctx->state = REASONING_BUDGET_FORCING;
+                    ctx->force_pos = 0;
+                    LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
+                }
+            }
             break;
     }
 }
@@ -144,6 +158,8 @@ static void common_reasoning_budget_apply(struct llama_sampler * smpl, llama_tok
     for (size_t i = 0; i < cur_p->size; i++) {
         if (cur_p->data[i].id != forced) {
             cur_p->data[i].logit = -INFINITY;
+        } else {
+            cur_p->data[i].logit = +INFINITY; // force the token
         }
     }
 }
@@ -218,34 +234,6 @@ static struct llama_sampler * common_reasoning_budget_init_state(
     );
 }
 
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        const std::vector<llama_token> & prefill_tokens) {
-    // Determine initial state from prefill: COUNTING if the prefill begins with
-    // the start sequence but does not also contain the end sequence after it.
-    common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE;
-    if (!prefill_tokens.empty() && !start_tokens.empty() &&
-            prefill_tokens.size() >= start_tokens.size() &&
-            std::equal(start_tokens.begin(), start_tokens.end(), prefill_tokens.begin())) {
-        initial_state = REASONING_BUDGET_COUNTING;
-        // If the end sequence also follows the start in the prefill, reasoning
-        // was opened and immediately closed — stay IDLE.
-        if (!end_tokens.empty() &&
-                prefill_tokens.size() >= start_tokens.size() + end_tokens.size()) {
-            auto end_start = prefill_tokens.end() - (ptrdiff_t) end_tokens.size();
-            if (end_start >= prefill_tokens.begin() + (ptrdiff_t) start_tokens.size() &&
-                    std::equal(end_tokens.begin(), end_tokens.end(), end_start)) {
-                initial_state = REASONING_BUDGET_IDLE;
-            }
-        }
-    }
-    return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
-}
-
 struct llama_sampler * common_reasoning_budget_init(
         const struct llama_vocab       * vocab,
         const std::vector<llama_token> & start_tokens,

common/reasoning-budget.h

@@ -29,10 +29,7 @@ enum common_reasoning_budget_state {
 //   end_tokens     - token sequence for natural deactivation
 //   forced_tokens  - token sequence forced when budget expires
 //   budget         - max tokens allowed in the reasoning block
-//   prefill_tokens - tokens already present in the prompt (generation prompt);
-//                    used to determine the initial state: COUNTING if they begin
-//                    with start_tokens (but don't also end with end_tokens),
-//                    IDLE otherwise. COUNTING with budget <= 0 is promoted to FORCING.
+//   initial_state  - initial state
 //
 struct llama_sampler * common_reasoning_budget_init(
         const struct llama_vocab       * vocab,
@@ -40,16 +37,6 @@ struct llama_sampler * common_reasoning_budget_init(
         const std::vector<llama_token> & end_tokens,
         const std::vector<llama_token> & forced_tokens,
         int32_t                          budget,
-        const std::vector<llama_token> & prefill_tokens = {});
-
-// Variant that takes an explicit initial state (used by tests and clone).
-// COUNTING with budget <= 0 is promoted to FORCING.
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        common_reasoning_budget_state    initial_state);
+        common_reasoning_budget_state    initial_state = REASONING_BUDGET_IDLE);
 
 common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl);

common/sampling.cpp

@@ -260,32 +260,35 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
         }
     }
 
+    // Compute prefill tokens from the generation prompt
+    std::vector<llama_token> prefill_tokens;
+    if (!params.generation_prompt.empty()) {
+        GGML_ASSERT(vocab != nullptr);
+        auto tokens = common_tokenize(vocab, params.generation_prompt, false, true);
+        for (size_t i = 0; i < tokens.size(); i++) {
+            std::string piece = common_token_to_piece(vocab, tokens[i], true);
+            if (i == 0 && std::isspace(piece[0]) && !std::isspace(params.generation_prompt[0])) {
+                // Some tokenizers will add a space before the first special token, need to exclude
+                continue;
+            }
+            LOG_DBG("%s: prefill token: %d = %s\n", __func__, tokens[i], piece.c_str());
+            prefill_tokens.push_back(tokens[i]);
+        }
+    }
+
     // Feed generation prompt tokens to the grammar sampler so it advances past
     // tokens the template already placed in the prompt.
     // Only applies to output-format and tool-call grammars; user-supplied grammars must not be prefilled.
-    std::vector<llama_token> prefill_tokens;
-    if (!params.generation_prompt.empty() && common_grammar_needs_prefill(params.grammar)) {
-        GGML_ASSERT(vocab != nullptr);
-        prefill_tokens = common_tokenize(vocab, params.generation_prompt, false, true);
-        if (!prefill_tokens.empty()) {
-            std::string first_token = common_token_to_piece(vocab, prefill_tokens[0], true);
-            if (std::isspace(first_token[0]) && !std::isspace(params.generation_prompt[0])) {
-                // Some tokenizers will add a space before the first special token, need to remove
-                prefill_tokens = std::vector<llama_token>(prefill_tokens.begin() + 1, prefill_tokens.end());
-            }
-        }
-
-        if (grmr && !params.grammar_lazy) {
-            try {
-                for (const auto & token : prefill_tokens) {
-                    llama_sampler_accept(grmr, token);
-                    LOG_DBG("%s: accepted prefill token (%d)\n", __func__, token);
-                }
-            } catch (std::exception &e) {
-                LOG_ERR("%s: error initializing grammar sampler for grammar:\n%s\n\nGeneration prompt:\n'%s'\n", __func__,
-                    common_grammar_value(params.grammar).c_str(), params.generation_prompt.c_str());
-                throw e;
+    if (grmr && !params.grammar_lazy && common_grammar_needs_prefill(params.grammar)) {
+        try {
+            for (const auto & token : prefill_tokens) {
+                llama_sampler_accept(grmr, token);
+                LOG_DBG("%s: grammar accepted prefill token (%d)\n", __func__, token);
             }
+        } catch (std::exception &e) {
+            LOG_ERR("%s: error initializing grammar sampler for grammar:\n%s\n\nGeneration prompt:\n'%s'\n", __func__,
+                common_grammar_value(params.grammar).c_str(), params.generation_prompt.c_str());
+            throw e;
         }
     }
 
@@ -296,8 +299,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
             params.reasoning_budget_start,
             params.reasoning_budget_end,
             params.reasoning_budget_forced,
-            params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens,
-            prefill_tokens);
+            params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens);
+
+        for (const auto & token : prefill_tokens) {
+            llama_sampler_accept(rbudget, token);
+            LOG_DBG("%s: reasoning-budget accepted prefill token (%d)\n", __func__, token);
+        }
     }
 
     if (params.has_logit_bias()) {
@@ -431,7 +438,7 @@ static bool grammar_should_apply(struct common_sampler * gsmpl) {
     return true;
 }
 
-void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
+void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated) {
     if (!gsmpl) {
         return;
     }
@@ -439,9 +446,11 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo
     const auto tm = gsmpl->tm();
 
     // grammar_should_apply() checks the reasoning budget state, so calculate this before we accept
-    accept_grammar = accept_grammar && grammar_should_apply(gsmpl);
+    const auto accept_grammar = is_generated && grammar_should_apply(gsmpl);
 
-    llama_sampler_accept(gsmpl->rbudget, token);
+    if (gsmpl->rbudget && is_generated) {
+        llama_sampler_accept(gsmpl->rbudget, token);
+    }
 
     if (gsmpl->grmr && accept_grammar) {
         llama_sampler_accept(gsmpl->grmr, token);

common/sampling.h

@@ -41,8 +41,8 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 
 void common_sampler_free(struct common_sampler * gsmpl);
 
-// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
-void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
+// if is_generated is true, the token is accepted by the sampling chain, the reasoning budget sampler, and the grammar sampler
+void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated);
 void                    common_sampler_reset (struct common_sampler * gsmpl);
 struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 

common/speculative.cpp

@@ -151,6 +151,9 @@ struct common_speculative_state {
             llama_tokens & result) = 0;
 
     virtual void accept(uint16_t n_accepted) = 0;
+
+    virtual int32_t n_max(const common_params_speculative & params) const = 0;
+    virtual int32_t n_min(const common_params_speculative & params) const = 0;
 };
 
 struct common_speculative_checkpoint {
@@ -164,8 +167,6 @@ struct common_speculative_checkpoint {
     size_t size() const {
         return data.size();
     }
-
-    size_t ckpt_size   = 0;
 };
 
 struct common_speculative_state_draft : public common_speculative_state {
@@ -173,7 +174,7 @@ struct common_speculative_state_draft : public common_speculative_state {
     llama_context * ctx_dft;
 
     bool use_ckpt = false;
-    struct common_speculative_checkpoint ckpt;
+    common_speculative_checkpoint ckpt;
 
     common_sampler * smpl;
 
@@ -246,29 +247,19 @@ struct common_speculative_state_draft : public common_speculative_state {
         llama_batch_free(batch);
     }
 
-    void begin(const llama_tokens & prompt) override {
-        if (use_ckpt && ckpt.size() > 0) {
-            // delete checkpoint
-            LOG_DBG("%s: delete checkpoint, prompt.size=%zu, pos_min=%d, pos_max=%d, n_tokens=%" PRId64 ", size=%.3f MiB\n",
-                    __func__, prompt.size(), ckpt.pos_min, ckpt.pos_max, ckpt.n_tokens, (float) ckpt.data.size() / 1024 / 1024);
-            ckpt.pos_min   = 0;
-            ckpt.pos_max   = 0;
-            ckpt.n_tokens  = 0;
-            ckpt.ckpt_size = 0;
-            ckpt.data.clear();
-        }
+    void begin(const llama_tokens & /*prompt*/) override {
     }
 
-    size_t draft_create_checkpoint(int n_tokens_prompt, int n_tokens_batch) {
+    size_t create_checkpoint(int n_tokens_prompt) {
         int slot_id = 0;
-        const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx_dft, slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+        const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx_dft, slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
 
         ckpt.pos_min  = llama_memory_seq_pos_min(llama_get_memory(ctx_dft), slot_id);
         ckpt.pos_max  = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), slot_id);
-        ckpt.n_tokens = n_tokens_prompt - n_tokens_batch;
+        ckpt.n_tokens = n_tokens_prompt;
         ckpt.data.resize(checkpoint_size);
 
-        const size_t n = llama_state_seq_get_data_ext(ctx_dft, ckpt.data.data(), checkpoint_size, slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+        const size_t n = llama_state_seq_get_data_ext(ctx_dft, ckpt.data.data(), checkpoint_size, slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
         if (n != checkpoint_size) {
             GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", checkpoint_size, n);
         }
@@ -278,13 +269,13 @@ struct common_speculative_state_draft : public common_speculative_state {
         return n;
     }
 
-    size_t draft_restore_checkpoint(size_t ckpt_size_part_expected) {
+    size_t restore_checkpoint() {
         int slot_id = 0;
         LOG_DBG("%s: pos_min = %d, pos_max = %d\n", __func__, ckpt.pos_min, ckpt.pos_max);
-        const size_t n = llama_state_seq_set_data_ext(ctx_dft, ckpt.data.data(), ckpt.size(), slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-        if (n != ckpt_size_part_expected) {
-            GGML_ABORT("%s: failed to restore context checkpoint (pos_min=%d, pos_max=%d, size=%zu, get_data_ext->%zu, set_data_ext->%zu",
-                        __func__, ckpt.pos_min, ckpt.pos_max, ckpt.size(), ckpt_size_part_expected, n);
+        const size_t n = llama_state_seq_set_data_ext(ctx_dft, ckpt.data.data(), ckpt.size(), slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+        if (n != ckpt.size()) {
+            GGML_ABORT("%s: failed to restore context checkpoint (pos_min=%d, pos_max=%d, size=%zu",
+                        __func__, ckpt.pos_min, ckpt.pos_max, ckpt.size());
         }
         llama_memory_seq_rm(llama_get_memory(ctx_dft), slot_id, ckpt.pos_max + 1, -1);
 
@@ -296,6 +287,8 @@ struct common_speculative_state_draft : public common_speculative_state {
             const llama_tokens & prompt_tgt,
             llama_token id_last,
             llama_tokens & result) override {
+        const auto & sparams = params.draft;
+
         auto * spec = this;
 
         auto & batch      = spec->batch;
@@ -309,7 +302,7 @@ struct common_speculative_state_draft : public common_speculative_state {
         int reuse_i = 0; // index of part to be reused in prompt_dft
         int reuse_n = 0; // length of part to be reused in prompt_dft
 
-        const int n_ctx = llama_n_ctx(ctx_dft) - params.n_max;
+        const int n_ctx = llama_n_ctx(ctx_dft) - sparams.n_max;
 
         llama_tokens prompt_cnv;
         if (!spec->vocab_cmpt) {
@@ -341,13 +334,18 @@ struct common_speculative_state_draft : public common_speculative_state {
 
         const int i_start = std::max<int>(0, (int) prompt_cur.size() - n_ctx);
 
+        if (use_ckpt && i_start > 0) {
+            LOG_WRN("%s: context shift is not supported with checkpoint-based contexts - skipping\n", __func__);
+            return;
+        }
+
         // reuse as much as possible from the old draft context
         // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
         for (int i = 0; i < (int) prompt_dft.size(); ++i) {
             int cur = 0;
             while (i_start + cur < (int) prompt_cur.size() &&
-                    i       + cur < (int) prompt_dft.size() &&
-                    prompt_cur[i_start + cur] == prompt_dft[i + cur]) {
+                   i       + cur < (int) prompt_dft.size() &&
+                   prompt_cur[i_start + cur] == prompt_dft[i + cur]) {
                 cur++;
             }
 
@@ -355,21 +353,26 @@ struct common_speculative_state_draft : public common_speculative_state {
                 reuse_i = i;
                 reuse_n = cur;
             }
+
+            if (use_ckpt) {
+                break;
+            }
         }
 
         LOG_DBG("%s: reuse_i = %d, reuse_n = %d, #prompt_dft = %zu, #prompt_cur = %zu\n",
                 __func__, reuse_i, reuse_n, prompt_dft.size(), prompt_cur.size());
-        if (use_ckpt && ckpt.ckpt_size == 0 && reuse_n > 0) {
-            LOG_DBG("%s: no checkpoint available, no reuse, (reuse_i=%d, reuse_n=%d) -> (0, 0)\n",
-                    __func__, reuse_i, reuse_n);
+        if (use_ckpt && ckpt.n_tokens > reuse_n) {
+            LOG_DBG("%s: checkpoint (n_tokens = %d) is outdated -> delete it\n", __func__, (int) ckpt.n_tokens);
+
             reuse_i = 0;
             reuse_n = 0;
+
+            ckpt = {};
         }
 
         result.clear();
-        result.reserve(params.n_max);
+        result.reserve(sparams.n_max);
 
-        bool needs_ckpt = use_ckpt && prompt_dft.size() > 0;
         if (reuse_n == 0 || (use_ckpt && reuse_i > 0)) {
             llama_memory_clear(mem_dft, false);
             prompt_dft.clear();
@@ -380,7 +383,7 @@ struct common_speculative_state_draft : public common_speculative_state {
                 for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
                     result.push_back(prompt_dft[i]);
 
-                    if (params.n_max <= (int) result.size()) {
+                    if (sparams.n_max <= (int) result.size()) {
                         break;
                     }
                 }
@@ -388,50 +391,38 @@ struct common_speculative_state_draft : public common_speculative_state {
                 return;
             }
 
-            bool do_restore = false;
-            if (prompt_dft.size() > prompt_cur.size() && reuse_i + reuse_n < (int64_t) prompt_dft.size()) {
-                // This can happen after a partial acceptance (speculative decoding with checkpoints)
-                LOG_DBG("%s: #prompt_dft=%zu, #prompt_cur=%zu, shorten draft\n",
-                        __func__, prompt_dft.size(), prompt_cur.size());
-                prompt_dft.resize(prompt_cur.size());
-                do_restore = true;
-            }
-
             if (reuse_i > 0) {
+                GGML_ASSERT(!use_ckpt);
+
                 bool is_removed = llama_memory_seq_rm (mem_dft, 0, 0, reuse_i);
                 if (!is_removed) {
                     LOG_ERR("%s: llama_memory_seq_rm failed, reuse_i=%d\n", __func__, reuse_i);
+                    return;
                 }
                 llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i);
 
                 prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
             }
 
-            if (reuse_n < (int) prompt_dft.size() || do_restore) {
+            if (reuse_n < (int) prompt_dft.size()) {
                 if (use_ckpt) {
-                    if (ckpt.n_tokens > (int64_t) prompt_dft.size()) {
-                        LOG_INF("%s: checkpoint is too large, prompt_tgt.size=%zu, ckpt.n_tokens=%" PRId64 ", reuse_n=%d, prompt_dft.size=%zu\n",
-                                __func__, prompt_tgt.size(), ckpt.n_tokens, reuse_n, prompt_dft.size());
+                    if (ckpt.n_tokens > 0) {
+                        LOG_DBG("%s: restoring checkpoint, reuse_n=%d, prompt_dft.size=%zu\n", __func__, reuse_n, prompt_dft.size());
+                        restore_checkpoint();
+                        reuse_n = ckpt.n_tokens;
+                        prompt_dft.resize(reuse_n);
                     }
-                    draft_restore_checkpoint(ckpt.ckpt_size);
-                    reuse_n = ckpt.n_tokens;
-                    prompt_dft.resize(reuse_n);
-                    needs_ckpt = false;
                 } else {
-                    bool is_removed = llama_memory_seq_rm (mem_dft, 0, reuse_n, -1);
+                    const bool is_removed = llama_memory_seq_rm(mem_dft, 0, reuse_n, -1);
                     if (!is_removed) {
-                        LOG_ERR("%s: llama_memory_seq_rm failed, reuse_n=%d, prompt_dft.size=%zu\n",
-                                __func__, reuse_n, prompt_dft.size());
+                        LOG_ERR("%s: llama_memory_seq_rm failed, reuse_n=%d, prompt_dft.size=%zu\n", __func__, reuse_n, prompt_dft.size());
+                        return;
                     }
                     prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
                 }
             }
         }
 
-        if (needs_ckpt) {
-            ckpt.ckpt_size = draft_create_checkpoint(prompt_dft.size(), batch.n_tokens);
-        }
-
         // prepare a batch to evaluate any new tokens in the prompt
         common_batch_clear(batch);
 
@@ -445,12 +436,17 @@ struct common_speculative_state_draft : public common_speculative_state {
         // we should rarely end-up here during normal decoding
         if (batch.n_tokens > 0) {
             //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
+            LOG_DBG("%s: draft prompt batch: %d tokens\n", __func__, batch.n_tokens);
 
             int ret = llama_decode(ctx_dft, batch);
             if (ret != 0 && ret != 1) {
                 LOG_WRN("%s: llama_decode returned %d, prompt_cur.size=%zu\n",
                         __func__, ret, prompt_cur.size());
             }
+
+            if (use_ckpt) {
+                create_checkpoint(prompt_dft.size());
+            }
         }
 
         const llama_pos n_past = prompt_dft.size();
@@ -462,7 +458,7 @@ struct common_speculative_state_draft : public common_speculative_state {
 
         prompt_dft.push_back(id_last);
 
-        LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
+        //LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
 
         int ret = llama_decode(ctx_dft, batch);
         if (ret != 0 && ret != 1) {
@@ -473,7 +469,7 @@ struct common_speculative_state_draft : public common_speculative_state {
         common_sampler_reset(smpl);
 
         // sample n_draft tokens from the draft model
-        for (int i = 0; i < params.n_max; ++i) {
+        for (int i = 0; i < sparams.n_max; ++i) {
             common_batch_clear(batch);
 
             common_sampler_sample(smpl, ctx_dft, 0, true);
@@ -490,14 +486,14 @@ struct common_speculative_state_draft : public common_speculative_state {
 
             common_sampler_accept(smpl, id, true);
 
-            result.push_back(id);
-
-            if (params.n_max <= (int) result.size()) {
+            // only collect very high-confidence draft tokens
+            if (cur_p->data[0].p < sparams.p_min) {
                 break;
             }
 
-            // only collect very high-confidence draft tokens
-            if (cur_p->data[0].p < params.p_min) {
+            result.push_back(id);
+
+            if (sparams.n_max <= (int) result.size()) {
                 break;
             }
 
@@ -518,10 +514,14 @@ struct common_speculative_state_draft : public common_speculative_state {
             detokenized = replace_to_tgt(detokenized);
             LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str());
             result = common_tokenize(ctx_tgt, detokenized, false, true);
-            if (result.size() > (size_t)params.n_max) {
-                result.resize(params.n_max);
+            if (result.size() > (size_t) sparams.n_max) {
+                result.resize(sparams.n_max);
             }
         }
+
+        if (result.size() < (size_t) sparams.n_min) {
+            result.clear();
+        }
     }
 
     void accept(uint16_t n_accepted) override {
@@ -529,6 +529,14 @@ struct common_speculative_state_draft : public common_speculative_state {
         GGML_UNUSED(n_accepted);
     }
 
+    int32_t n_max(const common_params_speculative & params) const override {
+        return params.draft.n_max;
+    }
+
+    int32_t n_min(const common_params_speculative & params) const override {
+        return params.draft.n_min;
+    }
+
     std::string replace_to_dft(const std::string & input) const {
         std::string result = input;
 
@@ -581,6 +589,14 @@ struct common_speculative_state_eagle3 : public common_speculative_state {
         // noop
         GGML_UNUSED(n_accepted);
     }
+
+    int32_t n_max(const common_params_speculative & params) const override {
+        return params.draft.n_max;
+    }
+
+    int32_t n_min(const common_params_speculative & params) const override {
+        return params.draft.n_min;
+    }
 };
 
 // state of self-speculation (simple implementation, not ngram-map)
@@ -610,19 +626,27 @@ struct common_speculative_state_ngram_simple : public common_speculative_state {
         // noop
         GGML_UNUSED(n_accepted);
     }
+
+    int32_t n_max(const common_params_speculative & /*params*/) const override {
+        return config.size_mgram;
+    }
+
+    int32_t n_min(const common_params_speculative & /*params*/) const override {
+        return config.size_mgram;
+    }
 };
 
 struct common_speculative_state_ngram_map_k : public common_speculative_state {
     // draft ngram map for speculative decoding without draft model
-    common_ngram_map map;
+    common_ngram_map config;
 
     common_speculative_state_ngram_map_k(
             enum common_speculative_type type,
-            common_ngram_map map)
-        : common_speculative_state(type), map(std::move(map)) {}
+            common_ngram_map config)
+        : common_speculative_state(type), config(std::move(config)) {}
 
     void begin(const llama_tokens & prompt) override {
-        common_ngram_map_begin(map, prompt);
+        common_ngram_map_begin(config, prompt);
     }
 
     void draft(
@@ -630,12 +654,20 @@ struct common_speculative_state_ngram_map_k : public common_speculative_state {
             const llama_tokens & prompt_tgt,
             llama_token id_last,
             llama_tokens & result) override {
-        common_ngram_map_draft(map, prompt_tgt, id_last, result);
+        common_ngram_map_draft(config, prompt_tgt, id_last, result);
         GGML_UNUSED(params);
     }
 
     void accept(uint16_t n_accepted) override {
-        common_ngram_map_accept(map, n_accepted);
+        common_ngram_map_accept(config, n_accepted);
+    }
+
+    int32_t n_max(const common_params_speculative & /*params*/) const override {
+        return config.size_value;
+    }
+
+    int32_t n_min(const common_params_speculative & /*params*/) const override {
+        return config.size_value;
     }
 };
 
@@ -692,7 +724,7 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
             const llama_tokens & prompt_tgt,
             llama_token id_last,
             llama_tokens & result) override {
-        GGML_UNUSED(params);
+        const auto & sparams = params.ngram_mod;
 
         n_draft_last = 0;
 
@@ -712,16 +744,16 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
             i_last = cur_len - n;
         }
 
-        result.resize(n + params.n_max);
+        result.resize(n + sparams.n_max);
         for (size_t i = 0; i < n - 1; ++i) {
             result[i] = prompt_tgt[cur_len - n + 1 + i];
         }
         result[n - 1] = id_last;
 
-        for (int i = 0; i < params.n_max; ++i) {
+        for (int i = 0; i < sparams.n_max; ++i) {
             const llama_token token = mod.get(result.data() + i);
             if (token == common_ngram_mod::EMPTY) {
-                if (i < params.n_min) {
+                if (i < sparams.n_min) {
                     result.clear();
                     return;
                 }
@@ -743,17 +775,15 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
     }
 
     void accept(uint16_t n_accepted) override {
-        if (verbose) {
-            LOG_INF("%s: accepted %d tokens from %zu drafted tokens\n", __func__, n_accepted, n_draft_last);
-        }
-
         // compute acceptance fraction if we have a recorded draft length
         if (n_draft_last > 0) {
             const double f_acc = (double)n_accepted / (double)n_draft_last;
             if (f_acc < 0.5) {
                 n_low++;
                 if (n_low >= 3) {
-                    LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, n_low);
+                    if (verbose) {
+                        LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, n_low);
+                    }
 
                     mod.reset();
                     n_low = 0;
@@ -764,6 +794,14 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
             }
         }
     }
+
+    int32_t n_max(const common_params_speculative & params) const override {
+        return params.ngram_mod.n_max;
+    }
+
+    int32_t n_min(const common_params_speculative & params) const override {
+        return params.ngram_mod.n_min;
+    }
 };
 
 struct common_speculative_state_ngram_cache : public common_speculative_state {
@@ -857,6 +895,14 @@ struct common_speculative_state_ngram_cache : public common_speculative_state {
         // TODO: noop
         GGML_UNUSED(n_accepted);
     }
+
+    int32_t n_max(const common_params_speculative & /*params*/) const override {
+        return n_draft;
+    }
+
+    int32_t n_min(const common_params_speculative & /*params*/) const override {
+        return 0;
+    }
 };
 
 struct common_speculative {
@@ -865,11 +911,13 @@ struct common_speculative {
     common_speculative_state * curr_impl = nullptr; // current implementation in use (for stats)
 };
 
-static common_ngram_map get_common_ngram_map(const common_speculative_config & config) {
-    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 min_hits   = config.params.ngram_min_hits;
+static common_ngram_map get_common_ngram_map(
+        common_speculative_type type,
+        const common_params_speculative_ngram_map & config) {
+    uint16_t size_key   = config.size_n;
+    uint16_t size_value = config.size_m;
+    bool     key_only   = type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K;
+    uint16_t min_hits   = config.min_hits;
 
     return common_ngram_map(size_key, size_value, key_only, min_hits);
 }
@@ -927,8 +975,8 @@ common_speculative * common_speculative_init(
         common_params_speculative & params,
         llama_context             * ctx_tgt) {
     llama_context * ctx_dft = nullptr;
-    if (params.model_dft) {
-        ctx_dft = llama_init_from_model(params.model_dft, params.cparams_dft);
+    if (params.draft.model) {
+        ctx_dft = llama_init_from_model(params.draft.model, params.draft.cparams);
         if (ctx_dft == nullptr) {
             LOG_ERR("%s", "failed to create draft context\n");
             return nullptr;
@@ -938,7 +986,7 @@ common_speculative * common_speculative_init(
     // Compute the implementations to use based on the config and their order of preference
     std::vector<common_speculative_config> configs = {}; // list of speculative configs to try
     {
-        bool has_draft = !params.mparams_dft.path.empty();
+        bool has_draft = !params.draft.mparams.path.empty();
         bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
 
         bool has_ngram_cache   = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_CACHE);
@@ -961,16 +1009,17 @@ common_speculative * common_speculative_init(
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params));
         }
         if (has_ngram_mod) {
-            // shared instance for all speculative decoding contexts
-            if (!params.ngram_mod) {
-                params.ngram_mod = std::make_shared<common_ngram_mod>(params.ngram_size_n, 4*1024*1024);
+            auto & sparams = params.ngram_mod;
 
-                LOG_INF("%s: initialized ngram_mod with n=%d, size=%zu (%.3f MB)\n", __func__,
-                        params.ngram_size_n, params.ngram_mod->size(),
-                        (float)(params.ngram_mod->size_bytes())/1024/1024);
+            if (!sparams.obj) {
+                sparams.obj = std::make_shared<common_ngram_mod>(sparams.n_match, 4*1024*1024);
 
-                if (params.ngram_size_n < 16) {
-                    LOG_WRN("%s: ngram_mod n=%d is too small - poor quality is possible, see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, params.ngram_size_n);
+                LOG_INF("%s: initialized ngram_mod with n_match=%d, size=%zu (%.3f MB)\n", __func__,
+                        sparams.n_match, sparams.obj->size(), (float)(sparams.obj->size_bytes())/1024/1024);
+
+                if (sparams.n_match < 16) {
+                    LOG_WRN("%s: ngram_mod n_match=%d is too small - poor quality is possible, "
+                            "see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, sparams.n_match);
                 }
             }
 
@@ -1000,7 +1049,7 @@ common_speculative * common_speculative_init(
                 impls.push_back(std::make_unique<common_speculative_state_draft>(config.type,
                     /* .ctx_tgt      = */ ctx_tgt,
                     /* .ctx_dft      = */ ctx_dft,
-                    /* .replacements = */ params.replacements,
+                    /* .replacements = */ params.draft.replacements,
                     /* .use_ckpt     = */ use_ckpt
                 ));
                 break;
@@ -1010,18 +1059,18 @@ common_speculative * common_speculative_init(
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
-                common_ngram_map ngram_map = get_common_ngram_map(config);
+                common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple);
 
                 uint16_t ngram_size_key   = ngram_map.size_key;
                 uint16_t mgram_size_value = ngram_map.size_value;
 
                 auto config_simple = common_ngram_simple_config {
-                    /* .size_ngram      = */ ngram_size_key,
-                    /* .size_mgram      = */ mgram_size_value
+                    /* .size_ngram = */ ngram_size_key,
+                    /* .size_mgram = */ mgram_size_value
                 };
                 auto state = std::make_unique<common_speculative_state_ngram_simple>(
-                    /* .type            = */ config.type,
-                    /* .state           = */ config_simple
+                    /* .type  = */ config.type,
+                    /* .state = */ config_simple
                 );
                 impls.push_back(std::move(state));
                 break;
@@ -1030,18 +1079,17 @@ common_speculative * common_speculative_init(
             case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: {
                 impls.push_back(std::make_unique<common_speculative_state_ngram_map_k>(
                     (config.type),
-                    get_common_ngram_map(config)
+                    get_common_ngram_map(config.type, config.params.ngram_map_k)
                 ));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: {
-                GGML_ASSERT(config.params.ngram_mod);
-                impls.push_back(std::make_unique<common_speculative_state_ngram_mod>(config.type, *config.params.ngram_mod));
+                GGML_ASSERT(config.params.ngram_mod.obj);
+                impls.push_back(std::make_unique<common_speculative_state_ngram_mod>(config.type, *config.params.ngram_mod.obj));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: {
-                auto state = create_state_ngram_cache(
-                        params.lookup_cache_static, params.lookup_cache_dynamic, config);
+                auto state = create_state_ngram_cache(params.ngram_cache.lookup_cache_static, params.ngram_cache.lookup_cache_dynamic, config);
                 impls.push_back(std::make_unique<common_speculative_state_ngram_cache>(state));
                 break;
             }
@@ -1099,6 +1147,15 @@ llama_tokens common_speculative_draft(
             impl->n_call_draft++;
         }
 
+        {
+            const int n_min = impl->n_min(params);
+
+            if (!result.empty() && (int) result.size() < n_min) {
+                LOG_DBG("%s: ignoring small draft: %d < %d\n", __func__, (int) result.size(), n_min);
+                result.clear();
+            }
+        }
+
         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(),
@@ -1108,7 +1165,7 @@ llama_tokens common_speculative_draft(
             impl->n_gen_drafts++;
             impl->n_gen_tokens += result.size();
 
-            break; // We have a draft, so break out of the loop and return it.
+            break; // we have a draft, so break out of the loop and return it.
         }
     }
 
@@ -1136,6 +1193,32 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
     }
 }
 
+int32_t common_speculative_n_max(const common_speculative * spec, const common_params_speculative & params) {
+    if (spec == nullptr) {
+        return 0;
+    }
+
+    int32_t n_max = 0;
+    for (const auto & impl : spec->impls) {
+        n_max = std::max(n_max, impl->n_max(params));
+    }
+
+    return n_max;
+}
+
+int32_t common_speculative_n_min(const common_speculative * spec, const common_params_speculative & params) {
+    if (spec == nullptr) {
+        return 0;
+    }
+
+    int32_t n_min = 0;
+    for (const auto & impl : spec->impls) {
+        n_min = std::max(n_min, impl->n_min(params));
+    }
+
+    return n_min;
+}
+
 void common_speculative_print_stats(const common_speculative * spec) {
     if (spec == nullptr) {
         return;

common/speculative.h

@@ -33,6 +33,9 @@ llama_tokens common_speculative_draft(
 // informs the speculative decoder that n_accepted tokens were accepted by the target model
 void common_speculative_accept(common_speculative * spec, uint16_t n_accepted);
 
+int32_t common_speculative_n_max(const common_speculative * spec, const common_params_speculative & params);
+int32_t common_speculative_n_min(const common_speculative * spec, const common_params_speculative & params);
+
 // print statistics about the speculative decoding
 void common_speculative_print_stats(const common_speculative * spec);
 

convert_hf_to_gguf_update.py

@@ -175,6 +175,7 @@ pre_computed_hashes = [
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
     {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
+    {"name": "qwen35",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openbmb/MiniCPM-V-4_6", "chkhsh": "1444df51289cfa8063b96f0e62b1125440111bc79a52003ea14b6eac7016fd5f"},
     {"name": "grok-2",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
     # jina-v2-de variants
     {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},

docs/multi-gpu.md

@@ -0,0 +1,127 @@
+# Using multiple GPUs with llama.cpp
+
+This guide explains how to run [llama.cpp](https://github.com/ggml-org/llama.cpp) across more than one GPU. It covers the split modes, the command-line flags that control them, the limitations you need to know about, and ready-to-use recipes for `llama-cli` and `llama-server`.
+
+The CLI arguments listed here are the same for both tools - or most llama.cpp binaries for that matter.
+
+---
+
+## When you need multi-GPU
+
+Reach for multi-GPU when one of these is true:
+
+- **The model doesn't fit in a single GPU's VRAM.** By spreading the weights across two or more GPUs the whole model can stay on accelerators. Otherwise part of the model will need to be run off of the comparatively slower system RAM.
+- **You want more throughput.** By distributing the computation across multiple GPUs, each individual GPU has to do less work. This can result in better prefill and/or token generation performance, depending on the split mode and interconnect speed vs. the speed of an individual GPU.
+
+---
+
+## The split modes
+
+Set with `--split-mode` / `-sm`.
+
+| Mode | What it does | When to use |
+|---|---|---|
+| `none` | Use a single GPU only. Pick which one with `--main-gpu`. | You explicitly want to confine the model to one GPU even though more are visible. |
+| `layer` (**default**) | Pipeline parallelism. Each GPU holds a contiguous slice of layers. The KV cache for layer *l* lives on the GPU that owns layer *l*. | Default and most compatible multi-GPU choice. You want more memory than a single GPU provides and your priority is a fast prefill. Can tolerate slow interconnect speeds between GPUs. |
+| `row` | **Deprecated.** Older row-split tensor-parallel path with comparatively poor performance. Splits only dense weights across GPUs. Superseded by `tensor` which should be universally superior if it can be used. | Avoid in new deployments. |
+| `tensor` | **EXPERIMENTAL.** Tensor parallelism that splits both weights *and* KV across the participating GPUs via a "meta device" abstraction. | You want more memory than a single GPU provides and your priority is fast token generation. Prefill speeds approach pipeline parallel speeds for large, dense models and fast GPU interconnect speeds. Treat as experimental as the code is less mature than pipeline parallelism. Performance should be good for multiple NVIDIA GPUs using the CUDA backend, no guarantees otherwise. |
+
+> Pipeline parallel (`layer`) vs. tensor parallel (`tensor`): pipeline-parallel runs different layers on different GPUs and processes tokens sequentially through the pipeline. This minimizes data transfers between GPUs but requires many tokens to scale well. Tensor-parallel splits each layer across GPUs and does multiple cross-GPU reductions per layer. This enables parallelizing any workload but is much more bottlenecked by the GPU interconnect speed. Pipeline-parallel maximizes batch throughput; tensor-parallel minimizes latency.
+
+---
+
+## Command-line arguments reference
+
+| Short | Long | Value | Default | Notes |
+|---|---|---|---|---|
+| `-sm` | `--split-mode` | `none` \| `layer` \| `tensor` | `layer` | See modes above. |
+| `-ts` | `--tensor-split` | comma-separated proportions, e.g. `3,1` | mode-dependent | How much of the model goes to each GPU. If omitted, `layer`/`row` use automatic splitting proportional to memory, while `tensor` splits tensor segments evenly. With `3,1` on two GPUs, GPU 0 gets 75 %, GPU 1 gets 25 %. The values follow the order in `--device`. |
+| `-mg` | `--main-gpu` | integer device index | `0` | The single GPU used in `--split-mode none`. |
+| `-ngl` | `--n-gpu-layers` / `--gpu-layers` | integer \| `auto` \| `all` | `auto` | Maximum number of layers to keep in VRAM. Use `999` or `all` to push everything possible to the GPUs. |
+| `-dev` | `--device` | comma-separated device names, or `none` | auto | Restrict which devices llama.cpp may use. See `--list-devices` for names. |
+| | `--list-devices` | - | - | Print the available devices and their memory. Run this first to learn the names you'd pass to `--device`. |
+| `-fa` | `--flash-attn` | `on` \| `off` \| `auto` | `auto` | Required when using `--split-mode tensor` and/or quantized V cache. Supported (and therefore enabled by default) for most combinations of models and backends. |
+| `-ctk` | `--cache-type-k` | `f32` \| `f16` \| `bf16` \| `q8_0` \| `q4_0` \| ... | `f16` | KV cache type for K. |
+| `-ctv` | `--cache-type-v` | same as `-ctk` | `f16` | KV cache type for V. |
+| `-fit` | `--fit` | `on` \| `off` | `on` | Auto-fit unset args to device memory. **Not supported with `tensor`. You may need to manually set the `--ctx-size` to make the model fit.**  |
+
+As for any CUDA program, the environment variable `CUDA_VISIBLE_DEVICES` can be used to control which GPUs to use for the CUDA backend: if you set it, llama.cpp only sees the specified GPUs. Use `--device` for selecting GPUs from among those visible to llama.cpp, this works for any backend.
+
+---
+
+## Recipes
+
+### 1. Default - pipeline parallel across all visible GPUs
+
+```bash
+llama-cli -m model.gguf
+llama-server -m model.gguf
+```
+
+Easiest configuration. KV cache spreads across the GPUs along with the layers. `--fit` (on by default) sizes things automatically.
+
+### 2. Pipeline parallel with a custom split ratio
+
+```bash
+llama-cli -m model.gguf -ts 3,1
+```
+
+Useful when GPUs have different memory: GPU 0 (3 parts) and GPU 1 (1 part). Proportions are normalized so `-ts 3,1` is the same as e.g. `-ts 75,25`.
+
+### 3. Single-GPU mode, picking a specific GPU
+
+```bash
+llama-cli --list-devices
+llama-cli -m model.gguf -dev CUDA1
+```
+
+Use only the device listed as `CUDA1` when calling with `--list-devices`.
+
+### 4. Tensor parallelism (experimental)
+
+```bash
+llama-cli -m model.gguf -sm tensor -ctk f16 -ctv f16
+```
+
+- `--flash-attn off` or (`--flash-attn auto` resolving to `off` when it isn't supported) is a hard error.
+- KV cache types must be non-quantized: `f32`, `f16`, or `bf16`. Support for quantized KV cache is not implemented and trying to use it will result in an error.
+- Mark this configuration as experimental in your tooling: validate output quality before deploying.
+- `--split-mode tensor`is not implemented for all architectures. The following will fail with *"LLAMA_SPLIT_MODE_TENSOR not implemented for architecture '...'"*:
+
+  - **MoE / hybrid:** Grok, MPT, OLMoE, DeepSeek2, GLM-DSA, Nemotron-H, Nemotron-H-MoE, Granite-Hybrid, LFM2-MoE, Minimax-M2, Mistral4, Kimi-Linear, Jamba, Falcon-H1
+  - **State-space / RWKV-style:** Mamba, Mamba2 (and the hybrid Mamba-attention models above)
+  - **Other:** PLAMO2, MiniCPM3, Gemma-3n, OLMo2, BitNet, T5
+
+### 5. With NCCL
+
+There's no runtime flag for NCCL - it's selected at build time (`-DGGML_CUDA_NCCL=ON`, this is the default). Note that NCCL is **not** automatically distributed with CUDA and you may need to install it manually - when in doubt check the CMake log to see whether or not it can find the package. When llama.cpp is compiled with NCCL support it uses it automatically for cross-GPU reductions in `tensor` mode. When NCCL is missing on a multi-GPU build, you'll see this one-time warning and performance will be lower:
+
+```
+NVIDIA Collective Communications Library (NCCL) is unavailable, multi GPU performance will be suboptimal
+```
+
+When using the "ROCm" backend (which is the ggml CUDA code translated for AMD via HIP), the AMD equivalent RCCL can be used by compiling with `-DGGML_HIP_RCCL=ON`. Note that RCCL is by default *disabled* because (unlike NCCL) it was not universally beneficial during testing.
+### 6. With CUDA peer-to-peer access (`GGML_CUDA_P2P`)
+
+CUDA peer-to-peer (P2P) lets GPUs transfer data directly between each other instead of going through system memory, which generally improves multi-GPU performance. It is **opt-in** at runtime - set the environment variable `GGML_CUDA_P2P` to any value to enable it:
+
+```bash
+GGML_CUDA_P2P=1 llama-cli -m model.gguf -sm tensor
+```
+
+P2P requires driver support (usually restricted to workstation/datacenter GPUs) and **may cause crashes or corrupted outputs on some motherboards or BIOS configurations** (e.g. when IOMMU is enabled). If you see instability after enabling it, unset the variable.
+
+---
+
+## Troubleshooting
+
+| Symptom | How to fix |
+|---|---|
+| Startup error *"SPLIT_MODE_TENSOR requires flash_attn to be enabled"* | Add `-fa on` or remove `-fa off`. |
+| Startup error *"simultaneous use of SPLIT_MODE_TENSOR and KV cache quantization not implemented"* | Use `-ctk f16 -ctv f16` (or `bf16`/`f32`) with `--split-mode tensor`. |
+| Startup error *"LLAMA_SPLIT_MODE_TENSOR not implemented for architecture 'X'"* | Architecture not on the TENSOR allow-list. Use `--split-mode layer`. |
+| Warning *"NCCL is unavailable, multi GPU performance will be suboptimal"* | llama.cpp wasn't built with NCCL. Either accept the lower performance or install NCCL and rebuild. |
+| CUDA OOM at startup or during prefill in `--split-mode tensor` | Auto-fit is disabled in this mode, so reduce memory pressure yourself. In order from least to most disruptive: lower `--ctx-size` (`-c`) (KV cache is roughly proportional to `n_ctx`); for `llama-server`, lower `--parallel` (`-np`) (a slot KV cache is allocated per concurrent sequence); as a last resort, reduce `--n-gpu-layers` (`-ngl`) (the remaining layers run on CPU and inference will be much slower). |
+| Performance is worse with multi-GPU than single-GPU | The performance is bottlenecked by GPU interconnect speed. For `--split-mode tensor`, verify that NCCL is being used. Try `--split-mode layer` (less communication than `tensor`). Increase GPU interconnect speed via more PCIe lanes or e.g. NVLink (if available). |
+| GPU not used at all | `--n-gpu-layers` is `0` or too low - try explicitly setting `-ngl all`. Or you are accidentally hiding the GPUs via an environment variable like `CUDA_VISIBLE_DEVICES=-1`. Or your build doesn't include support for the relevant backend. |
+| Crashes or corrupted outputs after setting `GGML_CUDA_P2P=1` | Some motherboards and BIOS settings (e.g. with IOMMU enabled) don't support CUDA peer-to-peer reliably. Unset `GGML_CUDA_P2P`. |

docs/multimodal/minicpmv4.6.md

@@ -0,0 +1,49 @@
+## MiniCPM-V 4.6
+
+### Prepare models and code
+
+Download [MiniCPM-V-4_6](https://huggingface.co/openbmb/MiniCPM-V-4_6) PyTorch model from huggingface to "MiniCPM-V-4_6" folder.
+
+The model must be the standard `transformers` v5.7.0+ checkpoint (no `trust_remote_code`); the architecture in `config.json` is `MiniCPMV4_6ForConditionalGeneration` with a `qwen3_5_text` text model and a SigLIP-based vision tower plus a window-attention `vit_merger`.
+
+### Build llama.cpp
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggml-org/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-V 4.6
+
+Unlike older MiniCPM-V variants, MiniCPM-V 4.6 is converted directly through `convert_hf_to_gguf.py`. The same script is invoked twice on the original Hugging Face directory: once to produce the language-model GGUF and once with `--mmproj` to produce the multimodal projector GGUF.
+
+```bash
+# language model
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_6 --outfile ../MiniCPM-V-4_6/ggml-model-f16.gguf
+
+# multimodal projector (vision tower + window-attention vit_merger + DownsampleMLP merger)
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_6 --mmproj --outfile ../MiniCPM-V-4_6/mmproj-model-f16.gguf
+
+# optional: quantize to Q4_K_M
+./build/bin/llama-quantize ../MiniCPM-V-4_6/ggml-model-f16.gguf ../MiniCPM-V-4_6/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_6/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_6/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4_6/mmproj-model-f16.gguf
+```

docs/ops.md

@@ -17,7 +17,7 @@ Legend:
 |                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -36,15 +36,15 @@ Legend:
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                             DIAG | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
@@ -101,11 +101,11 @@ Legend:
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                              SQR | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |

docs/speculative.md

@@ -33,18 +33,18 @@ An example to use this approach can be the rewriting of source code by a LLM.
 This implementation looks for the last n-gram in history that matches the current n-gram and creates a draft using the m tokens following the matched n-gram. It is the simplest self-speculative approach with minimal overhead.
 
 ```
-llama-server [...] --spec-type ngram-simple --draft-max 64
+llama-server [...] --spec-type ngram-simple --spec-draft-n-max 64
 ```
 
 #### n-gram Map Key (`ngram-map-k`)
 
-This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`, default is 1) before generating drafts.
+This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-map-k-min-hits`, default is 1) before generating drafts.
 
 The number of accepted tokens is stored for each used n-gram.
 
 **Example:**
 ```
-llama-server [...] --spec-type ngram-map-k --draft-max 64
+llama-server [...] --spec-type ngram-map-k --spec-draft-n-max 64
 ```
 
 #### n-gram Map Key-4-Values (`ngram-map-k4v`)
@@ -55,7 +55,7 @@ The number of accepted tokens is stored for each used n-gram.
 
 **Example:** Server options to be used if there are a lot of longer repetitions.
 ```
-llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-max 64
+llama-server [...] --spec-type ngram-map-k4v --spec-ngram-map-k4v-size-n 8 --spec-ngram-map-k4v-size-m 8 --spec-ngram-map-k4v-min-hits 2 --spec-draft-n-max 64
 ```
 
 ### n-gram Mod (`ngram-mod`)
@@ -80,9 +80,9 @@ Currently, a single hash pool is shared across all server slots, so different re
 # notes:
 # - small `n` are not recommended
 # - MoEs require long drafts
-# - dense models: can reduce `--draft-min` and `--draft-max`
+# - dense models: can reduce `--spec-ngram-mod-n-min` and `--spec-ngram-mod-n-max`
 
-llama-server ... --spec-type ngram-mod --spec-ngram-size-n 24 --draft-min 48 --draft-max 64
+llama-server ... --spec-type ngram-mod --spec-ngram-mod-n-match 24 --spec-ngram-mod-n-min 48 --spec-ngram-mod-n-max 64
 ```
 
 Applications:
@@ -105,21 +105,90 @@ Example Video:
 
 If a draft model is combined with a draftless decoding the draftless decoding has higher precedence.
 
+### General Speculative Parameters
+
 ```
---draft, --draft-n, --draft-max N       number of tokens to draft for speculative decoding (default: 16)
-                                        (env: LLAMA_ARG_DRAFT_MAX)
---draft-min, --draft-n-min N            minimum number of draft tokens to use for speculative decoding
-                                        (default: 0)
-                                        (env: LLAMA_ARG_DRAFT_MIN)
-[...]
 --spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         type of speculative decoding to use when no draft model is provided
                                         (default: none)
---spec-ngram-size-n N                   ngram size N for ngram-simple/ngram-map speculative decoding, length
-                                        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-min-hits N                 minimum hits for ngram-map speculative decoding (default: 1)
+                                        (env: LLAMA_ARG_SPEC_TYPE)
+--spec-default                          use default speculative decoding
+```
+
+### Draft Model Parameters
+
+```
+--spec-draft-model, -md, --model-draft  FNAME
+                                        draft model for speculative decoding (default: unused)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_MODEL)
+--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft  <user>/<model>[:quant]
+                                        HuggingFace repository for the draft model
+--spec-draft-n-max                      N
+                                        number of tokens to draft for speculative decoding (default: 16)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_N_MAX)
+--spec-draft-n-min                      N
+                                        minimum number of draft tokens to use for speculative decoding (default: 0)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_N_MIN)
+--spec-draft-p-split, --draft-p-split   P
+                                        speculative decoding split probability (default: 0.10)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT)
+--spec-draft-p-min, --draft-p-min       P
+                                        minimum speculative decoding probability (greedy) (default: 0.75)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_P_MIN)
+--spec-draft-ctx-size, -cd, --ctx-size-draft  N
+                                        size of the prompt context for the draft model (default: 0, 0 = loaded from model)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CTX_SIZE)
+--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft  N
+                                        max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
+                                        (env: LLAMA_ARG_N_GPU_LAYERS_DRAFT)
+--spec-draft-device, -devd, --device-draft  <dev1,dev2,..>
+                                        comma-separated list of devices to use for offloading the draft model
+--spec-draft-replace, --spec-replace    TARGET  DRAFT
+                                        translate the string in TARGET into DRAFT if the draft model and main model are not compatible
+```
+
+### n-gram Mod Parameters
+
+```
+--spec-ngram-mod-n-match                N
+                                        ngram-mod lookup length (default: 24)
+--spec-ngram-mod-n-min                  N
+                                        minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48)
+--spec-ngram-mod-n-max                  N
+                                        maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64)
+```
+
+### n-gram Simple Parameters
+
+```
+--spec-ngram-simple-size-n              N
+                                        ngram size N for ngram-simple speculative decoding, length of lookup n-gram (default: 12)
+--spec-ngram-simple-size-m              N
+                                        ngram size M for ngram-simple speculative decoding, length of draft m-gram (default: 48)
+--spec-ngram-simple-min-hits            N
+                                        minimum hits for ngram-simple speculative decoding (default: 1)
+```
+
+### n-gram Map Key Parameters
+
+```
+--spec-ngram-map-k-size-n               N
+                                        ngram size N for ngram-map-k speculative decoding, length of lookup n-gram (default: 12)
+--spec-ngram-map-k-size-m               N
+                                        ngram size M for ngram-map-k speculative decoding, length of draft m-gram (default: 48)
+--spec-ngram-map-k-min-hits             N
+                                        minimum hits for ngram-map-k speculative decoding (default: 1)
+```
+
+### n-gram Map Key-4-Values Parameters
+
+```
+--spec-ngram-map-k4v-size-n             N
+                                        ngram size N for ngram-map-k4v speculative decoding, length of lookup n-gram (default: 12)
+--spec-ngram-map-k4v-size-m             N
+                                        ngram size M for ngram-map-k4v speculative decoding, length of draft m-gram (default: 48)
+--spec-ngram-map-k4v-min-hits           N
+                                        minimum hits for ngram-map-k4v speculative decoding (default: 1)
 ```
 
 ### `--spec-type TYPE`
@@ -140,21 +209,40 @@ Specifies a type of speculative decoding without draft model.
 ./llama-server [...] --spec-type ngram-simple
 ```
 
-### `--spec-ngram-size-n N`
+### `--spec-ngram-*-size-n N`
 
 Sets the size N of the lookup n-gram for n-gram map based speculative decoding.
 The n-gram size N determines how many tokens in a row to look back when searching for matching patterns.
 
-### `--spec-ngram-size-m M`
+Each n-gram implementation has its own parameter:
+
+- `--spec-ngram-simple-size-n` for `ngram-simple`
+- `--spec-ngram-map-k-size-n` for `ngram-map-k`
+- `--spec-ngram-map-k4v-size-n` for `ngram-map-k4v`
+- `--spec-ngram-mod-n-match` for `ngram-mod`
+
+### `--spec-ngram-*-size-m M`
 
 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-min-hits H`
+Each n-gram implementation has its own parameter:
+
+- `--spec-ngram-simple-size-m` for `ngram-simple`
+- `--spec-ngram-map-k-size-m` for `ngram-map-k`
+- `--spec-ngram-map-k4v-size-m` for `ngram-map-k4v`
+
+### `--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).
 
+Each n-gram implementation has its own parameter:
+
+- `--spec-ngram-simple-min-hits` for `ngram-simple`
+- `--spec-ngram-map-k-min-hits` for `ngram-map-k`
+- `--spec-ngram-map-k4v-min-hits` for `ngram-map-k4v`
+
 ## Statistics
 Each speculative decoding implementation prints statistics.
 
@@ -180,4 +268,3 @@ statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts
 - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
 - `#acc tokens`: number of tokens accepted by the main model
 - `dur(b,g,a): durations of begin (new prompt), generation and accumulation (process acceptance).
-

examples/diffusion/CMakeLists.txt

@@ -1,5 +1,10 @@
+set(TARGET llama-diffusion)
+add_library(${TARGET} STATIC diffusion.cpp diffusion.h)
+target_link_libraries(${TARGET} PUBLIC llama llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PUBLIC cxx_std_17)
+
 set(TARGET llama-diffusion-cli)
 add_executable(${TARGET} diffusion-cli.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama-diffusion llama llama-common ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/diffusion/README.md

@@ -12,11 +12,11 @@ The diffusion CLI supports various parameters to control the generation process:
 ### Core Diffusion Parameters
 - `--diffusion-steps`: Number of diffusion steps (default: 256)
 - `--diffusion-algorithm`: Algorithm for token selection
-  - `0`: ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
-  - `1`: ENTROPY_BASED - Entropy-based selection
-  - `2`: MARGIN_BASED - Margin-based selection
-  - `3`: RANDOM - Random selection
-  - `4`: CONFIDENCE_BASED - Confidence-based selection (default)
+  - `0`: DIFFUSION_ALGORITHM_ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
+  - `1`: DIFFUSION_ALGORITHM_ENTROPY_BASED - Entropy-based selection
+  - `2`: DIFFUSION_ALGORITHM_MARGIN_BASED - Margin-based selection
+  - `3`: DIFFUSION_ALGORITHM_RANDOM - Random selection
+  - `4`: DIFFUSION_ALGORITHM_CONFIDENCE_BASED - Confidence-based selection (default)
   - More documentation here https://github.com/DreamLM/Dream
 - `--diffusion-visual`: Enable live visualization during generation
 

examples/diffusion/diffusion-cli.cpp

@@ -1,127 +1,23 @@
 #include "arg.h"
 #include "chat.h"
 #include "common.h"
+#include "diffusion.h"
 #include "llama.h"
 #include "log.h"
 
 #include <limits.h>
 
-#include <algorithm>
 #include <clocale>
-#include <cmath>
 #include <cstring>
-#include <limits>
-#include <random>
 #include <string>
 #include <vector>
 
-enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 };
-
-// Unified transfer scheduling methods
-enum transfer_schedule {
-    TIMESTEP_BASED = 0,  // Dream-style: (1.0 - s/t) * remaining
-    BLOCK_BASED    = 1,  // LLaDA-style: process in blocks with get_num_transfer_tokens
-};
-
-typedef bool (*diffusion_step_callback_t)(int32_t             step,
-                                          int32_t             total_steps,
-                                          const llama_token * tokens,
-                                          int32_t             n_tokens,
-                                          void *              user_data);
-
-struct diffusion_params {
-    int32_t                   steps                   = 0;
-    float                     temperature             = 0;
-    llama_token               mask_token_id           = LLAMA_TOKEN_NULL;
-    diffusion_step_callback_t step_callback           = nullptr;
-    void *                    step_callback_user_data = nullptr;
-    int32_t                   seed                    = 0;
-    bool                      visual_mode             = false;
-    bool                      shift_logits            = false;  // Shift logits by -1 after decode
-
-    float   top_p = 0.;
-    int32_t top_k = 0.;
-
-    diffusion_algorithm algorithm = CONFIDENCE_BASED;
-    transfer_schedule   schedule  = TIMESTEP_BASED;
-
-    float   cfg_scale        = 0.;     // Config scale for classifier-free guidance
-    float   eps              = 0.;     // Timestep scheduling
-    int32_t block_length     = 0;      // Block size (for block scheduling)
-    float   alg_temp         = 0;      // algorithm temperature (0.0 = deterministic)
-    bool    add_gumbel_noise = false;  // Add gumbel noise to the logits if temp > 0.0
-
-    int32_t max_length = 0;            // Maximum sequence length
-};
-
 struct callback_data {
     diffusion_params *  diff_params;
     const llama_vocab * vocab;
     int32_t             n_input;
 };
 
-static float calculate_confidence(const llama_token_data_array & cur_p,
-                                  diffusion_algorithm            algorithm,
-                                  std::mt19937 &                 rng) {
-    switch (algorithm) {
-        case CONFIDENCE_BASED:
-            return cur_p.data[cur_p.selected].p;  // Selected token probability
-
-        case ENTROPY_BASED:
-            {
-                float       entropy = 0.0f;
-                const float epsilon = 1e-10f;
-                for (size_t i = 0; i < cur_p.size; i++) {
-                    float prob = cur_p.data[i].p;
-                    entropy += prob * logf(prob + epsilon);
-                }
-                return -entropy;  // Higher entropy = lower confidence
-            }
-
-        case MARGIN_BASED:
-            return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
-
-        case RANDOM:
-            {
-                std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
-                return uniform(rng);  // Random confidence
-            }
-
-        case ORIGIN:
-            return cur_p.data[cur_p.selected].p;
-
-        default:
-            return 0.0f;
-    }
-}
-
-// Unified transfer count calculation function
-static int32_t calculate_transfer_count(int32_t                      step,
-                                        int32_t                      total_steps,
-                                        int32_t                      remaining_masked,
-                                        transfer_schedule            schedule,
-                                        float                        eps,
-                                        const std::vector<int32_t> & num_transfer_tokens = {}) {
-    switch (schedule) {
-        case TIMESTEP_BASED:
-            {
-                float t          = 1.0f - (float) step / total_steps * (1.0f - eps);
-                float s          = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
-                float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
-                return (int32_t) (remaining_masked * p_transfer);
-            }
-
-        case BLOCK_BASED:
-            if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
-                return num_transfer_tokens[step];
-            }
-            return remaining_masked / (total_steps - step);  // Fallback
-
-        default:
-            return remaining_masked / (total_steps - step);
-    }
-}
-
 static bool diffusion_step_callback(int32_t             step,
                                     int32_t             total_steps,
                                     const llama_token * tokens,
@@ -176,341 +72,6 @@ static bool diffusion_step_callback(int32_t             step,
     return true;
 }
 
-static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
-    if (temperature == 0.0f) {
-        return;
-    }
-
-    std::uniform_real_distribution<double> uniform(0.0, 1.0);
-    for (int32_t i = 0; i < n_vocab; i++) {
-        double noise        = uniform(rng);
-        // Prevent log(0)
-        noise               = std::max(noise, 1e-20);
-        double gumbel_noise = std::pow(-std::log(noise), temperature);
-        logits[i]           = std::exp(logits[i]) / gumbel_noise;
-    }
-}
-
-static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
-    std::vector<int32_t> num_transfer_tokens(steps);
-
-    int32_t base      = mask_count / steps;
-    int32_t remainder = mask_count % steps;
-
-    for (int32_t i = 0; i < steps; i++) {
-        num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
-    }
-
-    return num_transfer_tokens;
-}
-
-static void diffusion_generate(llama_context *          ctx,
-                               const llama_token *      input_tokens,
-                               llama_token *            output_tokens,
-                               int32_t                  n_input,
-                               const diffusion_params & params,
-                               int32_t &                n_generated) {
-    n_generated = 0;
-    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
-        return;
-    }
-
-    const llama_model * model = llama_get_model(ctx);
-
-    // Initialize with input and pad with mask tokens
-    std::copy(input_tokens, input_tokens + n_input, output_tokens);
-    std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
-
-    std::mt19937 rng(params.seed);
-
-    llama_set_causal_attn(ctx, false);
-
-    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
-
-    std::vector<llama_token_data> candidates(n_vocab);
-    std::vector<llama_token_data> conf_candidates;
-    conf_candidates.reserve(params.max_length);
-    std::vector<int32_t> mask_positions;
-    mask_positions.reserve(params.max_length);
-
-    // Setup sampler chain
-    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
-    if (params.top_k > 0) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
-    }
-    if (params.top_p < 1.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
-    }
-    if (params.temperature > 0.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
-    }
-    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
-
-    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
-
-    llama_batch batch = llama_batch_init(params.max_length, 0, 1);
-    batch.n_tokens    = params.max_length;
-
-    // Pre-allocate buffers for CFG if needed
-    int32_t                  logits_size = n_vocab * params.max_length;
-    std::vector<float>       cond_logits_buffer;
-    std::vector<llama_token> un_x_buffer;
-    if (params.cfg_scale > 0.0f) {
-        cond_logits_buffer.resize(logits_size);
-        un_x_buffer.resize(params.max_length);
-    }
-
-    // For block-based processing
-    std::vector<int32_t> num_transfer_tokens;
-    int32_t              num_blocks      = 1;
-    int32_t              steps_per_block = params.steps;
-
-    if (params.schedule == BLOCK_BASED) {
-        GGML_ASSERT(params.max_length % params.block_length == 0);
-        num_blocks = params.max_length / params.block_length;
-        GGML_ASSERT(params.steps % num_blocks == 0);
-        steps_per_block = params.steps / num_blocks;
-    }
-
-    std::vector<float> confidence(params.max_length);
-
-    int64_t total_sampling_time = 0;
-    int64_t total_time          = 0;
-    int64_t time_start          = ggml_time_us();
-
-    for (int block_num = 0; block_num < num_blocks; block_num++) {
-        int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
-        int32_t block_end   = (params.schedule == BLOCK_BASED) ?
-                                  std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
-                                  params.max_length;
-
-        // Count masked tokens in current block for block-based processing
-        if (params.schedule == BLOCK_BASED) {
-            int32_t block_mask_count = 0;
-            for (int i = block_start; i < block_end; i++) {
-                if (output_tokens[i] == params.mask_token_id) {
-                    block_mask_count++;
-                }
-            }
-            num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
-        }
-
-        for (int32_t step = 0; step < steps_per_block; step++) {
-            int32_t global_step = block_num * steps_per_block + step;
-
-            if (params.step_callback) {
-                if (!params.step_callback(
-                        global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
-                    break;
-                }
-            }
-
-            // Setup batch
-            for (int32_t i = 0; i < params.max_length; i++) {
-                batch.token[i]     = output_tokens[i];
-                batch.pos[i]       = i;
-                batch.n_seq_id[i]  = 1;
-                batch.seq_id[i][0] = 0;
-                batch.logits[i]    = 1;
-            }
-
-            float * logits = nullptr;
-
-            if (params.cfg_scale > 0.0f) {
-                int ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("Failed to generate conditional");
-                    break;
-                }
-                float * cond_logits_ptr = llama_get_logits(ctx);
-                std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
-
-                // Unconditional generation (mask input)
-                std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
-                for (int32_t i = 0; i < n_input; i++) {
-                    un_x_buffer[i] = params.mask_token_id;
-                }
-
-                for (int32_t i = 0; i < params.max_length; i++) {
-                    batch.token[i] = un_x_buffer[i];
-                }
-                ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("Failed to generate unconditional");
-                    break;
-                }
-                float * uncond_logits = llama_get_logits(ctx);
-
-                // Apply CFG
-                for (int32_t i = 0; i < logits_size; i++) {
-                    cond_logits_buffer[i] =
-                        uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
-                }
-                logits = cond_logits_buffer.data();
-            } else {
-                int ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
-                    break;
-                }
-                logits = llama_get_logits(ctx);
-            }
-
-            if (!logits) {
-                LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
-                break;
-            }
-
-            auto get_logits_for_pos = [&](int32_t pos) -> const float * {
-                if (params.shift_logits) {
-                    return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
-                }
-                return logits + (pos) *n_vocab;
-            };
-
-            int64_t time_start_sampling = ggml_time_us();
-
-            mask_positions.clear();
-            for (int32_t i = 0; i < params.max_length; i++) {
-                if (output_tokens[i] == params.mask_token_id) {
-                    // For block-based, only consider current block
-                    if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) {
-                        mask_positions.push_back(i);
-                    }
-                }
-            }
-
-            if (mask_positions.empty()) {
-                break;
-            }
-
-            if (params.add_gumbel_noise && params.temperature > 0.0f) {
-                add_gumbel_noise(logits, n_vocab, params.temperature, rng);
-            }
-
-            if (params.algorithm == ORIGIN) {
-                int32_t transfer_count = calculate_transfer_count(
-                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
-                float p_transfer = (float) transfer_count / mask_positions.size();
-
-                for (int32_t pos : mask_positions) {
-                    if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
-                        const float * pos_logits = get_logits_for_pos(pos);
-                        for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                            candidates[token_id].id    = token_id;
-                            candidates[token_id].logit = pos_logits[token_id];
-                            candidates[token_id].p     = 0.0f;
-                        }
-
-                        llama_token_data_array cur_p = {
-                            candidates.data(),
-                            (size_t) n_vocab,
-                            -1,
-                            false,
-                        };
-
-                        llama_sampler_apply(sampler, &cur_p);
-                        output_tokens[pos] = cur_p.data[cur_p.selected].id;
-                    }
-                }
-            } else {
-                std::vector<std::pair<float, int32_t>> confidences;
-                std::vector<llama_token>               sampled_tokens(mask_positions.size());
-
-                for (size_t i = 0; i < mask_positions.size(); i++) {
-                    int32_t       pos        = mask_positions[i];
-                    const float * pos_logits = get_logits_for_pos(pos);
-
-                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                        candidates[token_id].logit = pos_logits[token_id];
-                        candidates[token_id].p     = 0.0f;
-                        candidates[token_id].id    = token_id;
-                    }
-
-                    llama_token_data_array cur_p = {
-                        candidates.data(),
-                        candidates.size(),
-                        -1,
-                        false,
-                    };
-
-                    llama_sampler_apply(sampler, &cur_p);
-                    llama_token sampled_token = cur_p.data[cur_p.selected].id;
-
-                    float conf = calculate_confidence(cur_p, params.algorithm, rng);
-
-                    sampled_tokens[i] = sampled_token;
-                    confidences.emplace_back(conf, i);
-                }
-
-                int32_t transfer_count = calculate_transfer_count(
-                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
-
-                if (transfer_count > 0) {
-                    if (params.alg_temp == 0.0f) {
-                        std::partial_sort(confidences.begin(),
-                                          confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
-                                          confidences.end(),
-                                          [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
-                                              if (a.first != b.first) {
-                                                  return a.first > b.first;
-                                              }
-                                              return a.second < b.second;
-                                          });
-
-                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
-                            int32_t mask_idx   = confidences[i].second;
-                            int32_t pos        = mask_positions[mask_idx];
-                            output_tokens[pos] = sampled_tokens[mask_idx];
-                        }
-                    } else {
-                        conf_candidates.clear();
-                        for (size_t i = 0; i < confidences.size(); i++) {
-                            float conf_logit = confidences[i].first / params.alg_temp;
-                            conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
-                        }
-
-                        llama_token_data_array conf_array = {
-                            conf_candidates.data(),
-                            conf_candidates.size(),
-                            -1,
-                            false,
-                        };
-
-                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
-                            llama_sampler_apply(dist_sampler, &conf_array);
-                            int32_t selected_idx = conf_array.selected;
-                            int32_t mask_idx     = selected_idx;
-                            int32_t pos          = mask_positions[mask_idx];
-                            output_tokens[pos]   = sampled_tokens[mask_idx];
-
-                            conf_candidates[selected_idx].p = 0.0f;
-                            conf_array.selected             = -1;
-                        }
-                    }
-                }
-            }
-
-            int64_t time_end_sampling = ggml_time_us();
-            total_sampling_time += time_end_sampling - time_start_sampling;
-        }
-    }
-
-    int64_t time_end = ggml_time_us();
-    total_time += time_end - time_start;
-
-    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
-            total_time / 1000.0,
-            total_time / 1000.0 / params.steps,
-            total_sampling_time / 1000.0 / params.steps);
-
-    llama_batch_free(batch);
-    llama_sampler_free(sampler);
-    llama_sampler_free(dist_sampler);
-
-    n_generated = params.max_length;
-}
-
 static std::string format_input_text(const std::string & prompt, const std::string & system_prompt, bool use_chat_template, llama_model * model) {
     if (!use_chat_template) {
         return prompt;
@@ -631,10 +192,10 @@ int main(int argc, char ** argv) {
     GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0));
 
     if (params.diffusion.eps) {
-        diff_params.schedule = TIMESTEP_BASED;
+        diff_params.schedule = DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED;
         diff_params.eps      = params.diffusion.eps;
     } else if (params.diffusion.block_length) {
-        diff_params.schedule     = BLOCK_BASED;
+        diff_params.schedule     = DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED;
         diff_params.block_length = params.diffusion.block_length;
     }
 
@@ -653,8 +214,17 @@ int main(int argc, char ** argv) {
     callback_data cb_data               = { &diff_params, vocab, n_input };
     diff_params.step_callback_user_data = &cb_data;
 
-    const char * alg_names[]   = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" };
-    const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" };
+    const char * alg_names[]   = {
+        "DIFFUSION_ALGORITHM_ORIGIN",
+        "DIFFUSION_ALGORITHM_ENTROPY_BASED",
+        "DIFFUSION_ALGORITHM_MARGIN_BASED",
+        "DIFFUSION_ALGORITHM_RANDOM",
+        "DIFFUSION_ALGORITHM_CONFIDENCE_BASED",
+    };
+    const char * sched_names[] = {
+        "DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED",
+        "DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED",
+    };
     const char * alg_name =
         (diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN";
     const char * sched_name =
@@ -666,11 +236,11 @@ int main(int argc, char ** argv) {
     LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name);
     LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "schedule", diff_params.schedule, sched_name);
     LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "temperature", diff_params.temperature);
-    if (diff_params.schedule == TIMESTEP_BASED) {
+    if (diff_params.schedule == DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED) {
         LOG_INF("diffusion_params: - %-25s f32              = %.6f\n", "eps", diff_params.eps);
         LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "alg_temp", diff_params.alg_temp);
     }
-    if (diff_params.schedule == BLOCK_BASED) {
+    if (diff_params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) {
         LOG_INF("diffusion_params: - %-25s u32              = %d\n", "block_length", diff_params.block_length);
         LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "cfg_scale", diff_params.cfg_scale);
     }

examples/diffusion/diffusion.cpp

@@ -0,0 +1,408 @@
+#include "diffusion.h"
+
+#include "log.h"
+
+#include <algorithm>
+#include <cstddef>
+#include <cmath>
+#include <cstring>
+#include <random>
+#include <utility>
+#include <vector>
+
+static float calculate_confidence(const llama_token_data_array & cur_p,
+                                  diffusion_algorithm            algorithm,
+                                  std::mt19937 &                 rng) {
+    switch (algorithm) {
+        case DIFFUSION_ALGORITHM_CONFIDENCE_BASED:
+            return cur_p.data[cur_p.selected].p;  // Selected token probability
+
+        case DIFFUSION_ALGORITHM_ENTROPY_BASED:
+            {
+                float       entropy = 0.0f;
+                const float epsilon = 1e-10f;
+                for (size_t i = 0; i < cur_p.size; i++) {
+                    float prob = cur_p.data[i].p;
+                    entropy += prob * logf(prob + epsilon);
+                }
+                return -entropy;  // Higher entropy = lower confidence
+            }
+
+        case DIFFUSION_ALGORITHM_MARGIN_BASED:
+            return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
+
+        case DIFFUSION_ALGORITHM_RANDOM:
+            {
+                std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
+                return uniform(rng);  // Random confidence
+            }
+
+        case DIFFUSION_ALGORITHM_ORIGIN:
+            return cur_p.data[cur_p.selected].p;
+
+        default:
+            return 0.0f;
+    }
+}
+
+// Unified transfer count calculation function
+static int32_t calculate_transfer_count(int32_t                      step,
+                                        int32_t                      total_steps,
+                                        int32_t                      remaining_masked,
+                                        diffusion_transfer_schedule  schedule,
+                                        float                        eps,
+                                        const std::vector<int32_t> & num_transfer_tokens = {}) {
+    switch (schedule) {
+        case DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED:
+            {
+                float t          = 1.0f - (float) step / total_steps * (1.0f - eps);
+                float s          = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
+                float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
+                return (int32_t) (remaining_masked * p_transfer);
+            }
+
+        case DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED:
+            if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
+                return num_transfer_tokens[step];
+            }
+            return remaining_masked / (total_steps - step);  // Fallback
+
+        default:
+            return remaining_masked / (total_steps - step);
+    }
+}
+
+static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
+    if (temperature == 0.0f) {
+        return;
+    }
+
+    std::uniform_real_distribution<double> uniform(0.0, 1.0);
+    for (int32_t i = 0; i < n_vocab; i++) {
+        double noise        = uniform(rng);
+        // Prevent log(0)
+        noise               = std::max(noise, 1e-20);
+        double gumbel_noise = std::pow(-std::log(noise), temperature);
+        logits[i]           = std::exp(logits[i]) / gumbel_noise;
+    }
+}
+
+static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
+    std::vector<int32_t> num_transfer_tokens(steps);
+
+    int32_t base      = mask_count / steps;
+    int32_t remainder = mask_count % steps;
+
+    for (int32_t i = 0; i < steps; i++) {
+        num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
+    }
+
+    return num_transfer_tokens;
+}
+
+void diffusion_generate(llama_context *          ctx,
+                        const llama_token *      input_tokens,
+                        llama_token *            output_tokens,
+                        int32_t                  n_input,
+                        const diffusion_params & params,
+                        int32_t &                n_generated) {
+    n_generated = 0;
+    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
+        return;
+    }
+
+    const llama_model * model = llama_get_model(ctx);
+
+    // Initialize with input and pad with mask tokens
+    std::copy(input_tokens, input_tokens + n_input, output_tokens);
+    std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
+
+    std::mt19937 rng(params.seed);
+
+    llama_set_causal_attn(ctx, false);
+
+    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
+
+    std::vector<llama_token_data> candidates(n_vocab);
+    std::vector<llama_token_data> conf_candidates;
+    conf_candidates.reserve(params.max_length);
+    std::vector<int32_t> mask_positions;
+    mask_positions.reserve(params.max_length);
+
+    // Setup sampler chain
+    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
+    if (params.top_k > 0) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
+    }
+    if (params.top_p < 1.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
+    }
+    if (params.temperature > 0.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
+    }
+    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
+
+    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
+
+    llama_batch batch = llama_batch_init(params.max_length, 0, 1);
+    batch.n_tokens    = params.max_length;
+
+    // Pre-allocate buffers for CFG if needed
+    int32_t                  logits_size = n_vocab * params.max_length;
+    std::vector<float>       cond_logits_buffer;
+    std::vector<llama_token> un_x_buffer;
+    if (params.cfg_scale > 0.0f) {
+        cond_logits_buffer.resize(logits_size);
+        un_x_buffer.resize(params.max_length);
+    }
+
+    // For block-based processing
+    std::vector<int32_t> num_transfer_tokens;
+    int32_t              num_blocks      = 1;
+    int32_t              steps_per_block = params.steps;
+
+    if (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) {
+        GGML_ASSERT(params.max_length % params.block_length == 0);
+        num_blocks = params.max_length / params.block_length;
+        GGML_ASSERT(params.steps % num_blocks == 0);
+        steps_per_block = params.steps / num_blocks;
+    }
+
+    std::vector<float> confidence(params.max_length);
+
+    int64_t total_sampling_time = 0;
+    int64_t total_time          = 0;
+    int64_t time_start          = ggml_time_us();
+
+    for (int block_num = 0; block_num < num_blocks; block_num++) {
+        int32_t block_start = (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
+        int32_t block_end   = (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) ?
+                                  std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
+                                  params.max_length;
+
+        // Count masked tokens in current block for block-based processing
+        if (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) {
+            int32_t block_mask_count = 0;
+            for (int i = block_start; i < block_end; i++) {
+                if (output_tokens[i] == params.mask_token_id) {
+                    block_mask_count++;
+                }
+            }
+            num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
+        }
+
+        for (int32_t step = 0; step < steps_per_block; step++) {
+            int32_t global_step = block_num * steps_per_block + step;
+
+            if (params.step_callback) {
+                if (!params.step_callback(
+                        global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
+                    break;
+                }
+            }
+
+            // Setup batch
+            for (int32_t i = 0; i < params.max_length; i++) {
+                batch.token[i]     = output_tokens[i];
+                batch.pos[i]       = i;
+                batch.n_seq_id[i]  = 1;
+                batch.seq_id[i][0] = 0;
+                batch.logits[i]    = 1;
+            }
+
+            float * logits = nullptr;
+
+            if (params.cfg_scale > 0.0f) {
+                int ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("Failed to generate conditional");
+                    break;
+                }
+                float * cond_logits_ptr = llama_get_logits(ctx);
+                std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
+
+                // Unconditional generation (mask input)
+                std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
+                for (int32_t i = 0; i < n_input; i++) {
+                    un_x_buffer[i] = params.mask_token_id;
+                }
+
+                for (int32_t i = 0; i < params.max_length; i++) {
+                    batch.token[i] = un_x_buffer[i];
+                }
+                ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("Failed to generate unconditional");
+                    break;
+                }
+                float * uncond_logits = llama_get_logits(ctx);
+
+                // Apply CFG
+                for (int32_t i = 0; i < logits_size; i++) {
+                    cond_logits_buffer[i] =
+                        uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
+                }
+                logits = cond_logits_buffer.data();
+            } else {
+                int ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
+                    break;
+                }
+                logits = llama_get_logits(ctx);
+            }
+
+            if (!logits) {
+                LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
+                break;
+            }
+
+            auto get_logits_for_pos = [&](int32_t pos) -> const float * {
+                if (params.shift_logits) {
+                    return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
+                }
+                return logits + pos * n_vocab;
+            };
+
+            int64_t time_start_sampling = ggml_time_us();
+
+            mask_positions.clear();
+            for (int32_t i = 0; i < params.max_length; i++) {
+                if (output_tokens[i] == params.mask_token_id) {
+                    // For block-based, only consider current block
+                    if (params.schedule != DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED || (i >= block_start && i < block_end)) {
+                        mask_positions.push_back(i);
+                    }
+                }
+            }
+
+            if (mask_positions.empty()) {
+                break;
+            }
+
+            if (params.add_gumbel_noise && params.temperature > 0.0f) {
+                add_gumbel_noise(logits, n_vocab, params.temperature, rng);
+            }
+
+            if (params.algorithm == DIFFUSION_ALGORITHM_ORIGIN) {
+                int32_t transfer_count = calculate_transfer_count(
+                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
+                float p_transfer = (float) transfer_count / mask_positions.size();
+
+                for (int32_t pos : mask_positions) {
+                    if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
+                        const float * pos_logits = get_logits_for_pos(pos);
+                        for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                            candidates[token_id].id    = token_id;
+                            candidates[token_id].logit = pos_logits[token_id];
+                            candidates[token_id].p     = 0.0f;
+                        }
+
+                        llama_token_data_array cur_p = {
+                            candidates.data(),
+                            (size_t) n_vocab,
+                            -1,
+                            false,
+                        };
+
+                        llama_sampler_apply(sampler, &cur_p);
+                        output_tokens[pos] = cur_p.data[cur_p.selected].id;
+                    }
+                }
+            } else {
+                std::vector<std::pair<float, int32_t>> confidences;
+                std::vector<llama_token>               sampled_tokens(mask_positions.size());
+
+                for (size_t i = 0; i < mask_positions.size(); i++) {
+                    int32_t       pos        = mask_positions[i];
+                    const float * pos_logits = get_logits_for_pos(pos);
+
+                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                        candidates[token_id].logit = pos_logits[token_id];
+                        candidates[token_id].p     = 0.0f;
+                        candidates[token_id].id    = token_id;
+                    }
+
+                    llama_token_data_array cur_p = {
+                        candidates.data(),
+                        candidates.size(),
+                        -1,
+                        false,
+                    };
+
+                    llama_sampler_apply(sampler, &cur_p);
+                    llama_token sampled_token = cur_p.data[cur_p.selected].id;
+
+                    float conf = calculate_confidence(cur_p, params.algorithm, rng);
+
+                    sampled_tokens[i] = sampled_token;
+                    confidences.emplace_back(conf, i);
+                }
+
+                int32_t transfer_count = calculate_transfer_count(
+                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
+
+                if (transfer_count > 0) {
+                    if (params.alg_temp == 0.0f) {
+                        std::partial_sort(confidences.begin(),
+                                          confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
+                                          confidences.end(),
+                                          [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
+                                              if (a.first != b.first) {
+                                                  return a.first > b.first;
+                                              }
+                                              return a.second < b.second;
+                                          });
+
+                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
+                            int32_t mask_idx   = confidences[i].second;
+                            int32_t pos        = mask_positions[mask_idx];
+                            output_tokens[pos] = sampled_tokens[mask_idx];
+                        }
+                    } else {
+                        conf_candidates.clear();
+                        for (size_t i = 0; i < confidences.size(); i++) {
+                            float conf_logit = confidences[i].first / params.alg_temp;
+                            conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
+                        }
+
+                        llama_token_data_array conf_array = {
+                            conf_candidates.data(),
+                            conf_candidates.size(),
+                            -1,
+                            false,
+                        };
+
+                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
+                            llama_sampler_apply(dist_sampler, &conf_array);
+                            int32_t selected_idx = conf_array.selected;
+                            int32_t mask_idx     = selected_idx;
+                            int32_t pos          = mask_positions[mask_idx];
+                            output_tokens[pos]   = sampled_tokens[mask_idx];
+
+                            conf_candidates[selected_idx].p = 0.0f;
+                            conf_array.selected             = -1;
+                        }
+                    }
+                }
+            }
+
+            int64_t time_end_sampling = ggml_time_us();
+            total_sampling_time += time_end_sampling - time_start_sampling;
+        }
+    }
+
+    int64_t time_end = ggml_time_us();
+    total_time += time_end - time_start;
+
+    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
+            total_time / 1000.0,
+            total_time / 1000.0 / params.steps,
+            total_sampling_time / 1000.0 / params.steps);
+
+    llama_batch_free(batch);
+    llama_sampler_free(sampler);
+    llama_sampler_free(dist_sampler);
+
+    n_generated = params.max_length;
+}

examples/diffusion/diffusion.h

@@ -0,0 +1,57 @@
+#pragma once
+
+#include "llama.h"
+
+#include <cstdint>
+
+enum diffusion_algorithm {
+    DIFFUSION_ALGORITHM_ORIGIN           = 0,
+    DIFFUSION_ALGORITHM_ENTROPY_BASED    = 1,
+    DIFFUSION_ALGORITHM_MARGIN_BASED     = 2,
+    DIFFUSION_ALGORITHM_RANDOM           = 3,
+    DIFFUSION_ALGORITHM_CONFIDENCE_BASED = 4,
+};
+
+// Unified transfer scheduling methods
+enum diffusion_transfer_schedule {
+    DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED = 0,  // Dream-style: (1.0 - s/t) * remaining
+    DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED    = 1,  // LLaDA-style: process in blocks with get_num_transfer_tokens
+};
+
+typedef bool (*diffusion_step_callback_t)(int32_t             step,
+                                          int32_t             total_steps,
+                                          const llama_token * tokens,
+                                          int32_t             n_tokens,
+                                          void *              user_data);
+
+struct diffusion_params {
+    int32_t                   steps                   = 0;
+    float                     temperature             = 0;
+    llama_token               mask_token_id           = LLAMA_TOKEN_NULL;
+    diffusion_step_callback_t step_callback           = nullptr;
+    void *                    step_callback_user_data = nullptr;
+    int32_t                   seed                    = 0;
+    bool                      visual_mode             = false;
+    bool                      shift_logits            = false;  // Shift logits by -1 after decode
+
+    float   top_p = 0.;
+    int32_t top_k = 0.;
+
+    diffusion_algorithm         algorithm = DIFFUSION_ALGORITHM_CONFIDENCE_BASED;
+    diffusion_transfer_schedule schedule  = DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED;
+
+    float   cfg_scale        = 0.;     // Config scale for classifier-free guidance
+    float   eps              = 0.;     // Timestep scheduling
+    int32_t block_length     = 0;      // Block size (for block scheduling)
+    float   alg_temp         = 0;      // algorithm temperature (0.0 = deterministic)
+    bool    add_gumbel_noise = false;  // Add gumbel noise to the logits if temp > 0.0
+
+    int32_t max_length = 0;            // Maximum sequence length
+};
+
+void diffusion_generate(llama_context *          ctx,
+                        const llama_token *      input_tokens,
+                        llama_token *            output_tokens,
+                        int32_t                  n_input,
+                        const diffusion_params & params,
+                        int32_t &                n_generated);

examples/gen-docs/gen-docs.cpp

@@ -73,12 +73,12 @@ static void write_help(std::ostringstream & ss, const md_file & md) {
     auto ctx_arg = common_params_parser_init(params, md.ex);
 
     std::vector<common_arg *> common_options;
-    std::vector<common_arg *> sparam_options;
+    std::vector<common_arg *> sampling_options;
     std::vector<common_arg *> specific_options;
     for (auto & opt : ctx_arg.options) {
         // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
-        if (opt.is_sparam) {
-            sparam_options.push_back(&opt);
+        if (opt.is_sampling) {
+            sampling_options.push_back(&opt);
         } else if (opt.in_example(ctx_arg.ex)) {
             specific_options.push_back(&opt);
         } else {
@@ -93,7 +93,7 @@ static void write_help(std::ostringstream & ss, const md_file & md) {
     ss << "### Common params\n\n";
     write_table(ss, common_options);
     ss << "\n\n### Sampling params\n\n";
-    write_table(ss, sparam_options);
+    write_table(ss, sampling_options);
     ss << "\n\n### " << md.specific_section_header << "\n\n";
     write_table(ss, specific_options);
 

examples/lookup/lookup-create.cpp

@@ -37,9 +37,9 @@ int main(int argc, char ** argv){
 
     common_ngram_cache ngram_cache;
     common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
-    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.lookup_cache_static.c_str());
+    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.ngram_cache.lookup_cache_static.c_str());
 
-    common_ngram_cache_save(ngram_cache, params.speculative.lookup_cache_static);
+    common_ngram_cache_save(ngram_cache, params.speculative.ngram_cache.lookup_cache_static);
 
     return 0;
 }

examples/lookup/lookup-stats.cpp

@@ -24,7 +24,7 @@ int main(int argc, char ** argv){
         return 1;
     }
 
-    const int n_draft = params.speculative.n_max;
+    const int n_draft = params.speculative.draft.n_max;
 
     // init llama.cpp
     llama_backend_init();
@@ -49,18 +49,18 @@ int main(int argc, char ** argv){
     {
         const int64_t t_start_draft_us = ggml_time_us();
 
-        if (!params.speculative.lookup_cache_static.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
+                ngram_cache_static = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_static);
             } catch (std::ifstream::failure const &) {
-                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.ngram_cache.lookup_cache_static.c_str());
                 exit(1);
             }
         }
 
-        if (!params.speculative.lookup_cache_dynamic.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_dynamic);
             } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
         }
 

examples/lookup/lookup.cpp

@@ -25,7 +25,7 @@ int main(int argc, char ** argv){
     }
 
     // max. number of additional tokens to draft if match is found
-    const int n_draft = params.speculative.n_max;
+    const int n_draft = params.speculative.draft.n_max;
 
     // init llama.cpp
     llama_backend_init();
@@ -54,18 +54,18 @@ int main(int argc, char ** argv){
         const int64_t t_start_draft_us = ggml_time_us();
         common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
 
-        if (!params.speculative.lookup_cache_static.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
+                ngram_cache_static = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_static);
             } catch (std::ifstream::failure const &) {
-                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.ngram_cache.lookup_cache_static.c_str());
                 exit(1);
             }
         }
 
-        if (!params.speculative.lookup_cache_dynamic.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_dynamic);
             } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
         }
 
@@ -213,7 +213,7 @@ int main(int argc, char ** argv){
 
     // Update dynamic ngram cache with context ngram cache and save it to disk:
     common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
-    common_ngram_cache_save(ngram_cache_dynamic, params.speculative.lookup_cache_dynamic);
+    common_ngram_cache_save(ngram_cache_dynamic, params.speculative.ngram_cache.lookup_cache_dynamic);
 
     LOG("\n\n");
 

examples/save-load-state/save-load-state.cpp

@@ -38,8 +38,12 @@ int main(int argc, char ** argv) {
     std::string result0;
     std::string result1;
     std::string result2;
+    std::string result3;
 
     // init
+
+    ggml_backend_load_all();
+
     auto llama_init = common_init_from_params(params);
 
     auto * model = llama_init->model();
@@ -213,11 +217,83 @@ int main(int argc, char ** argv) {
         n_past += 1;
     }
 
+    // test on-device state save/load
+    auto params_ctx4 = common_context_params_to_llama(params);
+    params_ctx4.n_seq_max = 2;
+    llama_context * ctx4 = llama_init_from_model(model, params_ctx4);
+
+    llama_sampler * smpl4 = llama_sampler_chain_init(sparams);
+
+    llama_sampler_chain_add(smpl4, llama_sampler_init_dist(params.sampling.seed));
+
+    printf("\nsingle seq run: %s", params.prompt.c_str());
+
+    // load state (rng, logits, embedding and kv_cache) from file
+    n_token_count_out = 0;
+
+    if (!llama_state_load_file(ctx4, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
+        fprintf(stderr, "\n%s : failed to load state\n", __func__);
+        return 1;
+    }
+
+    fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
+
+    // restore state (last tokens)
+    n_past = n_token_count_out;
+    if (!common_replay_last_token(ctx4, tokens.back(), n_past)) {
+        return 1;
+    }
+    ++n_past;
+
+    // save seq 0 and load into seq 1
+    {
+        // save kv of seq 0
+        std::vector<uint8_t> seq_store(llama_state_seq_get_size_ext(ctx4, 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE));
+        const size_t ncopy = llama_state_seq_get_data_ext(ctx4, seq_store.data(), seq_store.size(), 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+        if (ncopy != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
+
+        // erase whole kv
+        llama_memory_clear(llama_get_memory(ctx4), true);
+        fprintf(stderr, "%s : kv cache cleared\n", __func__);
+
+        // restore kv into seq 0
+        const size_t nset = llama_state_seq_set_data_ext(ctx4, seq_store.data(), seq_store.size(), 1, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+        if (nset != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
+    }
+
+    // forth run
+    for (auto i = 0; i < params.n_predict; i++) {
+        auto next_token     = llama_sampler_sample(smpl4, ctx4, -1);
+        auto next_token_str = common_token_to_piece(ctx4, next_token);
+
+        printf("%s", next_token_str.c_str());
+        result3 += next_token_str;
+
+        common_batch_clear(batch);
+        common_batch_add(batch, next_token, n_past, {1}, true);
+
+        if (llama_decode(ctx4, batch)) {
+            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
+            llama_batch_free(batch);
+            return 1;
+        }
+        n_past += 1;
+    }
+
     printf("\n");
 
     llama_sampler_free(smpl);
     llama_sampler_free(smpl2);
     llama_sampler_free(smpl3);
+    llama_sampler_free(smpl4);
 
     llama_batch_free(batch);
 
@@ -226,12 +302,18 @@ int main(int argc, char ** argv) {
 
     llama_free(ctx2);
     llama_free(ctx3);
+    llama_free(ctx4);
 
     if (result0 != result2) {
         fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
         return 1;
     }
 
+    if (result0 != result3) {
+        fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
+        return 1;
+    }
+
     fprintf(stderr, "\n%s : success\n", __func__);
 
     return 0;

examples/speculative-simple/speculative-simple.cpp

@@ -43,7 +43,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (params.speculative.mparams_dft.path.empty()) {
+    if (params.speculative.draft.mparams.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;
     }
@@ -77,7 +77,7 @@ int main(int argc, char ** argv) {
 
     // TODO: simplify this logic
     {
-        const auto & params_spec = params.speculative;
+        const auto & params_spec = params.speculative.draft;
 
         auto params_dft = params;
 
@@ -85,15 +85,15 @@ int main(int argc, char ** argv) {
         params_dft.n_ctx        = params_spec.n_ctx;
         params_dft.n_batch      = llama_n_ctx_seq(ctx_tgt);
         params_dft.devices      = params_spec.devices;
-        params_dft.model        = params_spec.mparams_dft;
+        params_dft.model        = params_spec.mparams;
         params_dft.n_gpu_layers = params_spec.n_gpu_layers;
 
         if (params_spec.cpuparams.n_threads > 0) {
-            params_dft.cpuparams.n_threads       = params.speculative.cpuparams.n_threads;
-            params_dft.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
+            params_dft.cpuparams.n_threads       = params.speculative.draft.cpuparams.n_threads;
+            params_dft.cpuparams_batch.n_threads = params.speculative.draft.cpuparams_batch.n_threads;
         }
 
-        params_dft.tensor_buft_overrides = params.speculative.tensor_buft_overrides;
+        params_dft.tensor_buft_overrides = params.speculative.draft.tensor_buft_overrides;
 
         auto mparams_dft = common_model_params_to_llama(params_dft);
 
@@ -103,8 +103,8 @@ int main(int argc, char ** argv) {
             return 1;
         }
 
-        params.speculative.model_dft = model_dft.get();
-        params.speculative.cparams_dft = common_context_params_to_llama(params_dft);
+        params.speculative.draft.model = model_dft.get();
+        params.speculative.draft.cparams = common_context_params_to_llama(params_dft);
     }
 
     // Tokenize the prompt
@@ -187,16 +187,6 @@ int main(int argc, char ** argv) {
             // generate a new draft
             draft = common_speculative_draft(spec, params_spec, prompt_tgt, id_last);
 
-            if ((int) draft.size() > params_spec.n_max) {
-                LOG_WRN("draft size %zu exceeds max %d, truncating\n", draft.size(), params_spec.n_max);
-                draft.resize(params_spec.n_max);
-            }
-
-            if ((int) draft.size() < params_spec.n_min) {
-                LOG_DBG("ignoring small draft: %zu < %d\n", draft.size(), params_spec.n_min);
-                draft.clear();
-            }
-
             // save the original draft size
             n_draft = draft.size();
 
@@ -220,19 +210,12 @@ int main(int argc, char ** argv) {
             }
         }
 
-        GGML_ASSERT(n_draft > 0);
-
         // always have a token to evaluate from before - id_last
         common_batch_clear(batch_tgt);
         common_batch_add  (batch_tgt, id_last, n_past++, { 0 }, true);
 
         // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
         {
-            // do not waste time on small drafts
-            if (draft.size() < (size_t) params_spec.n_min) {
-                draft.clear();
-            }
-
             for (size_t i = 0; i < draft.size(); ++i) {
                 common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
             }
@@ -340,7 +323,7 @@ int main(int argc, char ** argv) {
     LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
 
     LOG_INF("\n");
-    LOG_INF("n_draft   = %d\n", params_spec.n_max);
+    LOG_INF("n_draft   = %d\n", params_spec.draft.n_max);
     LOG_INF("n_predict = %d\n", n_predict);
     LOG_INF("n_drafted = %d\n", n_drafted);
     LOG_INF("n_accept  = %d\n", n_accept);

examples/speculative/speculative.cpp

@@ -49,7 +49,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (params.speculative.mparams_dft.path.empty()) {
+    if (params.speculative.draft.mparams.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;
     }
@@ -58,7 +58,7 @@ int main(int argc, char ** argv) {
     const int n_seq_dft = params.n_parallel;
 
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
-    const float p_draft_split = params.speculative.p_split;
+    const float p_draft_split = params.speculative.draft.p_split;
 
     std::default_random_engine rng(params.sampling.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sampling.seed);
     std::uniform_real_distribution<> u_dist;
@@ -80,15 +80,15 @@ int main(int argc, char ** argv) {
     ctx_tgt   = llama_init_tgt->context();
 
     // load the draft model
-    params.devices = params.speculative.devices;
-    params.model = params.speculative.mparams_dft;
-    params.n_gpu_layers = params.speculative.n_gpu_layers;
-    if (params.speculative.cpuparams.n_threads > 0) {
-        params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
+    params.devices = params.speculative.draft.devices;
+    params.model = params.speculative.draft.mparams;
+    params.n_gpu_layers = params.speculative.draft.n_gpu_layers;
+    if (params.speculative.draft.cpuparams.n_threads > 0) {
+        params.cpuparams.n_threads = params.speculative.draft.cpuparams.n_threads;
     }
 
-    params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
-    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
+    params.cpuparams_batch.n_threads = params.speculative.draft.cpuparams_batch.n_threads;
+    params.tensor_buft_overrides     = params.speculative.draft.tensor_buft_overrides;
 
     auto llama_init_dft = common_init_from_params(params);
 
@@ -110,13 +110,21 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (
-        llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
-        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
-        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
-    ) {
-        LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
+    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+        (llama_vocab_get_add_bos(vocab_tgt) && llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft))) {
+        LOG_ERR("%s: draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
+                __func__,
+                llama_vocab_get_add_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_dft),
+                llama_vocab_bos(vocab_tgt), llama_vocab_bos(vocab_dft));
+        return 1;
+    }
+
+    if (llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
+        (llama_vocab_get_add_eos(vocab_tgt) && llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft))) {
+        LOG_ERR("%s: draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
+                __func__,
+                llama_vocab_get_add_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_dft),
+                llama_vocab_eos(vocab_tgt), llama_vocab_eos(vocab_dft));
         return 1;
     }
 
@@ -137,11 +145,12 @@ int main(int argc, char ** argv) {
         for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
             const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
             const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
+
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
                 LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
                 LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
-                        common_token_to_piece(ctx_tgt, i).c_str(),
-                        common_token_to_piece(ctx_dft, i).c_str());
+                        common_token_to_piece(vocab_tgt, i).c_str(),
+                        common_token_to_piece(vocab_dft, i).c_str());
                 return 1;
             }
         }
@@ -183,7 +192,7 @@ int main(int argc, char ** argv) {
     //GGML_ASSERT(n_vocab == llama_vocab_n_tokens(model_dft));
 
     // how many tokens to draft each time
-    int n_draft = params.speculative.n_max;
+    int n_draft = params.speculative.draft.n_max;
 
     int n_predict = 0;
     int n_drafted = 0;

examples/sycl/test.sh

@@ -119,7 +119,7 @@ if [ $GGML_SYCL_DEVICE -ne -1 ]; then
     echo "Use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
     GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
-    export ONEAPI_DEVICE_SELECTOR="level_zero:${$GGML_SYCL_DEVICE}"
+    export ONEAPI_DEVICE_SELECTOR="level_zero:${GGML_SYCL_DEVICE}"
     echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
 else
     echo "Use all Intel GPUs, including iGPU & dGPU"

ggml/CMakeLists.txt

@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
 
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 10)
+set(GGML_VERSION_MINOR 11)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 

ggml/include/ggml.h

@@ -438,6 +438,12 @@ extern "C" {
         GGML_PREC_F32     = 10,
     };
 
+    // op hint
+    enum ggml_op_hint {
+        GGML_HINT_NONE             = 0,
+        GGML_HINT_SRC0_IS_HADAMARD = 1,
+    };
+
     // model file types
     enum ggml_ftype {
         GGML_FTYPE_UNKNOWN        = -1,
@@ -1419,6 +1425,11 @@ extern "C" {
             struct ggml_tensor * a,
             enum ggml_prec       prec);
 
+    // change the hint of a matrix multiplication
+    GGML_API void ggml_mul_mat_set_hint(
+            struct ggml_tensor * a,
+            enum ggml_op_hint    hint);
+
     // indirect matrix multiplication
     GGML_API struct ggml_tensor * ggml_mul_mat_id(
             struct ggml_context * ctx,

ggml/src/CMakeLists.txt

@@ -470,11 +470,10 @@ endforeach()
 
 target_link_libraries(ggml-base PRIVATE Threads::Threads)
 
-find_library(MATH_LIBRARY m)
-if (MATH_LIBRARY)
-    if (NOT WIN32 OR NOT DEFINED ENV{ONEAPI_ROOT})
-        target_link_libraries(ggml-base PRIVATE ${MATH_LIBRARY})
-    endif()
+if (DEFINED MATH_LIBRARY)
+    target_link_libraries(ggml-base PRIVATE ${MATH_LIBRARY})
+elseif (NOT WIN32 AND NOT DEFINED ENV{ONEAPI_ROOT})
+    target_link_libraries(ggml-base PRIVATE m)
 endif()
 
 if (CMAKE_SYSTEM_NAME MATCHES "Android")

ggml/src/ggml-backend-meta.cpp

@@ -1826,7 +1826,24 @@ static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend,
                     continue;
                 }
 
-                i = get_i_delayed(i);
+                const int i_delayed = get_i_delayed(i);
+
+                // If we can delay the AllReduce we need to consider the interaction with zero-sized tensor slices.
+                // A backend with such a slice would normally have valid data after participating in the AllReduce with a node that has
+                //     its compute flag disabled and thus gets its data zeroed out.
+                // If the AllReduce is delayed then the nodes until that point also need to have their compute flag disabled.
+                if (i_delayed > i) {
+                    for (size_t j = 0; j < n_backends; j++) {
+                        auto & bcj = backend_ctx->backend_configs[j];
+                        if ((bcj.nodes[i]->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
+                            for (int ii = i + 1; ii <= i_delayed; ii++) {
+                                bcj.nodes[ii]->flags &= ~GGML_TENSOR_FLAG_COMPUTE;
+                            }
+                        }
+                    }
+                }
+
+                i = i_delayed;
 
                 for (size_t j = 0; j < n_backends; j++) {
                     auto & bcj = backend_ctx->backend_configs[j];
@@ -2083,8 +2100,8 @@ static const ggml_backend_i ggml_backend_meta_i = {
     /* .free                    = */ ggml_backend_meta_free,
     /* .set_tensor_async        = */ ggml_backend_meta_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_meta_get_tensor_async,
-    /* .get_tensor_2d_async     = */ nullptr,
     /* .set_tensor_2d_async     = */ nullptr,
+    /* .get_tensor_2d_async     = */ nullptr,
     /* .cpy_tensor_async        = */ nullptr,
     /* .synchronize             = */ ggml_backend_meta_synchronize,
     /* .graph_plan_create       = */ nullptr,

ggml/src/ggml-backend-reg.cpp

@@ -181,6 +181,12 @@ struct ggml_backend_registry {
             return;
         }
 
+        for (auto & entry : backends) {
+            if (entry.reg == reg) {
+                return;
+            }
+        }
+
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
             __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
@@ -192,6 +198,12 @@ struct ggml_backend_registry {
     }
 
     void register_device(ggml_backend_dev_t device) {
+        for (auto & dev : devices) {
+            if (dev == device) {
+                return;
+            }
+        }
+
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
 #endif

ggml/src/ggml-blas/ggml-blas.cpp

@@ -262,9 +262,9 @@ static struct ggml_backend_i blas_backend_i = {
     /* .get_name                = */ ggml_backend_blas_get_name,
     /* .free                    = */ ggml_backend_blas_free,
     /* .set_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
-    /* .set_tensor_2d_async     = */ NULL,
     /* .get_tensor_async        = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ NULL,

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -25,6 +25,7 @@
 #include "ggml-impl.h"
 #include "ggml.h"
 
+
 #include <aclnnop/aclnn_add.h>
 #include <aclnnop/aclnn_add_rms_norm.h>
 #include <aclnnop/aclnn_addcdiv.h>
@@ -45,7 +46,9 @@
 #include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <aclnnop/aclnn_ger.h>
 #include <aclnnop/aclnn_group_norm.h>
+#include <aclnnop/aclnn_gather_v2.h>
 #include <aclnnop/aclnn_grouped_matmul_v3.h>
+#include <aclnnop/aclnn_scatter.h>
 #include <aclnnop/aclnn_gt_scalar.h>
 #include <aclnnop/aclnn_im2col.h>
 #include <aclnnop/aclnn_index_copy.h>
@@ -62,6 +65,7 @@
 #include <aclnnop/aclnn_permute.h>
 #include <aclnnop/aclnn_pow.h>
 #include <aclnnop/aclnn_pow_tensor_tensor.h>
+#include <aclnnop/aclnn_recurrent_gated_delta_rule.h>
 #include <aclnnop/aclnn_reduce_sum.h>
 #include <aclnnop/aclnn_reflection_pad1d.h>
 #include <aclnnop/aclnn_repeat.h>
@@ -69,11 +73,15 @@
 #include <aclnnop/aclnn_rms_norm.h>
 #include <aclnnop/aclnn_roll.h>
 #include <aclnnop/aclnn_softmax.h>
+#include <aclnnop/aclnn_softmax_cross_entropy_with_logits.h>
 #include <aclnnop/aclnn_sub.h>
 #include <aclnnop/aclnn_sum.h>
 #include <aclnnop/aclnn_threshold.h>
 #include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triangular_solve.h>
 #include <aclnnop/aclnn_triu.h>
+#include <aclnnop/aclnn_logical_not.h>
+#include <aclnnop/aclnn_masked_fill_scalar.h>
 #include <aclnnop/aclnn_upsample_nearest_2d.h>
 #include <aclnnop/aclnn_weight_quant_batch_matmul_v2.h>
 #include <aclnnop/aclnn_zero.h>
@@ -151,6 +159,107 @@ void ggml_cann_op_unary_gated(std::function<void(ggml_backend_cann_context &, ac
     GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_dst.get(), acl_src1.get());
 }
 
+// Fused SwiGLU using aclnnSwiGlu: splits input along innermost dim, applies
+// SiLU to left half, multiplies by right half.
+//
+// Falls back to the generic two-kernel path when src[1] != nullptr (two
+// independent halves) or swapped != 0 (reversed activation order), as
+// aclnnSwiGlu only handles the single interleaved tensor in standard order.
+//
+// CANN tiling for SwiGlu requires (storageShapeDim + viewDims) to be even.
+// aclCreateTensor always uses storageShapeDim=1, so viewDims must be odd.
+// We use a 3D view (1+3=4, even) to satisfy this constraint while preserving
+// correct split semantics along the innermost (ne[0]) dimension.
+void ggml_cann_swiglu(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    auto silu_fn = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Silu, acl_src, acl_dst);
+    };
+
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    if (dst->src[1] != nullptr || swapped != 0) {
+        ggml_cann_op_unary_gated(silu_fn, ctx, dst);
+        return;
+    }
+
+    // aclnnSwiGlu requires the split dim (src->ne[0]) to be even; fall back otherwise.
+    if (dst->src[0]->ne[0] % 2 != 0) {
+        ggml_cann_op_unary_gated(silu_fn, ctx, dst);
+        return;
+    }
+
+    ggml_tensor * src0 = dst->src[0];
+    size_t elem_size = ggml_element_size(src0);
+
+    // src0 GGML: [2*ne0, ne1, ne2, ne3] → 3D view [2*ne0, ne1, ne2*ne3]
+    // CANN reversed: [ne2*ne3, ne1, 2*ne0], split along CANN dim 2 (last).
+    int64_t ne0_x2   = src0->ne[0];
+    int64_t ne1      = src0->ne[1];
+    int64_t ne23     = src0->ne[2] * src0->ne[3];
+    int64_t src3d_ne[] = { ne0_x2, ne1, ne23 };
+    size_t  src3d_nb[] = { (size_t)src0->nb[0], (size_t)src0->nb[1], (size_t)src0->nb[2] };
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type),
+                                                     elem_size, src3d_ne, src3d_nb, 3);
+
+    // dst GGML: [ne0, ne1, ne2, ne3] → 3D view [ne0, ne1, ne2*ne3]
+    int64_t ne0      = dst->ne[0];
+    int64_t dst3d_ne[] = { ne0, ne1, ne23 };
+    size_t  dst3d_nb[] = { (size_t)dst->nb[0], (size_t)dst->nb[1], (size_t)dst->nb[2] };
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ggml_cann_type_mapping(dst->type),
+                                                     elem_size, dst3d_ne, dst3d_nb, 3);
+
+    // CANN tensor [ne23, ne1, 2*ne0]: split along CANN dim 2 (last) = 2*ne0.
+    GGML_CANN_CALL_ACLNN_OP(ctx, SwiGlu, acl_src.get(), (int64_t)2, acl_dst.get());
+}
+
+// Fused GeGLU using aclnnGeGluV3: splits input along ne[0] (CANN last dim),
+// activates the LEFT half with GELU, multiplies by right half.
+// approximate: 0=tanh, 1=none(erf). activateLeft=true matches GGML convention.
+// outGelu is a required-but-discard output buffer.
+//
+// Falls back to the generic two-kernel path when src[1] != nullptr (two
+// independent halves) or swapped != 0 (reversed activation order), as
+// aclnnGeGluV3 only handles the single interleaved tensor in standard order.
+void ggml_cann_geglu(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t approximate) {
+    auto gelu_fn = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Gelu, acl_src, acl_dst);
+    };
+
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    if (dst->src[1] != nullptr || swapped != 0) {
+        ggml_cann_op_unary_gated(gelu_fn, ctx, dst);
+        return;
+    }
+
+    // aclnnGeGluV3 requires the split dim (src->ne[0]) to be even; fall back otherwise.
+    if (dst->src[0]->ne[0] % 2 != 0) {
+        ggml_cann_op_unary_gated(gelu_fn, ctx, dst);
+        return;
+    }
+
+    ggml_tensor * src0 = dst->src[0];
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    // Allocate a temporary buffer for the required outGelu output (same shape as dst).
+    // Build contiguous strides since the pool allocation is a fresh buffer.
+    size_t  elem_size    = ggml_element_size(dst);
+    int64_t ne[GGML_MAX_DIMS] = { dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3] };
+    size_t  nb[GGML_MAX_DIMS];
+    nb[0] = elem_size;
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        nb[i] = nb[i - 1] * ne[i - 1];
+    }
+    size_t gelu_out_size = nb[GGML_MAX_DIMS - 1] * ne[GGML_MAX_DIMS - 1];
+    ggml_cann_pool_alloc gelu_out_alloc(ctx.pool(), gelu_out_size);
+
+    acl_tensor_ptr acl_gelu_out = ggml_cann_create_tensor(
+        gelu_out_alloc.get(), ggml_cann_type_mapping(dst->type), elem_size, ne, nb, GGML_MAX_DIMS);
+    // V3 adds activateLeft param; true → Gelu(left)*right, matching GGML convention.
+    // GGML dim 0 → CANN last dim (index GGML_MAX_DIMS-1 = 3 for 4D tensor).
+    GGML_CANN_CALL_ACLNN_OP(ctx, GeGluV3, acl_src.get(), (int64_t)(GGML_MAX_DIMS - 1), approximate, true,
+                             acl_dst.get(), acl_gelu_out.get());
+}
+
 /**
  * @brief Repeats elements of a tensor along each dimension according to the
  * specified repeat array.
@@ -445,28 +554,33 @@ void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes);
     void *               buffer = temp_buffer_allocator.get();
 
-    int64_t div_ne[] = { 1, src->ne[1], src->ne[2], src->ne[3] };
-    size_t  div_nb[GGML_MAX_DIMS];
-    div_nb[0] = sizeof(float);
+    int64_t norm_ne[] = { 1, src->ne[1], src->ne[2], src->ne[3] };
+    size_t  norm_nb[GGML_MAX_DIMS];
+    norm_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; ++i) {
-        div_nb[i] = div_nb[i - 1] * div_ne[i - 1];
+        norm_nb[i] = norm_nb[i - 1] * norm_ne[i - 1];
     }
-    acl_tensor_ptr acl_div = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, div_ne, div_nb, GGML_MAX_DIMS);
+    acl_tensor_ptr acl_norm = ggml_cann_create_tensor(buffer, ACL_FLOAT, sizeof(float), norm_ne, norm_nb, GGML_MAX_DIMS);
 
     std::vector<int64_t> norm_dims  = { 3 };
     acl_int_array_ptr    dims_array = ggml_cann_create_int_array(norm_dims.data(), norm_dims.size());
 
     float          p_value  = 2.0f;
     acl_scalar_ptr p_scalar = ggml_cann_create_scalar(&p_value, aclDataType::ACL_FLOAT);
-    GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src.get(), p_scalar.get(), dims_array.get(), true, acl_div.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src.get(), p_scalar.get(), dims_array.get(), true, acl_norm.get());
 
-    // Clamp norm to at least eps: scale = 1/fmaxf(norm, eps)
-    acl_scalar_ptr acl_min = ggml_cann_create_scalar(&eps, aclDataType::ACL_FLOAT);
-    float          flt_max = FLT_MAX;
-    acl_scalar_ptr acl_max = ggml_cann_create_scalar(&flt_max, aclDataType::ACL_FLOAT);
-    GGML_CANN_CALL_ACLNN_OP(ctx, Clamp, acl_div.get(), acl_min.get(), acl_max.get(), acl_div.get());
+    ggml_cann_pool_alloc clamp_buffer_allocator(ctx.pool());
+    acl_tensor_ptr       acl_clamped;
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src.get(), acl_div.get(), acl_dst.get());
+    if (eps > 0.0f) {
+        void *         clamp_buf  = clamp_buffer_allocator.alloc(n_bytes);
+        acl_clamped               = ggml_cann_create_tensor(clamp_buf, ACL_FLOAT, sizeof(float), norm_ne, norm_nb, GGML_MAX_DIMS);
+        acl_scalar_ptr eps_scalar = ggml_cann_create_scalar(&eps, aclDataType::ACL_FLOAT);
+        GGML_CANN_CALL_ACLNN_OP(ctx, ClampMin, acl_norm.get(), eps_scalar.get(), acl_clamped.get());
+    }
+
+    aclTensor * acl_div_input = acl_clamped ? acl_clamped.get() : acl_norm.get();
+    GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src.get(), acl_div_input, acl_dst.get());
 }
 
 void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
@@ -482,56 +596,30 @@ void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor *
     logits_nb[1]              = logits_nb[0] * logits_ne[0];
     acl_tensor_ptr acl_logits = ggml_cann_create_tensor(src0->data, ACL_FLOAT, sizeof(float), logits_ne, logits_nb, 2);
 
-    size_t               log_softmax_type_size = sizeof(float);
-    int64_t              log_softmax_n_bytes   = nr * nc * log_softmax_type_size;
-    ggml_cann_pool_alloc log_softmax_allocator(ctx.pool(), log_softmax_n_bytes);
-    void *               log_softmax_buffer = log_softmax_allocator.get();
-
-    int64_t log_softmax_ne[] = { nc, nr };
-    size_t  log_softmax_nb[2];
-    log_softmax_nb[0]              = log_softmax_type_size;
-    log_softmax_nb[1]              = log_softmax_nb[0] * log_softmax_ne[0];
-    acl_tensor_ptr acl_log_softmax = ggml_cann_create_tensor(log_softmax_buffer, ACL_FLOAT, log_softmax_type_size,
-                                                             log_softmax_ne, log_softmax_nb, 2);
-
-    GGML_CANN_CALL_ACLNN_OP(ctx, LogSoftmax, acl_logits.get(), 1, acl_log_softmax.get());
-
     int64_t labels_ne[] = { nc, nr };
     size_t  labels_nb[2];
     labels_nb[0]              = ggml_type_size(src1->type);
     labels_nb[1]              = labels_nb[0] * labels_ne[0];
     acl_tensor_ptr acl_labels = ggml_cann_create_tensor(src1->data, ACL_FLOAT, sizeof(float), labels_ne, labels_nb, 2);
 
-    size_t               mul_type_size = sizeof(float);
-    int64_t              mul_n_bytes   = nr * nc * mul_type_size;
-    ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_n_bytes);
-    void *               mul_buffer = mul_allocator.get();
+    size_t               loss_per_sample_type_size = sizeof(float);
+    int64_t              loss_per_sample_n_bytes   = nr * loss_per_sample_type_size;
+    ggml_cann_pool_alloc loss_per_sample_allocator(ctx.pool(), loss_per_sample_n_bytes);
+    void *               loss_per_sample_buffer = loss_per_sample_allocator.get();
 
-    int64_t mul_ne[] = { nc, nr };
-    size_t  mul_nb[2];
-    mul_nb[0]                     = mul_type_size;
-    mul_nb[1]                     = mul_nb[0] * mul_ne[0];
-    acl_tensor_ptr acl_mul_result = ggml_cann_create_tensor(mul_buffer, ACL_FLOAT, mul_type_size, mul_ne, mul_nb, 2);
+    int64_t loss_per_sample_ne[] = { nr };
+    size_t  loss_per_sample_nb[1];
+    loss_per_sample_nb[0] = loss_per_sample_type_size;
+    acl_tensor_ptr acl_loss_per_sample = ggml_cann_create_tensor(
+        loss_per_sample_buffer, ACL_FLOAT, loss_per_sample_type_size, loss_per_sample_ne, loss_per_sample_nb, 1);
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_log_softmax.get(), acl_labels.get(), acl_mul_result.get());
+    size_t               backprop_n_bytes = nr * nc * sizeof(float);
+    ggml_cann_pool_alloc backprop_allocator(ctx.pool(), backprop_n_bytes);
+    void *               backprop_buffer = backprop_allocator.get();
+    acl_tensor_ptr acl_backprop = ggml_cann_create_tensor(backprop_buffer, ACL_FLOAT, sizeof(float), logits_ne, logits_nb, 2);
 
-    size_t               sum_per_sample_type_size = sizeof(float);
-    int64_t              sum_per_sample_n_bytes   = nr * sum_per_sample_type_size;
-    ggml_cann_pool_alloc sum_per_sample_allocator(ctx.pool(), sum_per_sample_n_bytes);
-    void *               sum_per_sample_buffer = sum_per_sample_allocator.get();
-
-    int64_t sum_per_sample_ne[] = { nr };
-    size_t  sum_per_sample_nb[1];
-    sum_per_sample_nb[0]              = sum_per_sample_type_size;
-    acl_tensor_ptr acl_sum_per_sample = ggml_cann_create_tensor(
-        sum_per_sample_buffer, ACL_FLOAT, sum_per_sample_type_size, sum_per_sample_ne, sum_per_sample_nb, 1);
-
-    std::vector<int64_t> sum_dims   = { 1 };
-    acl_int_array_ptr    dims_array = ggml_cann_create_int_array(sum_dims.data(), sum_dims.size());
-    bool                 keep_dims  = false;
-
-    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_mul_result.get(), dims_array.get(), keep_dims, ACL_FLOAT,
-                            acl_sum_per_sample.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, SoftmaxCrossEntropyWithLogits, acl_logits.get(), acl_labels.get(),
+                            acl_loss_per_sample.get(), acl_backprop.get());
 
     size_t               total_sum_type_size = sizeof(float);
     int64_t              total_sum_n_bytes   = 1 * total_sum_type_size;
@@ -547,11 +635,12 @@ void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor *
 
     std::vector<int64_t> total_sum_dims    = { 0 };
     acl_int_array_ptr total_sum_dims_array = ggml_cann_create_int_array(total_sum_dims.data(), total_sum_dims.size());
+    bool              keep_dims            = false;
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_sum_per_sample.get(), total_sum_dims_array.get(), keep_dims, ACL_FLOAT,
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_loss_per_sample.get(), total_sum_dims_array.get(), keep_dims, ACL_FLOAT,
                             acl_total_sum.get());
 
-    float          value        = -1.0f / static_cast<float>(nr);
+    float          value        = 1.0f / static_cast<float>(nr);
     acl_scalar_ptr scale_factor = ggml_cann_create_scalar(&value, aclDataType::ACL_FLOAT);
     acl_tensor_ptr acl_dst =
         ggml_cann_create_tensor(dst->data, ACL_FLOAT, sizeof(float), total_sum_ne, total_sum_nb, 1);
@@ -589,6 +678,33 @@ void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
                             acl_mean_out.get(), acl_rstd_out.get());
 }
 
+void ggml_cann_set(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    size_t nb1     = ((int32_t *) dst->op_params)[0];
+    size_t nb2     = ((int32_t *) dst->op_params)[1];
+    size_t nb3     = ((int32_t *) dst->op_params)[2];
+    size_t offset  = ((int32_t *) dst->op_params)[3];
+    bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
+
+    size_t param_nb[] = { ggml_element_size(src0), nb1, nb2, nb3 };
+
+    // Create a view of dst at the target offset with src1's dimensions
+    acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+    acl_tensor_ptr acl_src1 = ggml_cann_create_tensor(src1);
+
+    if (!inplace) {
+        // First copy src0 to dst entirely
+        size_t cpy_size = ggml_nbytes(dst);
+        ACL_CHECK(
+            aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    }
+
+    // Copy src1 into the target region of dst
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst.get(), acl_src1.get());
+}
+
 void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src0 = dst->src[0];
     ggml_tensor * src1 = dst->src[1];
@@ -652,6 +768,113 @@ void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     aclnn_reduce_sum(ctx, dst, reduce_dims, 4);
 }
 
+void ggml_cann_cumsum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    // GGML cumsum operates along dim 0 (innermost / ne[0]).
+    // ggml_cann_create_tensor reverses dimensions to [ne3,ne2,ne1,ne0],
+    // so GGML dim 0 maps to CANN dim 3 (the last dim of the 4-D tensor).
+    GGML_CANN_CALL_ACLNN_OP(ctx, Cumsum, acl_src.get(), (int64_t)3,
+                            ggml_cann_type_mapping(dst->type), acl_dst.get());
+}
+
+void ggml_cann_solve_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // A: [N, N, B2, B3] lower triangular
+    ggml_tensor * src1 = dst->src[1];  // B: [K, N, B2, B3]
+
+    acl_tensor_ptr acl_a = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_b = ggml_cann_create_tensor(src1);
+    acl_tensor_ptr acl_x = ggml_cann_create_tensor(dst);
+
+    // mOut: triangular copy of A (required output), same shape as A.
+    const size_t a_bytes = ggml_nbytes(src0);
+    ggml_cann_pool_alloc m_alloc(ctx.pool(), a_bytes);
+    acl_tensor_ptr acl_m = ggml_cann_create_tensor(
+        m_alloc.get(), ggml_cann_type_mapping(src0->type),
+        ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS);
+
+    // Solve AX = B: upper=false (lower tri), transpose=false, unitriangular=false.
+    GGML_CANN_CALL_ACLNN_OP(ctx, TriangularSolve,
+        acl_b.get(), acl_a.get(), false, false, false,
+        acl_x.get(), acl_m.get());
+}
+
+void ggml_cann_diag(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    GGML_ASSERT(src->ne[1] == 1);
+
+    const int64_t N       = src->ne[0];
+    const int64_t n_batch = src->ne[2] * src->ne[3];
+    const size_t  nb_f32  = sizeof(float);
+
+    // Fill dst with zeros.
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    {
+        float          zero = 0.0f;
+        acl_scalar_ptr acl_zero = ggml_cann_create_scalar(&zero, ACL_FLOAT);
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst.get(), acl_zero.get());
+    }
+
+    // Copy src vector onto the diagonal of dst via strided views.
+    // src viewed as [N, n_batch], contiguous strides.
+    int64_t ne_vec[2]      = { N, n_batch };
+    size_t  nb_src_vec[2]  = { nb_f32, N * nb_f32 };
+    // dst diagonal view: stride (N+1)*4 steps along the diagonal.
+    size_t  nb_dst_diag[2] = { (N + 1) * nb_f32, N * N * nb_f32 };
+
+    acl_tensor_ptr acl_src_vec  = ggml_cann_create_tensor(src->data, ACL_FLOAT, nb_f32, ne_vec, nb_src_vec, 2);
+    acl_tensor_ptr acl_dst_diag = ggml_cann_create_tensor(dst->data, ACL_FLOAT, nb_f32, ne_vec, nb_dst_diag, 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst_diag.get(), acl_src_vec.get());
+}
+
+void ggml_cann_fill(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    float c = ggml_get_op_params_f32(dst, 0);
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    acl_scalar_ptr acl_c   = ggml_cann_create_scalar(&c, ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst.get(), acl_c.get());
+}
+
+void ggml_cann_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    const int64_t S       = src->ne[0];
+    const int64_t n_batch = src->ne[2] * src->ne[3];
+    const size_t  nb_f32  = sizeof(float);
+
+    int64_t ne3d[3] = { S, S, n_batch };
+    size_t  nb3d[3] = { nb_f32, S * nb_f32, S * S * nb_f32 };
+
+    const ggml_tri_type ttype = (ggml_tri_type) ggml_get_op_params_i32(dst, 0);
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src->data, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
+
+    switch (ttype) {
+        case GGML_TRI_TYPE_LOWER:
+            // Tril(-1): preserve row > col (strict lower), zero upper + diagonal.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src.get(), (int64_t)-1, acl_dst.get());
+            break;
+        case GGML_TRI_TYPE_UPPER_DIAG:
+            // Triu(0): preserve row <= col (upper + diagonal), zero strict lower.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Triu, acl_src.get(), (int64_t)0, acl_dst.get());
+            break;
+        case GGML_TRI_TYPE_UPPER:
+            // Triu(1): preserve row < col (strict upper), zero lower + diagonal.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Triu, acl_src.get(), (int64_t)1, acl_dst.get());
+            break;
+        case GGML_TRI_TYPE_LOWER_DIAG:
+            // Tril(0): preserve row >= col (lower + diagonal), zero strict upper.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src.get(), (int64_t)0, acl_dst.get());
+            break;
+        default:
+            GGML_ABORT("unsupported tri type");
+    }
+}
+
 void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor *  src     = dst->src[0];
     acl_tensor_ptr acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
@@ -1695,152 +1918,90 @@ void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     aclnn_softmax(ctx, softmax_tensor.get(), 3, acl_dst.get());
 }
 
-/**
- * @brief Performs index select operation on a 4D tensor using the CANN backend.
- *
- * This function applies the `IndexSelect` operation along a specific dimension
- * of the source tensor (`src_buffer`) using the indices from the index tensor (`index`).
- * It iterates over the last two dimensions of the source tensor, creates the corresponding
- * CANN tensors for the source, index, and output slices, and executes the `IndexSelect`
- * operation for each slice.
- *
- * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer containing the 4D input tensor data.
- * @param src_ne The dimensions of the source tensor.
- * @param src_nb The strides (byte offsets) of the source tensor.
- * @param dst_buffer The destination buffer where the output tensor data will be written.
- * @param dst_ne The dimensions of the destination tensor.
- * @param dst_nb The strides (byte offsets) of the destination tensor.
- * @param index The index tensor specifying the indices to select from the source tensor.
- * @param type The data type of the source and destination tensors.
- */
-static void aclnn_index_select_4d(ggml_backend_cann_context & ctx,
-                                  void *                      src_buffer,
-                                  int64_t *                   src_ne,
-                                  size_t *                    src_nb,
-                                  void *                      dst_buffer,
-                                  int64_t *                   dst_ne,
-                                  size_t *                    dst_nb,
-                                  ggml_tensor *               index,
-                                  ggml_type                   type) {
-    for (int64_t i = 0; i < src_ne[3]; i++) {
-        for (int64_t j = 0; j < src_ne[2]; j++) {
-            // src
-            acl_tensor_ptr acl_src_tensor =
-                ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2);
-
-            // index
-            acl_tensor_ptr acl_index = ggml_cann_create_tensor(
-                (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
-                ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1);
-
-            // out
-            acl_tensor_ptr acl_out =
-                ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor.get(), 0, acl_index.get(), acl_out.get());
-        }
-    }
-}
-
-/**
- * @brief Performs inplace index copy operation on a 4D tensor using the CANN backend.
- *
- * This function applies the `IndexCopy` operation along a specific dimension of the
- * destination tensor (`dst_buffer`) by copying elements from the source tensor (`src_buffer`)
- * to positions specified by the index tensor (`index`).
- * It iterates over the last two dimensions of the tensors, creates the corresponding
- * CANN tensors for source, index, and destination slices, and performs the index copy
- * operation for each slice.
- *
- * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer containing the 4D input tensor data to be copied.
- * @param src_ne The dimensions of the source tensor.
- * @param src_nb The strides (byte offsets) of the source tensor.
- * @param dst_buffer The destination buffer where values will be copied to.
- * @param dst_ne The dimensions of the destination tensor.
- * @param dst_nb The strides (byte offsets) of the destination tensor.
- * @param index The index tensor specifying target positions in the destination tensor.
- * @param type The data type of the source and destination tensors.
- */
-static void aclnn_index_copy_4d(ggml_backend_cann_context & ctx,
-                                void *                      src_buffer,
-                                int64_t *                   src_ne,
-                                size_t *                    src_nb,
-                                void *                      dst_buffer,
-                                int64_t *                   dst_ne,
-                                size_t *                    dst_nb,
-                                ggml_tensor *               index,
-                                ggml_type                   type) {
-    for (int64_t i = 0; i < src_ne[3]; i++) {
-        for (int64_t j = 0; j < src_ne[2]; j++) {
-            // src
-            acl_tensor_ptr acl_src_tensor =
-                ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2);
-
-            // index
-            acl_tensor_ptr acl_index = ggml_cann_create_tensor(
-                (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
-                ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1);
-
-            // out
-            acl_tensor_ptr acl_out =
-                ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out.get(), 0, acl_index.get(), acl_src_tensor.get());
-        }
-    }
-}
 
 void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * src0 = dst->src[0];  // src
+    ggml_tensor * src0 = dst->src[0];  // weight
     ggml_tensor * src1 = dst->src[1];  // index
 
     GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16
                 || dst->type == GGML_TYPE_BF16);
 
+    // n_idx: number of row indices per (i2, i3) batch slice.
+    // ggml guarantees: src0->ne[2] == src1->ne[1], src0->ne[3] == src1->ne[2], src1->ne[3] == 1.
+    const int64_t n_idx = src1->ne[0];
+
+    // Gather all (i2, i3) batch slices from src into dst.
+    // ggml_cann_create_tensor reverses dims, so ACL sees [ne1, ne0].
+    // GatherV2 with dim=0 gathers along ACL dim-0 == ggml ne[1] (the vocabulary / row axis).
+    // nb: the 4 strides of the source buffer (nb[0..1] for the 2D slice shape,
+    //     nb[2..3] for computing per-batch-slice base pointer offsets).
+    auto gather_batched = [&](void * src_base, aclDataType acl_type, size_t type_size,
+                              const size_t * nb) {
+        int64_t src_ne[2]  = { src0->ne[0], src0->ne[1] };
+        size_t  src_nb_2d[2] = { nb[0], nb[1] };
+        int64_t dst_ne[2]  = { src0->ne[0], n_idx };
+        size_t  dst_nb_2d[2] = { dst->nb[0], dst->nb[1] };
+        int64_t idx_ne[1]  = { n_idx };
+        size_t  idx_nb[1]  = { (size_t)ggml_element_size(src1) };
+
+        for (int64_t i3 = 0; i3 < src0->ne[3]; i3++) {
+            for (int64_t i2 = 0; i2 < src0->ne[2]; i2++) {
+                acl_tensor_ptr acl_src = ggml_cann_create_tensor(
+                    (char *)src_base + i3 * nb[3] + i2 * nb[2],
+                    acl_type, type_size, src_ne, src_nb_2d, 2);
+                acl_tensor_ptr acl_idx = ggml_cann_create_tensor(
+                    (char *)src1->data + i3 * src1->nb[2] + i2 * src1->nb[1],
+                    ggml_cann_type_mapping(src1->type), (size_t)ggml_element_size(src1),
+                    idx_ne, idx_nb, 1);
+                acl_tensor_ptr acl_dst = ggml_cann_create_tensor(
+                    (char *)dst->data + i3 * dst->nb[3] + i2 * dst->nb[2],
+                    acl_type, type_size, dst_ne, dst_nb_2d, 2);
+                GGML_CANN_CALL_ACLNN_OP(ctx, GatherV2, acl_src.get(), 0, acl_idx.get(), acl_dst.get());
+            }
+        }
+    };
+
     switch (src0->type) {
         case GGML_TYPE_BF16:
         case GGML_TYPE_F16:
         case GGML_TYPE_F32:
             if (src0->type == dst->type) {
-                aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1,
-                                      dst->type);
+                gather_batched(src0->data,
+                               ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                               src0->nb);
             } else {
-                acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
-                ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * ggml_element_size(dst));
-                void *               src_trans_buffer = src_buffer_allocator.get();
-                size_t               src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = dst->nb[0];
+                // Cast src0 to dst type, then gather.
+                ggml_cann_pool_alloc src_cast_allocator(ctx.pool(),
+                                                        ggml_nelements(src0) * ggml_element_size(dst));
+                size_t src_cast_nb[GGML_MAX_DIMS];
+                src_cast_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+                    src_cast_nb[i] = src_cast_nb[i - 1] * src0->ne[i - 1];
                 }
-                acl_tensor_ptr src_trans_tensor =
-                    ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(dst->type),
-                                            ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
-                aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
-                aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
-                                      dst->type);
+                acl_tensor_ptr acl_src0     = ggml_cann_create_tensor(src0);
+                acl_tensor_ptr acl_src_cast = ggml_cann_create_tensor(
+                    src_cast_allocator.get(), ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                    src0->ne, src_cast_nb, GGML_MAX_DIMS);
+                aclnn_cast(ctx, acl_src0.get(), acl_src_cast.get(), ggml_cann_type_mapping(dst->type));
+
+                gather_batched(src_cast_allocator.get(),
+                               ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                               src_cast_nb);
             }
             break;
         case GGML_TYPE_Q8_0:
             {
-                // add 1 dim for bcast mul.
+                // Dequantize Q8_0 to dst type, then gather.
                 size_t  weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], dequant_nb[GGML_MAX_DIMS + 1];
                 int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], *dequant_ne;
-                int64_t scale_offset = 0;
-                // [3,4,5,64] -> [3,4,5,2,32]
-                weight_ne[0]         = QK8_0;
-                weight_ne[1]         = src0->ne[0] / QK8_0;
-                weight_nb[0]         = sizeof(int8_t);
-                weight_nb[1]         = weight_nb[0] * weight_ne[0];
+                weight_ne[0] = QK8_0;
+                weight_ne[1] = src0->ne[0] / QK8_0;
+                weight_nb[0] = sizeof(int8_t);
+                weight_nb[1] = weight_nb[0] * weight_ne[0];
                 for (int i = 2; i < GGML_MAX_DIMS + 1; i++) {
                     weight_ne[i] = src0->ne[i - 1];
                     weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1];
                 }
-                // [3,4,5,64] -> [3,4,5,2,1]
                 scale_ne[0] = 1;
                 scale_ne[1] = src0->ne[0] / QK8_0;
                 scale_nb[0] = sizeof(uint16_t);
@@ -1849,31 +2010,33 @@ void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
                     scale_ne[i] = src0->ne[i - 1];
                     scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1];
                 }
-                // [3,4,5,64] -> [3,4,5,2,32]
                 dequant_ne    = weight_ne;
                 dequant_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS + 1; i++) {
                     dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1];
                 }
-                scale_offset = ggml_nelements(src0) * sizeof(int8_t);
-                ggml_cann_pool_alloc dequant_buffer_allocator(ctx.pool(),
-                                                              ggml_nelements(src0) * ggml_type_size(dst->type));
-                acl_tensor_ptr       acl_weight_tensor = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t),
-                                                                                 weight_ne, weight_nb, GGML_MAX_DIMS + 1);
-                acl_tensor_ptr       acl_scale_tensor =
-                    ggml_cann_create_tensor(src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
-                                            GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
-                acl_tensor_ptr dequant_tensor =
-                    ggml_cann_create_tensor(dequant_buffer_allocator.get(), ggml_cann_type_mapping(dst->type),
-                                            ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
-                aclnn_mul(ctx, acl_weight_tensor.get(), acl_scale_tensor.get(), dequant_tensor.get());
-                dequant_nb[0] = ggml_type_size(dst->type);
+                const int64_t scale_offset = ggml_nelements(src0) * sizeof(int8_t);
+                ggml_cann_pool_alloc dequant_allocator(ctx.pool(),
+                                                       ggml_nelements(src0) * ggml_type_size(dst->type));
+                acl_tensor_ptr acl_weight = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t),
+                                                                     weight_ne, weight_nb, GGML_MAX_DIMS + 1);
+                acl_tensor_ptr acl_scale  = ggml_cann_create_tensor(
+                    src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
+                    GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
+                acl_tensor_ptr acl_dequant = ggml_cann_create_tensor(
+                    dequant_allocator.get(), ggml_cann_type_mapping(dst->type),
+                    ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
+                aclnn_mul(ctx, acl_weight.get(), acl_scale.get(), acl_dequant.get());
+
+                // Reinterpret dequant buffer as 4D [src0->ne] with contiguous strides.
                 dequant_ne    = src0->ne;
+                dequant_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS; i++) {
                     dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
                 }
-                aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), dequant_ne, dequant_nb, dst->data, dst->ne,
-                                      dst->nb, src1, dst->type);
+                gather_batched(dequant_allocator.get(),
+                               ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                               dequant_nb);
                 break;
             }
         default:
@@ -1883,31 +2046,70 @@ void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
 }
 
 void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * src0 = dst->src[0];  // src
-    ggml_tensor * src1 = dst->src[1];  // index
+    ggml_tensor * src0 = dst->src[0];  // source values
+    ggml_tensor * src1 = dst->src[1];  // row indices
+
+    // n_idx: number of source rows to scatter per batch slice.
+    // ggml guarantees: src0->ne[1] == src1->ne[0].
+    const int64_t n_idx = src1->ne[0];
+
+    // Copy n_idx rows of src [ne0, n_idx] into dst [ne0, ne1] at positions given by a 1D index.
+    // ggml_cann_create_tensor reverses dims, so ACL sees [ne1, ne0] for dst.
+    // InplaceIndexCopy with dim=0 copies along ACL dim-0 == ggml ne[1] (the row axis).
+    // src_nb: the 4 strides of the source buffer (nb[0..1] for the 2D slice shape,
+    //         nb[2..3] for computing per-batch-slice base pointer offsets).
+    auto scatter_batched = [&](void * src_base, aclDataType acl_type, size_t type_size,
+                               const size_t * src_nb) {
+        int64_t d_ne[2]    = { dst->ne[0], dst->ne[1] };
+        size_t  d_nb[2]    = { dst->nb[0], dst->nb[1] };
+        int64_t s_ne[2]    = { dst->ne[0], n_idx };
+        size_t  s_nb_2d[2] = { src_nb[0], src_nb[1] };
+        int64_t i_ne[1]    = { n_idx };
+        size_t  i_nb[1]    = { (size_t)ggml_element_size(src1) };
+
+        for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) {
+            for (int64_t i2 = 0; i2 < dst->ne[2]; i2++) {
+                acl_tensor_ptr acl_dst = ggml_cann_create_tensor(
+                    (char *)dst->data + i3 * dst->nb[3] + i2 * dst->nb[2],
+                    acl_type, type_size, d_ne, d_nb, 2);
+                acl_tensor_ptr acl_idx = ggml_cann_create_tensor(
+                    (char *)src1->data + (i3 % src1->ne[2]) * src1->nb[2] + (i2 % src1->ne[1]) * src1->nb[1],
+                    ggml_cann_type_mapping(src1->type), (size_t)ggml_element_size(src1),
+                    i_ne, i_nb, 1);
+                acl_tensor_ptr acl_src = ggml_cann_create_tensor(
+                    (char *)src_base + i3 * src_nb[3] + i2 * src_nb[2],
+                    acl_type, type_size, s_ne, s_nb_2d, 2);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_dst.get(), 0, acl_idx.get(), acl_src.get());
+            }
+        }
+    };
 
     switch (dst->type) {
         case GGML_TYPE_F32:
-            {
-                aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, dst->type);
-                break;
-            }
+            scatter_batched(src0->data,
+                            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                            src0->nb);
+            break;
         case GGML_TYPE_F16:
         case GGML_TYPE_BF16:
             {
-                acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
-                ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t));
-                void *               src_trans_buffer = src_buffer_allocator.get();
-                size_t               src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = sizeof(uint16_t);
+                // Cast src0 (F32) to dst type first.
+                ggml_cann_pool_alloc src_cast_allocator(ctx.pool(),
+                                                        ggml_nelements(src0) * ggml_type_size(dst->type));
+                size_t src_cast_nb[GGML_MAX_DIMS];
+                src_cast_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+                    src_cast_nb[i] = src_cast_nb[i - 1] * src0->ne[i - 1];
                 }
-                acl_tensor_ptr src_trans_tensor = ggml_cann_create_tensor(
-                    src_trans_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
-                aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
-                aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
-                                    dst->type);
+                acl_tensor_ptr acl_src0     = ggml_cann_create_tensor(src0);
+                acl_tensor_ptr acl_src_cast = ggml_cann_create_tensor(
+                    src_cast_allocator.get(), ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                    src0->ne, src_cast_nb, GGML_MAX_DIMS);
+                aclnn_cast(ctx, acl_src0.get(), acl_src_cast.get(), ggml_cann_type_mapping(dst->type));
+
+                scatter_batched(src_cast_allocator.get(),
+                                ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                src_cast_nb);
                 break;
             }
         default:
@@ -3268,29 +3470,50 @@ void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst
     int64_t           paddingsArray[2] = { opts[0], opts[1] };
     acl_int_array_ptr paddings         = ggml_cann_create_int_array(paddingsArray, 2);
 
-    for (int64_t i = 0; i < src0->ne[3]; i++) {
-        acl_tensor_ptr acl_src =
-            ggml_cann_create_tensor((char *) src0->data + i * src0->ne[3], ggml_cann_type_mapping(src0->type),
-                                    ggml_element_size(src0), src0->ne, src0->nb, 3);
+    // Collapsing ne[2]*ne[3] into a single batch dimension requires that dim3
+    // is contiguous with respect to dim2 in both src and dst.
+    GGML_ASSERT(src0->nb[3] == src0->nb[2] * src0->ne[2]);
+    GGML_ASSERT(dst->nb[3]  == dst->nb[2]  * dst->ne[2]);
 
-        acl_tensor_ptr acl_dst =
-            ggml_cann_create_tensor((char *) dst->data + i * src0->ne[3], ggml_cann_type_mapping(dst->type),
-                                    ggml_element_size(dst), dst->ne, dst->nb, 3);
+    int64_t src_ne_3d[3] = { src0->ne[0], src0->ne[1], src0->ne[2] * src0->ne[3] };
+    int64_t dst_ne_3d[3] = { dst->ne[0],  dst->ne[1],  dst->ne[2]  * dst->ne[3]  };
 
-        GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src.get(), paddings.get(), acl_dst.get());
-    }
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type),
+                                                     ggml_element_size(src0), src_ne_3d, src0->nb, 3);
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ggml_cann_type_mapping(dst->type),
+                                                     ggml_element_size(dst), dst_ne_3d, dst->nb, 3);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src.get(), paddings.get(), acl_dst.get());
 }
 
 void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src0 = dst->src[0];
     ggml_tensor * src1 = dst->src[1];
 
+    // Write element-wise equality (0 or 1) into a temporary buffer to avoid
+    // modifying src0 in-place.  Use the same type as src0 so ReduceSum can
+    // consume it directly without a type cast.
+    ggml_cann_pool_alloc eq_alloc(ctx.pool(), ggml_nelements(src0) * ggml_element_size(src0));
+    size_t eq_nb[GGML_MAX_DIMS];
+    eq_nb[0] = ggml_element_size(src0);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        eq_nb[i] = eq_nb[i - 1] * src0->ne[i - 1];
+    }
+    acl_tensor_ptr acl_eq = ggml_cann_create_tensor(
+        eq_alloc.get(), ggml_cann_type_mapping(src0->type), ggml_element_size(src0),
+        src0->ne, eq_nb, GGML_MAX_DIMS);
+
     acl_tensor_ptr acl_self  = ggml_cann_create_tensor(src0);
     acl_tensor_ptr acl_other = ggml_cann_create_tensor(src1);
+    GGML_CANN_CALL_ACLNN_OP(ctx, EqTensor, acl_self.get(), acl_other.get(), acl_eq.get());
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceEqTensor, acl_self.get(), acl_other.get());
-
-    ggml_cann_sum(ctx, dst);
+    // Sum the 0/1 values into dst.
+    acl_tensor_ptr    acl_dst    = ggml_cann_create_tensor(dst);
+    int64_t           dims[4]    = { 0, 1, 2, 3 };
+    acl_int_array_ptr dims_arr   = ggml_cann_create_int_array(dims, 4);
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_eq.get(), dims_arr.get(), true,
+                            ggml_cann_type_mapping(dst->type), acl_dst.get());
 }
 
 void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
@@ -3306,6 +3529,27 @@ void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     GGML_CANN_CALL_ACLNN_OP(ctx, GtScalar, acl_src.get(), alpha.get(), acl_dst.get());
 }
 
+void ggml_cann_softplus(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    float          beta_val      = 1.0f;
+    float          threshold_val = 20.0f;
+    acl_scalar_ptr beta          = ggml_cann_create_scalar(&beta_val,      ACL_FLOAT);
+    acl_scalar_ptr threshold     = ggml_cann_create_scalar(&threshold_val, ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Softplus, acl_src.get(), beta.get(), threshold.get(), acl_dst.get());
+}
+
+void ggml_cann_geglu_quick(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    auto gelu_quick_fn = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst);
+    };
+    ggml_cann_op_unary_gated(gelu_quick_fn, ctx, dst);
+}
+
 /**
  * @brief Performs expert-specific matrix multiplication (MoE) with
  * floating-point precision using the CANN backend.
@@ -3892,46 +4136,65 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst
 }
 
 static void ggml_cann_out_prod_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * src0 = dst->src[0];  // weight
-    ggml_tensor * src1 = dst->src[1];  // input
+    ggml_tensor * src0 = dst->src[0];  // weight  [ne00=m, ne01=K, ne02, ne03]
+    ggml_tensor * src1 = dst->src[1];  // input   [ne10=n, ne11=K, ne12, ne13]
     GGML_TENSOR_BINARY_OP_LOCALS
 
-    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
-    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_dst.get());
+    // dst[i,j] = sum_k src0[i,k] * src1[j,k]  i.e. dst = src0 @ src1^T.
+    //
+    // ggml_cann_create_tensor reverses dimension order, so ACL sees:
+    //   acl_src0 slice:   ggml[m,K]  ->  ACL[K,m]
+    //   acl_src1 slice:   ggml[n,K]  ->  ACL[K,n]
+    //   acl_dst  slice:   ggml[m,n]  ->  ACL[n,m]
+    //
+    // Build a transposed view of src1 by swapping ne[0]/ne[1]:
+    //   src1_t:  ggml[K,n] (swapped strides)  ->  ACL[n,K]
+    //
+    // Matmul(src1_t [n,K], src0 [K,m]) = [n,m] = acl_dst  ✓
+    //
+    // The outer batch loop is kept because src0 may have fewer batch slices than
+    // dst (ne02 <= ne2, ne03 <= ne3): this is a strided-broadcast not supported
+    // by standard CANN Matmul broadcasting.
+
+    const aclDataType src0_acl_type = ggml_cann_type_mapping(src0->type);
+    const aclDataType src1_acl_type = ggml_cann_type_mapping(src1->type);
+    const aclDataType dst_acl_type  = ggml_cann_type_mapping(dst->type);
+    const size_t      src0_type_sz  = ggml_type_size(src0->type);
+    const size_t      src1_type_sz  = ggml_type_size(src1->type);
+    const size_t      dst_type_sz   = ggml_type_size(dst->type);
 
     const int64_t dps2 = ne2 / ne02;
     const int64_t dps3 = ne3 / ne03;
+
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = 0; i2 < ne2; i2++) {
             const int64_t i02 = i2 / dps2;
             const int64_t i03 = i3 / dps3;
 
-            const int64_t  i12 = i2;
-            const int64_t  i13 = i3;
-            acl_tensor_ptr accumulator =
-                ggml_cann_create_tensor((char *) dst->data + i2 * nb2 + i3 * nb3, ggml_cann_type_mapping(dst->type),
-                                        ggml_type_size(dst->type), dst->ne, dst->nb, 2);
+            // src0 2D slice at [i02, i03]: ggml [m, K] -> ACL [K, m]
+            int64_t src0_ne[2] = { ne00, ne01 };
+            size_t  src0_nb[2] = { nb00, nb01 };
+            acl_tensor_ptr acl_src0_s = ggml_cann_create_tensor(
+                (char *) src0->data + i02 * nb02 + i03 * nb03,
+                src0_acl_type, src0_type_sz, src0_ne, src0_nb, 2);
 
-            // The outer product needs to be accumulated in this dimension.
-            for (int64_t i1 = 0; i1 < ne11; i1++) {
-                acl_tensor_ptr acl_input = ggml_cann_create_tensor(
-                    (char *) src1->data + i1 * nb11 + i12 * nb12 + i13 * nb13, ggml_cann_type_mapping(src0->type),
-                    ggml_type_size(src0->type), src1->ne, src1->nb, 1);
+            // src1 transposed 2D slice at [i2, i3]: swap ne/nb -> ggml[K,n] -> ACL[n,K]
+            int64_t src1_t_ne[2] = { ne11, ne10 };
+            size_t  src1_t_nb[2] = { nb11, nb10 };
+            acl_tensor_ptr acl_src1_t = ggml_cann_create_tensor(
+                (char *) src1->data + i2 * nb12 + i3 * nb13,
+                src1_acl_type, src1_type_sz, src1_t_ne, src1_t_nb, 2);
 
-                acl_tensor_ptr acl_weight = ggml_cann_create_tensor(
-                    (char *) src0->data + i1 * nb01 + i02 * nb02 + i03 * nb03, ggml_cann_type_mapping(src0->type),
-                    ggml_type_size(src0->type), src0->ne, src0->nb, 1);
+            // dst 2D slice at [i2, i3]: ggml [m, n] -> ACL [n, m]
+            int64_t dst_ne[2] = { ne0, ne1 };
+            size_t  dst_nb[2] = { nb0, nb1 };
+            acl_tensor_ptr acl_dst_s = ggml_cann_create_tensor(
+                (char *) dst->data + i2 * nb2 + i3 * nb3,
+                dst_acl_type, dst_type_sz, dst_ne, dst_nb, 2);
 
-                ggml_cann_pool_alloc output_allocator(ctx.pool());
-                void *               output_buffer = output_allocator.alloc(ggml_nbytes(dst));
-                acl_tensor_ptr       acl_out = ggml_cann_create_tensor(output_buffer, ggml_cann_type_mapping(dst->type),
-                                                                       ggml_type_size(dst->type), dst->ne, dst->nb, 2);
-
-                GGML_CANN_CALL_ACLNN_OP(ctx, Ger, acl_input.get(), acl_weight.get(), acl_out.get());
-                float       alpha_value = 1.0f;
-                aclScalar * alpha       = aclCreateScalar(&alpha_value, ACL_FLOAT);
-                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, accumulator.get(), acl_out.get(), alpha);
-            }
+            // Matmul(src1_t [n,K], src0 [K,m]) = [n,m] = acl_dst_s  ✓
+            GGML_CANN_CALL_ACLNN_OP(ctx, Matmul,
+                acl_src1_t.get(), acl_src0_s.get(), acl_dst_s.get(), (int8_t) 1);
         }
     }
 }
@@ -4170,3 +4433,4 @@ void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor *
         }
     }
 }
+

ggml/src/ggml-cann/aclnn_ops.h

@@ -32,6 +32,9 @@
 #include <aclnnop/aclnn_cat.h>
 #include <aclnnop/aclnn_clamp.h>
 #include <aclnnop/aclnn_cos.h>
+#include <aclnnop/aclnn_cumsum.h>
+#include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triu.h>
 #include <aclnnop/aclnn_exp.h>
 #include <aclnnop/aclnn_gelu.h>
 #include <aclnnop/aclnn_gelu_v2.h>
@@ -47,6 +50,9 @@
 #include <aclnnop/aclnn_sign.h>
 #include <aclnnop/aclnn_silu.h>
 #include <aclnnop/aclnn_sin.h>
+#include <aclnnop/aclnn_softplus.h>
+#include <aclnnop/aclnn_swi_glu.h>
+#include <aclnnop/aclnn_geglu.h>
 #include <aclnnop/aclnn_slice.h>
 #include <aclnnop/aclnn_sqrt.h>
 #include <aclnnop/aclnn_tanh.h>
@@ -69,6 +75,9 @@
  */
 void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+void ggml_cann_swiglu(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+void ggml_cann_geglu(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t approximate);
+
 /**
  * @brief   Applies the Leaky ReLU activation function to a tensor using the CANN
  *          backend.
@@ -325,6 +334,48 @@ void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
 void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+/**
+ * @brief   Computes the cumulative sum of a ggml tensor along dim 0 using the
+ *          CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_CUMSUM`.
+ */
+void ggml_cann_cumsum(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Computes a triangular mask (tril/triu) of a square ggml tensor
+ *          using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_TRI`.
+ */
+void ggml_cann_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Solves a triangular linear system AX=B using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_SOLVE_TRI`.
+ */
+void ggml_cann_solve_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Creates a diagonal matrix from a vector using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_DIAG`.
+ */
+void ggml_cann_diag(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Fills a tensor with a constant scalar value using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_FILL`.
+ */
+void ggml_cann_fill(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
 /**
  * @brief   Upsamples a ggml tensor using nearest neighbor interpolation using
  *          the CANN backend.
@@ -461,6 +512,9 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor *
 // @see ggml_cann_dup.
 void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+// @see ggml_cann_acc, but copies src1 into dst instead of adding.
+void ggml_cann_set(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
 /**
  * @brief   Computes the softmax activation with optional masking.
  *
@@ -813,6 +867,8 @@ void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst);
  *            dst->op is expected to be `GGML_OP_STEP`.
  */
 void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+void ggml_cann_softplus(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+void ggml_cann_geglu_quick(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
 /**
  * @brief   Performs the Flash Attention extended operator using the CANN backend.

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

@@ -1428,6 +1428,22 @@ static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
     return false;
 }
 
+/**
+ * @brief Set a region of a tensor's device memory to a specified value.
+ *
+ * @param buffer The CANN buffer containing the tensor.
+ * @param tensor Pointer to the tensor whose memory will be set.
+ * @param value The value to which each byte in the region will be set.
+ * @param offset Byte offset within the tensor's data to start setting.
+ * @param size Number of bytes to set.
+ */
+static void ggml_backend_cann_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+    ACL_CHECK(aclrtMemset((char *) tensor->data + offset, size, value, size));
+}
+
 /**
  * @brief Clear a CANN buffer by setting all its memory to a specified value.
  *
@@ -1454,7 +1470,7 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
     /* .free_buffer     = */ ggml_backend_cann_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_cann_buffer_get_base,
     /* .init_tensor     = */ ggml_backend_cann_buffer_init_tensor,
-    /* .memset_tensor   = */ NULL,
+    /* .memset_tensor   = */ ggml_backend_cann_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cann_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cann_buffer_get_tensor,
     /* .set_tensor_2d   = */ NULL,
@@ -1835,6 +1851,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
                 case GGML_UNARY_OP_STEP:
                     ggml_cann_step(ctx, dst);
                     break;
+                case GGML_UNARY_OP_SOFTPLUS:
+                    ggml_cann_softplus(ctx, dst);
+                    break;
                 default:
                     return false;
             }
@@ -1845,20 +1864,16 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
                     GGML_CANN_CALL_OP_UNARY_GATED(Relu);
                     break;
                 case GGML_GLU_OP_GEGLU:
+                    ggml_cann_geglu(ctx, dst, 0);  // approximate=0 → tanh
+                    break;
                 case GGML_GLU_OP_GEGLU_ERF:
-                    // aclnnGelu internally uses the erf-based approximation.
-                    GGML_CANN_CALL_OP_UNARY_GATED(Gelu);
+                    ggml_cann_geglu(ctx, dst, 1);  // approximate=1 → erf
                     break;
                 case GGML_GLU_OP_SWIGLU:
-                    GGML_CANN_CALL_OP_UNARY_GATED(Silu);
+                    ggml_cann_swiglu(ctx, dst);
                     break;
                 case GGML_GLU_OP_GEGLU_QUICK:
-                    {
-                        auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
-                            GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst);
-                        };
-                        ggml_cann_op_unary_gated(lambda, ctx, dst);
-                    }
+                    ggml_cann_geglu_quick(ctx, dst);
                     break;
                 default:
                     return false;
@@ -1920,6 +1935,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
         case GGML_OP_CPY:
             ggml_cann_cpy(ctx, dst);
             break;
+        case GGML_OP_SET:
+            ggml_cann_set(ctx, dst);
+            break;
         case GGML_OP_CONT:
             ggml_cann_dup(ctx, dst);
             break;
@@ -1989,6 +2007,21 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
         case GGML_OP_SSM_CONV:
             ggml_cann_ssm_conv(ctx, dst);
             break;
+        case GGML_OP_CUMSUM:
+            ggml_cann_cumsum(ctx, dst);
+            break;
+        case GGML_OP_TRI:
+            ggml_cann_tri(ctx, dst);
+            break;
+        case GGML_OP_FILL:
+            ggml_cann_fill(ctx, dst);
+            break;
+        case GGML_OP_DIAG:
+            ggml_cann_diag(ctx, dst);
+            break;
+        case GGML_OP_SOLVE_TRI:
+            ggml_cann_solve_tri(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -2324,6 +2357,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend,
     if (use_cann_graph) {
         // If no matching graph is found, the graph needs to be recaptured.
         graph_capture_required = !cann_ctx->graph_lru_cache.find_and_move_to_front(cgraph);
+
         if (graph_capture_required) {
             // If no matching graph is found, add a new ACL graph.
             ggml_cann_graph * new_graph = ggml_cann_graph::create_from_cgraph(cgraph);
@@ -2382,6 +2416,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                 case GGML_UNARY_OP_SGN:
                 case GGML_UNARY_OP_STEP:
                 case GGML_UNARY_OP_GELU_ERF:
+                case GGML_UNARY_OP_SOFTPLUS:
                     return true;
                 default:
                     return false;
@@ -2572,6 +2607,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
+        case GGML_OP_SET:
         case GGML_OP_GROUP_NORM:
             return true;
         case GGML_OP_PAD:
@@ -2649,6 +2685,16 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
             }
         case GGML_OP_SSM_CONV:
             return true;
+        case GGML_OP_CUMSUM:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_TRI:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_FILL:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_DIAG:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_SOLVE_TRI:
+            return op->src[0]->type == GGML_TYPE_F32;
         default:
             return false;
     }
@@ -2700,8 +2746,8 @@ static const ggml_backend_i ggml_backend_cann_interface = {
     /* .free                    = */ ggml_backend_cann_free,
     /* .set_tensor_async        = */ ggml_backend_cann_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cann_get_tensor_async,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ ggml_backend_cann_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cann_synchronize,
     /* .graph_plan_create       = */ NULL,

ggml/src/ggml-cpu/CMakeLists.txt

@@ -485,6 +485,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             if (GGML_RV_ZIHINTPAUSE)
                 string(APPEND MARCH_STR "_zihintpause")
             endif()
+            if (GGML_CPU_RISCV64_SPACEMIT)
+                # `xsmtvdotii' is only required for GCC >= 15.
+                if (CMAKE_C_COMPILER_ID STREQUAL "GNU" AND
+                    CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 15)
+                    string(APPEND MARCH_STR "_xsmtvdotii")
+                endif()
+            endif()
 
             list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
         else()
@@ -571,13 +578,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.22.0")
-        set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "54049037570ab0ee0a0d126b2ba5ece1")
+        set(KLEIDIAI_COMMIT_TAG "v1.24.0")
+        set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/releases/download/${KLEIDIAI_COMMIT_TAG}/kleidiai-${KLEIDIAI_COMMIT_TAG}-src.tar.gz")
+        set(KLEIDIAI_RELEASE_ARCHIVE_MD5  "2f02ebe29573d45813e671eb304f2a00")
 
         set(KLEIDIAI_FETCH_ARGS
             URL ${KLEIDIAI_DOWNLOAD_URL}
-            URL_HASH MD5=${KLEIDIAI_ARCHIVE_MD5}
+            URL_HASH MD5=${KLEIDIAI_RELEASE_ARCHIVE_MD5}
         )
         if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.24")
             list(APPEND KLEIDIAI_FETCH_ARGS DOWNLOAD_EXTRACT_TIMESTAMP NEW)

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

@@ -203,7 +203,6 @@
 #elif defined(__riscv)
 // quants.c
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
-#define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4

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

@@ -5023,6 +5023,71 @@ void ggml_gemm_q8_0_4x8_q8_0(int                        n,
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
+#if defined(__aarch64__) && defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (svcntb() * 8 == 256) {
+        const block_q8_0x4 * b_ptr_base = (const block_q8_0x4 *) vx;
+
+        static const uint32_t idx_arr[8] = {0, 1, 4, 5, 2, 3, 6, 7};
+        svuint32_t idx = svld1(svptrue_b32(), idx_arr);
+        static const uint32_t idx_arr1[8] = {0, 1, 2, 3, 1, 2, 3, 0};
+        svuint32_t idx_sc1 = svld1(svptrue_b32(), idx_arr1);
+        static const uint32_t idx_arr2[8] = {0, 1, 2, 3, 0, 1, 2, 3};
+        svuint32_t idx_sc2 = svld1(svptrue_b32(), idx_arr2);
+
+        for (int y = 0; y < nr; y += 4) {
+            const block_q8_0x4 * a_ptr_base = (const block_q8_0x4 *) vy + (y / 4) * nb;
+
+            for (int x = 0; x < nc; x += ncols_interleaved) {
+                const block_q8_0x4 * b_ptr = b_ptr_base + (x / 4) * nb;
+                const block_q8_0x4 * a_ptr = a_ptr_base;
+
+                svfloat32_t acc_f32_01 = svdup_f32(0);
+                svfloat32_t acc_f32_23 = svdup_f32(0);
+
+                for (int b = 0; b < nb; b++) {
+
+                    svint32_t acc_01 = svdup_s32(0);
+                    svint32_t acc_23 = svdup_s32(0);
+
+                    // Process 4 chunks of 8 positions each
+                    for (int chunk = 0; chunk < 4; chunk++) {
+                        svint8_t s_a01 = svld1rq_s8(svptrue_b8(), a_ptr->qs + chunk * 32);
+                        svint8_t s_a23 = svld1rq_s8(svptrue_b8(), a_ptr->qs + chunk * 32 + 16);
+                        svint8_t s_b0123 = svld1_s8(svptrue_b8(), b_ptr->qs + chunk * 32);
+
+                        acc_01 = svmmla_s32(acc_01, s_a01, s_b0123);
+                        acc_23 = svmmla_s32(acc_23, s_a23, s_b0123);
+                    }
+
+                    // Reorder outputs from 2×2 tiles to row-major
+                    // acc[01] = [r0c0, r0c1, r1c0, r1c1, r0c2, r0c3, r1c2, r1c3]
+                    // acc[23] = [r2c0, r2c1, r3c0, r3c1, r2c2, r2c3, r3c2, r3c3]
+
+                    svint32_t row01 = svtbl_s32(acc_01, idx);
+                    svint32_t row23 = svtbl_s32(acc_23, idx);
+
+                    svfloat16_t temp1 = svld1_f16(svptrue_pat_b16(SV_VL4), (const __fp16 *) a_ptr->d);
+                    svfloat16_t temp2 = svld1_f16(svptrue_pat_b16(SV_VL4), (const __fp16 *) b_ptr->d);
+                    svfloat32_t sv_a_d = svtbl_f32(svcvt_f32_f16_x(svptrue_b32(), svzip1_f16(temp1, temp1)), idx_sc1);
+                    svfloat32_t sv_b_d = svtbl_f32(svcvt_f32_f16_x(svptrue_b32(), svzip1_f16(temp2, temp2)), idx_sc2);
+
+                    acc_f32_01 = svmla_f32_x(svptrue_b32(), acc_f32_01, svcvt_f32_s32_x(svptrue_b32(), row01), svmul_lane_f32(sv_b_d, sv_a_d, 0));
+                    acc_f32_23 = svmla_f32_x(svptrue_b32(), acc_f32_23, svcvt_f32_s32_x(svptrue_b32(), row23), svmul_lane_f32(sv_b_d, sv_a_d, 2));
+                    a_ptr++;
+                    b_ptr++;
+                }
+
+                svbool_t pg4 = svptrue_pat_b32(SV_VL4);
+                svst1_f32(pg4, s + (y+0) * bs + x, acc_f32_01);
+                svst1_f32(pg4, s + (y+1) * bs + x, svext_f32(acc_f32_01, acc_f32_01, 4));
+                svst1_f32(pg4, s + (y+2) * bs + x, acc_f32_23);
+                svst1_f32(pg4, s + (y+3) * bs + x, svext_f32(acc_f32_23, acc_f32_23, 4));
+            }
+        }
+        return;
+    }
+#endif  // SVE compile-time end
+
 #if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
     const block_q8_0x4 * b_ptr_base = (const block_q8_0x4 *) vx;
 

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

@@ -480,6 +480,104 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }
 
+#if defined(__riscv_v)
+static NOINLINE void ggml_vec_dot_q1_0_q8_0_vl256(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy) {
+    const int qk = QK1_0;
+    const int nb = n / qk;
+    assert(n % qk == 0);
+
+    const block_q1_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    //LMUL = 1, VLMAX = 32
+    const size_t vl32 = __riscv_vsetvl_e8m1(32);
+    assert(vl32 == 32);
+
+    const vint16m1_t zero = __riscv_vmv_v_x_i16m1(0, 1);
+
+    float sumf = 0;
+
+    for (int ib = 0; ib < nb; ++ib) {
+        const float d0 = GGML_CPU_FP16_TO_FP32(x[ib].d);
+
+        float acc = 0;
+
+        for (int k = 0; k < 4; ++k) {
+            const block_q8_0 * GGML_RESTRICT yb = &y[ib * 4 + k];
+            const vbool8_t is_not_zero = __riscv_vlm_v_b8(x[ib].qs + 4 * k, vl32);
+
+            const vint8m1_t qy = __riscv_vle8_v_i8m1(yb->qs, vl32);
+            const vint8m1_t neg_qy = __riscv_vneg_v_i8m1(qy, vl32);
+            const vint8m1_t sy = __riscv_vmerge_vvm_i8m1(neg_qy, qy, is_not_zero, vl32);
+
+            const vint16m1_t red = __riscv_vwredsum_vs_i8m1_i16m1(sy, zero, vl32);
+            acc += GGML_CPU_FP16_TO_FP32(yb->d) * (float)__riscv_vmv_x_s_i16m1_i16(red);
+        }
+
+        sumf += d0 * acc;
+    }
+
+    *s = sumf;
+}
+
+static NOINLINE void ggml_vec_dot_q1_0_q8_0_vl128(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy) {
+    const int qk = QK1_0;
+    const int nb = n / qk;
+    assert(n % qk == 0);
+
+    const block_q1_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    //LMUL = 2, VLMAX = 32
+    const size_t vl32 = __riscv_vsetvl_e8m2(32);
+    assert(vl32 == 32);
+
+    const vint16m1_t zero = __riscv_vmv_v_x_i16m1(0, 1);
+
+    float sumf = 0;
+
+    for (int ib = 0; ib < nb; ++ib) {
+        const float d0 = GGML_CPU_FP16_TO_FP32(x[ib].d);
+
+        float acc = 0;
+
+        for (int k = 0; k < 4; ++k) {
+            const block_q8_0 * GGML_RESTRICT yb = &y[ib * 4 + k];
+            const vbool4_t is_not_zero = __riscv_vlm_v_b4(x[ib].qs + 4 * k, vl32);
+
+            const vint8m2_t qy = __riscv_vle8_v_i8m2(yb->qs, vl32);
+            const vint8m2_t neg_qy =__riscv_vneg_v_i8m2(qy, vl32);
+            const vint8m2_t sy = __riscv_vmerge_vvm_i8m2(neg_qy, qy, is_not_zero, vl32);
+
+            const vint16m1_t red = __riscv_vwredsum_vs_i8m2_i16m1(sy, zero, vl32);
+            acc += GGML_CPU_FP16_TO_FP32(yb->d) * (float)__riscv_vmv_x_s_i16m1_i16(red);
+        }
+
+        sumf += d0 * acc;
+    }
+
+    *s = sumf;
+}
+#endif
+
+void ggml_vec_dot_q1_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+#if defined(__riscv_v)
+    assert(nrc == 1);
+
+    const size_t vlen_bits = __riscv_vlenb() * 8;
+
+    if (vlen_bits >= 256) {
+        ggml_vec_dot_q1_0_q8_0_vl256(n, s, vx, vy);
+    } else if (vlen_bits >= 128) {
+        ggml_vec_dot_q1_0_q8_0_vl128(n, s, vx, vy);
+    } else {
+        ggml_vec_dot_q1_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+    }
+#else
+    ggml_vec_dot_q1_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
+}
+
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     assert(nrc == 1);
     UNUSED(nrc);

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

@@ -1245,6 +1245,12 @@ void ggml_compute_forward_mul_mat(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
+    const int32_t hint = ggml_get_op_params_i32(dst, 1);
+    if (hint == GGML_HINT_SRC0_IS_HADAMARD && !params->use_ref) {
+        ggml_compute_forward_fwht(params, dst);
+        return;
+    }
+
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -2959,6 +2965,45 @@ struct ggml_cplan ggml_graph_plan(
     return cplan;
 }
 
+
+// Try to fuse the current node with subsequent nodes for better performance.
+// Returns the number of nodes skipped by fusion (>=1), or 0 if no fusion was applied.
+static bool ggml_cpu_disable_fusion = false;  // initialized once in ggml_cpu_init(), read-only afterwards
+
+static int ggml_cpu_try_fuse_ops(
+        const struct ggml_cgraph * cgraph,
+        const int node_n,
+        const struct ggml_compute_params * params,
+        const struct ggml_cplan * cplan) {
+
+    if (ggml_cpu_disable_fusion || cplan->use_ref) {
+        return 0;
+    }
+
+    struct ggml_tensor * node = cgraph->nodes[node_n];
+
+    if (node->op == GGML_OP_RMS_NORM) {
+        // RMS_NORM + MUL fusion
+        const enum ggml_op fuse_ops[] = { GGML_OP_RMS_NORM, GGML_OP_MUL };
+        if (ggml_can_fuse(cgraph, node_n, fuse_ops, 2)) {
+            struct ggml_tensor * mul_node = cgraph->nodes[node_n + 1];
+            const struct ggml_tensor * mul_w = (mul_node->src[0] == node)
+                ? mul_node->src[1] : mul_node->src[0];
+            if (node->src[0]->type  == GGML_TYPE_F32 &&
+                mul_node->type      == GGML_TYPE_F32 &&
+                mul_w->type         == GGML_TYPE_F32 &&
+                mul_w->ne[0]        == node->ne[0]   &&
+                mul_w->nb[0]        == sizeof(float)) {
+
+                ggml_compute_forward_rms_norm_mul_fused(params, node, mul_node);
+                return 1;
+            }
+        }
+    }
+
+    return 0;
+}
+
 static thread_ret_t ggml_graph_compute_thread(void * data) {
     struct ggml_compute_state * state = (struct ggml_compute_state *) data;
     struct ggml_threadpool    * tp    = state->threadpool;
@@ -2995,7 +3040,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             continue;
         }
 
-        ggml_compute_forward(&params, node);
+        // TODO: move fused-op detection into ggml_graph_plan so fusion decisions are made once at planning time
+        // Try fused ops, fall back to normal compute
+        const int n_fused = ggml_cpu_try_fuse_ops(cgraph, node_n, &params, cplan);
+        if (n_fused > 0) {
+            node_n += n_fused;
+        } else {
+            ggml_compute_forward(&params, node);
+        }
 
         if (state->ith == 0 && cplan->abort_callback &&
                 cplan->abort_callback(cplan->abort_callback_data)) {
@@ -3757,6 +3809,11 @@ void ggml_cpu_init(void) {
         ggml_init_riscv_arch_features();
 #endif
 
+        {
+            const char * env = getenv("GGML_CPU_DISABLE_FUSION");
+            ggml_cpu_disable_fusion = (env != NULL && atoi(env) == 1);
+        }
+
         is_first_call = false;
     }
 

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -195,8 +195,8 @@ static const struct ggml_backend_i ggml_backend_cpu_i = {
     /* .free                    = */ ggml_backend_cpu_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,

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

@@ -2321,6 +2321,9 @@ class tinyBLAS_Q0_PPC {
     }
 
     void matmul(int64_t m, int64_t n) {
+    #if defined(_AIX) || defined(__BIG_ENDIAN__)
+        mnpack(0, m, 0, n);
+    #else
         const int64_t mc = 64;
         const int64_t kc = 64;
         int64_t nc = 64;
@@ -2334,7 +2337,6 @@ class tinyBLAS_Q0_PPC {
         } else {
             n_aligned = (n / 64) * 64;
         }
-
         if (n_aligned > 0) {
             if (n_aligned % 64 == 0)      nc = 64;
             else if (n_aligned == n)      nc = n;
@@ -2352,6 +2354,7 @@ class tinyBLAS_Q0_PPC {
         } else {
             mnpack(0, m, 0, n);
         }
+    #endif
     }
 
   private:
@@ -3191,12 +3194,16 @@ class tinyBLAS_PPC {
     }
 
     void matmul(int64_t m, int64_t n) {
+    #if defined(_AIX) || defined(__BIG_ENDIAN__)
+        mnpack(0, m, 0, n);
+    #else
         int64_t mc = 256; int64_t nc = 256; int64_t kc = 256;
         if (m % mc == 0 && n % nc == 0 && k % kc == 0) {
             matmul_tiled(m, n, mc, nc, kc);
         } else {
             mnpack(0, m, 0, n);
         }
+    #endif
     }
 
   private:

ggml/src/ggml-cpu/ops.cpp

@@ -3713,11 +3713,27 @@ void ggml_compute_forward_norm(
 
 // ggml_compute_forward_group_rms_norm
 
+// fusion kinds that can be combined with the rms_norm computation in a single pass.
+// extend this enum when adding new fused variants (e.g. FUSE_ADD, FUSE_MUL_ADD, ...).
+enum ggml_rms_norm_fuse_op {
+    GGML_RMS_NORM_FUSE_OP_NONE,
+    GGML_RMS_NORM_FUSE_OP_MUL,
+};
+
+template <ggml_rms_norm_fuse_op FUSE_OP>
 static void ggml_compute_forward_rms_norm_f32(
         const ggml_compute_params * params,
-        ggml_tensor * dst) {
+        ggml_tensor * dst_rms_norm,
+        ggml_tensor * dst_fused = nullptr) {
 
-    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src0 = dst_rms_norm->src[0];
+    const ggml_tensor * src1 = nullptr;
+    ggml_tensor       * dst  = dst_rms_norm;
+
+    if constexpr (FUSE_OP == GGML_RMS_NORM_FUSE_OP_MUL) {
+        src1 = (dst_fused->src[0] == dst_rms_norm) ? dst_fused->src[1] : dst_fused->src[0];
+        dst  = dst_fused;
+    }
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
@@ -3726,11 +3742,10 @@ static void ggml_compute_forward_rms_norm_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    GGML_TENSOR_UNARY_OP_LOCALS
+    GGML_TENSOR_BINARY_OP_LOCALS
 
     float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
+    memcpy(&eps, dst_rms_norm->op_params, sizeof(float));
     GGML_ASSERT(eps >= 0.0f);
 
     // TODO: optimize
@@ -3740,25 +3755,32 @@ static void ggml_compute_forward_rms_norm_f32(
                 const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
 
                 ggml_float sum = 0.0;
+                // worth switching to explicit SIMD?
                 for (int64_t i00 = 0; i00 < ne00; i00++) {
                     sum += (ggml_float)(x[i00] * x[i00]);
                 }
 
-                const float mean = sum/ne00;
-
-                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
-
-                memcpy(y, x, ne00 * sizeof(float));
-                // for (int i00 = 0; i00 < ne00; i00++) {
-                //     y[i00] = x[i00];
-                // }
-
+                const float mean  = sum/ne00;
                 const float scale = 1.0f/sqrtf(mean + eps);
 
                 // if you hit this, likely you got an inf somewhere earlier
                 assert(scale > 0.0f);
 
-                ggml_vec_scale_f32(ne00, y, scale);
+                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
+
+                if constexpr (FUSE_OP == GGML_RMS_NORM_FUSE_OP_MUL) {
+                    const int64_t i11 = i01 % ne11;
+                    const int64_t i12 = i02 % ne12;
+                    const int64_t i13 = i03 % ne13;
+                    const float * w = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        y[i00] = x[i00] * scale * w[i00];
+                    }
+                } else {
+                    memcpy(y, x, ne00 * sizeof(float));
+                    ggml_vec_scale_f32(ne00, y, scale);
+                }
             }
         }
     }
@@ -3773,7 +3795,31 @@ void ggml_compute_forward_rms_norm(
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rms_norm_f32(params, dst);
+                ggml_compute_forward_rms_norm_f32<GGML_RMS_NORM_FUSE_OP_NONE>(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
+// Fused RMS_NORM + MUL: computes dst = rms_norm(src0) * src1 in a single pass.
+// This avoids materializing the intermediate rms_norm result in memory.
+void ggml_compute_forward_rms_norm_mul_fused(
+        const ggml_compute_params * params,
+        ggml_tensor * dst_rms_norm,
+        ggml_tensor * dst_mul) {
+
+    GGML_ASSERT(dst_mul != nullptr);
+    GGML_ASSERT(dst_mul->src[0] == dst_rms_norm || dst_mul->src[1] == dst_rms_norm);
+
+    const ggml_tensor * src0 = dst_rms_norm->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_rms_norm_f32<GGML_RMS_NORM_FUSE_OP_MUL>(params, dst_rms_norm, dst_mul);
             } break;
         default:
             {
@@ -11212,3 +11258,91 @@ void ggml_compute_forward_opt_step_sgd(const ggml_compute_params * params, ggml_
             }
     }
 }
+
+static void ggml_compute_forward_fwht_f32(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t n = ne10;
+    GGML_ASSERT((n & (n - 1)) == 0); // must be power of 2
+
+    const int64_t nr = ne11 * ne12 * ne13;
+    const int64_t rows_per_thread = (nr + nth - 1) / nth;
+    const int64_t start_row = ith * rows_per_thread;
+    const int64_t end_row = MIN(start_row + rows_per_thread, nr);
+
+    const float scale = 1.0f / sqrtf((float)n);
+
+#if defined(GGML_SIMD)
+    const GGML_F32_VEC v_minus_one = GGML_F32_VEC_SET1(-1.0f);
+#endif
+
+    for (int64_t r = start_row; r < end_row; r++) {
+        const int64_t i13 = r / (ne11 * ne12);
+        const int64_t i12 = (r - i13 * ne11 * ne12) / ne11;
+        const int64_t i11 = r - i13 * ne11 * ne12 - i12 * ne11;
+
+        const float * src_row = (const float *) ((const char *) src1->data + i11 * nb11 + i12 * nb12 + i13 * nb13);
+        float * dst_row = (float *) ((char *) dst->data + i11 * nb1 + i12 * nb2 + i13 * nb3);
+
+        for (int64_t j = 0; j < n; j++) {
+            dst_row[j] = src_row[j] * scale;
+        }
+
+        // Scalar passes
+#if defined(GGML_SIMD)
+        const int step = GGML_F32_EPR;
+#else
+        const int step = n;
+#endif
+        for (int64_t len = 1; len < step && len < n; len <<= 1) {
+            for (int64_t i = 0; i < n; i += 2 * len) {
+                for (int64_t j = 0; j < len; j++) {
+                    float u = dst_row[i + j];
+                    float v = dst_row[i + len + j];
+                    dst_row[i + j] = u + v;
+                    dst_row[i + len + j] = u - v;
+                }
+            }
+        }
+
+        // SIMD passes using GGML_F32_VEC_* macros for multi-architecture support
+#if defined(GGML_SIMD)
+        for (int64_t len = step; len < n; len <<= 1) {
+            for (int64_t i = 0; i < n; i += 2 * len) {
+                for (int64_t j = 0; j < len; j += step) {
+                    GGML_F32_VEC u = GGML_F32_VEC_LOAD(dst_row + i + j);
+                    GGML_F32_VEC v = GGML_F32_VEC_LOAD(dst_row + i + len + j);
+
+                    GGML_F32_VEC_STORE(dst_row + i + j,       GGML_F32_VEC_ADD(u, v));
+                    GGML_F32_VEC_STORE(dst_row + i + len + j, GGML_F32_VEC_FMA(u, v, v_minus_one));
+                }
+            }
+        }
+#endif
+    }
+}
+
+void ggml_compute_forward_fwht(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src1 = dst->src[1];
+
+    switch (src1->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_fwht_f32(params, dst);
+            }
+            break;
+        default:
+            {
+                GGML_ABORT("fatal error - fwht is F32 only");
+            }
+    }
+}

ggml/src/ggml-cpu/ops.h

@@ -44,6 +44,7 @@ void ggml_compute_forward_concat(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_silu_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_rms_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_rms_norm_mul_fused(const struct ggml_compute_params * params, struct ggml_tensor * dst_rms_norm, struct ggml_tensor * dst_mul);
 void ggml_compute_forward_rms_norm_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_group_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_l2_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -111,6 +112,7 @@ void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params *
 void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_fwht(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_opt_step_sgd(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 #ifdef __cplusplus
 }

ggml/src/ggml-cuda/common.cuh

@@ -830,6 +830,18 @@ static __device__ __forceinline__ float ggml_cuda_ue4m3_to_fp32(uint8_t x) {
 #endif // defined(GGML_USE_HIP) && defined(CDNA3) && defined(FP8_AVAILABLE) && HIP_VERSION >= 60200000
 }
 
+static __device__ __forceinline__ uint8_t ggml_cuda_fp32_to_ue4m3(float x) {
+#if defined(BLACKWELL_MMA_AVAILABLE) // This is used for NVFP4 subblock scale quantizations only
+    if (!(x > 0.0f)) {
+        return 0;
+    }
+    const __nv_fp8_e4m3 xf(x);
+    return xf.__x;
+#else
+     NO_DEVICE_CODE; // Used only for NVFP4 Scales for Activations, only for Blackwell
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
+}
+
 __device__ __forceinline__ uint8_t ggml_cuda_float_to_fp4_e2m1(float x, float e) {
     const uint8_t sign_bit = (x < 0.0f) << 3;
     float         ax       = fabsf(x) * e;

ggml/src/ggml-cuda/fattn-mma-f16.cuh

@@ -66,6 +66,9 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  32, 128, 128, 128, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  32, 128, 128, 128, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32, 256, 256, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 256, 256, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
@@ -85,6 +88,9 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
@@ -118,6 +124,9 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2,  64, 160, 128,  64, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 128, 2,  64, 160, 128,  64, 2, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 128, 128, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  32, 128, 128, 128, 1, false);
@@ -1217,7 +1226,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         float KQ_max_scale[cols_per_thread];
 #pragma unroll
         for (int col = 0; col < cols_per_thread; ++col) {
-            const int jc = cols_per_warp == 8 ? T_C_KQ::get_j(col) : T_C_KQ::get_i(2*col);
+            const int jc = (threadIdx.y/np)*cols_per_warp + (cols_per_warp == 8 ? T_C_KQ::get_j(col) : T_C_KQ::get_i(2*col));
             const float sink = sinks_f[jc % ncols2];
 
             const float KQ_max_new = fmaxf(KQ_max[col], sink);
@@ -1825,6 +1834,10 @@ extern DECL_FATTN_MMA_F16_CASE(576, 512, 1, 16);
 extern DECL_FATTN_MMA_F16_CASE(576, 512, 2, 16);
 extern DECL_FATTN_MMA_F16_CASE(576, 512, 4, 16);
 
+// Mistral Small 4 (DKQ=320, DV=256), GQA=32-only build:
+extern DECL_FATTN_MMA_F16_CASE(320, 256,  1, 32);
+extern DECL_FATTN_MMA_F16_CASE(320, 256,  2, 32);
+
 // For GLM 4.7 Flash
 extern DECL_FATTN_MMA_F16_CASE(576, 512,  4,  4);
 extern DECL_FATTN_MMA_F16_CASE(576, 512,  8,  4);

ggml/src/ggml-cuda/fattn-tile.cu

@@ -38,6 +38,10 @@ void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor
             GGML_ASSERT(V->ne[0] == K->ne[0]);
             ggml_cuda_flash_attn_ext_tile_case<256, 256>(ctx, dst);
         } break;
+        case 320: {
+            GGML_ASSERT(V->ne[0] == 256);
+            ggml_cuda_flash_attn_ext_tile_case<320, 256>(ctx, dst);
+        } break;
         case 512: {
             GGML_ASSERT(V->ne[0] == K->ne[0]);
             ggml_cuda_flash_attn_ext_tile_case<512, 512>(ctx, dst);

ggml/src/ggml-cuda/fattn-tile.cuh

@@ -68,6 +68,8 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 16, 256, 2,  64,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
@@ -128,6 +130,8 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  32,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 16, 256, 2,  32,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  32,  64)
@@ -195,6 +199,8 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  32, 128)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 32, 512, 1, 128,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
@@ -264,6 +270,8 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 5,  32, 256)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 3,  64, 128)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 32, 256, 2, 128,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 4,  64,  64)
@@ -1116,7 +1124,7 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggm
     constexpr size_t nbytes_shared = 0;
 
 #ifdef GGML_USE_HIP
-    if constexpr (DV <= 128) {
+    if constexpr (DKQ <= 128) {
         if (Q->ne[1] > 32/ncols2) {
             constexpr int cols_per_block = 64;
             const int nwarps    = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
@@ -1130,7 +1138,7 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggm
 #endif // GGML_USE_HIP
 
 #ifndef GGML_USE_HIP
-    if constexpr (DV <= 256)
+    if constexpr (DKQ <= 256)
 #endif // GGML_USE_HIP
     {
         if (Q->ne[1] > 16/ncols2) {
@@ -1144,14 +1152,16 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggm
         }
     }
 
-    if (Q->ne[1] > 8/ncols2) {
-        constexpr int cols_per_block = 16;
-        const int nwarps    = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
-        const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
-        fattn_kernel_t fattn_kernel = flash_attn_tile<DKQ, DV, cols_per_block/ncols2, ncols2, use_logit_softcap>;
-        launch_fattn<DV, cols_per_block/ncols2, ncols2>
-            (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size);
-        return;
+    if constexpr (ncols2 <= 16) {
+        if (Q->ne[1] > 8/ncols2) {
+            constexpr int cols_per_block = 16;
+            const int nwarps    = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
+            const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
+            fattn_kernel_t fattn_kernel = flash_attn_tile<DKQ, DV, cols_per_block/ncols2, ncols2, use_logit_softcap>;
+            launch_fattn<DV, cols_per_block/ncols2, ncols2>
+                (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size);
+            return;
+        }
     }
 
     if constexpr (ncols2 <= 8) {
@@ -1210,6 +1220,25 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
     const int gqa_limit = nvidia && gqa_ratio <= 4 && DV <= 256 ? 16 : INT_MAX;
     const bool use_gqa_opt = mask && max_bias == 0.0f && Q->ne[1] <= gqa_limit && K->ne[1] % FATTN_KQ_STRIDE == 0;
 
+    if constexpr (DKQ == 320) {
+        // This branch is only used for Mistral Small 4 which has a GQA ratio of 32.
+        // On AMD, simply use that GQA ratio with 32 columns / block since we always have enough SRAM.
+        // On NVIDIA however, the tile kernel is only used for GPUs that can't use the mma kernel (Pascal and older).
+        // Therefore, use a GQA ratio of 16 with 16 columns / block to stay below 48 kiB of SRAM / block.
+#ifdef GGML_USE_HIP
+        if (use_gqa_opt && gqa_ratio % 32 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 32, use_logit_softcap>(ctx, dst);
+            return;
+        }
+#else
+        if (use_gqa_opt && gqa_ratio % 16 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 16, use_logit_softcap>(ctx, dst);
+            return;
+        }
+#endif // GGML_USE_HIP
+        GGML_ABORT("flash-attn tile (320/256): expected GQA ratio multiple of 32");
+    }
+
     if constexpr (DKQ == 576) {
         if (use_gqa_opt && gqa_ratio % 16 == 0) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 16, use_logit_softcap>(ctx, dst);
@@ -1221,7 +1250,7 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
         }
     }
 
-    if constexpr (DKQ <= 512) {
+    if constexpr (DKQ <= 512 && DKQ != 320) {
         if (use_gqa_opt && gqa_ratio % 8 == 0) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 8, use_logit_softcap>(ctx, dst);
             return;
@@ -1275,5 +1304,6 @@ extern DECL_FATTN_TILE_CASE( 96,  96);
 extern DECL_FATTN_TILE_CASE(112, 112);
 extern DECL_FATTN_TILE_CASE(128, 128);
 extern DECL_FATTN_TILE_CASE(256, 256);
+extern DECL_FATTN_TILE_CASE(320, 256);
 extern DECL_FATTN_TILE_CASE(512, 512);
 extern DECL_FATTN_TILE_CASE(576, 512);

ggml/src/ggml-cuda/fattn.cu

@@ -143,6 +143,22 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg
             GGML_ASSERT(V->ne[0] == 256);
             ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<256, 256>(ctx, dst);
             break;
+        case 320:
+            // For Mistral Small 4, go straight to the ncols1 switch (ncols2=32-only build).
+            GGML_ASSERT(V->ne[0] == 256);
+            {
+                float max_bias = 0.0f;
+                memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
+
+                const bool use_gqa_opt = mask && max_bias == 0.0f;
+                GGML_ASSERT(use_gqa_opt);
+                GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
+                const int gqa_ratio = Q->ne[2] / K->ne[2];
+                GGML_ASSERT(gqa_ratio % 32 == 0);
+
+                ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<320, 256, 32>(ctx, dst);
+            }
+            break;
         case 512:
             GGML_ASSERT(V->ne[0] == 512);
             ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<512, 512>(ctx, dst);
@@ -352,6 +368,14 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
                 return BEST_FATTN_KERNEL_NONE;
             }
             break;
+        case 320:
+            if (V->ne[0] != 256 || !gqa_opt_applies) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            if (gqa_ratio % 32 != 0) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
         case 512:
             if (V->ne[0] != K->ne[0]) {
                 return BEST_FATTN_KERNEL_NONE;

ggml/src/ggml-cuda/getrows.cu

@@ -6,17 +6,18 @@ template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void k_get_rows(
         const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
         const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
-        /*const int64_t ne10,*/ const int64_t ne11, const int64_t ne12, /*const int64_t ne13,*/
+        /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/
         /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    for (int64_t z = blockIdx.z; z < ne11*ne12; z += gridDim.z) {
+    for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
         for (int64_t i00 = 2*(blockIdx.y*blockDim.x + threadIdx.x); i00 < ne00; i00 += gridDim.y*blockDim.x) {
             // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
             const int i10 =  blockIdx.x;
-            const int i11 =  z / ne12; // TODO fastdiv
-            const int i12 =  z % ne12;
+            const uint2 dm  = fast_div_modulo((uint32_t)z, ne12_fdv);
+            const int i11 =  dm.x;
+            const int i12 =  dm.y;
 
             const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
 
@@ -42,17 +43,18 @@ template<typename src0_t, typename dst_t>
 static __global__ void k_get_rows_float(
         const src0_t * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
         const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
-        /*const int64_t ne10,*/ const int64_t ne11, const int64_t ne12, /*const int64_t ne13,*/
+        /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/
         /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    for (int64_t z = blockIdx.z; z < ne11*ne12; z += gridDim.z) {
+    for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
         for (int64_t i00 = blockIdx.y*blockDim.x + threadIdx.x; i00 < ne00; i00 += gridDim.y*blockDim.x) {
             // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
             const int i10 = blockIdx.x;
-            const int i11 = z / ne12; // TODO fastdiv
-            const int i12 = z % ne12;
+            const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv);
+            const int i11 = dm.x;
+            const int i12 = dm.y;
 
             if (i00 >= ne00) {
                 return;
@@ -115,10 +117,14 @@ static void get_rows_cuda_q(
 
     GGML_ASSERT(ne00 % 2 == 0);
 
+    GGML_ASSERT(ne12 > 0);
+    GGML_ASSERT(ne11 <= std::numeric_limits<uint32_t>::max() / ne12);
+    const uint3 ne12_fdv = init_fastdiv_values(ne12);
+
     k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
         src0_d, src1_d, dst_d,
         ne00, /*ne01, ne02, ne03,*/
-        /*ne10,*/ ne11, ne12, /*ne13,*/
+        /*ne10,*/ ne11, ne12_fdv, /*ne13,*/
         /* s0,*/ s1, s2, s3,
         /* nb00,*/ nb01, nb02, nb03,
         s10, s11, s12/*, s13*/);
@@ -146,10 +152,14 @@ static void get_rows_cuda_float(
     const size_t s12 = nb12 / sizeof(int32_t);
     // const size_t s13 = nb13 / sizeof(int32_t);
 
+    GGML_ASSERT(ne12 > 0);
+    GGML_ASSERT(ne11 <= std::numeric_limits<uint32_t>::max() / ne12);
+    const uint3 ne12_fdv = init_fastdiv_values(ne12);
+
     k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
         src0_d, src1_d, dst_d,
         ne00, /*ne01, ne02, ne03,*/
-        /*ne10,*/ ne11, ne12, /*ne13,*/
+        /*ne10,*/ ne11, ne12_fdv, /*ne13,*/
         /* s0,*/ s1, s2, s3,
         /* nb00,*/ nb01, nb02, nb03,
         s10, s11, s12/*, s13*/);

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

@@ -3556,6 +3556,9 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
      && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_SILU) {
         const ggml_tensor * ssm_conv = cgraph->nodes[node_idx];
         const ggml_tensor * silu     = cgraph->nodes[node_idx+1];
+        if (ggml_get_unary_op(silu) != unary_ops.begin()[0]) {
+            return false;
+        }
 
         if (ssm_conv->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) {
             return false;
@@ -3564,6 +3567,31 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
         return true;
     }
 
+    if (ops.size() == 3 && ops.begin()[0] == GGML_OP_SSM_CONV && ops.begin()[1] == GGML_OP_ADD
+     && ops.begin()[2] == GGML_OP_UNARY && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_SILU) {
+        const ggml_tensor * ssm_conv = cgraph->nodes[node_idx];
+        const ggml_tensor * add      = cgraph->nodes[node_idx+1];
+        const ggml_tensor * silu     = cgraph->nodes[node_idx+2];
+        if (ggml_get_unary_op(silu) != unary_ops.begin()[0]) {
+            return false;
+        }
+
+        if (ssm_conv->type != GGML_TYPE_F32 || add->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) {
+            return false;
+        }
+
+        // ADD must consume ssm_conv's output and broadcast a 1-D channel-wise bias.
+        const ggml_tensor * bias = (add->src[0] == ssm_conv) ? add->src[1] : add->src[0];
+        if (bias->type != GGML_TYPE_F32 || !ggml_is_contiguous(bias)) {
+            return false;
+        }
+        if (ggml_nelements(bias) != ssm_conv->ne[0] || bias->ne[0] != ssm_conv->ne[0]) {
+            return false;
+        }
+
+        return true;
+    }
+
     if (ops.size() == 2 && ops.begin()[0] == GGML_OP_UNARY && ops.begin()[1] == GGML_OP_MUL
      && unary_ops.size() == 1 && (unary_ops.begin()[0] == GGML_UNARY_OP_SILU || unary_ops.begin()[0] == GGML_UNARY_OP_SIGMOID || unary_ops.begin()[0] == GGML_UNARY_OP_SOFTPLUS)) {
         const ggml_tensor * unary = cgraph->nodes[node_idx];
@@ -3640,6 +3668,362 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
     return false;
 }
 
+// try and fuse nodes and return the number of nodes to skip
+static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, int i) {
+
+    static bool disable_fusion = getenv("GGML_CUDA_DISABLE_FUSION") != nullptr && std::atoi(getenv("GGML_CUDA_DISABLE_FUSION"));
+    if (disable_fusion) {
+        return 0;
+    }
+
+    ggml_tensor * node = cgraph->nodes[i];
+
+    //topk-moe
+    if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
+            cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
+        ggml_cuda_topk_moe_args args;
+        const bool              can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args);
+        std::vector<ggml_op>    ops;
+
+        if (can_fuse) {
+            const ggml_tensor * logits  = node->src[0];
+            ggml_tensor *       weights = nullptr;
+            ggml_tensor *       ids     = nullptr;
+            const ggml_tensor * bias    = nullptr;
+            const ggml_tensor * clamp   = nullptr;
+            const ggml_tensor * scale   = nullptr;
+
+            if (!args.delayed_softmax) {
+                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
+                int     out_nodes[2];  // nodes which can't be elided
+
+                if (args.prob_bias) {
+                    bias = cgraph->nodes[i + 2]->src[1];
+                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW,
+                                            GGML_OP_GET_ROWS });
+                    out_nodes[0] = i + 4;
+                    ids          = cgraph->nodes[i + 4];
+                } else {
+                    ops.insert(ops.end(),
+                               { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS });
+                    out_nodes[0] = i + 3;
+                    ids          = cgraph->nodes[i + 3];
+                }
+
+                if (args.norm) {
+                    ops.insert(ops.end(),
+                               { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV, GGML_OP_RESHAPE });
+                    clamp = cgraph->nodes[i + ops.size() - 3];
+                }
+                if (args.scale) {
+                    ops.insert(ops.end(), { GGML_OP_SCALE });
+                    scale = cgraph->nodes[i + ops.size() - 1];
+                }
+
+                weights      = cgraph->nodes[i + ops.size() - 1];
+                out_nodes[1] = i + ops.size() - 1;
+
+                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
+                        ggml_cuda_should_use_topk_moe(node, logits, weights, ids) &&
+                        ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/true)) {
+                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
+                    return ops.size() - 1;
+                }
+            } else if (!args.norm && !args.prob_bias) {
+                //special case gpt-oss, no norm, no bias.
+                ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
+                                        GGML_OP_SOFT_MAX, GGML_OP_RESHAPE });
+                weights                     = cgraph->nodes[i + 5];
+                ids                         = cgraph->nodes[i + 1];
+                const ggml_tensor * softmax = cgraph->nodes[i + 4];
+
+                int out_nodes[2] = { i + 1, i + 5 };
+                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
+                        ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) &&
+                        ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/true)) {
+                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
+                    return ops.size() - 1;
+                }
+            }
+        }
+    }
+
+    //RoPE + view + set-rows
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
+        ggml_tensor * rope     = cgraph->nodes[i];
+        ggml_tensor * set_rows = cgraph->nodes[i + 2];
+
+        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
+        return 2;
+    }
+
+    // multi-(add or mul)
+    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
+        int     n_fuse = 0;
+        ggml_op ops[8];
+        std::fill(ops, ops + 8, node->op);
+
+        for (; n_fuse <= 6; ++n_fuse) {
+            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
+                break;
+            }
+            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
+                break;
+            }
+            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
+                break;
+            }
+        }
+
+        n_fuse++;
+
+        if (n_fuse > 1) {
+            ggml_tensor fused_node;
+            memcpy(&fused_node, node, sizeof(ggml_tensor));
+            for (int j = 0; j < n_fuse - 1; ++j) {
+                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
+            }
+            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
+            if (node->op == GGML_OP_ADD) {
+                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
+            } else {
+                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
+            }
+            return n_fuse - 1;
+        }
+    }
+
+    bool fused_mul_mat_vec = false;
+    int  fused_node_count  = 0;
+
+    // gate + glu + up
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
+            ggml_tensor * glu         = cgraph->nodes[i + 4];
+            ggml_tensor * gate_bias_n = glu->src[0];
+            ggml_tensor * up_bias_n   = glu->src[1];
+
+            //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
+            ggml_tensor * gate_n = nullptr;
+            ggml_tensor * up_n   = nullptr;
+
+            if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
+                gate_n = cgraph->nodes[i];
+                up_n   = cgraph->nodes[i + 2];
+            } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
+                gate_n = cgraph->nodes[i + 2];
+                up_n   = cgraph->nodes[i];
+            } else {
+                continue;
+            }
+
+            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
+                if (op_bias == GGML_OP_ADD) {
+                    if (bias_node->src[0] == mul_node) {
+                        return bias_node->src[1];
+                    }
+                    if (bias_node->src[1] == mul_node) {
+                        return bias_node->src[0];
+                    }
+                    return (ggml_tensor *) nullptr;
+                }
+                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
+                GGML_ASSERT(bias_node->src[0] == mul_node);
+                return bias_node->src[1];
+            };
+
+            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
+            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
+
+            if (!up_bias_tensor || !gate_bias_tensor) {
+                continue;
+            }
+
+            // we don't support repeating adds
+            if (bias_op == GGML_OP_ADD && (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
+                                           !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = up_n->src[0];
+            const ggml_tensor * src1 = up_n->src[1];
+            const ggml_tensor * ids  = up_n->src[2];
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate      = gate_n->src[0];
+                fusion_data.x_bias    = up_bias_tensor;
+                fusion_data.gate_bias = gate_bias_tensor;
+                fusion_data.glu_op    = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 5;
+                break;
+            }
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate      = gate_n->src[0];
+                fusion_data.x_bias    = up_bias_tensor;
+                fusion_data.gate_bias = gate_bias_tensor;
+                fusion_data.glu_op    = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 5;
+                break;
+            }
+        } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
+            ggml_tensor * glu  = cgraph->nodes[i + 2];
+            ggml_tensor * gate = glu->src[0];
+            ggml_tensor * up   = glu->src[1];
+
+            bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1]) ||
+                      (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
+
+            if (!ok) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = up->src[0];
+            const ggml_tensor * src1 = up->src[1];
+            const ggml_tensor * ids  = up->src[2];
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate   = gate->src[0];
+                fusion_data.glu_op = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 3;
+                break;
+            }
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate   = gate->src[0];
+                fusion_data.glu_op = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 3;
+                break;
+            }
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    fused_mul_mat_vec = false;
+    fused_node_count  = 0;
+
+    // gate + add + glu + up + add
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
+            continue;
+        }
+
+        ggml_tensor * mm_node   = cgraph->nodes[i];
+        ggml_tensor * bias_node = cgraph->nodes[i + 1];
+
+        ggml_tensor * bias_tensor = nullptr;
+        if (bias_op == GGML_OP_ADD) {
+            if (bias_node->src[0] == mm_node) {
+                bias_tensor = bias_node->src[1];
+            } else if (bias_node->src[1] == mm_node) {
+                bias_tensor = bias_node->src[0];
+            } else {
+                continue;
+            }
+        } else {
+            if (bias_node->src[0] != mm_node) {
+                continue;
+            }
+            bias_tensor = bias_node->src[1];
+        }
+
+        const ggml_tensor * src0 = mm_node->src[0];
+        const ggml_tensor * src1 = mm_node->src[1];
+        const ggml_tensor * ids  = mm_node->src[2];
+
+        if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
+            continue;
+        }
+
+        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
+            continue;
+        }
+
+        ggml_cuda_mm_fusion_args_host fusion_data{};
+        fusion_data.x_bias = bias_tensor;
+
+        if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
+            ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+            fused_mul_mat_vec = true;
+            fused_node_count  = 2;
+            break;
+        }
+
+        if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
+            ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+            fused_mul_mat_vec = true;
+            fused_node_count  = 2;
+            break;
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD }, {})) {
+        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2]);
+        return 2;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL }, {})) {
+        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_ADD, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
+        ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2]);
+        return 2;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
+        ggml_cuda_op_ssm_conv(*cuda_ctx, node, /*bias_add_node=*/ nullptr, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) ||
+        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) ||
+        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) {
+        ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_SQR }, { GGML_UNARY_OP_RELU })) {
+        ggml_cuda_op_relu_sqr(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
+        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i + 2], node);
+        return 2;
+    }
+
+    return 0;
+}
+
 static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required, const void * graph_key) {
     bool graph_evaluated_or_captured = false;
 
@@ -3786,355 +4170,11 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                     continue;
                 }
 
-                // start of fusion operations
-                static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
-                if (!disable_fusion) {
-                    ggml_cuda_topk_moe_args args;
+                int nodes_to_skip = ggml_cuda_try_fuse(cuda_ctx, cgraph, i);
 
-                    if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
-                        cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
-                        const bool can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args);
-
-                        std::vector<ggml_op> ops;
-
-                        if (can_fuse) {
-                            const ggml_tensor * logits  = node->src[0];
-                            ggml_tensor *       weights = nullptr;
-                            ggml_tensor *       ids     = nullptr;
-                            const ggml_tensor * bias    = nullptr;
-                            const ggml_tensor * clamp   = nullptr;
-                            const ggml_tensor * scale   = nullptr;
-
-                            if (!args.delayed_softmax) {
-                                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
-                                int     out_nodes[2];  // nodes which can't be elided
-
-                                if (args.prob_bias) {
-                                    bias = cgraph->nodes[i + 2]->src[1];
-                                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT,
-                                                            GGML_OP_VIEW, GGML_OP_GET_ROWS });
-                                    out_nodes[0] = i + 4;
-                                    ids          = cgraph->nodes[i + 4];
-                                } else {
-                                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW,
-                                                            GGML_OP_GET_ROWS });
-                                    out_nodes[0] = i + 3;
-                                    ids          = cgraph->nodes[i + 3];
-                                }
-
-                                if (args.norm) {
-                                    ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
-                                                            GGML_OP_DIV, GGML_OP_RESHAPE });
-                                    clamp = cgraph->nodes[i + ops.size() - 3];
-                                }
-                                if (args.scale) {
-                                    ops.insert(ops.end(), { GGML_OP_SCALE });
-                                    scale = cgraph->nodes[i + ops.size() - 1];
-                                }
-
-                                weights      = cgraph->nodes[i + ops.size() - 1];
-                                out_nodes[1] = i + ops.size() - 1;
-
-                                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
-                                    ggml_cuda_should_use_topk_moe(node, logits, weights, ids) &&
-                                    ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/ true)) {
-                                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
-                                    i += ops.size() - 1;
-                                    continue;
-                                }
-                            } else if (!args.norm && !args.prob_bias) {
-                                //special case gpt-oss, no norm, no bias.
-                                ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS,
-                                                        GGML_OP_RESHAPE, GGML_OP_SOFT_MAX, GGML_OP_RESHAPE });
-                                weights                     = cgraph->nodes[i + 5];
-                                ids                         = cgraph->nodes[i + 1];
-                                const ggml_tensor * softmax = cgraph->nodes[i + 4];
-
-                                int out_nodes[2] = { i + 1, i + 5 };
-                                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
-                                    ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) &&
-                                    ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/ true)) {
-                                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
-                                    i += ops.size() - 1;
-                                    continue;
-                                }
-                            }
-                        }
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
-                        ggml_tensor * rope = cgraph->nodes[i];
-                        ggml_tensor * set_rows = cgraph->nodes[i + 2];
-
-                        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
-                        i += 2;
-                        continue;
-                    }
-
-                    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
-                        int n_fuse = 0;
-                        ggml_op ops[8];
-                        std::fill(ops, ops + 8, node->op);
-
-                        for (; n_fuse <= 6; ++n_fuse){
-                            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
-                                break;
-                            }
-                            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
-                                break;
-                            }
-                            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
-                                break;
-                            }
-                        }
-
-                        n_fuse++;
-
-                        if (n_fuse > 1) {
-                            ggml_tensor fused_node;
-                            memcpy(&fused_node, node, sizeof(ggml_tensor));
-                            for (int j = 0; j < n_fuse - 1; ++j) {
-                                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
-                            }
-                            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
-                            if (node->op == GGML_OP_ADD) {
-                                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
-                            } else {
-                                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
-                            }
-                            i += n_fuse - 1;
-
-                            continue;
-                        }
-                    }
-
-                    bool fused_mul_mat_vec = false;
-                    int fused_node_count = 0;
-
-                    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
-                        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
-
-                        if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
-                            ggml_tensor * glu         = cgraph->nodes[i + 4];
-                            ggml_tensor * gate_bias_n = glu->src[0];
-                            ggml_tensor * up_bias_n   = glu->src[1];
-
-                            //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
-                            ggml_tensor * gate_n      = nullptr;
-                            ggml_tensor * up_n        = nullptr;
-
-                            if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
-                                gate_n = cgraph->nodes[i];
-                                up_n   = cgraph->nodes[i + 2];
-                            } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
-                                gate_n = cgraph->nodes[i + 2];
-                                up_n   = cgraph->nodes[i];
-                            } else {
-                                continue;
-                            }
-
-                            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
-                                if (op_bias == GGML_OP_ADD) {
-                                    if (bias_node->src[0] == mul_node) {
-                                        return bias_node->src[1];
-                                    }
-                                    if (bias_node->src[1] == mul_node) {
-                                        return bias_node->src[0];
-                                    }
-                                    return (ggml_tensor *) nullptr;
-                                }
-                                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
-                                GGML_ASSERT(bias_node->src[0] == mul_node);
-                                return bias_node->src[1];
-                            };
-
-                            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
-                            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
-
-                            if (!up_bias_tensor || !gate_bias_tensor) {
-                                continue;
-                            }
-
-                            // we don't support repeating adds
-                            if (bias_op == GGML_OP_ADD &&
-                                (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
-                                 !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
-                                continue;
-                            }
-
-                            const ggml_tensor * src0 = up_n->src[0];
-                            const ggml_tensor * src1 = up_n->src[1];
-                            const ggml_tensor * ids  = up_n->src[2];
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate      = gate_n->src[0];
-                                fusion_data.x_bias    = up_bias_tensor;
-                                fusion_data.gate_bias = gate_bias_tensor;
-                                fusion_data.glu_op    = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 5;
-                                break;
-                            }
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate      = gate_n->src[0];
-                                fusion_data.x_bias    = up_bias_tensor;
-                                fusion_data.gate_bias = gate_bias_tensor;
-                                fusion_data.glu_op    = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 5;
-                                break;
-                            }
-                        } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
-                            ggml_tensor * glu  = cgraph->nodes[i + 2];
-                            ggml_tensor * gate = glu->src[0];
-                            ggml_tensor * up   = glu->src[1];
-
-                            bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1])
-                                || (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
-
-                            if (!ok) continue;
-
-                            const ggml_tensor * src0 = up->src[0];
-                            const ggml_tensor * src1 = up->src[1];
-                            const ggml_tensor * ids  = up->src[2];
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate   = gate->src[0];
-                                fusion_data.glu_op = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 3;
-                                break;
-                            }
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate   = gate->src[0];
-                                fusion_data.glu_op = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 3;
-                                break;
-                            }
-                        }
-                    }
-
-                    if (fused_mul_mat_vec) {
-                        i += fused_node_count - 1;
-                        continue;
-                    }
-
-                    fused_mul_mat_vec = false;
-                    fused_node_count = 0;
-
-                    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
-                        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
-
-                        if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
-                            continue;
-                        }
-
-                        ggml_tensor * mm_node   = cgraph->nodes[i];
-                        ggml_tensor * bias_node = cgraph->nodes[i + 1];
-
-                        ggml_tensor * bias_tensor = nullptr;
-                        if (bias_op == GGML_OP_ADD) {
-                            if (bias_node->src[0] == mm_node) {
-                                bias_tensor = bias_node->src[1];
-                            } else if (bias_node->src[1] == mm_node) {
-                                bias_tensor = bias_node->src[0];
-                            } else {
-                                continue;
-                            }
-                        } else {
-                            if (bias_node->src[0] != mm_node) {
-                                continue;
-                            }
-                            bias_tensor = bias_node->src[1];
-                        }
-
-                        const ggml_tensor * src0 = mm_node->src[0];
-                        const ggml_tensor * src1 = mm_node->src[1];
-                        const ggml_tensor * ids  = mm_node->src[2];
-
-                        if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
-                            continue;
-                        }
-
-                        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
-                            continue;
-                        }
-
-                        ggml_cuda_mm_fusion_args_host fusion_data{};
-                        fusion_data.x_bias = bias_tensor;
-
-                        if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
-                            ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
-                            fused_mul_mat_vec = true;
-                            fused_node_count = 2;
-                            break;
-                        }
-
-                        if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
-                            ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
-                            fused_mul_mat_vec = true;
-                            fused_node_count = 2;
-                            break;
-                        }
-                    }
-
-                    if (fused_mul_mat_vec) {
-                        i += fused_node_count - 1;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD}, {})) {
-                        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
-                        i += 2;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL}, {})) {
-                        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
-                        ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) ||
-                        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) ||
-                        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) {
-                        ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_SQR }, { GGML_UNARY_OP_RELU })) {
-                        ggml_cuda_op_relu_sqr(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
-                        i += 2;
-                        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i], node);
-                        continue;
-                    }
+                if (nodes_to_skip != 0) {
+                    i += nodes_to_skip;
+                    continue;
                 }
 #ifndef NDEBUG
                 assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
@@ -4548,8 +4588,8 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .free                    = */ ggml_backend_cuda_free,
     /* .set_tensor_async        = */ ggml_backend_cuda_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cuda_get_tensor_async,
-    /* .get_tensor_2d_async     = */ ggml_backend_cuda_set_tensor_2d_async,
-    /* .set_tensor_2d_async     = */ ggml_backend_cuda_get_tensor_2d_async,
+    /* .set_tensor_2d_async     = */ ggml_backend_cuda_set_tensor_2d_async,
+    /* .get_tensor_2d_async     = */ ggml_backend_cuda_get_tensor_2d_async,
     /* .cpy_tensor_async        = */ ggml_backend_cuda_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cuda_synchronize,
     /* .graph_plan_create       = */ NULL,
@@ -5391,8 +5431,8 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                 CUDA_CHECK(cudaGetDeviceProperties(&prop, i));
                 dev_ctx->description = prop.name;
 
-                char pci_bus_id[16] = {};
-                snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.0", prop.pciDomainID, prop.pciBusID, prop.pciDeviceID);
+                char pci_bus_id[32] = {};
+                CUDA_CHECK(cudaDeviceGetPCIBusId(pci_bus_id, sizeof(pci_bus_id), i));
                 dev_ctx->pci_bus_id = pci_bus_id;
                 dev_ctx->op_offload_min_batch_size = min_batch_size;
 

ggml/src/ggml-cuda/mma.cuh

@@ -1015,25 +1015,35 @@ namespace ggml_cuda_mma {
 #endif // AMD_MFMA_AVAILABLE
     }
 
-    static __device__ __forceinline__ void mma_block_scaled(tile<16, 8, float> &     D,
-                                                            const tile<16, 8, int> & A,
-                                                            const tile<8, 8, int> &  B,
-                                                            uint32_t                 a_scale,
-                                                            uint32_t                 b_scale) {
+    template <ggml_type type>
+    static __device__ __forceinline__ void mma_block_scaled_fp4(tile<16, 8, float> &     D,
+                                                                const tile<16, 8, int> & A,
+                                                                const tile<8, 8, int> &  B,
+                                                                uint32_t                 a_scale,
+                                                                uint32_t                 b_scale) {
 #ifdef BLACKWELL_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         float *     Dxi = (float *) D.x;
 
-        asm volatile(
-            "mma.sync.aligned.kind::mxf4.block_scale.scale_vec::2X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue8m0 "
-            "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
-            "%10, {0, 0}, %11, {0, 0};"
-            : "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
-            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
+        if constexpr (type == GGML_TYPE_MXFP4) {
+            asm volatile(
+                "mma.sync.aligned.kind::mxf4.block_scale.scale_vec::2X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue8m0 "
+                "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
+                "%10, {0, 0}, %11, {0, 0};"
+                : "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
+                : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
+        } else {
+            asm volatile(
+                "mma.sync.aligned.kind::mxf4nvf4.block_scale.scale_vec::4X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue4m3 "
+                "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
+                "%10, {0, 0}, %11, {0, 0};"
+                : "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
+                : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
+        }
 #else
         GGML_UNUSED_VARS(D, A, B, a_scale, b_scale);
-#endif  // BLACKWELL_MMA_AVAILABLE
+#endif // BLACKWELL_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(

ggml/src/ggml-cuda/mmq.cu

@@ -122,7 +122,7 @@ void ggml_cuda_mul_mat_q(
                             || GGML_CUDA_CC_IS_CDNA(cc);
 
     // TODO: tighter pool buffer size vs q8 path
-    const bool use_native_mxfp4 = blackwell_mma_available(cc) && src0->type == GGML_TYPE_MXFP4;
+    const bool use_native_fp4 = blackwell_mma_available(cc) && (src0->type == GGML_TYPE_MXFP4 || src0->type == GGML_TYPE_NVFP4);
 
     if (!ids) {
         const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 +
@@ -133,9 +133,9 @@ void ggml_cuda_mul_mat_q(
             const int64_t s11 = src1->nb[1] / ts_src1;
             const int64_t s12 = src1->nb[2] / ts_src1;
             const int64_t s13 = src1->nb[3] / ts_src1;
-            if (use_native_mxfp4) {
+            if (use_native_fp4) {
                 static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1));
-                quantize_mmq_mxfp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
+                quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
                                         ne11, ne12, ne13, stream);
 
             } else {
@@ -146,10 +146,8 @@ void ggml_cuda_mul_mat_q(
         }
 
         // Stride depends on quantization format
-        const int64_t s12 = use_native_mxfp4 ?
-                                ne11 * ne10_padded * sizeof(block_fp4_mmq) /
-                                    (8 * QK_MXFP4 * sizeof(int))  // block_fp4_mmq holds 256 values (8 blocks of 32)
-                                :
+        const int64_t s12 = use_native_fp4 ?
+                                ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) :  // block_fp4_mmq holds 256 values
                                 ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
         const int64_t s13 = ne12*s12;
 
@@ -198,8 +196,8 @@ void ggml_cuda_mul_mat_q(
         const int64_t s12 = src1->nb[2] / ts_src1;
         const int64_t s13 = src1->nb[3] / ts_src1;
 
-        if (use_native_mxfp4) {
-            quantize_mmq_mxfp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
+        if (use_native_fp4) {
+            quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
                                     ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
         } else {
             quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
@@ -208,8 +206,9 @@ void ggml_cuda_mul_mat_q(
         CUDA_CHECK(cudaGetLastError());
     }
 
-    const int64_t s12 = use_native_mxfp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (8 * QK_MXFP4 * sizeof(int)) :
-                                           ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
+    static_assert(QK_K == 8 * QK_MXFP4, "QK_K needs to be 8 * QK_MXFP4");
+    const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) :
+                                         ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
     const int64_t s13 = ne12*s12;
 
     // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid.

ggml/src/ggml-cuda/mmq.cuh

@@ -10,9 +10,9 @@
 using namespace ggml_cuda_mma;
 
 #define MMQ_DP4A_MAX_BATCH_SIZE 64 // Max. batch size to use for dp4a MMQ kernels when FP16 tensor cores are available.
-#define MMQ_ITER_K 256
-#define MMQ_ITER_K_MXFP4_FP4    512
-#define MMQ_NWARPS 8
+#define MMQ_ITER_K             256
+#define MMQ_ITER_K_FP4         512
+#define MMQ_NWARPS               8
 
 typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride);
 typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00);
@@ -46,9 +46,12 @@ struct block_q8_1_mmq {
     int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each
 };
 
+// this struct is used for fp4 data types (currently only used for Blackwell)
+// mxfp4 has block size 32, each int32 of d4 contains 2 e8m0 scales in the lower 16 bits
+// nvfp4 has block size 16, each int32 of d4 contains 4 ue4m3 scales
 struct block_fp4_mmq {
-    uint32_t d4[4];       // 8 E8M0 scales (1 per 32 values), 2 packed per uint32: d4[0]={s0,s1}, d4[1]={s2,s3}, etc.
-    int8_t   qs[4 * 32];  // 256 FP4 values packed as 4-bit pairs (2 per byte), 8 blocks of 32 values
+    uint32_t d4[4];
+    int8_t   qs[4 * 32];  // 256 FP4 values packed as 4-bit pairs (2 per byte)
 };
 
 static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
@@ -143,10 +146,11 @@ static int get_mmq_y_host(const int cc) {
 
 static constexpr __device__ int get_iter_k([[maybe_unused]] const ggml_type type) {
 #if defined(BLACKWELL_MMA_AVAILABLE)
-    return type == GGML_TYPE_MXFP4 ? MMQ_ITER_K_MXFP4_FP4 : MMQ_ITER_K;
-#else
-    return MMQ_ITER_K;
+if (type == GGML_TYPE_NVFP4 || type == GGML_TYPE_MXFP4) {
+    return MMQ_ITER_K_FP4;
+}
 #endif // defined(BLACKWELL_MMA_AVAILABLE)
+    return MMQ_ITER_K;
 }
 
 static constexpr __device__ int get_mmq_y_device() {
@@ -213,8 +217,8 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
 }
 
 #define MMQ_MMA_TILE_X_K_Q8_0  (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
-#define MMQ_MMA_TILE_X_K_FP4   (2*MMQ_TILE_NE_K + 8                                       + 4) // MXFP4
-#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4) // NVFP4
+#define MMQ_MMA_TILE_X_K_FP4   (2*MMQ_TILE_NE_K + 8                                       + 4) // MXFP4 and NVFP4 Blackwell
+#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4) // NVFP4 Generic
 #define MMQ_MMA_TILE_X_K_Q8_1  (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
 #define MMQ_MMA_TILE_X_K_Q2_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K                           + 4)
 #define MMQ_MMA_TILE_X_K_Q3_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4)
@@ -240,7 +244,11 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
         case GGML_TYPE_Q8_0:    return MMQ_MMA_TILE_X_K_Q8_0;
         // tile sizes are the same for Q8_1 and FP4 for blackwell
         case GGML_TYPE_MXFP4:   return MMQ_MMA_TILE_X_K_Q8_1;
+#if defined(BLACKWELL_MMA_AVAILABLE)
+        case GGML_TYPE_NVFP4:   return MMQ_MMA_TILE_X_K_FP4;
+#else
         case GGML_TYPE_NVFP4:   return MMQ_MMA_TILE_X_K_NVFP4;
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
         case GGML_TYPE_Q2_K:    return MMQ_MMA_TILE_X_K_Q2_K;
         case GGML_TYPE_Q3_K:    return MMQ_MMA_TILE_X_K_Q3_K;
         case GGML_TYPE_Q4_K:    return MMQ_MMA_TILE_X_K_Q8_1;
@@ -934,6 +942,128 @@ static __device__ __forceinline__ void load_tiles_mxfp4_fp4(const char * __restr
     }
 }
 
+#ifdef BLACKWELL_MMA_AVAILABLE
+template <int mmq_y, bool need_check>
+static __device__ __forceinline__ void load_tiles_nvfp4_nvfp4(const char * __restrict__ x,
+                                                            int * __restrict__ x_tile,
+                                                            const int kbx0,
+                                                            const int i_max,
+                                                            const int stride) {
+    constexpr int nwarps = mmq_get_nwarps_device();
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+    constexpr int iter_k = get_iter_k(GGML_TYPE_NVFP4);
+    constexpr int threads_per_row = iter_k / QK_NVFP4; // each thread processes 1 block
+    constexpr int rows_per_warp = warp_size / threads_per_row;
+
+    uint32_t * x_u32 = (uint32_t *) x_tile;
+
+    const int txi = threadIdx.x;
+    const int kbx = txi % threads_per_row;
+    const int row_in_warp = txi / threads_per_row;
+
+    const block_nvfp4 * bxi_base = (const block_nvfp4 *) x + kbx0 + kbx;
+    uint32_t * x_u32_scale = x_u32 + 64 + kbx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += rows_per_warp * nwarps) {
+        int i = i0 + threadIdx.y * rows_per_warp + row_in_warp;
+
+        if constexpr (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_nvfp4 * bxi = bxi_base + i * stride;
+        const int row_base = i * MMQ_MMA_TILE_X_K_FP4;
+        const int q_base = row_base + 8 * kbx;
+
+        const uint32_t * src_qs = reinterpret_cast<const uint32_t *>(bxi->qs);
+
+#pragma unroll
+        for (int sub = 0; sub < QK_NVFP4 / QK_NVFP4_SUB; ++sub) {
+            x_u32[q_base + 2 * sub + 0] = src_qs[2 * sub + 0];
+            x_u32[q_base + 2 * sub + 1] = src_qs[2 * sub + 1];
+        }
+
+        x_u32_scale[row_base] = get_int_b4(bxi->d, 0);
+    }
+}
+
+// Shared MMA kernel for MXFP4 and NVFP4 on Blackwell.
+// Both quantizations encode values as e2m1 (FP4) and produce one uint32 scale per
+// m16n8k64 MMA call; only the PTX kind (scale_vec::2X ue8m0 vs scale_vec::4X ue4m3)
+// and the per-type stride constant differ.
+template <int mmq_x, int mmq_y, ggml_type type>
+static __device__ __forceinline__ void vec_dot_fp4_fp4_mma(const int * __restrict__ x,
+                                                           const int * __restrict__ y,
+                                                           float * __restrict__ sum,
+                                                           const int k00) {
+    static_assert(type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4,
+                  "vec_dot_fp4_fp4_mma: type must be MXFP4 or NVFP4");
+
+    typedef tile<16, 8, int>   tile_A;
+    typedef tile<8, 8, int>    tile_B;
+    typedef tile<16, 8, float> tile_C;
+
+    constexpr int stride        = MMQ_MMA_TILE_X_K_FP4;
+    constexpr int granularity   = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx           = rows_per_warp / tile_C::I;
+    constexpr int nfrags        = MMQ_TILE_NE_K / tile_A::J;
+
+    y += (threadIdx.y % ntx) * (tile_C::J * MMQ_TILE_Y_K);
+
+    const int *      x_qs = (const int *) x;
+    const uint32_t * x_sc = (const uint32_t *) (x_qs + 2 * MMQ_TILE_NE_K);
+    const int *      y_qs = (const int *) y + 4;
+    const uint32_t * y_sc = (const uint32_t *) y;
+
+    // 2 threads per quad supply the packed scale register to the block_scale MMA,
+    // see https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-block-scaling
+    const int tidx_A = threadIdx.x / 4 + (threadIdx.x % 2) * 8;
+    const int tidx_B = threadIdx.x / 4;
+    const int i0     = (threadIdx.y / ntx) * rows_per_warp;
+
+    tile_A   A[ntx][nfrags];
+    uint32_t scaleA[ntx][nfrags];
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int frag = 0; frag < nfrags; ++frag) {
+            const int k0 = k00 + frag * tile_A::J;
+            load_ldmatrix(A[n][frag], x_qs + (i0 + n * tile_A::I) * stride + k0, stride);
+            scaleA[n][frag] = x_sc[(i0 + n * tile_A::I + tidx_A) * stride + k0 / tile_A::J];
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx * tile_C::J) {
+        tile_B   B[nfrags];
+        uint32_t scaleB[nfrags];
+
+#pragma unroll
+        for (int frag = 0; frag < nfrags; ++frag) {
+            const int k0 = frag * tile_B::J;
+            load_generic(B[frag], y_qs + j0 * MMQ_TILE_Y_K + k0, MMQ_TILE_Y_K);
+            scaleB[frag] = y_sc[(j0 + tidx_B) * MMQ_TILE_Y_K + frag];
+        }
+
+#pragma unroll
+        for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+            for (int frag = 0; frag < nfrags; ++frag) {
+                tile_C C = {};
+                mma_block_scaled_fp4<type>(C, A[n][frag], B[frag], scaleA[n][frag], scaleB[frag]);
+#pragma unroll
+                for (int l = 0; l < tile_C::ne; ++l) {
+                    sum[(j0 / tile_C::J + n) * tile_C::ne + l] += C.x[l];
+                }
+            }
+        }
+    }
+}
+#endif // BLACKWELL_MMA_AVAILABLE
+
 
 template <int mmq_y, bool need_check>
 static __device__ __forceinline__ void load_tiles_nvfp4(const char * __restrict__ x,
@@ -1163,77 +1293,6 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
 #endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
 }
 
-template <int mmq_x, int mmq_y>
-static __device__ __forceinline__ void vec_dot_mxfp4_mxfp4_mma(const int * __restrict__ x,
-                                                               const int * __restrict__ y,
-                                                               float * __restrict__ sum,
-                                                               const int k00) {
-    typedef tile<16, 8, int>   tile_A;
-    typedef tile<8, 8, int>    tile_B;
-    typedef tile<16, 8, float> tile_C;  // Output is float for native scaled MMA
-
-    constexpr int granularity   = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx           = rows_per_warp / tile_C::I;  // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (tile_C::J * MMQ_TILE_Y_FP4_K);
-
-    // Match layout from load_tiles_mxfp4_fp4
-    const int *      x_qs = (const int *) x;
-    const uint32_t * x_sc = (const uint32_t *) (x_qs + 2 * MMQ_TILE_NE_K);
-    const int *      y_qs = (const int *) y + 4;
-    const uint32_t * y_sc = (const uint32_t *) y;
-
-    // tile_A has a length of 64 logical values vs. 32 values in block_mxfp4
-    tile_A   A[ntx][MMQ_TILE_NE_K / (2 * QI_MXFP4)];
-    uint32_t scaleA[ntx][MMQ_TILE_NE_K / (2 * QI_MXFP4)];
-
-    // Block scale
-    // Each thread has to point to a 4 byte scale value
-    // https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-block-scaling
-
-    const int i0 = (threadIdx.y / ntx) * rows_per_warp;
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 2 * QI_MXFP4) {
-            const int k0 = k00 + k01;
-
-            load_ldmatrix(A[n][k01 / (2 * QI_MXFP4)], x_qs + (i0 + n * tile_A::I) * MMQ_MMA_TILE_X_K_FP4 + k0,
-                          MMQ_MMA_TILE_X_K_FP4);
-
-            // based on block-scaling document, 2 threads in each quad need to supply to the scale value
-            const int tidx         = threadIdx.x / 4 + (threadIdx.x % 2) * 8;
-            scaleA[n][k01 / (2 * QI_MXFP4)] =
-                *(x_sc + (i0 + n * tile_A::I + tidx) * MMQ_MMA_TILE_X_K_FP4 + k0 / (2 * QI_MXFP4));
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx * tile_C::J) {
-#pragma unroll
-        for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 2 * QI_MXFP4) {
-            tile_B   B;
-            uint32_t scaleB;  // 2xN scales
-
-            load_generic(B, y_qs + j0 * MMQ_TILE_Y_FP4_K + k01, MMQ_TILE_Y_FP4_K);
-
-            scaleB = y_sc[(j0 + threadIdx.x / 4) * MMQ_TILE_Y_FP4_K + k01 / (2 * QI_MXFP4)];
-
-#pragma unroll
-            for (int n = 0; n < ntx; ++n) {
-                tile_C C;
-
-                mma_block_scaled(C, A[n][k01 / (2 * QI_MXFP4)], B, scaleA[n][k01 / (2 * QI_MXFP4)], scaleB);
-#pragma unroll
-                for (int l = 0; l < tile_C::ne; ++l) {
-                    sum[(j0 / tile_C::J + n) * tile_C::ne + l] += C.x[l];
-                }
-            }
-        }
-    }
-}
 
 template <int mmq_x, int mmq_y>
 static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a(
@@ -3259,7 +3318,7 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
     static constexpr int              vdr          = VDR_MXFP4_Q8_1_MMQ;
 #ifdef BLACKWELL_MMA_AVAILABLE
     static constexpr load_tiles_mmq_t load_tiles  = load_tiles_mxfp4_fp4<mmq_y, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma = vec_dot_mxfp4_mxfp4_mma<mmq_x, mmq_y>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma = vec_dot_fp4_fp4_mma<mmq_x, mmq_y, GGML_TYPE_MXFP4>;
 #else
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_mxfp4<mmq_y, need_check>;
     static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, MMQ_Q8_1_DS_LAYOUT_D4>;
@@ -3270,8 +3329,13 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
 template <int mmq_x, int mmq_y, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_NVFP4> {
     static constexpr int              vdr          = VDR_NVFP4_Q8_1_MMQ;
+#ifdef BLACKWELL_MMA_AVAILABLE
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_nvfp4_nvfp4<mmq_y, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_fp4_fp4_mma<mmq_x, mmq_y, GGML_TYPE_NVFP4>;
+#else
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_nvfp4<mmq_y, need_check>;
     static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y>;
+#endif // BLACKWELL_MMA_AVAILABLE
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y>;
 };
 
@@ -3406,7 +3470,7 @@ static __device__ __forceinline__ void mul_mat_q_process_tile(
 
 #if defined(BLACKWELL_MMA_AVAILABLE)
     // FP4 tile stores 8 blocks
-    constexpr int ne_block = (type == GGML_TYPE_MXFP4) ? 8 * QK_MXFP4 : 4 * QK8_1;
+    constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_K : 4 * QK8_1;
 #else
     constexpr int ne_block = 4 * QK8_1;
 #endif  // defined(BLACKWELL_MMA_AVAILABLE)

ggml/src/ggml-cuda/mmvq.cu

@@ -115,6 +115,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_pascal_older(gg
         case GGML_TYPE_IQ4_NL:  return 6;
         case GGML_TYPE_IQ4_XS:  return 5;
         case GGML_TYPE_MXFP4:   return 4;
+        case GGML_TYPE_NVFP4:   return 4;
         case GGML_TYPE_Q2_K:    return 4;
         case GGML_TYPE_Q3_K:    return 4;
         case GGML_TYPE_Q4_0:    return 6;
@@ -135,6 +136,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_turing_plus(ggm
         case GGML_TYPE_IQ3_S:   return 6;
         case GGML_TYPE_IQ3_XXS: return 7;
         case GGML_TYPE_MXFP4:   return 7;
+        case GGML_TYPE_NVFP4:   return 8;
         case GGML_TYPE_Q2_K:    return 7;
         case GGML_TYPE_Q3_K:    return 5;
         default:                return MMVQ_MAX_BATCH_SIZE;
@@ -221,6 +223,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna4(ggml_type
         case GGML_TYPE_IQ4_NL:  return 7;
         case GGML_TYPE_IQ4_XS:  return 5;
         case GGML_TYPE_MXFP4:   return 5;
+        case GGML_TYPE_NVFP4:   return 5;
         case GGML_TYPE_Q3_K:    return 4;
         case GGML_TYPE_Q4_0:    return 7;
         case GGML_TYPE_Q4_1:    return 7;

ggml/src/ggml-cuda/quantize.cu

@@ -70,6 +70,102 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
     return static_cast<uint8_t>(biased);
 }
 
+
+static __global__ void quantize_mmq_nvfp4(
+        const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy,
+        const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t ne0, const int64_t ne1, const int64_t ne2) {
+#if defined(BLACKWELL_MMA_AVAILABLE)
+
+    const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB;
+    if (i0_base >= ne0) {
+        return;
+    }
+
+    const int64_t i1 = blockIdx.x;
+    const int64_t i2 = blockIdx.z % ne2;
+    const int64_t i3 = blockIdx.z / ne2;
+    const int64_t i01 = ids ? ids[i1] : i1;
+    const int64_t k_block = i0_base / QK_K;
+    const int64_t blocks_per_col = (ne0 + QK_K - 1) / QK_K;
+    if (k_block >= blocks_per_col) {
+        return;
+    }
+
+    const int64_t ib = blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x;
+    block_fp4_mmq * y = (block_fp4_mmq *) vy;
+    block_fp4_mmq * yb = y + ib;
+
+    const int sub = (i0_base % QK_K) / QK_NVFP4_SUB;
+
+    float vals_raw[QK_NVFP4_SUB];
+    float amax_raw = 0.0f;
+    const int64_t base_idx = i3 * s03 + i2 * s02 + i01 * s01;
+#pragma unroll
+    for (int k = 0; k < QK_NVFP4_SUB; k++) {
+        const int64_t i00 = i0_base + k;
+        if (i00 < ne00) {
+            const float v = x[base_idx + i00];
+            vals_raw[k] = v;
+            amax_raw = fmaxf(amax_raw, fabsf(v));
+        } else {
+            vals_raw[k] = 0.0f;
+        }
+    }
+
+    static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2};
+    const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f);
+
+    float best_err = FLT_MAX;
+    uint8_t fp8_code = 0;
+    float subblock_scale = 0.0f;
+
+#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell.
+    for (int i = 0; i < 5; i++) {
+        const int test_code = first_fp8_code + test_offsets[i];
+        if (test_code < 0 || test_code > 0x7e) {
+            continue;
+        }
+        const uint8_t code = (uint8_t) test_code;
+        const float test_scale = ggml_cuda_ue4m3_to_fp32(code);
+        const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
+        float cur_err = 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+            const float v = vals_raw[k];
+            const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
+            const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale;
+            cur_err = fmaf(err_diff, err_diff, cur_err);
+        }
+
+        if (cur_err < best_err) {
+            best_err = cur_err;
+            fp8_code = test_code;
+            subblock_scale = test_scale;
+        }
+    }
+
+    const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+    uint32_t q0 = 0;
+    uint32_t q1 = 0;
+#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1
+    for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
+        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  0], inv_scale) << (8 * k);
+        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  8], inv_scale) << (8 * k + 4);
+        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  4], inv_scale) << (8 * k);
+        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4);
+    }
+
+    uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
+    yqs[2 * sub + 0] = q0;
+    yqs[2 * sub + 1] = q1;
+    reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
+#else
+    NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only.
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
+
+}
+
 // quantize values in the format mxfp4 is stored which is interleaved nibbles
 // i.e. a block a0-a31 is represented as a0a16,a1a17 ...a15a31
 static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x,
@@ -316,28 +412,32 @@ void quantize_mmq_q8_1_cuda(
     }
 }
 
-void quantize_mmq_mxfp4_cuda(const float *                    x,
-                             const int32_t *                  ids,
-                             void *                           vy,
-                             [[maybe_unused]] const ggml_type type_src0,
-                             const int64_t                    ne00,
-                             const int64_t                    s01,
-                             const int64_t                    s02,
-                             const int64_t                    s03,
-                             const int64_t                    ne0,
-                             const int64_t                    ne1,
-                             const int64_t                    ne2,
-                             const int64_t                    ne3,
-                             cudaStream_t                     stream) {
-    GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
+void quantize_mmq_fp4_cuda(
+        const float * x, const int32_t * ids, void * vy, const ggml_type type_src0,
+        const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
+    GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4);
+    GGML_ASSERT(ne0 > 0);
 
-    constexpr int nwarps = 8;
-    constexpr int vals_per_warp  = 2 * QK_MXFP4;
-    constexpr int vals_per_block = nwarps * vals_per_warp;
+    if (type_src0 == GGML_TYPE_NVFP4) {
+        GGML_ASSERT(ne00 % QK_NVFP4 == 0);
+        constexpr int nvfp4_block_size = 128;
+        const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
+        const dim3 block_size(nvfp4_block_size, 1, 1);
+        const dim3 num_blocks(ne1, block_num_y, ne2 * ne3);
+        quantize_mmq_nvfp4<<<num_blocks, block_size, 0, stream>>>(
+            x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
+    } else {
+        GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
 
-    const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block;
-    const dim3    num_blocks(ne1, block_num_y, ne2 * ne3);
-    const dim3    block_size(WARP_SIZE, nwarps, 1);
+        constexpr int nwarps = 8;
+        constexpr int vals_per_warp  = 2 * QK_MXFP4;
+        constexpr int vals_per_block = nwarps * vals_per_warp;
 
-    quantize_mmq_mxfp4<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
+        const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block;
+        const dim3    num_blocks(ne1, block_num_y, ne2 * ne3);
+        const dim3    block_size(WARP_SIZE, nwarps, 1);
+
+        quantize_mmq_mxfp4<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
+    }
 }

ggml/src/ggml-cuda/quantize.cuh

@@ -26,7 +26,7 @@ void quantize_mmq_q8_1_cuda(
         ggml_type type_src0, int64_t ne00, int64_t s01, int64_t s02, int64_t s03,
         int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, cudaStream_t stream);
 
-void quantize_mmq_mxfp4_cuda(const float *   x,
+void quantize_mmq_fp4_cuda(const float *   x,
                              const int32_t * ids,
                              void *          vy,
                              ggml_type       type_src0,

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

@@ -3,6 +3,7 @@
 
 template <bool apply_silu, size_t split_d_inner, size_t d_conv>
 static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float * __restrict__ src1,
+                                    const float * __restrict__ bias,
                                     const int src0_nb0, const int src0_nb1, const int src0_nb2, const int src1_nb1,
                                     float * __restrict__ dst, const int dst_nb0, const int dst_nb1, const int dst_nb2,
                                     const int64_t n_t) {
@@ -27,6 +28,8 @@ static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float
         w[j] = w_block[tid * stride_w + j];
     }
 
+    float b = bias != nullptr ? bias[bidy * split_d_inner + tid] : 0.0f;
+
     for (int64_t i = 0; i < n_t; i++) {
         float sumf = 0.0f;
 
@@ -42,12 +45,14 @@ static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float
         for (size_t j = 0; j < d_conv; j++) {
             sumf += x[(i + j) % d_conv] * w[j];
         }
+        sumf += b;
         y_block[i * stride_y + tid] = apply_silu ? ggml_cuda_op_silu_single(sumf) : sumf;
     }
 }
 
 template <bool apply_silu, size_t split_d_inner, size_t d_conv, int64_t split_n_t>
 static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0, const float * __restrict__ src1,
+                                               const float * __restrict__ bias,
                                                const int src0_nb0, const int src0_nb1, const int src0_nb2,
                                                const int src1_nb1, float * __restrict__ dst, const int dst_nb0,
                                                const int dst_nb1, const int dst_nb2, const int64_t n_t) {
@@ -97,6 +102,8 @@ static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0,
         w[j] = w_block[tid * stride_w + j];
     }
 
+    float b = bias != nullptr ? bias[bidy * split_d_inner + tid] : 0.0f;
+
     // Compute from shared memory
     for (int64_t i = 0; i < local_n_t; i++) {
         float sumf = 0.0f;
@@ -104,12 +111,13 @@ static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0,
         for (size_t j = 0; j < d_conv; j++) {
             sumf += smem[tid * n_cols + i + j] * w[j];
         }
+        sumf += b;
         y_block[i * stride_y + tid] = apply_silu ? ggml_cuda_op_silu_single(sumf) : sumf;
     }
 }
 
 template <bool apply_silu>
-static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int src0_nb0, const int src0_nb1,
+static void ssm_conv_f32_cuda(const float * src0, const float * src1, const float * bias, const int src0_nb0, const int src0_nb1,
                               const int src0_nb2, const int src1_nb1, float * dst, const int dst_nb0, const int dst_nb1,
                               const int dst_nb2, const int64_t nc, const int64_t nr, const int64_t n_t,
                               const int64_t n_s, cudaStream_t stream) {
@@ -120,14 +128,14 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
         constexpr int kNC = decltype(NC)::value;
         if (n_t <= 32) {
             const dim3 blocks(n_s, (nr + threads - 1) / threads, 1);
-            ssm_conv_f32<apply_silu, threads, kNC><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
+            ssm_conv_f32<apply_silu, threads, kNC><<<blocks, threads, 0, stream>>>(src0, src1, bias, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
                                                                        dst, dst_nb0, dst_nb1, dst_nb2, n_t);
         } else {
             const int64_t split_n_t = 32;
             dim3          blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t);
             const size_t  smem_size = threads * (kNC - 1 + split_n_t) * sizeof(float);
             ssm_conv_long_token_f32<apply_silu, threads, kNC, split_n_t><<<blocks, threads, smem_size, stream>>>(
-                src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
+                src0, src1, bias, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
         }
     };
 
@@ -140,11 +148,18 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
     }
 }
 
-void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * silu_dst) {
+void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * bias_add_node, ggml_tensor * silu_dst) {
     const struct ggml_tensor * src0 = dst->src[0];  // conv_x
     const struct ggml_tensor * src1 = dst->src[1];  // conv1d.weight
+    const bool fuse_bias = bias_add_node != nullptr;
     const bool fuse_silu = silu_dst != nullptr;
 
+    // bias always comes with silu.
+    GGML_ASSERT(!fuse_bias || fuse_silu);
+
+    // The bias (when fused) is the non-conv operand of the ADD node.
+    const struct ggml_tensor * bias = fuse_bias ? (bias_add_node->src[0] == dst ? bias_add_node->src[1] : bias_add_node->src[0]) : nullptr;
+
     // When fusing, write to silu_dst (the node downstream references).
     const struct ggml_tensor * out = fuse_silu ? silu_dst : dst;
 
@@ -160,16 +175,23 @@ void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, g
 
     const float * src0_d = (const float *) src0->data;
     const float * src1_d = (const float *) src1->data;
+    const float * bias_d = fuse_bias ? (const float *) bias->data : nullptr;
     float *       dst_d  = (float *) out->data;
     cudaStream_t  stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(out->type == GGML_TYPE_F32);
+    if (fuse_bias) {
+        GGML_ASSERT(bias->type == GGML_TYPE_F32);
+        GGML_ASSERT(ggml_is_contiguous(bias));
+        GGML_ASSERT(ggml_nelements(bias) == nr);
+    }
+
     if (fuse_silu) {
-        ssm_conv_f32_cuda<true>(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
+        ssm_conv_f32_cuda<true>(src0_d, src1_d, bias_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
                           out->nb[2], nc, nr, n_t, n_s, stream);
     } else {
-        ssm_conv_f32_cuda<false>(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
+        ssm_conv_f32_cuda<false>(src0_d, src1_d, bias_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
                           out->nb[2], nc, nr, n_t, n_s, stream);
     }
 }

ggml/src/ggml-cuda/ssm-conv.cuh

@@ -1,3 +1,3 @@
 #include "common.cuh"
 
-void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * silu_dst = nullptr);
+void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * bias_add_node = nullptr, ggml_tensor * silu_dst = nullptr);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu

@@ -2,4 +2,5 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(320, 256, 1, 32);
 DECL_FATTN_MMA_F16_CASE(576, 512, 1, 32);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu

@@ -2,4 +2,5 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(320, 256, 2, 32);
 DECL_FATTN_MMA_F16_CASE(576, 512, 2, 32);

ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq320-dv256.cu

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.cuh"
+
+DECL_FATTN_TILE_CASE(320, 256);

ggml/src/ggml-cuda/template-instances/generate_cu_files.py

@@ -3,7 +3,7 @@
 from glob import glob
 import os
 
-HEAD_SIZES_KQ = [40, 64, 72, 80, 96, 112, 128, 256, 512, 576]
+HEAD_SIZES_KQ = [40, 64, 72, 80, 96, 112, 128, 256, 320, 512, 576]
 
 TYPES_KV = ["GGML_TYPE_F16", "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_BF16"]
 
@@ -62,7 +62,7 @@ for filename in glob("*.cu"):
     os.remove(filename)
 
 for head_size_kq in HEAD_SIZES_KQ:
-    head_size_v = head_size_kq if head_size_kq != 576 else 512
+    head_size_v = 256 if head_size_kq == 320 else (head_size_kq if head_size_kq != 576 else 512)
     with open(f"fattn-tile-instance-dkq{head_size_kq}-dv{head_size_v}.cu", "w") as f:
         f.write(SOURCE_FATTN_TILE.format(head_size_kq=head_size_kq, head_size_v=head_size_v))
 
@@ -84,13 +84,16 @@ for ncols in [8, 16, 32, 64]:
                     continue
                 if head_size_kq == 72:
                     continue
-                if head_size_kq == 512 and ncols2 not in (4, 8):
+                # Skip compilation of unused ncols2 values for niche head sizes:
+                if head_size_kq == 320 and ncols2 != 32: # Mistral Small 4
                     continue
-                if head_size_kq != 576 and ncols2 in (16, 32):
+                if head_size_kq == 512 and ncols2 not in (4, 8): # Gemma 4
                     continue
-                if head_size_kq == 576 and ncols2 not in (4, 16, 32):
+                if head_size_kq == 576 and ncols2 not in (4, 16, 32): # Deepseek, GLM 4.7 Flash
                     continue
-                head_size_v = head_size_kq if head_size_kq != 576 else 512
+                if head_size_kq not in (320, 576) and ncols2 in (16, 32):
+                    continue
+                head_size_v = 256 if head_size_kq == 320 else (head_size_kq if head_size_kq != 576 else 512)
                 f.write(SOURCE_FATTN_MMA_CASE.format(ncols1=ncols1, ncols2=ncols2, head_size_kq=head_size_kq, head_size_v=head_size_v))
 
 for type in TYPES_MMQ:

ggml/src/ggml-cuda/vendors/hip.h

@@ -55,6 +55,7 @@
 #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
 #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
 #define cudaDeviceGetAttribute hipDeviceGetAttribute
+#define cudaDeviceGetPCIBusId hipDeviceGetPCIBusId
 #define cudaDeviceProp hipDeviceProp_t
 #define cudaDeviceSynchronize hipDeviceSynchronize
 #define cudaError_t hipError_t

ggml/src/ggml-cuda/vendors/musa.h

@@ -39,6 +39,7 @@
 #define cudaDeviceCanAccessPeer musaDeviceCanAccessPeer
 #define cudaDeviceDisablePeerAccess musaDeviceDisablePeerAccess
 #define cudaDeviceEnablePeerAccess musaDeviceEnablePeerAccess
+#define cudaDeviceGetPCIBusId musaDeviceGetPCIBusId
 #define cudaDeviceProp musaDeviceProp
 #define cudaDeviceSynchronize musaDeviceSynchronize
 #define cudaError_t musaError_t

ggml/src/ggml-hexagon/CMakeLists.txt

@@ -22,7 +22,8 @@ message(STATUS "hexagon: using ${HEXAGON_SDK_ROOT} and ${HEXAGON_TOOLS_ROOT} for
 include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake)
 include(ExternalProject)
 
-option(GGML_HEXAGON_HTP_DEBUG "ggml-hexagon: enable HTP debug output" OFF)
+option(GGML_HEXAGON_HTP_DEBUG  "ggml-hexagon: enable HTP debug output" OFF)
+option(GGML_HEXAGON_FA_EXP2_HF "ggml-hexagon: use FP16 exp2 polynomial in FA softmax instead of F32 exp round-trip" OFF)
 set(GGML_HEXAGON_HTP_CERT  "$ENV{HEXAGON_HTP_CERT}" CACHE PATH "ggml-hexagon: enable HTP library signing using certificate")
 set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml-hexagon: quantize group size (32, 64, or 128)")
 

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

@@ -48,14 +48,16 @@ using intvec  = std::vector<int>;
 using uintvec = std::vector<unsigned int>;
 using u32vec  = std::vector<uint32_t>;
 
-static size_t opt_ndev         = 1;
-static size_t opt_nhvx         = 0; // use all
-static int    opt_arch         = 0; // autodetect
-static int    opt_etm          = 0;
-static int    opt_verbose      = 0;
-static int    opt_profile      = 0; // profiling mode (0-disabled, 1-basic, 2-pmu)
-static int    opt_hostbuf      = 1; // hostbuf ON by default
-static int    opt_use_hmx      = 1; // when set, enable HMX; when 0, use HVX only
+static int    opt_arch    = 0; // autodetect
+static size_t opt_ndev    = 1;
+static size_t opt_nhvx    = 0; // use all
+static int    opt_use_hmx = 1; // when set, enable HMX; when 0, use HVX only
+static size_t opt_vmem    = HTP_OP_MAX_VMEM_DEFAULT;  // max available va space for buffer mappings
+static size_t opt_mbuf    = 1ul * 1024 * 1024 * 1024; // max buffer size
+static int    opt_etm     = 0;
+static int    opt_verbose = 0;
+static int    opt_profile = 0; // profiling mode (0-disabled, 1-basic, 2-pmu)
+static int    opt_hostbuf = 1; // hostbuf ON by default
 
 // Default PMU events, if profiling with PMU (mode=2) is enabled
 // See https://docs.qualcomm.com/doc/80-N2040-60/topic/pmu-events.html
@@ -66,6 +68,7 @@ static u32vec opt_pmu_evt { 0x3, 0x111, 0x100, 0x105, 0x240, 0x256, 0x7D, 0x8C }
 static int opt_opstage  = HTP_OPSTAGE_QUEUE | HTP_OPSTAGE_COMPUTE;
 static int opt_opbatch  = 1024; // max number of ops in a batch
 static int opt_opqueue  = 16;   // max number of pending batches
+
 static std::regex* opt_opfilter = NULL; // regex of ops to not claim
 
 #define HEX_VERBOSE(...) \
@@ -110,7 +113,7 @@ static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct
     if (!opt_verbose) return;
 
     op_desc desc(op);
-    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s : %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
+    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s: %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
                 ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, supp ? "yes" : "no");
 }
 
@@ -118,8 +121,6 @@ static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_t
                                       uint32_t op_usec, uint32_t op_cycles, const uint32_t pmu[]) {
     if (!opt_profile) return;
 
-    op_desc desc(op);
-
     char pmu_str[256] = "";
     if (opt_profile > 1) {
         static_assert(HTP_PROF_PMU_NCNT == 8, "current implementation assumes 8 PMU counters");
@@ -127,6 +128,7 @@ static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_t
                 pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
     }
 
+    op_desc desc(op);
     GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u%s\n", sess_name.c_str(),
             ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, op_usec, op_cycles, pmu_str);
 }
@@ -191,33 +193,30 @@ struct ggml_hexagon_shared_buffer {
     bool                   mapped;
     bool                   pinned;
 
-    void mmap(bool pinned = false) {
-        int err = fastrpc_mmap(sess->domain_id, this->fd, (void *) this->base, 0, this->size, FASTRPC_MAP_FD_DELAYED);
+    void mmap() {
+        fastrpc_map_flags flags = this->pinned ? FASTRPC_MAP_FD : FASTRPC_MAP_FD_DELAYED;
+
+        int err = fastrpc_mmap(sess->domain_id, this->fd, (void *) this->base, 0, this->size, flags);
         if (err != 0) {
             GGML_LOG_ERROR("ggml-hex: %s buffer mapping failed : domain_id %d size %zu fd %d error 0x%08x\n", sess->c_name(),
                     sess->domain_id, this->size, this->fd, (unsigned) err);
             throw std::runtime_error("ggml-hex: fastrpc_mmap failed (see log for details)");
         }
 
-        if (pinned) {
-            err = htp_iface_mmap(sess->handle, this->fd, this->size, pinned);
-            if (err != 0) {
-                GGML_LOG_ERROR("ggml-hex: %s buffer pinning failed : domain_id %d size %zu fd %d error 0x%08x\n", sess->c_name(),
-                        sess->domain_id, this->size, this->fd, (unsigned) err);
-                throw std::runtime_error("ggml-hex: htp_iface_mmap failed (see log for details)");
-            }
-        }
-
-        this->mapped = true;
-        this->pinned = pinned;
         HEX_VERBOSE("ggml-hex: %s mapped buffer: base %p size %zu fd %d pinned %u\n",
                 sess->c_name(), (void *) this->base, this->size, this->fd, pinned);
+
+        this->mapped = true;
     }
 
     void unmap() {
         if (!this->mapped) return;
 
-        htp_iface_munmap(sess->handle, this->fd);
+        if (!this->pinned) {
+            // HTP might still hold a reference, tell it drop it
+            htp_iface_munmap(sess->handle, this->fd);
+        }
+
         fastrpc_munmap(sess->domain_id, this->fd, (void *) this->base, this->size);
 
         HEX_VERBOSE("ggml-hex: %s unmapped buffer: base %p size %zu fd %d\n", sess->c_name(),
@@ -227,7 +226,7 @@ struct ggml_hexagon_shared_buffer {
         this->fd     = -1;
     }
 
-    void alloc(size_t size, bool pinned = false) {
+    void alloc(size_t size) {
         if (this->base) return;
 
         this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS, size);
@@ -245,8 +244,7 @@ struct ggml_hexagon_shared_buffer {
 
         HEX_VERBOSE("ggml-hex: %s allocated buffer: base %p size %zu fd %d pinned %d\n", sess->c_name(),
                     (void *) this->base, this->size, this->fd, (int) pinned);
-
-        mmap(pinned);
+        mmap();
     }
 
     void free() {
@@ -262,15 +260,14 @@ struct ggml_hexagon_shared_buffer {
     }
 
     ggml_hexagon_shared_buffer(ggml_hexagon_session * sess, size_t size, bool pinned = false) {
-        size += 4 * 1024;  // extra page for padding
-
         this->sess   = sess;
         this->size   = 0;
         this->base   = nullptr;
         this->fd     = -1;
         this->mapped = false;
+        this->pinned = pinned;
 
-        alloc(size, pinned);
+        alloc(size);
     }
 
     ~ggml_hexagon_shared_buffer() {
@@ -1475,6 +1472,7 @@ static ggml_backend_buffer_t ggml_backend_hexagon_buffer_type_alloc_buffer(
             ggml_backend_buffer_type_t buffer_type, size_t size) {
     auto sess = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->sess;
     try {
+        size += 4 * 1024;  // guard page
         ggml_hexagon_shared_buffer * sbuf = new ggml_hexagon_shared_buffer(sess, size);
         return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, sbuf, size);
     } catch (const std::exception & exc) {
@@ -1487,6 +1485,7 @@ static ggml_backend_buffer_t ggml_backend_hexagon_repack_buffer_type_alloc_buffe
             ggml_backend_buffer_type_t buffer_type, size_t size) {
     auto sess = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->sess;
     try {
+        size += 4 * 1024;  // guard page
         ggml_hexagon_shared_buffer * sbuf = new ggml_hexagon_shared_buffer(sess, size);
         return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, sbuf, size);
     } catch (const std::exception & exc) {
@@ -1505,7 +1504,7 @@ static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffe
 }
 
 static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
-    return 1UL * 1024 * 1024 * 1024;  // 1GB per buffer
+    return opt_mbuf; // typically 1GB per buffer
     GGML_UNUSED(buffer_type);
 }
 
@@ -1573,14 +1572,14 @@ struct ggml_hexagon_opbatch {
         d_map.clear();
     }
 
-    ggml_hexagon_opbatch(ggml_hexagon_session *sess, size_t batch_size) {
+    ggml_hexagon_opbatch(ggml_hexagon_session *sess, size_t batch_size, size_t max_vmem) {
         this->sess = sess;
 
         n_bufs_max = HTP_OP_MAX_BUFS;
         n_ops_max  = batch_size;
         n_tens_max = n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS;
 
-        b_vmem_max = HTP_OP_MAX_VMEM;
+        b_vmem_max = max_vmem;
 
         ops.resize(n_ops_max);
 
@@ -1592,6 +1591,9 @@ struct ggml_hexagon_opbatch {
         t_map.reserve(n_tens_max);
         d_map.reserve(n_tens_max);
 
+        GGML_LOG_INFO("ggml-hex: %s op batching: n-bufs %u n-tensors %u n-ops %u vmem %zu\n",
+                sess->c_name(), n_bufs_max, n_tens_max, n_ops_max, b_vmem_max);
+
         reset();
     }
 
@@ -1925,6 +1927,8 @@ void ggml_hexagon_session::flush_batch() {
     // Bump pending flag (cleared in the session::flush once we get the response)
     this->op_pending++;  // atomic inc
 
+    HEX_VERBOSE("ggml-hex: %s queue-opbatch: %p size %u\n", this->c_name(), dbuf.ptr, dbuf.size);
+
     int err = dspqueue_write(this->queue, 0, 1, &dbuf, sizeof(req), (const uint8_t*) &req, DSPQUEUE_TIMEOUT);
     if (err != 0) {
         GGML_ABORT("ggml-hex: %s dspqueue_write failed: 0x%08x\n", this->c_name(), (unsigned) err);
@@ -1944,6 +1948,35 @@ void ggml_hexagon_session::flush(bool all) {
     flush_pending(all);
 }
 
+static size_t ggml_hexagon_measure_max_vmem(ggml_hexagon_session *sess) {
+    // Allocate a bunch pinned buffers till failure.
+    // This is kind of expensive but handy for figuring out exactly how much we can mmap on a specific device.
+    // Typically we're going to allocate all/most of these buffers anyway for the model weights.
+
+    std::vector<ggml_hexagon_shared_buffer *> sbufs;
+
+    const size_t MiB = 1024 * 1024;
+    const size_t GiB = MiB  * 1024;
+
+    size_t vmem = 0;
+    size_t step = 256u * MiB;
+
+    try {
+        sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
+        sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
+        sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
+
+        while (1) {
+            sbufs.push_back(new ggml_hexagon_shared_buffer(sess, step, true));
+            vmem += step;
+        }
+    } catch (...) { }
+
+    for (auto b : sbufs) { delete b; }
+
+    return vmem - step; // backoff to account for overhead from internal mappings
+}
+
 void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
     this->valid_session = false;
     this->valid_handle  = false;
@@ -1957,7 +1990,7 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
 
     this->op_pending  = 0;
 
-    GGML_LOG_INFO("ggml-hex: allocating new session: %s\n", this->name.c_str());
+    GGML_LOG_DEBUG("ggml-hex: %s allocating new session\n", this->name.c_str());
 
     domain * my_domain = get_domain(this->domain_id);
     if (my_domain == NULL) {
@@ -2033,9 +2066,6 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
 
     this->valid_handle = true;
 
-    GGML_LOG_INFO("ggml-hex: new session: %s : session-id %d domain-id %d uri %s handle 0x%lx\n", this->name.c_str(),
-                  this->session_id, this->domain_id, session_uri, (unsigned long) this->handle);
-
     // Enable FastRPC QoS mode
     {
         struct remote_rpc_control_latency l;
@@ -2047,6 +2077,9 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
         }
     }
 
+    GGML_LOG_INFO("ggml-hex: %s new session : session-id %d domain-id %d uri %s handle 0x%lx\n", this->c_name(),
+                  this->session_id, this->domain_id, session_uri, (unsigned long) this->handle);
+
     const size_t req_q_size = (sizeof(htp_opbatch_req) * opt_opqueue * 2) + 1024;
     const size_t rsp_q_size = (sizeof(htp_opbatch_rsp) * opt_opqueue * 2) + 1024;
 
@@ -2091,13 +2124,19 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
     }
 
     // Allocate buffers and state for op batching
-    this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch);
     this->op_queue = new ggml_hexagon_opqueue(this, opt_opbatch, opt_opqueue);
 
-    // Start processing op batch requests
-    err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_use_hmx);
+    if (!opt_vmem) {
+        opt_vmem = ggml_hexagon_measure_max_vmem(this);
+        GGML_LOG_INFO("ggml-hex: %s measured max vmem %zu\n", this->c_name(), opt_vmem);
+    }
+
+    this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch, opt_vmem);
+
+    // Start dspqueue/opbatch processing
+    err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_use_hmx, opt_vmem);
     if (err != 0) {
-        GGML_LOG_ERROR("ggml-hex: failed to start session: 0x%08x\n", (unsigned) err);
+        GGML_LOG_ERROR("ggml-hex: %s failed to start session: 0x%08x\n", this->c_name(), (unsigned) err);
         throw std::runtime_error("ggml-hex: iface start failed (see log for details)");
     }
     this->valid_iface = true;
@@ -2108,17 +2147,17 @@ void ggml_hexagon_session::release() noexcept(true) {
 
     int err;
 
-    delete this->op_batch;
-    delete this->op_queue;
-
-    // Stop the DSP-side service and close the queue
     if (this->valid_iface) {
+        // Stop dspqueue/opbatch processing
         err = htp_iface_stop(this->handle);
         if (err != 0) {
             GGML_ABORT("ggml-hex: htp_iface_stop failed: 0x%08x\n", (unsigned) err);
         }
     }
 
+    delete this->op_batch;
+    delete this->op_queue;
+
     if (opt_etm) {
         err = htp_iface_etm(this->handle, 0);
         if (err != 0) {
@@ -2215,8 +2254,7 @@ static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_sess
         return false;
     }
 
-    if (dst->ne[2] != 1 || dst->ne[3] != 1) {
-        // FA during prompt still needs work
+    if (dst->ne[3] != 1) {
         return false;
     }
 
@@ -2382,8 +2420,8 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses
         return false;
     }
 
-    // TODO: add support for non-contigiuos tensors
-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+    // TODO: add support for non-contiguous elements within a row
+    if (!ggml_is_contiguous_rows(src0) || !ggml_is_contiguous_rows(dst)) {
         return false;
     }
 
@@ -2997,8 +3035,8 @@ static struct ggml_backend_i hexagon_backend_i = {
     /* .free                    = */ ggml_backend_hexagon_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ ggml_backend_hexagon_synchronize,
     /* .graph_plan_create       = */ NULL,
@@ -3380,21 +3418,6 @@ struct ggml_hexagon_registry {
 ggml_hexagon_registry::ggml_hexagon_registry(ggml_backend_reg_t reg) {
     GGML_LOG_INFO("ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev %zu\n", opt_ndev);
 
-    if (!opt_arch) {
-        int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch);
-        if (err != 0) {
-            GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err);
-            opt_arch = 73;
-        }
-    }
-
-#if defined(__ANDROID__)
-    if (opt_arch < 75) {
-        opt_ndev = 1;
-        GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n");
-    }
-#endif
-
     GGML_LOG_INFO("ggml-hex: Hexagon Arch version v%d\n", opt_arch);
 
     // Create devices / sessions
@@ -3480,32 +3503,67 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     static_assert((unsigned int) HTP_TYPE_IQ4_NL == (unsigned int) GGML_TYPE_IQ4_NL,
                   "please update hexagon_type to match ggml_type");
 
-    const char * str_verbose = getenv("GGML_HEXAGON_VERBOSE");
-    const char * str_hostbuf = getenv("GGML_HEXAGON_HOSTBUF");
-    const char * str_opstage = getenv("GGML_HEXAGON_OPSTAGE");
-    const char * str_opbatch = getenv("GGML_HEXAGON_OPBATCH");
-    const char * str_opqueue = getenv("GGML_HEXAGON_OPQUEUE");
-    const char * str_opfilter= getenv("GGML_HEXAGON_OPFILTER");
-    const char * str_profile = getenv("GGML_HEXAGON_PROFILE");
-    const char * str_etm     = getenv("GGML_HEXAGON_ETM");
-    const char * str_nhvx    = getenv("GGML_HEXAGON_NHVX");
-    const char * str_use_hmx = getenv("GGML_HEXAGON_USE_HMX");
-    const char * str_ndev    = getenv("GGML_HEXAGON_NDEV");
-    const char * str_arch    = getenv("GGML_HEXAGON_ARCH");
+    const char * str_verbose  = getenv("GGML_HEXAGON_VERBOSE");
+    const char * str_hostbuf  = getenv("GGML_HEXAGON_HOSTBUF");
+    const char * str_opstage  = getenv("GGML_HEXAGON_OPSTAGE");
+    const char * str_opbatch  = getenv("GGML_HEXAGON_OPBATCH");
+    const char * str_opqueue  = getenv("GGML_HEXAGON_OPQUEUE");
+    const char * str_opfilter = getenv("GGML_HEXAGON_OPFILTER");
+    const char * str_profile  = getenv("GGML_HEXAGON_PROFILE");
+    const char * str_etm      = getenv("GGML_HEXAGON_ETM");
+    const char * str_nhvx     = getenv("GGML_HEXAGON_NHVX");
+    const char * str_use_hmx  = getenv("GGML_HEXAGON_USE_HMX");
+    const char * str_ndev     = getenv("GGML_HEXAGON_NDEV");
+    const char * str_arch     = getenv("GGML_HEXAGON_ARCH");
+    const char * str_vmem     = getenv("GGML_HEXAGON_VMEM");
+    const char * str_mbuf     = getenv("GGML_HEXAGON_MBUF");
+
+    // Init Arch first since it affects other defaults
+    if (!str_arch) {
+        int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch);
+        if (err != 0) {
+            GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err);
+            opt_arch = 73;
+        }
+    } else {
+        if (str_arch[0] == 'v' || str_arch[0] == 'V') {
+            str_arch++;
+        }
+        opt_arch = strtoul(str_arch, NULL, 0);
+    }
+
+    size_t MiB = 1024 * 1024;
+
+    // Update vmem default
+    opt_vmem = opt_arch >= 75 ? HTP_OP_MAX_VMEM_DEFAULT : 3000 * MiB;
 
     auto RE_ICASE = std::regex_constants::icase;
 
-    opt_opfilter     = str_opfilter ? new std::regex(str_opfilter, RE_ICASE) : NULL;
-    opt_verbose      = str_verbose  ? atoi(str_verbose)             : 0;
-    opt_hostbuf      = str_hostbuf  ? atoi(str_hostbuf)             : opt_hostbuf;
-    opt_opstage      = str_opstage  ? strtoul(str_opstage, NULL, 0) : opt_opstage;
-    opt_opbatch      = str_opbatch  ? strtoul(str_opbatch, NULL, 0) : opt_opbatch;
-    opt_opqueue      = str_opqueue  ? strtoul(str_opqueue, NULL, 0) : opt_opqueue;
-    opt_etm          = str_etm      ? atoi(str_etm)                 : 0;
-    opt_nhvx         = str_nhvx     ? strtoul(str_nhvx, NULL, 0)    : opt_nhvx;
-    opt_use_hmx      = str_use_hmx  ? atoi(str_use_hmx)             : opt_use_hmx;
-    opt_ndev         = str_ndev     ? strtoul(str_ndev, NULL, 0)    : opt_ndev;
-    opt_hostbuf      = str_hostbuf  ? atoi(str_hostbuf)             : opt_hostbuf;
+    opt_opfilter  = str_opfilter ? new std::regex(str_opfilter, RE_ICASE) : NULL;
+    opt_verbose   = str_verbose  ? atoi(str_verbose)                      : 0;
+    opt_hostbuf   = str_hostbuf  ? atoi(str_hostbuf)                      : opt_hostbuf;
+    opt_opstage   = str_opstage  ? strtoul(str_opstage, NULL, 0)          : opt_opstage;
+    opt_opbatch   = str_opbatch  ? strtoul(str_opbatch, NULL, 0)          : opt_opbatch;
+    opt_opqueue   = str_opqueue  ? strtoul(str_opqueue, NULL, 0)          : opt_opqueue;
+    opt_profile   = str_profile  ? atoi(str_profile)                      : 0;
+    opt_etm       = str_etm      ? atoi(str_etm)                          : 0;
+    opt_nhvx      = str_nhvx     ? strtoul(str_nhvx, NULL, 0)             : opt_nhvx;
+    opt_use_hmx   = str_use_hmx  ? atoi(str_use_hmx)                      : opt_use_hmx;
+    opt_ndev      = str_ndev     ? strtoul(str_ndev, NULL, 0)             : opt_ndev;
+    opt_hostbuf   = str_hostbuf  ? atoi(str_hostbuf)                      : opt_hostbuf;
+    opt_mbuf      = str_mbuf     ? strtoul(str_mbuf, NULL, 0) * MiB       : opt_mbuf;
+    opt_vmem      = str_vmem     ? strtoul(str_vmem, NULL, 0) * MiB       : opt_vmem;
+
+    if (opt_ndev > GGML_HEXAGON_MAX_SESSIONS) {
+        opt_ndev = GGML_HEXAGON_MAX_SESSIONS;
+    }
+
+#if defined(__ANDROID__)
+    if (opt_arch < 75) {
+        opt_ndev = 1;
+        GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n");
+    }
+#endif
 
     if (str_profile) {
         opt_pmu_evt = [&]() -> std::vector<uint32_t> {
@@ -3520,17 +3578,6 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
                 vec_to_str<uint32_t, 16>(opt_pmu_evt).c_str());
     }
 
-    if (opt_ndev > GGML_HEXAGON_MAX_SESSIONS) {
-        opt_ndev = GGML_HEXAGON_MAX_SESSIONS;
-    }
-
-    if (str_arch) {
-        if (str_arch[0] == 'v') {
-            str_arch++;
-        }
-        opt_arch = strtoul(str_arch, NULL, 0);
-    }
-
     reg->context = new ggml_hexagon_registry(reg);
 }
 

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

@@ -44,6 +44,11 @@ target_compile_definitions(${HTP_LIB} PRIVATE
     $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>
     FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
 
+if (GGML_HEXAGON_FA_EXP2_HF)
+    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
+    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
+endif()
+
 # HMX acceleration: available on v73+ architectures
 set(HTP_HMX_VERSIONS v73 v75 v79 v81)
 list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
@@ -52,11 +57,13 @@ if (_hmx_idx GREATER_EQUAL 0)
     target_sources(${HTP_LIB} PRIVATE
         hmx-queue.c
         hmx-matmul-ops.c
+        hmx-flash-attn-ops.c
     )
 
     # -mhmx enables HMX instruction set (needed by files that include hmx-utils.h)
     set_source_files_properties(
         hmx-matmul-ops.c
+        hmx-flash-attn-ops.c
         PROPERTIES COMPILE_OPTIONS "-mhmx"
     )
 

ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake

@@ -138,15 +138,15 @@ set(CMAKE_SHARED_LIBRARY_SONAME_C_FLAG   "-Wl,-soname,")
 set(CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG "-Wl,-soname,")
 
 #Compiler Options
-set(COMMON_FLAGS "-mcpu=hexagon${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH} -fvectorize -Wall -Werror -fno-zero-initialized-in-bss -G0 -fdata-sections -fpic ${XQF_ARGS}")
+set(COMMON_FLAGS "-mcpu=hexagon${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH} -fvectorize -flto -Wall -Werror -fno-zero-initialized-in-bss -G0 -fdata-sections -fpic ${XQF_ARGS}")
 
 set(CMAKE_CXX_FLAGS_DEBUG          "${COMMON_FLAGS} -O0 -D_DEBUG -g")
-set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${COMMON_FLAGS} -O3 -g")
-set(CMAKE_CXX_FLAGS_RELEASE        "${COMMON_FLAGS} -O3")
+set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${COMMON_FLAGS} -O2 -g")
+set(CMAKE_CXX_FLAGS_RELEASE        "${COMMON_FLAGS} -O2")
 
 set(CMAKE_C_FLAGS_DEBUG            "${COMMON_FLAGS} -O0 -D_DEBUG -g")
-set(CMAKE_C_FLAGS_RELWITHDEBINFO   "${COMMON_FLAGS} -O3 -g")
-set(CMAKE_C_FLAGS_RELEASE          "${COMMON_FLAGS} -O3")
+set(CMAKE_C_FLAGS_RELWITHDEBINFO   "${COMMON_FLAGS} -O2 -g")
+set(CMAKE_C_FLAGS_RELEASE          "${COMMON_FLAGS} -O2")
 
 set(CMAKE_ASM_FLAGS_DEBUG          "${COMMON_FLAGS} ${CMAKE_CXX_FLAGS_DEBUG}")
 set(CMAKE_ASM_FLAGS_RELEASE        "${COMMON_FLAGS} ${CMAKE_CXX_FLAGS_RELEASE}")

ggml/src/ggml-hexagon/htp/flash-attn-ops.c

@@ -17,13 +17,14 @@
 #include "htp-ctx.h"
 #include "htp-ops.h"
 #include "htp-ops.h"
+#include "hmx-ops.h"
 
 // Must be multiple of 32
 #define FLASH_ATTN_BLOCK_SIZE (32 * 2)
 
 // This is a bit of a hack because the compiler is strugling to properly inline
 // the default hvx_vec_f32_to_f16 with output into the local array.
-static void __attribute__((noinline)) hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
+static __attribute__((noinline)) void hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
 {
     *(HVX_Vector *) ptr = hvx_vec_f32_to_f16(v0, v1);
 }
@@ -621,6 +622,17 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
+#ifdef HTP_HAS_HMX
+    // HMX path: prefill (neq1 >= 32), head_dim multiple of 32, F16 KV
+    if (k->type == HTP_TYPE_F16 && v->type == HTP_TYPE_F16 && k->ne[0] % 32 == 0 && q->ne[1] >= 32) {
+        int ret = hmx_flash_attn_ext(octx);
+        if (ret == HTP_STATUS_OK) {
+            return ret;
+        }
+        // VTCM too small or other failure -> fall through to HVX path
+    }
+#endif
+
     struct htp_fa_context factx;
     factx.octx = octx;
 

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

@@ -74,6 +74,12 @@ static inline size_t hex_smax(size_t a, size_t b) {
     return a > b ? a : b;
 }
 
+static inline void hex_swap_ptr(void ** p1, void ** p2) {
+    void * t = *p1;
+    *p1      = *p2;
+    *p2      = t;
+}
+
 static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride, uint32_t height) {
     const uint64_t control = Q6_P_combine_RR(stride, Q6_R_combine_RlRl(width, height));
     Q6_l2fetch_AP((void *) p, control);