model: fix step3.5 n_rot (#20318)

* llama: dynamic head_dim and n_rot for SWA

* also add gguf_writer wrappers

* fix build

* build_rope_shift arg reorder

CONTRIBUTING.md

@@ -39,6 +39,7 @@ Before submitting your PR:
     - For intricate features, consider opening a feature request first to discuss and align expectations
     - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
+- If you are a new contributor, limit your open PRs to 1.
 
 After submitting your PR:
 - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability

common/chat-auto-parser-generator.cpp

@@ -1,6 +1,7 @@
 #include "chat-auto-parser.h"
 #include "chat-peg-parser.h"
 #include "chat.h"
+#include "common.h"
 #include "json-schema-to-grammar.h"
 #include "nlohmann/json.hpp"
 
@@ -51,13 +52,15 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
     bool has_tools =
         autoparser.tools.format.mode != tool_format::NONE && inputs.tools.is_array() && !inputs.tools.empty();
     std::string trigger_marker = !autoparser.tools.format.section_start.empty() ? autoparser.tools.format.section_start :
-                                                                                autoparser.tools.format.per_call_start;
-    bool        include_grammar =
-        has_tools && ((inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO && !trigger_marker.empty()) ||
-                      inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED);
+                                                                                  autoparser.tools.format.per_call_start;
+
+    bool has_response_format = !inputs.json_schema.empty() && inputs.json_schema.is_object();
+    bool include_grammar = has_response_format || (has_tools &&
+            ((inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO && !trigger_marker.empty()) ||
+              inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
 
     if (include_grammar) {
-        data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+        data.grammar_lazy = !has_response_format && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
         data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
             foreach_function(inputs.tools, [&](const json & tool) {
                 const auto & function = tool.at("function");
@@ -68,7 +71,7 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
         });
 
         // Set grammar triggers based on tool section markers (fall back to per-call markers)
-        if (data.grammar_lazy) {  // only do triggers on lazy grammar
+        if (data.grammar_lazy) {
             data.grammar_triggers = {
                 { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, trigger_marker }
             };
@@ -104,8 +107,11 @@ common_peg_arena autoparser::build_parser(const templates_params & inputs) const
         bool has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
 
         if (has_response_format) {
-            return ctx.reasoning_parser + p.space() +
-                   p.content(p.schema(p.json(), "response-format", inputs.json_schema)) + p.end();
+            auto response_format = p.rule("response-format", p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
+            return ctx.reasoning_parser + p.space() + p.choice({
+                p.literal("```json") + p.space() + response_format + p.space() + p.literal("```"),
+                response_format
+            }) + p.end();
         }
 
         if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && jinja_caps.supports_tool_calls) {

common/chat-auto-parser-helpers.cpp

@@ -162,7 +162,7 @@ diff_split calculate_diff_split(const std::string & left, const std::string & ri
         right_fully_consumed = true;
     }
 
-    auto eat_segment = [](std::string & str, segment & seg) -> std::string { return str.append(seg.value); };
+    auto eat_segment = [](std::string str, const segment & seg) -> std::string { return std::move(str) + seg.value; };
 
     bool can_have_text_suffix = left_end->type == segment_type::TEXT && right_end->type == segment_type::TEXT;
     bool can_have_text_prefix = right_start->type == segment_type::TEXT && left_start->type == segment_type::TEXT;

common/chat-peg-parser.cpp

@@ -167,8 +167,8 @@ void tag_based_peg_mapper::from_ast(const common_peg_ast_arena & arena, const co
     });
 }
 
-tagged_parse_result tagged_peg_parser::parse_and_extract(const std::string & input, bool is_partial) const {
-    common_peg_parse_context ctx(input, is_partial);
+tagged_parse_result tagged_peg_parser::parse_and_extract(const std::string & input, common_peg_parse_flags extra_flags) const {
+    common_peg_parse_context ctx(input, flags | extra_flags);
     auto parse_result = arena.parse(ctx);
 
     tag_based_peg_mapper mapper;
@@ -179,11 +179,10 @@ tagged_parse_result tagged_peg_parser::parse_and_extract(const std::string & inp
 
 tagged_parse_result tagged_peg_parser::parse_anywhere_and_extract(const std::string & input) const {
     if (input.empty()) {
-        return parse_and_extract(input, false);
+        return parse_and_extract(input);
     }
     for (size_t i = 0; i < input.size(); i++) {
-        common_peg_parse_context ctx(input, false);
-        ctx.debug = debug;
+        common_peg_parse_context ctx(input, flags);
         auto parse_result = arena.parse(ctx, i);
         if (parse_result.success() || i == input.size() - 1) {
             tag_based_peg_mapper mapper;
@@ -477,6 +476,74 @@ common_peg_parser common_chat_peg_builder::standard_constructed_tools(
     return force_tool_calls ? section : optional(section);
 }
 
+// Python-style tool calls: name(arg1="value1", arg2=123)
+// Used only by LFM2 for now, so we don't merge it into autoparser
+common_peg_parser common_chat_peg_builder::python_style_tool_calls(
+    const nlohmann::json & tools,
+    bool                   parallel_tool_calls) {
+    if (!tools.is_array() || tools.empty()) {
+        return eps();
+    }
+
+    auto tool_choices = choice();
+
+    for (const auto & tool_def : tools) {
+        if (!tool_def.contains("function")) {
+            continue;
+        }
+        const auto &   function = tool_def.at("function");
+        std::string    name     = function.at("name");
+        nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object();
+
+        auto args = eps();
+        if (params.contains("properties") && !params["properties"].empty()) {
+            auto arg_choice = choice();
+            for (const auto & el : params["properties"].items()) {
+                const std::string & prop_name = el.key();
+                const auto & prop_def = el.value();
+                bool is_string_type = (prop_def.contains("type") && prop_def["type"] == "string");
+
+                auto arg_name_parser = literal(prop_name);
+
+                common_peg_parser arg_value_parser = eps();
+                auto string_value_parser = choice({
+                    literal("\"") + tool_arg_string_value(json_string_content()) + literal("\""),
+                    literal("'") + tool_arg_string_value(json_string_content()) + literal("'")
+                });
+
+                if (is_string_type) {
+                    arg_value_parser = string_value_parser;
+                } else {
+                    arg_value_parser = tool_arg_value(python_value());
+                }
+
+                // Full argument: name="value" or name=value
+                auto arg_rule = tool_arg(
+                    tool_arg_open(eps()) +
+                    tool_arg_name(arg_name_parser) +
+                    literal("=") +
+                    arg_value_parser +
+                    tool_arg_close(eps())
+                );
+                arg_choice |= arg_rule;
+            }
+
+            args = arg_choice + zero_or_more("," + space() + arg_choice);
+        }
+
+        auto tool_parser = tool(tool_open(tool_name(literal(name)) + literal("(")) +
+            space() + tool_args(args) + space() + tool_close(literal(")"))
+        );
+
+        tool_choices |= rule("tool-" + name, tool_parser);
+    }
+
+    if (parallel_tool_calls) {
+        return "[" + space() + tool_choices + zero_or_more("," + space() + tool_choices) + space() + "]";
+    }
+    return "[" + space() + tool_choices + space() + "]";
+}
+
 // Helper: Parse dot notation key into prefix and field name
 static std::pair<std::string, std::string> parse_key_spec(const std::string & key) {
     auto dot_pos = key.find('.');

common/chat-peg-parser.h

@@ -112,6 +112,11 @@ class common_chat_peg_builder : public common_peg_parser_builder {
                                                  bool                                       parallel_tool_calls,
                                                  bool                                       force_tool_calls);
 
+    // Helper for Python-style function call format: name(arg1="value1", arg2=123)
+    // Used by LFM2 and similar templates
+    common_peg_parser python_style_tool_calls(const nlohmann::json & tools,
+                                              bool                   parallel_tool_calls);
+
   private:
     // Implementation helpers for standard_json_tools — one per JSON tool call layout mode
     common_peg_parser build_json_tools_function_is_key(const nlohmann::json & tools,
@@ -155,19 +160,19 @@ struct tagged_parse_result {
 
 struct tagged_peg_parser {
     common_peg_arena arena;
-    bool debug = false;
+    common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_NONE;
 
     tagged_peg_parser & withDebug() {
-      debug = true;
+      flags |= COMMON_PEG_PARSE_FLAG_DEBUG;
       return *this;
     }
 
     tagged_peg_parser & withoutDebug() {
-      debug = false;
+      flags = flags & ~COMMON_PEG_PARSE_FLAG_DEBUG;
       return *this;
     }
 
-    tagged_parse_result parse_and_extract(const std::string & input, bool is_partial = false) const;
+    tagged_parse_result parse_and_extract(const std::string & input, common_peg_parse_flags extra_flags = COMMON_PEG_PARSE_FLAG_NONE) const;
     tagged_parse_result parse_anywhere_and_extract(const std::string & input) const;
 };
 

common/chat.cpp

@@ -1274,8 +1274,95 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
     return data;
 }
 
+// LFM2 format:
+// - Reasoning: <think>{reasoning}</think> (optional, only if enable_thinking is true)
+// - Content: text after reasoning (optional)
+// - Tool calls: <|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|>
+// Tool calls can appear multiple times (parallel tool calls)
+static common_chat_params common_chat_params_init_lfm2(const common_chat_template &    tmpl,
+                                                       const autoparser::templates_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.preserved_tokens  = {
+        "<|tool_list_start|>",
+        "<|tool_list_end|>",
+        "<|tool_call_start|>",
+        "<|tool_call_end|>",
+        "<think>",
+        "</think>",
+    };
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+
+    const std::string TOOL_CALL_START = "<|tool_call_start|>";
+    const std::string TOOL_CALL_END   = "<|tool_call_end|>";
+    const std::string THINK_START     = "<think>";
+    const std::string THINK_END       = "</think>";
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+
+        auto end = p.end();
+
+        auto reasoning = p.eps();
+        if (extract_reasoning && inputs.enable_thinking) {
+            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
+        }
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return reasoning + p.content(p.rest()) + end;
+        }
+
+        auto tool_calls = p.rule("tool-calls",
+            p.trigger_rule("tool-call", p.literal(TOOL_CALL_START) +
+                p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls) +
+                p.literal(TOOL_CALL_END)
+            )
+        );
+
+        auto content = p.content(p.until(TOOL_CALL_START));
+
+        return reasoning + content + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START }
+        };
+    }
+
+    return data;
+}
+
 namespace workaround {
 
+static void map_developer_role_to_system(json & messages) {
+    for (auto & message : messages) {
+        if (message.contains("role")) {
+            if (message["role"] == "developer") {
+                message["role"] = "system";
+            }
+        }
+    }
+}
+
+
 // if first message is system and template does not support it, merge it with next message
 static void system_message_not_supported(json & messages) {
     if (!messages.empty() && messages.front().at("role") == "system") {
@@ -1353,6 +1440,10 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
     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
+        workaround::map_developer_role_to_system(params.messages);
+    }
     workaround::func_args_not_string(params.messages);
 
     if (!tmpl.original_caps().supports_system_role) {
@@ -1422,6 +1513,14 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         return common_chat_params_init_kimi_k2(tmpl, params);
     }
 
+    // LFM2 - uses <|tool_list_start|>/<|tool_list_end|> markers and <|tool_call_start|>[name(args)]<|tool_call_end|> format
+    // Detection: template has "<|tool_list_start|>" and "<|tool_list_end|>" markers
+    if (src.find("<|tool_list_start|>") != std::string::npos &&
+        src.find("<|tool_list_end|>") != std::string::npos) {
+        LOG_DBG("Using specialized template: LFM2\n");
+        return common_chat_params_init_lfm2(tmpl, params);
+    }
+
     try {
         LOG_DBG("Using differential autoparser\n");
         struct autoparser::autoparser autoparser;
@@ -1527,8 +1626,12 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
 
     LOG_DBG("Parsing PEG input with format %s: %s\n", common_chat_format_name(params.format), input.c_str());
 
-    common_peg_parse_context ctx(input, is_partial);
-    ctx.debug   = params.debug;
+    common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_LENIENT;
+    if (params.debug) {
+        flags |= COMMON_PEG_PARSE_FLAG_DEBUG;
+    }
+
+    common_peg_parse_context ctx(input, flags);
     auto result = parser.parse(ctx);
 
     if (result.fail()) {
@@ -1541,7 +1644,7 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
             auto mapper = common_chat_peg_mapper(msg);
             mapper.from_ast(ctx.ast, result);
 
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "\nAST for partial parse (fail):\n%s\n", ctx.ast.dump().c_str());
                 fflush(stderr);
             }
@@ -1557,7 +1660,7 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
     auto mapper = common_chat_peg_mapper(msg);
     mapper.from_ast(ctx.ast, result);
 
-    if (ctx.debug) {
+    if (ctx.is_debug()) {
         fprintf(stderr, "\nAST for %s parse:\n%s\n", is_partial ? "partial" : "full", ctx.ast.dump().c_str());
         fflush(stderr);
     }

common/http.h

@@ -7,6 +7,7 @@ struct common_http_url {
     std::string user;
     std::string password;
     std::string host;
+    int port;
     std::string path;
 };
 
@@ -47,6 +48,20 @@ static common_http_url common_http_parse_url(const std::string & url) {
         parts.host = rest;
         parts.path = "/";
     }
+
+    auto colon_pos = parts.host.find(':');
+
+    if (colon_pos != std::string::npos) {
+        parts.port = std::stoi(parts.host.substr(colon_pos + 1));
+        parts.host = parts.host.substr(0, colon_pos);
+    } else if (parts.scheme == "http") {
+        parts.port = 80;
+    } else if (parts.scheme == "https") {
+        parts.port = 443;
+    } else {
+        throw std::runtime_error("unsupported URL scheme: " + parts.scheme);
+    }
+
     return parts;
 }
 
@@ -68,7 +83,7 @@ static std::pair<httplib::Client, common_http_url> common_http_client(const std:
     }
 #endif
 
-    httplib::Client cli(parts.scheme + "://" + parts.host);
+    httplib::Client cli(parts.scheme + "://" + parts.host + ":" + std::to_string(parts.port));
 
     if (!parts.user.empty()) {
         cli.set_basic_auth(parts.user, parts.password);

common/peg-parser.cpp

@@ -349,7 +349,7 @@ struct parser_executor {
         auto pos = start_pos;
         for (auto i = 0u; i < p.literal.size(); ++i) {
             if (pos >= ctx.input.size()) {
-                if (!ctx.is_partial) {
+                if (!ctx.is_lenient()) {
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
                 }
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
@@ -364,7 +364,7 @@ struct parser_executor {
     }
 
     common_peg_parse_result operator()(const common_peg_sequence_parser & p) {
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             LOG_DBG("%sSEQ start at %zu '%s' (%zu children)\n", debug_indent().c_str(), start_pos,
                     debug_input_snippet(start_pos).c_str(), p.children.size());
         }
@@ -375,26 +375,19 @@ struct parser_executor {
 
         for (size_t i = 0; i < p.children.size(); i++) {
             const auto & child_id = p.children[i];
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sSEQ child %zu: %s\n", debug_indent().c_str(), i, arena.dump(child_id).c_str());
             }
             auto result = arena.parse(child_id, ctx, pos);
 
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sSEQ child %zu: %s at %zu->%zu\n", debug_indent().c_str(), i,
                         common_peg_parse_result_type_name(result.type), result.start, result.end);
             }
 
             if (result.fail()) {
                 ctx.parse_depth--;
-                if (ctx.is_partial && result.end >= ctx.input.size()) {
-                    if (ctx.debug) {
-                        fprintf(stderr, "%sSEQ -> NEED_MORE (child failed at end)\n", debug_indent().c_str());
-                    }
-                    return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end,
-                                                   std::move(nodes));
-                }
-                if (ctx.debug) {
+                if (ctx.is_debug()) {
                     fprintf(stderr, "%sSEQ -> FAIL\n", debug_indent().c_str());
                 }
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, result.end);
@@ -406,7 +399,7 @@ struct parser_executor {
 
             if (result.need_more_input()) {
                 ctx.parse_depth--;
-                if (ctx.debug) {
+                if (ctx.is_debug()) {
                     fprintf(stderr, "%sSEQ -> NEED_MORE\n", debug_indent().c_str());
                 }
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes));
@@ -416,14 +409,14 @@ struct parser_executor {
         }
 
         ctx.parse_depth--;
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             fprintf(stderr, "%sSEQ -> SUCCESS at %zu->%zu\n", debug_indent().c_str(), start_pos, pos);
         }
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes));
     }
 
     common_peg_parse_result operator()(const common_peg_choice_parser & p) {
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             fprintf(stderr, "%sCHOICE start at %zu '%s' (%zu options)\n", debug_indent().c_str(), start_pos,
                     debug_input_snippet(start_pos).c_str(), p.children.size());
         }
@@ -432,17 +425,17 @@ struct parser_executor {
         auto pos = start_pos;
         for (size_t i = 0; i < p.children.size(); i++) {
             const auto & child_id = p.children[i];
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sCHOICE option %zu: %s\n", debug_indent().c_str(), i, arena.dump(child_id).c_str());
             }
             auto result = arena.parse(child_id, ctx, pos);
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sCHOICE option %zu: %s\n", debug_indent().c_str(), i,
                         common_peg_parse_result_type_name(result.type));
             }
             if (!result.fail()) {
                 ctx.parse_depth--;
-                if (ctx.debug) {
+                if (ctx.is_debug()) {
                     fprintf(stderr, "%sCHOICE -> %s (option %zu)\n", debug_indent().c_str(),
                             common_peg_parse_result_type_name(result.type), i);
                 }
@@ -451,14 +444,14 @@ struct parser_executor {
         }
 
         ctx.parse_depth--;
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             fprintf(stderr, "%sCHOICE -> FAIL (no options matched)\n", debug_indent().c_str());
         }
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
     }
 
     common_peg_parse_result operator()(const common_peg_repetition_parser & p) {
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             fprintf(stderr, "%sREPEAT start at %zu '%s' (min=%d, max=%d)\n", debug_indent().c_str(), start_pos,
                     debug_input_snippet(start_pos).c_str(), p.min_count, p.max_count);
         }
@@ -471,7 +464,7 @@ struct parser_executor {
         // Try to match up to max_count times (or unlimited if max_count is -1)
         while (p.max_count == -1 || match_count < p.max_count) {
             if (pos >= ctx.input.size()) {
-                if (ctx.debug) {
+                if (ctx.is_debug()) {
                     fprintf(stderr, "%sREPEAT: at end of input, count=%d\n", debug_indent().c_str(), match_count);
                 }
                 break;
@@ -479,7 +472,7 @@ struct parser_executor {
 
             auto result = arena.parse(p.child, ctx, pos);
 
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sREPEAT iter %d: %s at %zu->%zu, nodes=%zu\n", debug_indent().c_str(), match_count,
                         common_peg_parse_result_type_name(result.type), result.start, result.end, result.nodes.size());
                 fprintf(stderr, "%sREPEAT CHILD: %s\n", debug_indent().c_str(), arena.dump(p.child).c_str());
@@ -488,7 +481,7 @@ struct parser_executor {
             if (result.success()) {
                 // Prevent infinite loop on empty matches
                 if (result.end == pos) {
-                    if (ctx.debug) {
+                    if (ctx.is_debug()) {
                         fprintf(stderr, "%s  REPEAT: empty match, stopping\n", debug_indent().c_str());
                     }
                     break;
@@ -509,7 +502,7 @@ struct parser_executor {
                 }
 
                 ctx.parse_depth--;
-                if (ctx.debug) {
+                if (ctx.is_debug()) {
                     fprintf(stderr, "%sREPEAT -> NEED_MORE (count=%d, nodes=%zu)\n", debug_indent().c_str(),
                             match_count, nodes.size());
                 }
@@ -517,7 +510,7 @@ struct parser_executor {
             }
 
             // Child failed - stop trying
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sREPEAT: child failed, stopping\n", debug_indent().c_str());
             }
             break;
@@ -526,14 +519,14 @@ struct parser_executor {
         // Check if we got enough matches
         if (p.min_count > 0 && match_count < p.min_count) {
             ctx.parse_depth--;
-            if (pos >= ctx.input.size() && ctx.is_partial) {
-                if (ctx.debug) {
+            if (pos >= ctx.input.size() && ctx.is_lenient()) {
+                if (ctx.is_debug()) {
                     fprintf(stderr, "%sREPEAT -> NEED_MORE (not enough matches: %d < %d)\n", debug_indent().c_str(),
                             match_count, p.min_count);
                 }
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos, std::move(nodes));
             }
-            if (ctx.debug) {
+            if (ctx.is_debug()) {
                 fprintf(stderr, "%sREPEAT -> FAIL (not enough matches: %d < %d)\n", debug_indent().c_str(), match_count,
                         p.min_count);
             }
@@ -541,7 +534,7 @@ struct parser_executor {
         }
 
         ctx.parse_depth--;
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             fprintf(stderr, "%sREPEAT -> SUCCESS (count=%d, nodes=%zu)\n", debug_indent().c_str(), match_count,
                     nodes.size());
         }
@@ -576,7 +569,7 @@ struct parser_executor {
         auto result = common_parse_utf8_codepoint(ctx.input, start_pos);
 
         if (result.status == utf8_parse_result::INCOMPLETE) {
-            if (!ctx.is_partial) {
+            if (!ctx.is_lenient()) {
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
             }
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos);
@@ -615,7 +608,7 @@ struct parser_executor {
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
                 }
                 // Not enough matches yet
-                if (!ctx.is_partial) {
+                if (!ctx.is_lenient()) {
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
                 }
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
@@ -656,7 +649,7 @@ struct parser_executor {
 
         // Check if we got enough matches
         if (match_count < p.min_count) {
-            if (pos >= ctx.input.size() && ctx.is_partial) {
+            if (pos >= ctx.input.size() && ctx.is_lenient()) {
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
             }
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos);
@@ -668,7 +661,7 @@ struct parser_executor {
     static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos) {
         ++pos; // consume '\'
         if (pos >= ctx.input.size()) {
-            if (!ctx.is_partial) {
+            if (!ctx.is_lenient()) {
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
             }
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
@@ -698,7 +691,7 @@ struct parser_executor {
         ++pos; // consume 'u'
         for (int i = 0; i < 4; ++i) {
             if (pos >= ctx.input.size()) {
-                if (!ctx.is_partial) {
+                if (!ctx.is_lenient()) {
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
                 }
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
@@ -732,7 +725,7 @@ struct parser_executor {
                 auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos);
 
                 if (utf8_result.status == utf8_parse_result::INCOMPLETE) {
-                    if (!ctx.is_partial) {
+                    if (!ctx.is_lenient()) {
                         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
                     }
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
@@ -747,7 +740,7 @@ struct parser_executor {
         }
 
         // Reached end without finding closing quote
-        if (!ctx.is_partial) {
+        if (!ctx.is_lenient()) {
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos);
         }
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
@@ -774,7 +767,7 @@ struct parser_executor {
                 auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos);
 
                 if (utf8_result.status == utf8_parse_result::INCOMPLETE) {
-                    if (!ctx.is_partial) {
+                    if (!ctx.is_lenient()) {
                         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
                     }
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
@@ -789,7 +782,7 @@ struct parser_executor {
         }
 
         // Reached end without finding closing quote
-        if (!ctx.is_partial) {
+        if (!ctx.is_lenient()) {
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos);
         }
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
@@ -807,7 +800,7 @@ struct parser_executor {
 
             if (utf8_result.status == utf8_parse_result::INCOMPLETE) {
                 // Incomplete UTF-8 sequence
-                if (!ctx.is_partial) {
+                if (!ctx.is_lenient()) {
                     // Input is complete but UTF-8 is incomplete = malformed
                     return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
                 }
@@ -837,7 +830,7 @@ struct parser_executor {
             last_valid_pos = pos;
         }
 
-        if (last_valid_pos == ctx.input.size() && ctx.is_partial) {
+        if (last_valid_pos == ctx.input.size() && ctx.is_lenient()) {
             // Reached the end of a partial stream, there might still be more input that we need to consume.
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, last_valid_pos);
         }
@@ -876,7 +869,7 @@ struct parser_executor {
 
     common_peg_parse_result operator()(const common_peg_tag_parser & p) {
         // Parse the child
-        if (ctx.debug) {
+        if (ctx.is_debug()) {
             fprintf(stderr, "%sTAG: %s\n", debug_indent().c_str(), p.tag.c_str());
         }
         auto result = arena.parse(p.child, ctx, start_pos);

common/peg-parser.h

@@ -139,22 +139,43 @@ struct common_peg_parse_result {
     bool success() const { return type == COMMON_PEG_PARSE_RESULT_SUCCESS; }
 };
 
+enum common_peg_parse_flags {
+    COMMON_PEG_PARSE_FLAG_NONE    = 0,
+    COMMON_PEG_PARSE_FLAG_LENIENT = 1 << 0,
+    COMMON_PEG_PARSE_FLAG_DEBUG   = 1 << 1,
+};
+
+inline common_peg_parse_flags operator|(common_peg_parse_flags a, common_peg_parse_flags b) {
+    return static_cast<common_peg_parse_flags>(int(a) | int(b));
+}
+
+inline common_peg_parse_flags & operator|=(common_peg_parse_flags & a, common_peg_parse_flags b) {
+    return a = a | b;
+}
+
+inline common_peg_parse_flags operator&(common_peg_parse_flags a, common_peg_parse_flags b) {
+    return static_cast<common_peg_parse_flags>(int(a) & int(b));
+}
+
+inline common_peg_parse_flags operator~(common_peg_parse_flags a) {
+    return static_cast<common_peg_parse_flags>(~int(a));
+}
+
 struct common_peg_parse_context {
     std::string input;
-    bool is_partial;
-    bool debug = false;  // Enable debug output for parser tracing
+    common_peg_parse_flags flags;
     common_peg_ast_arena ast;
 
     int parse_depth;
 
-    common_peg_parse_context()
-        : is_partial(false), parse_depth(0) {}
+    common_peg_parse_context(common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_NONE)
+        : flags(flags), parse_depth(0) {}
 
-    common_peg_parse_context(const std::string & input)
-        : input(input), is_partial(false), parse_depth(0) {}
+    common_peg_parse_context(const std::string & input, common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_NONE)
+        : input(input), flags(flags), parse_depth(0) {}
 
-    common_peg_parse_context(const std::string & input, bool is_partial)
-        : input(input), is_partial(is_partial), parse_depth(0) {}
+    bool is_lenient() const { return flags & COMMON_PEG_PARSE_FLAG_LENIENT; }
+    bool is_debug() const { return flags & COMMON_PEG_PARSE_FLAG_DEBUG; }
 };
 
 class common_peg_arena;

docs/ops.md

@@ -47,6 +47,7 @@ Legend:
 |                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                  GATED_DELTA_NET | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -92,7 +93,7 @@ Legend:
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |

docs/ops/Vulkan.csv

@@ -1,8 +1,8 @@
 "backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name"
 "Vulkan0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
-"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan"
-"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan"
+"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
+"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
 "Vulkan0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
 "Vulkan0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
@@ -85,8 +85,8 @@
 "Vulkan0","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","Vulkan"
 "Vulkan0","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
-"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan"
-"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan"
+"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
+"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
 "Vulkan0","NEG","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","NEG","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
 "Vulkan0","STEP","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
@@ -13591,3 +13591,16 @@
 "Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan"
 "Vulkan0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan"
 "Vulkan0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","0","no","Vulkan"
+"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","0","no","Vulkan"

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

@@ -205,7 +205,14 @@ static ggml_cuda_device_info ggml_cuda_init() {
     GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES);
 
     int64_t total_vram = 0;
-    GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
+    for (int id = 0; id < info.device_count; ++id) {
+        cudaDeviceProp prop;
+        CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
+        total_vram += prop.totalGlobalMem;
+    }
+    GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices (Total VRAM: %zu MiB):\n",
+                  __func__, info.device_count, (size_t)(total_vram / (1024 * 1024)));
+    total_vram = 0;
 
     std::vector<std::pair<int, std::string>> turing_devices_without_mma;
     for (int id = 0; id < info.device_count; ++id) {
@@ -243,6 +250,12 @@ static ggml_cuda_device_info ggml_cuda_init() {
 #else
         info.devices[id].supports_cooperative_launch = false;
 #endif // !(GGML_USE_MUSA)
+
+        // cudaMemGetInfo returns info for the current device
+        size_t free_mem;
+        CUDA_CHECK(cudaSetDevice(id));
+        CUDA_CHECK(cudaMemGetInfo(&free_mem, NULL));
+
 #if defined(GGML_USE_HIP)
         info.devices[id].smpbo = prop.sharedMemPerBlock;
 
@@ -257,22 +270,25 @@ static ggml_cuda_device_info ggml_cuda_init() {
                 info.devices[id].cc += prop.minor * 0x10;
             }
         }
-        GGML_LOG_INFO("  Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d\n",
+        GGML_LOG_INFO("  Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d, VRAM: %zu MiB (%zu MiB free)\n",
                       id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff,
-                      device_vmm ? "yes" : "no", prop.warpSize);
+                      device_vmm ? "yes" : "no", prop.warpSize,
+                      (size_t)(prop.totalGlobalMem / (1024 * 1024)), free_mem / (1024 * 1024));
 #elif defined(GGML_USE_MUSA)
         // FIXME: Ensure compatibility with varying warp sizes across different MUSA archs.
         info.devices[id].warp_size = 32;
         info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
         info.devices[id].cc = GGML_CUDA_CC_OFFSET_MTHREADS + prop.major * 0x100;
         info.devices[id].cc += prop.minor * 0x10;
-        GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
-                        id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
+        GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB (%zu MiB free)\n",
+                      id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no",
+                      (size_t)(prop.totalGlobalMem / (1024 * 1024)), free_mem / (1024 * 1024));
 #else
         info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
         info.devices[id].cc = 100*prop.major + 10*prop.minor;
-        GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
-                        id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
+        GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB (%zu MiB free)\n",
+                      id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no",
+                      (size_t)(prop.totalGlobalMem / (1024 * 1024)), free_mem / (1024 * 1024));
         std::string device_name(prop.name);
         if (device_name == "NVIDIA GeForce MX450") {
             turing_devices_without_mma.push_back({ id, device_name });
@@ -4976,9 +4992,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_GATED_LINEAR_ATTN:
-        case GGML_OP_GATED_DELTA_NET:
         case GGML_OP_RWKV_WKV7:
             return true;
+        case GGML_OP_GATED_DELTA_NET:
+            //TODO: enable once MUSA compiler is solved https://github.com/ggml-org/llama.cpp/pull/19504#issuecomment-4018634327
+#ifdef GGML_USE_MUSA
+            return false;
+#else
+            return true;
+#endif // GGML_USE_MUSA
         case GGML_OP_FLASH_ATTN_EXT:
             return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op);
         case GGML_OP_CROSS_ENTROPY_LOSS:

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

@@ -1717,12 +1717,29 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_upscale(ggml_met
     char base[256];
     char name[256];
 
-    snprintf(base, 256, "kernel_upscale_%s", ggml_type_name(op->src[0]->type));
-    snprintf(name, 256, "%s", base);
+    const int32_t mode_flags = ggml_get_op_params_i32(op, 0);
+    const ggml_scale_mode mode = (ggml_scale_mode) (mode_flags & 0xFF);
+
+    const bool antialias = (mode_flags & GGML_SCALE_FLAG_ANTIALIAS);
+
+    if (mode == GGML_SCALE_MODE_BILINEAR) {
+        snprintf(base, 256, "kernel_upscale_bilinear_%s", ggml_type_name(op->src[0]->type));
+    } else if (mode == GGML_SCALE_MODE_BICUBIC) {
+        snprintf(base, 256, "kernel_upscale_bicubic_%s", ggml_type_name(op->src[0]->type));
+    } else {
+        snprintf(base, 256, "kernel_upscale_nearest_%s", ggml_type_name(op->src[0]->type));
+    }
+    snprintf(name, 256, "%s_aa=%d", base, antialias);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_bool(cv, antialias, FC_UPSCALE + 0);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
     return res;

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

@@ -1108,7 +1108,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                    op->type == GGML_TYPE_F32 &&
                    (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32);
         case GGML_OP_UPSCALE:
-            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST && !(op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS);
+            return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_POOL_1D:
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_POOL_2D:

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

@@ -83,6 +83,7 @@
 #define FC_UNARY                       1200
 #define FC_BIN                         1300
 #define FC_SUM_ROWS                    1400
+#define FC_UPSCALE                     1500
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8
@@ -890,6 +891,7 @@ typedef struct {
     float    sf1;
     float    sf2;
     float    sf3;
+    float    poffs;
 } ggml_metal_kargs_upscale;
 
 typedef struct {

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

@@ -1963,6 +1963,7 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
           (
            op->src[0]->type == GGML_TYPE_F32  || // TODO: helper function
            op->src[0]->type == GGML_TYPE_F16  ||
+           op->src[0]->type == GGML_TYPE_BF16 ||
            op->src[0]->type == GGML_TYPE_Q4_0 ||
            op->src[0]->type == GGML_TYPE_Q4_1 ||
            op->src[0]->type == GGML_TYPE_Q5_0 ||
@@ -1977,6 +1978,8 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
            op->src[0]->type == GGML_TYPE_Q4_K ||
            op->src[0]->type == GGML_TYPE_Q5_K ||
            op->src[0]->type == GGML_TYPE_Q6_K ||
+           op->src[0]->type == GGML_TYPE_Q2_K ||
+           op->src[0]->type == GGML_TYPE_Q3_K ||
            false) && (ne11 >= 4 && ne11 <= 8)
          )
         )
@@ -3729,32 +3732,43 @@ int ggml_metal_op_upscale(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
-    const float sf0 = (float)ne0/op->src[0]->ne[0];
-    const float sf1 = (float)ne1/op->src[0]->ne[1];
-    const float sf2 = (float)ne2/op->src[0]->ne[2];
-    const float sf3 = (float)ne3/op->src[0]->ne[3];
+    float sf0 = (float)ne0/op->src[0]->ne[0];
+    float sf1 = (float)ne1/op->src[0]->ne[1];
+    float sf2 = (float)ne2/op->src[0]->ne[2];
+    float sf3 = (float)ne3/op->src[0]->ne[3];
+
+    const int32_t mode_flags = ggml_get_op_params_i32(op, 0);
+
+    float poffs = 0.5f;
+
+    if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
+        poffs = 0.0f;
+        sf0 = ne0 > 1 && ne00 > 1 ? (float)(ne0 - 1) / (ne00 - 1) : sf0;
+        sf1 = ne1 > 1 && ne01 > 1 ? (float)(ne1 - 1) / (ne01 - 1) : sf1;
+    }
 
     ggml_metal_kargs_upscale args = {
-        /*.ne00 =*/ ne00,
-        /*.ne01 =*/ ne01,
-        /*.ne02 =*/ ne02,
-        /*.ne03 =*/ ne03,
-        /*.nb00 =*/ nb00,
-        /*.nb01 =*/ nb01,
-        /*.nb02 =*/ nb02,
-        /*.nb03 =*/ nb03,
-        /*.ne0 =*/ ne0,
-        /*.ne1 =*/ ne1,
-        /*.ne2 =*/ ne2,
-        /*.ne3 =*/ ne3,
-        /*.nb0 =*/ nb0,
-        /*.nb1 =*/ nb1,
-        /*.nb2 =*/ nb2,
-        /*.nb3 =*/ nb3,
-        /*.sf0 =*/ sf0,
-        /*.sf1 =*/ sf1,
-        /*.sf2 =*/ sf2,
-        /*.sf3 =*/ sf3
+        /*.ne00  =*/ ne00,
+        /*.ne01  =*/ ne01,
+        /*.ne02  =*/ ne02,
+        /*.ne03  =*/ ne03,
+        /*.nb00  =*/ nb00,
+        /*.nb01  =*/ nb01,
+        /*.nb02  =*/ nb02,
+        /*.nb03  =*/ nb03,
+        /*.ne0   =*/ ne0,
+        /*.ne1   =*/ ne1,
+        /*.ne2   =*/ ne2,
+        /*.ne3   =*/ ne3,
+        /*.nb0   =*/ nb0,
+        /*.nb1   =*/ nb1,
+        /*.nb2   =*/ nb2,
+        /*.nb3   =*/ nb3,
+        /*.sf0   =*/ sf0,
+        /*.sf1   =*/ sf1,
+        /*.sf2   =*/ sf2,
+        /*.sf3   =*/ sf3,
+        /*.poffs =*/ poffs,
     };
 
     auto pipeline = ggml_metal_library_get_pipeline_upscale(lib, op);

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

@@ -3481,6 +3481,13 @@ template [[host_name("kernel_mul_mv_ext_f16_f32_r1_3")]]    kernel mul_mv_ext_q4
 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_4")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, half4,        4,  dequantize_f16_t4>;
 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_5")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, half4,        4,  dequantize_f16_t4>;
 
+#if defined(GGML_METAL_HAS_BF16)
+template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_2")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, bfloat4,      4,  dequantize_bf16_t4>;
+template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_3")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, bfloat4,      4,  dequantize_bf16_t4>;
+template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_4")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, bfloat4,      4,  dequantize_bf16_t4>;
+template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_5")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, bfloat4,      4,  dequantize_bf16_t4>;
+#endif
+
 template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_2")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q4_0,   32, dequantize_q4_0_t4>;
 template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_3")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q4_0,   32, dequantize_q4_0_t4>;
 template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_4")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q4_0,   32, dequantize_q4_0_t4>;
@@ -3531,6 +3538,16 @@ template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_3")]] kernel mul_mv_ext_q4x4
 template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q6_K, 256, dequantize_q6_K>;
 template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q6_K, 256, dequantize_q6_K>;
 
+template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q2_K, 256, dequantize_q2_K>;
+template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q2_K, 256, dequantize_q2_K>;
+template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q2_K, 256, dequantize_q2_K>;
+template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q2_K, 256, dequantize_q2_K>;
+
+template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q3_K, 256, dequantize_q3_K>;
+template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q3_K, 256, dequantize_q3_K>;
+template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q3_K, 256, dequantize_q3_K>;
+template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q3_K, 256, dequantize_q3_K>;
+
 template<typename T0, typename T1, short NR0, typename args_t>
 void kernel_mul_mv_t_t_impl(
         args_t args,
@@ -4530,7 +4547,9 @@ kernel void kernel_conv_transpose_2d<half>(
     uint3   tpitg[[thread_position_in_threadgroup]],
     uint3     ntg[[threads_per_threadgroup]]);
 
-kernel void kernel_upscale_f32(
+constant bool FC_upscale_aa [[function_constant(FC_UPSCALE + 0)]];
+
+kernel void kernel_upscale_nearest_f32(
     constant ggml_metal_kargs_upscale & args,
     device  const char * src0,
     device        char * dst,
@@ -4556,6 +4575,156 @@ kernel void kernel_upscale_f32(
     }
 }
 
+static inline float bilinear_tri(float x) {
+    return MAX(0.0f, 1.0f - fabs(x));
+}
+
+kernel void kernel_upscale_bilinear_f32(
+    constant ggml_metal_kargs_upscale & args,
+    device  const char * src0,
+    device        char * dst,
+    uint3 tgpig[[threadgroup_position_in_grid]],
+    uint3 tpitg[[thread_position_in_threadgroup]],
+    uint3   ntg[[threads_per_threadgroup]]) {
+
+    const int64_t i3 = tgpig.z;
+    const int64_t i2 = tgpig.y;
+    const int64_t i1 = tgpig.x;
+
+    const int64_t i03 = i3 / args.sf3;
+    const int64_t i02 = i2 / args.sf2;
+
+    const float   f01  = ((float)i1 + args.poffs) / args.sf1 - args.poffs;
+    const int64_t i01  = MAX(0, MIN(args.ne01 - 1, (int64_t)floor(f01)));
+    const int64_t i01p = MAX(0, MIN(args.ne01 - 1, i01 + 1));
+    const float   fd1  = MAX(0.0f, MIN(1.0f, f01 - (float)i01));
+
+    src0 += i03*args.nb03 + i02*args.nb02;
+
+    device float * dst_ptr = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
+
+    if (FC_upscale_aa) {
+        const float support0  = MAX(1.0f, 1.0f / args.sf0);
+        const float invscale0 = 1.0f / support0;
+        const float support1  = MAX(1.0f, 1.0f / args.sf1);
+        const float invscale1 = 1.0f / support1;
+
+        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+            const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
+
+            int64_t x_min = MAX((int64_t)0, (int64_t)floor(f00 - support0 + args.poffs));
+            int64_t x_max = MIN(args.ne00,  (int64_t)ceil (f00 + support0 + args.poffs));
+
+            int64_t y_min = MAX((int64_t)0, (int64_t)floor(f01 - support1 + args.poffs));
+            int64_t y_max = MIN(args.ne01,  (int64_t)ceil (f01 + support1 + args.poffs));
+
+            float sum = 0.0f;
+            float wsum = 0.0f;
+
+            for (int64_t sy = y_min; sy < y_max; ++sy) {
+                const float wy = MAX(0.0f, 1.0f - fabs((float)sy - f01) * invscale1);
+                for (int64_t sx = x_min; sx < x_max; ++sx) {
+                    const float wx = MAX(0.0f, 1.0f - fabs((float)sx - f00) * invscale0);
+                    const float w  = wx * wy;
+                    const device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00);
+                    sum  += (*src_ptr) * w;
+                    wsum += w;
+                }
+            }
+
+            const float v = (wsum > 0.0f) ? (sum / wsum) : 0.0f;
+            dst_ptr[i0] = v;
+        }
+    } else {
+        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+            const float   f00  = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
+            const int64_t i00  = MAX(0, MIN(args.ne00 - 1, (int64_t)floor(f00)));
+            const int64_t i00p = MAX(0, MIN(args.ne00 - 1, i00 + 1));
+            const float   fd0  = MAX(0.0f, MIN(1.0f, f00 - (float)i00));
+
+            device const float * src00 = (device const float *)(src0 + i01*args.nb01  + i00*args.nb00);
+            device const float * src10 = (device const float *)(src0 + i01*args.nb01  + i00p*args.nb00);
+            device const float * src01 = (device const float *)(src0 + i01p*args.nb01 + i00*args.nb00);
+            device const float * src11 = (device const float *)(src0 + i01p*args.nb01 + i00p*args.nb00);
+
+            const float v =
+                (*src00) * (1.0f - fd0) * (1.0f - fd1) +
+                (*src10) * fd0          * (1.0f - fd1) +
+                (*src01) * (1.0f - fd0) * fd1 +
+                (*src11) * fd0          * fd1;
+
+            dst_ptr[i0] = v;
+        }
+    }
+}
+
+static inline float bicubic_weight1(float x) {
+    const float a = -0.75f;
+    return ((a + 2) * x - (a + 3)) * x * x + 1;
+}
+
+static inline float bicubic_weight2(float x) {
+    const float a = -0.75f;
+    return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a;
+}
+
+kernel void kernel_upscale_bicubic_f32(
+    constant ggml_metal_kargs_upscale & args,
+    device  const char * src0,
+    device        char * dst,
+    uint3 tgpig[[threadgroup_position_in_grid]],
+    uint3 tpitg[[thread_position_in_threadgroup]],
+    uint3   ntg[[threads_per_threadgroup]]) {
+
+    const int64_t i3 = tgpig.z;
+    const int64_t i2 = tgpig.y;
+    const int64_t i1 = tgpig.x;
+
+    const int64_t i03 = i3 / args.sf3;
+    const int64_t i02 = i2 / args.sf2;
+
+    const float   f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs;
+    const int64_t i01 = (int64_t)floor(f01);
+    const float   fd1 = f01 - (float)i01;
+
+    const float w_y0 = bicubic_weight2(fd1 + 1.0f);
+    const float w_y1 = bicubic_weight1(fd1);
+    const float w_y2 = bicubic_weight1(1.0f - fd1);
+    const float w_y3 = bicubic_weight2(2.0f - fd1);
+
+    const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02;
+
+    device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1);
+
+    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+        const float   f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
+        const int64_t i00 = (int64_t)floor(f00);
+        const float   fd0 = f00 - (float)i00;
+
+        const float w_x0 = bicubic_weight2(fd0 + 1.0f);
+        const float w_x1 = bicubic_weight1(fd0);
+        const float w_x2 = bicubic_weight1(1.0f - fd0);
+        const float w_x3 = bicubic_weight2(2.0f - fd0);
+
+        float sum = 0.0f;
+
+        for (int dy = -1; dy <= 2; ++dy) {
+            const int64_t iy = MAX(0, MIN(args.ne01 - 1, i01 + dy));
+            const float wy = (dy == -1) ? w_y0 : (dy == 0) ? w_y1 : (dy == 1) ? w_y2 : w_y3;
+
+            for (int dx = -1; dx <= 2; ++dx) {
+                const int64_t ix = MAX(0, MIN(args.ne00 - 1, i00 + dx));
+                const float wx = (dx == -1) ? w_x0 : (dx == 0) ? w_x1 : (dx == 1) ? w_x2 : w_x3;
+
+                const device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00);
+                sum += (*src_ptr) * wx * wy;
+            }
+        }
+
+        dst_ptr[i0] = sum;
+    }
+}
+
 kernel void kernel_pad_f32(
     constant ggml_metal_kargs_pad & args,
     device  const char * src0,

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

@@ -763,6 +763,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_ceil[2];
     vk_pipeline pipeline_floor[2];
     vk_pipeline pipeline_trunc[2];
+    vk_pipeline pipeline_sgn[2];
 
     vk_pipeline pipeline_add1_f16_f16;
     vk_pipeline pipeline_add1_f16_f32;
@@ -4393,6 +4394,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     CREATE_UNARY(ceil)
     CREATE_UNARY(floor)
     CREATE_UNARY(trunc)
+    CREATE_UNARY(sgn)
 #undef CREATE_UNARY
 
 #define CREATE_UNARY_RTE(name)  \
@@ -9281,6 +9283,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
                 return ctx->device->pipeline_floor[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_TRUNC:
                 return ctx->device->pipeline_trunc[dst->type == GGML_TYPE_F16];
+            case GGML_UNARY_OP_SGN:
+                return ctx->device->pipeline_sgn[dst->type == GGML_TYPE_F16];
             default:
                 break;
         }
@@ -12875,6 +12879,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_UNARY_OP_CEIL:
         case GGML_UNARY_OP_FLOOR:
         case GGML_UNARY_OP_TRUNC:
+        case GGML_UNARY_OP_SGN:
             ggml_vk_unary(ctx, compute_ctx, src0, node);
             break;
         case GGML_UNARY_OP_XIELU:
@@ -13253,6 +13258,10 @@ static void ggml_backend_vk_buffer_memset_tensor(ggml_backend_buffer_t buffer, g
     ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
     vk_buffer buf = buf_ctx->dev_buffer;
 
+    if (size == 0) {
+        return;
+    }
+
     uint32_t val32 = (uint32_t)value * 0x01010101;
     ggml_vk_buffer_memset(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, val32, size);
 }
@@ -13262,6 +13271,10 @@ static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml
     ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
     vk_buffer buf = buf_ctx->dev_buffer;
 
+    if (size == 0) {
+        return;
+    }
+
     ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
 }
 
@@ -13269,12 +13282,20 @@ static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, cons
     VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
     ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
 
+    if (size == 0) {
+        return;
+    }
+
     vk_buffer buf = buf_ctx->dev_buffer;
 
     ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
 }
 
 static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    if (ggml_nbytes(src) == 0) {
+        return true;
+    }
+
     if (ggml_backend_buffer_is_vk(src->buffer)) {
         ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context;
         ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
@@ -13464,6 +13485,10 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
 
+    if (size == 0) {
+        return;
+    }
+
     ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;
 
     vk_context cpy_ctx;
@@ -13507,6 +13532,10 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
 
+    if (size == 0) {
+        return;
+    }
+
     ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;
 
     vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);
@@ -13533,9 +13562,14 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_
 }
 
 static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
-    VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async()");
+    VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async(" << src << " -> " << dst << ", size=" << ggml_nbytes(src) << ")");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend_dst->context;
 
+    // Skip zero-size tensors
+    if (ggml_nbytes(src) == 0) {
+        return true;
+    }
+
     if (dst->buffer->buft != ggml_backend_vk_get_default_buffer_type(backend_dst)) {
         return false;
     }
@@ -14975,6 +15009,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_UNARY_OP_CEIL:
                 case GGML_UNARY_OP_FLOOR:
                 case GGML_UNARY_OP_TRUNC:
+                case GGML_UNARY_OP_SGN:
                     return ggml_is_contiguous(op->src[0]) &&
                            (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
@@ -16141,6 +16176,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             case GGML_UNARY_OP_TRUNC:
                 tensor_clone = ggml_trunc(ggml_ctx, src_clone[0]);
                 break;
+            case GGML_UNARY_OP_SGN:
+                tensor_clone = ggml_sgn(ggml_ctx, src_clone[0]);
+                break;
             default:
                 std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
                 GGML_ABORT("fatal error");

ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp

@@ -0,0 +1,21 @@
+#version 450
+
+#include "generic_head.glsl"
+#include "types.glsl"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    data_d[i] = D_TYPE(sign(float(data_a[i])));
+}

ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp

@@ -871,6 +871,8 @@ void process_shaders() {
     string_to_spv("elu_f32",        "elu.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
     string_to_spv("xielu_f16",      "xielu.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("xielu_f32",      "xielu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("sgn_f16",        "sgn.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("sgn_f32",        "sgn.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
     string_to_spv("tri_f16",        "tri.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("tri_f32",        "tri.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});

gguf-py/gguf/constants.py

@@ -177,6 +177,8 @@ class Keys:
         TEMPERATURE_LENGTH           = "{arch}.attention.temperature_length"
         KEY_LENGTH_MLA               = "{arch}.attention.key_length_mla"
         VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
+        KEY_LENGTH_SWA               = "{arch}.attention.key_length_swa"
+        VALUE_LENGTH_SWA             = "{arch}.attention.value_length_swa"
         SHARED_KV_LAYERS             = "{arch}.attention.shared_kv_layers"
         SLIDING_WINDOW_PATTERN       = "{arch}.attention.sliding_window_pattern"
         TEMPERATURE_SCALE            = "{arch}.attention.temperature_scale"
@@ -188,6 +190,7 @@ class Keys:
 
     class Rope:
         DIMENSION_COUNT           = "{arch}.rope.dimension_count"
+        DIMENSION_COUNT_SWA       = "{arch}.rope.dimension_count_swa"
         DIMENSION_SECTIONS        = "{arch}.rope.dimension_sections"
         FREQ_BASE                 = "{arch}.rope.freq_base"
         FREQ_BASE_SWA             = "{arch}.rope.freq_base_swa"

gguf-py/gguf/gguf_writer.py

@@ -773,6 +773,12 @@ class GGUFWriter:
     def add_value_length_mla(self, length: int) -> None:
         self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA.format(arch=self.arch), length)
 
+    def add_key_length_swa(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KEY_LENGTH_SWA.format(arch=self.arch), length)
+
+    def add_value_length_swa(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.VALUE_LENGTH_SWA.format(arch=self.arch), length)
+
     def add_indexer_head_count(self, count: int) -> None:
         self.add_uint32(Keys.Attention.Indexer.HEAD_COUNT.format(arch=self.arch), count)
 
@@ -946,6 +952,9 @@ class GGUFWriter:
     def add_rope_dimension_count(self, count: int) -> None:
         self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
 
+    def add_rope_dimension_count_swa(self, count: int) -> None:
+        self.add_uint32(Keys.Rope.DIMENSION_COUNT_SWA.format(arch=self.arch), count)
+
     def add_rope_dimension_sections(self, dims: Sequence[int]) -> None:
         self.add_array(Keys.Rope.DIMENSION_SECTIONS.format(arch=self.arch), dims)
 

models/templates/LFM2-8B-A1B.jinja

@@ -6,7 +6,7 @@
 	{%- set messages = messages[1:] -%}
 {%- endif -%}
 {%- if tools -%}
-	{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "You can use the following tools: <|tool_list_start|>[" -%}
+	{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%}
 	{%- for tool in tools -%}
 		{%- if tool is not string -%}
 			{%- set tool = tool | tojson -%}
@@ -17,7 +17,6 @@
 		{%- endif -%}
 	{%- endfor -%}
 	{%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%}
-	{{- '**IMPORTANT**: The syntax for calling the tools is: <|tool_call_start|>JSON tool call goes here<|tool_call_end|>. Please only call tools in the specified manner.' -}}
 {%- endif -%}
 {%- if ns.system_prompt -%}
 	{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
@@ -30,18 +29,9 @@
 	{%- endif -%}
 	{%- if message["role"] == "tool" -%}
 		{%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%}
-	{%- elif message["role"] == "assistant" -%}
-		{%- if message.tool_calls %}
-			{%- for tool_call in message.tool_calls %}
-				{%- if tool_call.function %}
-					{%- set tool_call = tool_call.function %}
-				{%- endif %}
-				{{- '\n<|tool_call_start|>\n{"name": "' + tool_call.name + '", "arguments": ' + (tool_call.arguments if tool_call.arguments is string else tool_call.arguments | tojson) + '}\n<|tool_call_end|>\n' }}
-			{%- endfor %}
-		{%- endif %}
 	{%- endif -%}
 	{{- content + "<|im_end|>\n" -}}
 {%- endfor -%}
 {%- if add_generation_prompt -%}
 	{{- "<|im_start|>assistant\n" -}}
-{%- endif -%}
+{%- endif -%}

models/templates/llama-cpp-lfm2.jinja

@@ -1,37 +0,0 @@
-{{- bos_token -}}
-{%- set system_prompt = "" -%}
-{%- set ns = namespace(system_prompt="") -%}
-{%- if messages[0]["role"] == "system" -%}
-	{%- set ns.system_prompt = messages[0]["content"] -%}
-	{%- set messages = messages[1:] -%}
-{%- endif -%}
-{%- if tools -%}
-	{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%}
-	{%- for tool in tools -%}
-		{%- if tool is not string -%}
-			{%- set tool = tool | tojson -%}
-		{%- endif -%}
-		{%- set ns.system_prompt = ns.system_prompt + tool -%}
-		{%- if not loop.last -%}
-			{%- set ns.system_prompt = ns.system_prompt + ", " -%}
-		{%- endif -%}
-	{%- endfor -%}
-	{%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%}
-{%- endif -%}
-{%- if ns.system_prompt -%}
-	{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
-{%- endif -%}
-{%- for message in messages -%}
-	{{- "<|im_start|>" + message["role"] + "\n" -}}
-	{%- set content = message["content"] -%}
-	{%- if content is not string -%}
-		{%- set content = content | tojson -%}
-	{%- endif -%}
-	{%- if message["role"] == "tool" -%}
-		{%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%}
-	{%- endif -%}
-	{{- content + "<|im_end|>\n" -}}
-{%- endfor -%}
-{%- if add_generation_prompt -%}
-	{{- "<|im_start|>assistant\n" -}}
-{%- endif -%}

src/llama-arch.cpp

@@ -230,11 +230,14 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_TEMPERATURE_SCALE,            "%s.attention.temperature_scale"            },
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
+    { LLM_KV_ATTENTION_KEY_LENGTH_SWA,               "%s.attention.key_length_swa"               },
+    { LLM_KV_ATTENTION_VALUE_LENGTH_SWA,             "%s.attention.value_length_swa"             },
     { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,           "%s.attention.indexer.head_count"           },
     { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,           "%s.attention.indexer.key_length"           },
     { LLM_KV_ATTENTION_INDEXER_TOP_K,                "%s.attention.indexer.top_k"                },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,           "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_DIMENSION_COUNT_SWA,       "%s.rope.dimension_count_swa"             },
     { LLM_KV_ROPE_DIMENSION_SECTIONS,        "%s.rope.dimension_sections"              },
     { LLM_KV_ROPE_FREQ_BASE,                 "%s.rope.freq_base"                       },
     { LLM_KV_ROPE_FREQ_BASE_SWA,             "%s.rope.freq_base_swa"                   },

src/llama-arch.h

@@ -234,11 +234,14 @@ enum llm_kv {
     LLM_KV_ATTENTION_TEMPERATURE_SCALE,
     LLM_KV_ATTENTION_KEY_LENGTH_MLA,
     LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
+    LLM_KV_ATTENTION_KEY_LENGTH_SWA,
+    LLM_KV_ATTENTION_VALUE_LENGTH_SWA,
     LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
     LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
     LLM_KV_ATTENTION_INDEXER_TOP_K,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
+    LLM_KV_ROPE_DIMENSION_COUNT_SWA,
     LLM_KV_ROPE_DIMENSION_SECTIONS,
     LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_FREQ_BASE_SWA,

src/llama-context.cpp

@@ -2876,19 +2876,23 @@ llama_context * llama_init_from_model(
 
     if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_k)) {
         const uint32_t blck_size = ggml_blck_size(params.type_k);
-        if (model->hparams.n_embd_head_k % blck_size != 0) {
-            LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n",
-                __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k);
-            return nullptr;
+        for (uint32_t il = 0; il < model->hparams.n_layer; ++il) {
+            if (model->hparams.n_embd_head_k(il) % blck_size != 0) {
+                LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n",
+                    __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k(il));
+                return nullptr;
+            }
         }
     }
 
     if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_v)) {
         const uint32_t blck_size = ggml_blck_size(params.type_v);
-        if (model->hparams.n_embd_head_v % blck_size != 0) {
-            LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_k=%u\n",
-                __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v);
-            return nullptr;
+        for (uint32_t il = 0; il < model->hparams.n_layer; ++il) {
+            if (model->hparams.n_embd_head_v(il) % blck_size != 0) {
+                LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_v=%u\n",
+                    __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v(il));
+                return nullptr;
+            }
         }
     }
 

src/llama-graph.cpp

@@ -849,13 +849,13 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     ubatch           (params.ubatch),
     n_embd           (hparams.n_embd),
     n_layer          (hparams.n_layer),
-    n_rot            (hparams.n_rot),
+    n_rot            (hparams.n_rot()),
     n_ctx            (cparams.n_ctx),
     n_head           (hparams.n_head()),
     n_head_kv        (hparams.n_head_kv()),
-    n_embd_head_k    (hparams.n_embd_head_k),
+    n_embd_head_k    (hparams.n_embd_head_k()),
     n_embd_k_gqa     (hparams.n_embd_k_gqa()),
-    n_embd_head_v    (hparams.n_embd_head_v),
+    n_embd_head_v    (hparams.n_embd_head_v()),
     n_embd_v_gqa     (hparams.n_embd_v_gqa()),
     n_expert         (hparams.n_expert),
     n_expert_used    (cparams.warmup ? hparams.n_expert : hparams.n_expert_used),
@@ -1151,7 +1151,6 @@ ggml_tensor * llm_graph_context::build_ffn(
     return cur;
 }
 
-// TODO remove redundant scale_w argument
 ggml_tensor * llm_graph_context::build_moe_ffn(
          ggml_tensor * cur,
          ggml_tensor * gate_inp,
@@ -1163,7 +1162,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
              int64_t   n_expert_used,
      llm_ffn_op_type   type_op,
                 bool   norm_w,
-                bool   scale_w,
                float   w_scale,
          llama_expert_gating_func_type gating_op,
                  int   il,
@@ -1180,7 +1178,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
         n_expert_used,
         type_op,
         norm_w,
-        scale_w,
         w_scale,
         gating_op,
         il,
@@ -1204,7 +1201,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
              int64_t   n_expert_used,
      llm_ffn_op_type   type_op,
                 bool   norm_w,
-                bool   scale_w,
                float   w_scale,
         llama_expert_gating_func_type gating_op,
                  int   il,
@@ -1332,7 +1328,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
 
         weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens);
     }
-    if (scale_w) {
+    if (w_scale != 0.0f && w_scale != 1.0f) {
         weights = ggml_scale(ctx0, weights, w_scale);
         cb(weights, "ffn_moe_weights_scaled", il);
     }

src/llama-graph.h

@@ -810,7 +810,6 @@ struct llm_graph_context {
                  int64_t   n_expert_used,
          llm_ffn_op_type   type_op,
                     bool   norm_w,
-                    bool   scale_w,
                    float   w_scale,
             llama_expert_gating_func_type gating_op,
                      int   il,
@@ -832,7 +831,6 @@ struct llm_graph_context {
                  int64_t   n_expert_used,
          llm_ffn_op_type   type_op,
                     bool   norm_w,
-                    bool   scale_w,
                    float   w_scale,
             llama_expert_gating_func_type gating_op,
                      int   il,

src/llama-hparams.cpp

@@ -62,6 +62,14 @@ uint32_t llama_hparams::n_gqa(uint32_t il) const {
     return n_head/n_head_kv;
 }
 
+uint32_t llama_hparams::n_rot(uint32_t il) const {
+    if (il < n_layer) {
+        return is_swa(il) ? n_rot_swa : n_rot_full;
+    }
+
+    GGML_ABORT("fatal error");
+}
+
 uint32_t llama_hparams::n_embd_inp() const {
     uint32_t n_embd_inp = n_embd;
 
@@ -76,16 +84,32 @@ uint32_t llama_hparams::n_embd_out() const {
     return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd;
 }
 
+uint32_t llama_hparams::n_embd_head_k(uint32_t il) const {
+    if (il < n_layer) {
+        return is_swa(il) ? n_embd_head_k_swa : n_embd_head_k_full;
+    }
+
+    GGML_ABORT("fatal error");
+}
+
+uint32_t llama_hparams::n_embd_head_v(uint32_t il) const {
+    if (il < n_layer) {
+        return is_swa(il) ? n_embd_head_v_swa : n_embd_head_v_full;
+    }
+
+    GGML_ABORT("fatal error");
+}
+
 uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
     const uint32_t n_head_kv = this->n_head_kv(il);
 
-    return n_embd_head_k * n_head_kv;
+    return n_embd_head_k(il) * n_head_kv;
 }
 
 uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
     const uint32_t n_head_kv = this->n_head_kv(il);
 
-    return n_embd_head_v * n_head_kv;
+    return n_embd_head_v(il) * n_head_kv;
 }
 
 bool llama_hparams::is_n_embd_k_gqa_variable() const {
@@ -197,11 +221,11 @@ bool llama_hparams::is_mla() const {
 }
 
 uint32_t llama_hparams::n_embd_head_k_mla() const {
-    return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k;
+    return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k();
 }
 
 uint32_t llama_hparams::n_embd_head_v_mla() const {
-    return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v;
+    return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v();
 }
 
 bool llama_hparams::has_kv(uint32_t il) const {

src/llama-hparams.h

@@ -44,13 +44,20 @@ struct llama_hparams {
     uint32_t n_embd;
     uint32_t n_layer;
     int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
-    uint32_t n_rot;
-    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
-    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
     uint32_t n_rel_attn_bkts = 0;
 
+    // different head size for full_attention and SWA layers
+    uint32_t n_embd_head_k_full; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
+    uint32_t n_embd_head_v_full; // dimension of values (d_v) aka n_embd_head
+    uint32_t n_embd_head_k_swa;
+    uint32_t n_embd_head_v_swa;
+
+    // different RoPE dimensions for full_attention and SWA layers
+    uint32_t n_rot_full;
+    uint32_t n_rot_swa;
+
     // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
     uint32_t n_embd_head_k_mla_impl = 0;
     uint32_t n_embd_head_v_mla_impl = 0;
@@ -247,12 +254,18 @@ struct llama_hparams {
 
     uint32_t n_gqa(uint32_t il = 0) const;
 
+    uint32_t n_rot(uint32_t il = 0) const;
+
     // dimension of main + auxiliary input embeddings
     uint32_t n_embd_inp() const;
 
     // dimension of output embeddings
     uint32_t n_embd_out() const;
 
+    // dimension of key/value embeddings for each head (per layer)
+    uint32_t n_embd_head_k(uint32_t il = 0) const;
+    uint32_t n_embd_head_v(uint32_t il = 0) const;
+
     // dimension of key embeddings across all k-v heads
     uint32_t n_embd_k_gqa(uint32_t il = 0) const;
 

src/llama-kv-cache.cpp

@@ -1033,8 +1033,8 @@ ggml_tensor * llama_kv_cache::get_k(ggml_context * ctx, int32_t il, uint32_t n_k
     const uint32_t ns = sinfo.s1 - sinfo.s0 + 1;
 
     return ggml_view_4d(ctx, k,
-            hparams.n_embd_head_k, hparams.n_head_kv(il), n_kv, ns,
-            ggml_row_size(k->type, hparams.n_embd_head_k),
+            hparams.n_embd_head_k(il), hparams.n_head_kv(il), n_kv, ns,
+            ggml_row_size(k->type, hparams.n_embd_head_k(il)),
             ggml_row_size(k->type, n_embd_k_gqa),
             ggml_row_size(k->type, n_embd_k_gqa*kv_size),
             ggml_row_size(k->type, n_embd_k_gqa*kv_size)*sinfo.s0);
@@ -1056,8 +1056,8 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k
     if (!v_trans) {
         // note: v->nb[1] <= v->nb[2]
         return ggml_view_4d(ctx, v,
-                hparams.n_embd_head_v, hparams.n_head_kv(il), n_kv, ns,
-                ggml_row_size(v->type, hparams.n_embd_head_v),          // v->nb[1]
+                hparams.n_embd_head_v(il), hparams.n_head_kv(il), n_kv, ns,
+                ggml_row_size(v->type, hparams.n_embd_head_v(il)),          // v->nb[1]
                 ggml_row_size(v->type, n_embd_v_gqa),                   // v->nb[2]
                 ggml_row_size(v->type, n_embd_v_gqa*kv_size),           // v->nb[3]
                 ggml_row_size(v->type, n_embd_v_gqa*kv_size)*sinfo.s0);
@@ -1065,8 +1065,8 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k
 
     // note: v->nb[1] > v->nb[2]
     return ggml_view_4d(ctx, v,
-            n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v, ns,
-            ggml_row_size(v->type, kv_size*hparams.n_embd_head_v),  // v->nb[1]
+            n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v(il), ns,
+            ggml_row_size(v->type, kv_size*hparams.n_embd_head_v(il)),  // v->nb[1]
             ggml_row_size(v->type, kv_size),                        // v->nb[2]
             ggml_row_size(v->type, kv_size*n_embd_v_gqa),           // v->nb[3]
             ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0);
@@ -1544,7 +1544,8 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
                 ggml_tensor * shift,
                 ggml_tensor * factors,
                       float   freq_base,
-                      float   freq_scale) const {
+                      float   freq_scale,
+                   uint32_t   il) const {
     const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;
 
     const auto & yarn_ext_factor  = cparams.yarn_ext_factor;
@@ -1552,7 +1553,7 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
     const auto & yarn_beta_slow   = cparams.yarn_beta_slow;
     const auto & yarn_attn_factor = cparams.yarn_attn_factor;
 
-    const auto & n_rot     = hparams.n_rot;
+    const auto & n_rot     = hparams.n_rot(il);
     const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE
                                 // @ngxson : this is a workaround
                                 // for M-RoPE, we want to rotate the whole vector when doing KV shift
@@ -1606,13 +1607,6 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
     auto * ctx = res->get_ctx();
     auto * gf  = res->get_gf();
 
-    const auto & n_embd_head_k = hparams.n_embd_head_k;
-  //const auto & n_embd_head_v = hparams.n_embd_head_v;
-
-    const auto & n_rot = hparams.n_rot;
-
-    const auto n_embd_nope = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0;
-
     auto inp = std::make_unique<llm_graph_input_k_shift>(this);
 
     inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, (int64_t) get_size()*n_stream);
@@ -1626,6 +1620,10 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
         const int64_t n_head_kv    = hparams.n_head_kv(il);
         const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
 
+        const auto n_rot         = hparams.n_rot(il);
+        const auto n_embd_head_k = hparams.n_embd_head_k(il);
+        const auto n_embd_nope   = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0;
+
         const float freq_base_l  = model.get_rope_freq_base (cparams, il);
         const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
 
@@ -1638,7 +1636,7 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
                 ggml_row_size(layer.k->type, n_embd_k_gqa),
                 ggml_row_size(layer.k->type, n_embd_nope));
 
-        ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l);
+        ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l, il);
 
         ggml_build_forward_expand(gf, cur);
     }

src/llama-kv-cache.h

@@ -264,7 +264,8 @@ private:
                     ggml_tensor * shift,
                     ggml_tensor * factors,
                           float   freq_base,
-                          float   freq_scale) const;
+                          float   freq_scale,
+                       uint32_t   il) const;
 
     ggml_cgraph * build_graph_shift(
                llm_graph_result * res,

src/llama-model-loader.cpp

@@ -918,7 +918,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
             } break;
         case GGML_OP_ROPE:
             {
-                const int n_embd_head = hparams.n_embd_head_v;
+                const int n_embd_head = hparams.n_embd_head_v();
                 const int n_head = hparams.n_head();
                 ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512);
                 ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);

src/llama-model-saver.cpp

@@ -186,8 +186,10 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV,           hparams.n_head_kv_arr, true);
     add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS,          hparams.f_max_alibi_bias);
     add_kv(LLM_KV_ATTENTION_CLAMP_KQV,               hparams.f_clamp_kqv);
-    add_kv(LLM_KV_ATTENTION_KEY_LENGTH,              hparams.n_embd_head_k);
-    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,            hparams.n_embd_head_v);
+    add_kv(LLM_KV_ATTENTION_KEY_LENGTH,              hparams.n_embd_head_k_full);
+    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,            hparams.n_embd_head_v_full);
+    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA,          hparams.n_embd_head_k_swa);
+    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,        hparams.n_embd_head_v_swa);
     add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS,           hparams.f_norm_eps);
     add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
     add_kv(LLM_KV_ATTENTION_CAUSAL,                  hparams.causal_attn);
@@ -199,7 +201,8 @@ void llama_model_saver::add_kv_from_model() {
 
     const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
 
-    add_kv(LLM_KV_ROPE_DIMENSION_COUNT,              hparams.n_rot);
+    add_kv(LLM_KV_ROPE_DIMENSION_COUNT,              hparams.n_rot_full);
+    add_kv(LLM_KV_ROPE_DIMENSION_COUNT_SWA,          hparams.n_rot_swa);
     add_kv(LLM_KV_ROPE_FREQ_BASE,                    hparams.rope_freq_base_train);
     // add_kv(LLM_KV_ROPE_SCALE_LINEAR,                 rope_scaling_factor); // old name
     add_kv(LLM_KV_ROPE_SCALING_TYPE,                 llama_rope_scaling_type_name(hparams.rope_scaling_type_train));

src/llama-model.cpp

@@ -459,26 +459,37 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
         // gpt-j n_rot = rotary_dim
 
-        hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
-        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+        hparams.n_embd_head_k_full = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full, false);
 
-        hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
-        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+        hparams.n_embd_head_v_full = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full, false);
 
         // sanity check for n_rot (optional)
-        hparams.n_rot = hparams.n_embd_head_k;
+        hparams.n_rot_full = hparams.n_embd_head_k_full;
 
-        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
+        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full, false);
 
         if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON || arch == LLM_ARCH_LLAMA_EMBED) {
-            if (hparams.n_rot != hparams.n_embd_head_k) {
-                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
+            if (hparams.n_rot_full != hparams.n_embd_head_k_full) {
+                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot_full, hparams.n_embd_head_k_full));
             }
         }
     } else {
-        hparams.n_rot = 0;
-        hparams.n_embd_head_k = 0;
-        hparams.n_embd_head_v = 0;
+        hparams.n_rot_full = 0;
+        hparams.n_embd_head_k_full = 0;
+        hparams.n_embd_head_v_full = 0;
+    }
+
+    // head size and n_rot for SWA layers
+    {
+        hparams.n_embd_head_k_swa = hparams.n_embd_head_k_full;
+        hparams.n_embd_head_v_swa = hparams.n_embd_head_v_full;
+        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa, false);
+        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa, false);
+
+        hparams.n_rot_swa = hparams.n_rot_full;
+        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
     }
 
     // for differentiating model types
@@ -1114,10 +1125,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                         break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
-
-                // Load attention parameters
-                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH,   hparams.n_embd_head_k, false);
-                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
             } break;
         case LLM_ARCH_PLAMO3:
             {
@@ -1212,7 +1219,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L173
                 hparams.f_attention_scale = type == LLM_TYPE_27B
                     ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
-                    : 1.0f / std::sqrt(float(hparams.n_embd_head_k));
+                    : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
             } break;
         case LLM_ARCH_GEMMA3:
             {
@@ -1245,7 +1252,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L289
                 hparams.f_attention_scale = type == LLM_TYPE_27B
                     ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
-                    : 1.0f / std::sqrt(float(hparams.n_embd_head_k));
+                    : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
             } break;
         case LLM_ARCH_GEMMA3N:
             {
@@ -1294,7 +1301,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     case 24: type = LLM_TYPE_0_3B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
-                hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k));
+                hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
 
             } break;
         case LLM_ARCH_STARCODER2:
@@ -1570,6 +1577,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
                 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
                 ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
 
                 switch (hparams.n_ff_exp) {
                     case 1408: type = LLM_TYPE_16B; break;
@@ -2076,6 +2084,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
                 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
                 ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
                 ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
 
                 switch (hparams.n_layer) {
@@ -2485,7 +2494,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,    hparams.n_embd_head_k_mla_impl);
                 ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,  hparams.n_embd_head_v_mla_impl);
                 ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
-                ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT,        hparams.n_rot);
                 ml.get_key(LLM_KV_SSM_CONV_KERNEL,             hparams.ssm_d_conv);
                 ml.get_key(LLM_KV_KDA_HEAD_DIM,                hparams.n_embd_head_kda);
 
@@ -2516,6 +2524,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
 
                 hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
 
+                // full_attention layer only use half of the RoPE dimensions
+                hparams.n_rot_full = hparams.n_rot_full / 2;
+
                 // MoE + SWA parameters
                 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
                 ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
@@ -2659,13 +2670,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
         const int64_t n_embd        = hparams.n_embd;
         const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
         const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
-        const int64_t n_embd_head_k = hparams.n_embd_head_k;
-        const int64_t n_embd_head_v = hparams.n_embd_head_v;
+        const int64_t n_embd_head_k = hparams.n_embd_head_k();
+        const int64_t n_embd_head_v = hparams.n_embd_head_v();
         const int64_t n_ff          = hparams.n_ff();
         const int64_t n_embd_gqa    = n_embd_v_gqa;
         const int64_t n_vocab       = vocab.n_tokens();
         const int64_t n_token_types = vocab.n_token_types();
-        const int64_t n_rot         = hparams.n_rot;
+        const int64_t n_rot         = hparams.n_rot();
         const int64_t n_expert      = hparams.n_expert;
         const int64_t n_expert_used = hparams.n_expert_used;
         const int64_t n_ctx_train   = hparams.n_ctx_train;
@@ -2965,8 +2976,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_MINICPM3:
                 {
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
-                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
 
                     const int64_t q_lora_rank  = hparams.n_lora_q;
                     const int64_t kv_lora_rank = hparams.n_lora_kv;
@@ -3838,8 +3849,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t dt_dim              = std::max(64, int(hparams.n_embd / 16));
 
                     // attention parameters
-                    const uint32_t qk_dim = hparams.n_embd_head_k;
-                    const uint32_t v_dim  = hparams.n_embd_head_v;
+                    const uint32_t qk_dim = hparams.n_embd_head_k();
+                    const uint32_t v_dim  = hparams.n_embd_head_v();
 
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
@@ -3899,8 +3910,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_PLAMO3:
                 {
-                    const int64_t head_dim_q = hparams.n_embd_head_k;
-                    const int64_t head_dim_v = hparams.n_embd_head_v;
+                    const int64_t head_dim_q = hparams.n_embd_head_k();
+                    const int64_t head_dim_v = hparams.n_embd_head_v();
 
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
@@ -4647,7 +4658,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_SEED_OSS:
                 {
-                    const uint32_t head_dim             = hparams.n_embd_head_k;
+                    const uint32_t head_dim             = hparams.n_embd_head_k();
                     const int64_t n_qo_dim              = n_head * head_dim;
                     const int64_t n_kv_dim              = n_head_kv * head_dim;
 
@@ -4876,7 +4887,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
                     const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
 
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
                     const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
                     GGML_ASSERT(n_embd_head_qk_nope >= 1);
 
@@ -4955,8 +4966,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_PLM:
                 {
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
-                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
                     const int64_t kv_lora_rank = hparams.n_lora_kv;
 
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5394,7 +5405,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
                     const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
 
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
                     const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
 
                     const int64_t q_lora_rank  = hparams.n_lora_q;
@@ -5678,7 +5689,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t n_expert       = hparams.n_expert;
                     const int64_t n_expert_used  = hparams.n_expert_used;
                     const int64_t n_ff_shexp     = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : n_ff_exp;
-                    const int64_t head_dim       = hparams.n_embd_head_k;
+                    const int64_t head_dim       = hparams.n_embd_head_k();
                     const int64_t n_qo_dim       = n_head * head_dim;
                     const int64_t n_kv_dim       = n_head_kv * head_dim;
 
@@ -6966,7 +6977,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
                              // Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA)
                              // Note: hparams.n_rot may be 72 (from conversion) but actual is 64
-                             const int64_t qk_rope_head_dim = hparams.n_rot;  // From config: qk_rope_head_dim
+                             const int64_t qk_rope_head_dim = hparams.n_rot();  // From config: qk_rope_head_dim
                              layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0);
                              // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled)
                              layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
@@ -7337,7 +7348,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
                     uint32_t n_rot_max = 0;
                     for (int i = 0; i < n_layer; ++i) {
-                        n_rot_max = std::max(n_rot_max, hparams.n_rot);
+                        n_rot_max = std::max(n_rot_max, hparams.n_rot(i));
                     }
                     if (n_rot_max == 0) {
                         n_rot_max = n_rot;
@@ -7672,11 +7683,11 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: n_layer               = %u\n",     __func__, hparams.n_layer);
         LLAMA_LOG_INFO("%s: n_head                = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
         LLAMA_LOG_INFO("%s: n_head_kv             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_rot                 = %u\n",     __func__, hparams.n_rot);
+        LLAMA_LOG_INFO("%s: n_rot                 = %u\n",     __func__, hparams.n_rot_full);
         LLAMA_LOG_INFO("%s: n_swa                 = %u\n",     __func__, hparams.n_swa);
         LLAMA_LOG_INFO("%s: is_swa_any            = %u\n",     __func__, hparams.is_swa_any());
-        LLAMA_LOG_INFO("%s: n_embd_head_k         = %u\n",     __func__, hparams.n_embd_head_k);
-        LLAMA_LOG_INFO("%s: n_embd_head_v         = %u\n",     __func__, hparams.n_embd_head_v);
+        LLAMA_LOG_INFO("%s: n_embd_head_k         = %u\n",     __func__, hparams.n_embd_head_k_full);
+        LLAMA_LOG_INFO("%s: n_embd_head_v         = %u\n",     __func__, hparams.n_embd_head_v_full);
         LLAMA_LOG_INFO("%s: n_gqa                 = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
         LLAMA_LOG_INFO("%s: n_embd_k_gqa          = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
         LLAMA_LOG_INFO("%s: n_embd_v_gqa          = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
@@ -7700,6 +7711,9 @@ void llama_model::print_info() const {
         if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
             LLAMA_LOG_INFO("%s: freq_base_swa         = %.1f\n",   __func__, hparams.rope_freq_base_train_swa);
             LLAMA_LOG_INFO("%s: freq_scale_swa        = %g\n",     __func__, hparams.rope_freq_scale_train_swa);
+            LLAMA_LOG_INFO("%s: n_embd_head_k_swa     = %u\n",     __func__, hparams.n_embd_head_k_swa);
+            LLAMA_LOG_INFO("%s: n_embd_head_v_swa     = %u\n",     __func__, hparams.n_embd_head_v_swa);
+            LLAMA_LOG_INFO("%s: n_rot_swa             = %u\n",     __func__, hparams.n_rot_swa);
         }
         LLAMA_LOG_INFO("%s: n_ctx_orig_yarn       = %u\n",     __func__, hparams.n_ctx_orig_yarn);
         LLAMA_LOG_INFO("%s: rope_yarn_log_mul     = %.4f\n",   __func__, hparams.rope_yarn_log_mul);

src/llama-quant.cpp

@@ -778,7 +778,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             ml.load_data_for(tensor);
         }
 
-        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
+        LLAMA_LOG_INFO("[%4d/%4d] %-36s - [%s], type = %6s, ",
                ++idx, ml.n_tensors,
                ggml_get_name(tensor),
                llama_format_tensor_shape(tensor).c_str(),

src/models/afmoe.cpp

@@ -1,8 +1,8 @@
 #include "models.h"
 
 llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -127,7 +127,6 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para
                     n_expert, n_expert_used,
                     LLM_FFN_SILU,
                     hparams.expert_weights_norm,           // norm_w (route_norm=True)
-                    hparams.expert_weights_scale,          // scale_w
                     hparams.expert_weights_scale,          // w_scale (route_scale=2.826)
                     (llama_expert_gating_func_type) hparams.expert_gating_func,
                     il);

src/models/apertus.cpp

@@ -3,10 +3,10 @@
 
 
 llm_build_apertus::llm_build_apertus(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/arcee.cpp

@@ -2,10 +2,10 @@
 
 
 llm_build_arcee::llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/arctic.cpp

@@ -1,11 +1,10 @@
 #include "models.h"
 
-
 llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -104,7 +103,7 @@ llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_pa
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(cur, "ffn_moe_out", il);

src/models/baichuan.cpp

@@ -2,10 +2,10 @@
 
 
 llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/bailingmoe.cpp

@@ -1,6 +1,5 @@
 #include "models.h"
 
-
 llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -97,7 +96,7 @@ llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_
                     nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, hparams.expert_weights_norm,
-                    false, hparams.expert_weights_scale,
+                    hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il);
         cb(moe_out, "ffn_moe_out", il);

src/models/bailingmoe2.cpp

@@ -1,13 +1,11 @@
 #include "models.h"
 
-
-
 llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -90,7 +88,7 @@ llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const ll
                 model.layers[il].ffn_exp_probs_b,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, hparams.expert_weights_norm,
-                hparams.expert_weights_scale, hparams.expert_weights_scale,
+                hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il);
             cb(moe_out, "ffn_moe_out", il);

src/models/bert.cpp

@@ -1,12 +1,10 @@
 #include "models.h"
 
-
-
 llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -129,9 +127,17 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params
         // feed-forward network
         if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
             // MoE branch
-            cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr,
-                                model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used,
-                                LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    nullptr,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    hparams.n_expert, hparams.n_expert_used,
+                    LLM_FFN_GELU, false,
+                    hparams.expert_weights_scale,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
             cb(cur, "ffn_moe_out", il);
         } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE ||
                    model.arch == LLM_ARCH_JINA_BERT_V3) {

src/models/bitnet.cpp

@@ -2,9 +2,9 @@
 
 
 llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/bloom.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/chameleon.cpp

@@ -3,10 +3,10 @@
 #include <float.h>
 
 llm_build_chameleon::llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/chatglm.cpp

@@ -2,10 +2,10 @@
 
 
 llm_build_chatglm::llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/codeshell.cpp

@@ -1,11 +1,11 @@
 #include "models.h"
 
 llm_build_codeshell::llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/cogvlm.cpp

@@ -2,11 +2,11 @@
 
 llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const float   kq_scale    = 1.0f / sqrtf(float(n_embd_head));
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * inpL;
     ggml_tensor * cur;

src/models/cohere2-iswa.cpp

@@ -1,9 +1,9 @@
 #include "models.h"
 
 llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     const float f_logit_scale = hparams.f_logit_scale;
 

src/models/command-r.cpp

@@ -4,9 +4,9 @@
 
 llm_build_command_r::llm_build_command_r(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     const float f_logit_scale = hparams.f_logit_scale;
 

src/models/dbrx.cpp

@@ -1,12 +1,11 @@
 #include "models.h"
 
-
 llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -89,7 +88,7 @@ llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(cur, "ffn_moe_out", il);

src/models/deci.cpp

@@ -3,10 +3,10 @@
 
 
 llm_build_deci::llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/deepseek.cpp

@@ -1,13 +1,11 @@
 #include "models.h"
 
-
-
 llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -100,7 +98,7 @@ llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_grap
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, false,
-                false, hparams.expert_weights_scale,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
             cb(moe_out, "ffn_moe_out", il);

src/models/deepseek2.cpp

@@ -8,7 +8,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
     const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
     const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();
 
-    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
     const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
 
     const uint32_t kv_lora_rank = hparams.n_lora_kv;
@@ -216,7 +216,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 model.layers[il].ffn_exp_probs_b,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, hparams.expert_weights_norm,
-                hparams.expert_weights_scale, hparams.expert_weights_scale,
+                hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il,
                 nullptr,

src/models/dots1.cpp

@@ -1,13 +1,11 @@
 #include "models.h"
 
-
-
 llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -91,7 +89,7 @@ llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_para
                 model.layers[il].ffn_exp_probs_b,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, hparams.expert_weights_norm,
-                hparams.expert_weights_scale, hparams.expert_weights_scale,
+                hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il);
             cb(moe_out, "ffn_moe_out", il);

src/models/dream.cpp

@@ -5,10 +5,10 @@
 llm_build_dream::llm_build_dream(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     //copied from qwen2
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/ernie4-5-moe.cpp

@@ -1,13 +1,11 @@
 #include "models.h"
 
-
-
 llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -103,7 +101,7 @@ llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const
                                         model.layers[il].ffn_exp_probs_b,
                                         n_expert, n_expert_used,
                                         LLM_FFN_SILU, true,
-                                        false, 0.0,
+                                        hparams.expert_weights_scale,
                                         LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                                         il);
             cb(moe_out, "ffn_moe_out", il);

src/models/ernie4-5.cpp

@@ -2,10 +2,10 @@
 
 llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/eurobert.cpp

@@ -1,9 +1,9 @@
 #include "models.h"
 
 llm_build_eurobert::llm_build_eurobert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/exaone-moe.cpp

@@ -1,12 +1,11 @@
 #include "models.h"
 
-
 llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_k;
+    const int64_t n_embd_head = hparams.n_embd_head_k();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -100,7 +99,7 @@ llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_
                 model.layers[il].ffn_exp_probs_b,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, hparams.expert_weights_norm,
-                hparams.expert_weights_scale, hparams.expert_weights_scale,
+                hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il);
             cb(moe_out, "ffn_moe_out", il);

src/models/exaone.cpp

@@ -4,10 +4,10 @@
 
 llm_build_exaone::llm_build_exaone(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/exaone4.cpp

@@ -4,10 +4,10 @@
 template <bool iswa>
 llm_build_exaone4<iswa>::llm_build_exaone4(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_k;
+    const int64_t n_embd_head = hparams.n_embd_head_k();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/falcon-h1.cpp

@@ -2,7 +2,7 @@
 
 llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) :
     llm_build_mamba_base(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/falcon.cpp

@@ -2,11 +2,11 @@
 
 
 llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/gemma-embedding.cpp

@@ -2,7 +2,7 @@
 
 llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_k;
+    const int64_t n_embd_head = hparams.n_embd_head_k();
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/gemma.cpp

@@ -2,7 +2,7 @@
 
 
 llm_build_gemma::llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/gemma2-iswa.cpp

@@ -1,7 +1,7 @@
 #include "models.h"
 
 llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_k;
+    const int64_t n_embd_head = hparams.n_embd_head_k();
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/gemma3.cpp

@@ -2,7 +2,7 @@
 
 template <bool iswa>
 llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_k;
+    const int64_t n_embd_head = hparams.n_embd_head_k();
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/gemma3n-iswa.cpp

@@ -3,7 +3,7 @@
 llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params),
     model(model),
-    n_embd_head(model.hparams.n_embd_head_k),
+    n_embd_head(model.hparams.n_embd_head_k()),
     n_embd_altup(model.hparams.n_embd_altup),
     n_altup(model.hparams.n_altup),
     i_altup_act(model.hparams.i_altup_act) {

src/models/glm4-moe.cpp

@@ -1,9 +1,9 @@
 #include "models.h"
 
 llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     int sections[4];
     std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
@@ -128,7 +128,7 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
                     model.layers[il].ffn_exp_probs_b,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, hparams.expert_weights_norm,
-                    hparams.expert_weights_scale, hparams.expert_weights_scale,
+                    hparams.expert_weights_scale,
                     (llama_expert_gating_func_type) hparams.expert_gating_func,
                     il);
             cb(routed_out, "ffn_moe_out", il);

src/models/glm4.cpp

@@ -3,10 +3,10 @@
 
 
 llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     int sections[4];
     std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);

src/models/gpt2.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_gpt2::llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * pos;

src/models/gptneox.cpp

@@ -2,10 +2,10 @@
 
 
 llm_build_gptneox::llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/granite-hybrid.cpp

@@ -1,10 +1,9 @@
 #include "models.h"
 
-
 llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params) :
     llm_build_mamba_base(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -160,7 +159,7 @@ ggml_tensor * llm_build_granite_hybrid::build_layer_ffn(ggml_tensor *       cur,
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(moe_out, "ffn_moe_out", il);

src/models/granite.cpp

@@ -1,15 +1,14 @@
 #include "models.h"
 
-
 llm_build_granite::llm_build_granite(
     const llama_model & model,
     const llm_graph_params & params)
     : llm_graph_context(params) {
 
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -175,7 +174,7 @@ ggml_tensor * llm_build_granite::build_layer_ffn(
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(moe_out, "ffn_moe_out", il);

src/models/grok.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -99,7 +99,7 @@ llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_GELU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(moe_out, "ffn_moe_out", il);

src/models/grovemoe.cpp

@@ -1,14 +1,12 @@
 #include "models.h"
 
-
-
 llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    const int64_t n_embd_head    = hparams.n_embd_head_v;
+    const int64_t n_embd_head    = hparams.n_embd_head_v();
     const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -90,7 +88,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il,
                 probs);
@@ -106,7 +104,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap
                     nullptr,
                     n_chunk_expert, n_expert_used > n_chunk_expert ? n_chunk_expert : n_expert_used,
                     LLM_FFN_SILU, true,
-                    false, 0.0,
+                    hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il,
                     probs);

src/models/hunyuan-dense.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_hunyuan_dense::llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/hunyuan-moe.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -119,8 +119,7 @@ llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const ll
                 n_expert, n_expert_used,
                 LLM_FFN_SILU,
                 true, // norm_topk_prob
-                false,
-                0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(cur_moe, "ffn_moe_out", il);

src/models/internlm2.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_internlm2::llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/jais.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_jais::llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/jais2.cpp

@@ -3,10 +3,10 @@
 // JAIS-2 model graph builder
 // Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings
 llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/jamba.cpp

@@ -1,7 +1,7 @@
 #include "models.h"
 
 llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -76,7 +76,7 @@ llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_para
                     nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, false,
-                    false, 0.0,
+                    hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il);
             cb(cur, "ffn_moe_out", il);

src/models/kimi-linear.cpp

@@ -1,5 +1,4 @@
 #include "models.h"
-#include "ggml.h"
 
 #include "llama-memory-recurrent.h"
 
@@ -103,7 +102,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
     const int64_t kv_lora_rank = hparams.n_lora_kv;
     // qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot
     // Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim]
-    const int64_t n_embd_head_qk_rope = hparams.n_rot;  // config.qk_rope_head_dim
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();  // config.qk_rope_head_dim
     const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;  // 192 - 64 = 128
     // Attention scale for MLA
     const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla);
@@ -341,7 +340,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
                 hparams.n_expert,
                 hparams.n_expert_used,
                 LLM_FFN_SILU, true,
-                hparams.expert_weights_scale, hparams.expert_weights_scale,
+                hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il);
             cb(moe_out, "ffn_moe_out", il);

src/models/lfm2.cpp

@@ -23,17 +23,23 @@ llm_build_lfm2<iswa>::llm_build_lfm2(const llama_model & model, const llm_graph_
     };
     auto build_moe_feed_forward = [&model, this](ggml_tensor * cur, int il) -> ggml_tensor * {
         return build_moe_ffn(cur,
-                            model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
-                            model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
-                            model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, false, 0.0,
-                            static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func), il);
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                hparams.expert_weights_scale,
+                static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
+                il);
     };
     auto build_attn_block = [&model, this](ggml_tensor *   cur,
                                            ggml_tensor *   inp_pos,
                                            inp_attn_type * inp_attn,
                                            int             il) -> ggml_tensor * {
         GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il));
-        const auto n_embd_head = hparams.n_embd_head_v;
+        const auto n_embd_head = hparams.n_embd_head_v();
         const auto n_head_kv   = hparams.n_head_kv(il);
 
         auto * q = build_lora_mm(model.layers[il].wq, cur);

src/models/llada-moe.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -90,7 +90,7 @@ llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_gr
                 nullptr,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, false,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                 il);
         cb(cur, "ffn_moe_out", il);

src/models/llada.cpp

@@ -2,10 +2,10 @@
 
 llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     // LLaDA is similar to LLaMA but uses non-causal attention for diffusion
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/llama-iswa.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -134,7 +134,7 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_
                     nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, false,
-                    false, 0.0,
+                    hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
                     il);
 

src/models/llama.cpp

@@ -2,10 +2,10 @@
 
 template <bool embed>
 llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -130,7 +130,7 @@ llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_gra
                     nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
-                    false, 0.0,
+                    hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il);
             cb(cur, "ffn_moe_out", il);

src/models/maincoder.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/mamba-base.cpp

@@ -155,7 +155,6 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp,
 
     const auto kv_head = mctx_cur->get_head();
 
-    const int64_t n_embd   = hparams.n_embd;
     const int64_t d_conv   = hparams.ssm_d_conv;
     const int64_t d_inner  = hparams.ssm_d_inner;
     const int64_t d_state  = hparams.ssm_d_state;
@@ -170,7 +169,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp,
     GGML_ASSERT(ubatch.equal_seqs());
     GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
     GGML_ASSERT(d_inner % n_head == 0);
-    GGML_ASSERT(d_inner % (n_group*n_embd) == 0);
+    GGML_ASSERT(d_inner % (n_group*d_state) == 0);
 
     ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
     ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);

src/models/mimo2-iswa.cpp

@@ -1,4 +1,3 @@
-
 #include "models.h"
 
 llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
@@ -88,10 +87,17 @@ llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_
             cb(cur, "ffn_out", il);
         } else {
             // MoE branch
-            cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
-                                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
-                                model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, false,
-                                0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il);
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    hparams.expert_weights_scale,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
+                    il);
             cb(cur, "ffn_moe_out", il);
         }
 

src/models/minicpm3.cpp

@@ -5,10 +5,10 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap
     const int64_t n_embd_base = 256;
     const float scale_embd  = 12.0f;
     const float scale_depth = 1.4f;
-    const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k));
+    const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k()));
 
-    const uint32_t n_embd_head_qk_rope = hparams.n_rot;
-    const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+    const uint32_t n_embd_head_qk_rope = hparams.n_rot();
+    const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
 
     const uint32_t kv_lora_rank = hparams.n_lora_kv;
 
@@ -51,21 +51,21 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap
                     LLM_NORM_RMS, il);
             cb(q, "q", il);
 
-            // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
+            // {q_lora_rank, n_head * hparams.n_embd_head_k()} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k(), n_tokens}
             q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
             cb(q, "q", il);
 
             // split into {n_head * n_embd_head_qk_nope, n_tokens}
             ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
-                    ggml_row_size(q->type, hparams.n_embd_head_k),
-                    ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                    ggml_row_size(q->type, hparams.n_embd_head_k()),
+                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),
                     0);
             cb(q_nope, "q_nope", il);
 
             // and {n_head * n_embd_head_qk_rope, n_tokens}
             ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
-                    ggml_row_size(q->type, hparams.n_embd_head_k),
-                    ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                    ggml_row_size(q->type, hparams.n_embd_head_k()),
+                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),
                     ggml_row_size(q->type, n_embd_head_qk_nope));
             cb(q_pe, "q_pe", il);
 
@@ -97,15 +97,15 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap
 
             // split into {n_head * n_embd_head_qk_nope, n_tokens}
             ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
-                    ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
-                    ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                    ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()),
+                    ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())),
                     0);
             cb(k_nope, "k_nope", il);
 
             // and {n_head * n_embd_head_v, n_tokens}
-            ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
-                    ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
-                    ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+            ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens,
+                    ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())),
+                    ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head),
                     ggml_row_size(kv->type, (n_embd_head_qk_nope)));
             cb(v_states, "v_states", il);
 

src/models/minimax-m2.cpp

@@ -1,11 +1,10 @@
-
 #include "models.h"
 
 llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    // GGML_ASSERT(n_embd_head == n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -91,7 +90,7 @@ llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_
                 model.layers[il].ffn_exp_probs_b,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, true,
-                false, 0.0,
+                hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il);
         cb(cur, "ffn_moe_out", il);

src/models/mistral3.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
 
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -127,7 +127,7 @@ llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_grap
                     nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
-                    false, 0.0,
+                    hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il);
             cb(cur, "ffn_moe_out", il);

src/models/modern-bert.cpp

@@ -1,10 +1,10 @@
 #include "models.h"
 
 llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

src/models/mpt.cpp

@@ -3,10 +3,10 @@
 
 
 llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_head = hparams.n_embd_head_v();
     const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
     ggml_tensor * cur;
     ggml_tensor * pos;