llama-quant : correct n_attention_wv usage (#20357)

* llama-quant : correct `n_attention_wv` usage

In #19770, I introduced a regression in the way the
`quantize_state_impl` counter values were initialized. I was
incrementing and using `n_attention_wv` in the same loop, when it should
have been fixed by the time we're deciding tensor types in
`llama_tensor_get_type_impl` (for `use_more_bits`).

I never observed a difference in any of [my
tests](https://github.com/ggml-org/llama.cpp/pull/19770#issuecomment-4000424712)
- it was only after @bartowski kindly pointed this out that I realized
it was incorrect. (Thanks!)

* simplify

.github/workflows/build.yml

@@ -93,7 +93,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L main -E "test-llama-archs" --verbose --timeout 900
 
   macOS-latest-cmake-x64:
     runs-on: macos-15-intel

CODEOWNERS

@@ -11,6 +11,8 @@
 /common/base64.hpp.*                    @ggerganov
 /common/build-info.*                    @ggerganov
 /common/chat.*                          @pwilkin
+/common/chat-auto*.*                    @pwilkin
+/common/chat-diff-analyzer.*            @pwilkin
 /common/chat-peg-parser.*               @aldehir
 /common/common.*                        @ggerganov
 /common/console.*                       @ggerganov
@@ -89,12 +91,13 @@
 /src/llama-vocab.*                      @CISC
 /src/models/                            @CISC
 /tests/                                 @ggerganov
-/tests/test-chat-.*                     @pwilkin
+/tests/test-chat.*                      @pwilkin
 /tools/batched-bench/                   @ggerganov
 /tools/cli/                             @ngxson
 /tools/completion/                      @ggerganov
 /tools/mtmd/                            @ngxson
 /tools/perplexity/                      @ggerganov
+/tools/parser/                          @pwilkin
 /tools/quantize/                        @ggerganov
 /tools/rpc/                             @rgerganov
 /tools/server/*                         @ngxson @ggerganov # no subdir

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

README.md

@@ -259,6 +259,8 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
 - [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
 - [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes.
+- [LLMKube](https://github.com/defilantech/llmkube) - Kubernetes operator for llama.cpp with multi-GPU and Apple Silicon Metal
+  support"
 </details>
 
 <details>

common/arg.cpp

@@ -2427,11 +2427,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 );
             }
             if (split_arg.size() == 1) {
-                std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024);
+                std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoull(split_arg[0]) * 1024*1024);
                 return;
             }
             for (size_t i = 0; i < split_arg.size(); i++) {
-                params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024;
+                params.fit_params_target[i] = std::stoull(split_arg[i]) * 1024*1024;
             }
         }
     ).set_env("LLAMA_ARG_FIT_TARGET"));
@@ -2666,7 +2666,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.out_file = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE}));
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE, LLAMA_EXAMPLE_RESULTS}));
     add_opt(common_arg(
         {"-ofreq", "--output-frequency"}, "N",
         string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
@@ -3607,6 +3607,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(common_arg(
+        {"--check"},
+        string_format("check rather than generate results (default: %s)", params.check ? "true" : "false"),
+        [](common_params & params) {
+            params.check = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_RESULTS}));
     add_opt(common_arg(
         {"--save-logits"},
         string_format("save final logits to files for verification (default: %s)", params.save_logits ? "true" : "false"),

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 }
             };
@@ -87,7 +90,7 @@ common_peg_arena autoparser::build_parser(const templates_params & inputs) const
         // pre-register a json-string rule that accepts both quote styles. This must happen
         // before any call to p.json() so that all JSON parsing inherits the flexible rule.
         if (tools.format.uses_python_dicts) {
-            p.rule("json-string", [&]() { return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); });
+            p.rule("json-string", p.quoted_string());
         }
 
         parser_build_context ctx(p, inputs);
@@ -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) {
@@ -239,8 +245,6 @@ common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context
         if (!function.close.empty()) {
             func_parser = func_parser + function.close;
         }
-        func_parser = p.atomic(func_parser);
-
         tool_choice |= p.rule("tool-" + name, func_parser);
     });
 
@@ -302,8 +306,9 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
             params.at("required").get_to(required);
         }
 
-        // Build parser for each argument
-        std::vector<common_peg_parser> arg_parsers;
+        // Build parser for each argument, separating required and optional
+        std::vector<common_peg_parser> required_parsers;
+        std::vector<common_peg_parser> optional_parsers;
         for (const auto & [param_name, param_schema] : properties.items()) {
             bool        is_required = required.find(param_name) != required.end();
             std::string type        = "object";
@@ -328,31 +333,59 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                                         p.space()) +
                 p.tool_arg_close(p.literal(arguments.value_suffix)));
 
+            auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
             if (is_required) {
-                arg_parsers.push_back(p.rule("tool-" + name + "-arg-" + param_name, arg));
+                required_parsers.push_back(named_arg);
             } else {
-                arg_parsers.push_back(p.optional(p.rule("tool-" + name + "-arg-" + param_name, arg)));
+                optional_parsers.push_back(named_arg);
             }
         }
 
-        // Build arg sequence with space() between consecutive args
+        // Build required arg sequence in definition order
         common_peg_parser args_seq = p.eps();
-        for (size_t i = 0; i < arg_parsers.size(); i++) {
+        for (size_t i = 0; i < required_parsers.size(); i++) {
             if (i > 0) {
                 args_seq = args_seq + p.space();
             }
-            args_seq = args_seq + arg_parsers[i];
+            args_seq = args_seq + required_parsers[i];
+        }
+
+        // Build optional args with flexible ordering
+        if (!optional_parsers.empty()) {
+            common_peg_parser any_opt = p.choice();
+            for (const auto & opt : optional_parsers) {
+                any_opt |= opt;
+            }
+            args_seq = args_seq + p.repeat(p.space() + any_opt, 0, (int) optional_parsers.size());
         }
 
         // Build call_id parser based on position (if supported)
         common_peg_parser call_id_section = p.eps();
+        bool have_call_id = false;
         if (call_id.pos == call_id_position::BETWEEN_FUNC_AND_ARGS && !call_id.prefix.empty() &&
             !call_id.suffix.empty()) {
-            call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix))) + call_id.suffix;
+            have_call_id = true;
+            call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix)) + call_id.suffix);
         }
 
-        auto func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
-                           call_id_section + p.space() + args_seq;
+        bool matched_atomic = false;
+        common_peg_parser func_parser = p.eps();
+        if (!function.name_suffix.empty()) {
+            func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
+                call_id_section + p.space() + args_seq;
+            matched_atomic = true;
+        } else if (have_call_id) {
+            func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
+                call_id_section) + p.space() + args_seq;
+            matched_atomic = true;
+        } else if (!arguments.name_prefix.empty() && properties.size() > 0) {
+            func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
+                call_id_section + p.space() + p.peek(p.literal(arguments.name_prefix))) + args_seq;
+            matched_atomic = true;
+        } else {
+            func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
+                call_id_section + p.space() + args_seq;
+        }
 
         if (!function.close.empty()) {
             func_parser = func_parser + p.space() + p.tool_close(p.literal(function.close));
@@ -366,8 +399,10 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
             func_parser =
                 func_parser + p.tool_close(p.space());  // force this to process tool closing callbacks in mapper
         }
+        if (!matched_atomic) {
+            func_parser = p.atomic(func_parser);
+        }
 
-        func_parser = p.atomic(func_parser);
         tool_choice |= p.rule("tool-" + name, func_parser);
     });
 

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(string_content('"')) + literal("\""),
+                    literal("'") + tool_arg_string_value(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('.');
@@ -510,7 +577,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
         if (!call_id_key.empty()) {
             auto id_parser = atomic(
                 literal("\"" + call_id_key + "\"") + space() + literal(":") + space() +
-                literal("\"") + tool_id(json_string_content()) + literal("\"")
+                literal("\"") + tool_id(string_content('"')) + literal("\"")
             );
             inner_fields.push_back(optional(id_parser + space() + optional(literal(",") + space())));
         }
@@ -519,7 +586,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
             auto gen_id_parser = atomic(
                 literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() +
                 choice({
-                    literal("\"") + tool_id(json_string_content()) + literal("\""),
+                    literal("\"") + tool_id(string_content('"')) + literal("\""),
                     tool_id(json_number())
                 })
             );
@@ -608,7 +675,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
             if (id_spec.first.empty()) {
                 auto id_parser = atomic(
                     literal("\"" + call_id_key + "\"") + space() + literal(":") + space() +
-                    literal("\"") + tool_id(json_string_content()) + literal("\"")
+                    literal("\"") + tool_id(string_content('"')) + literal("\"")
                 );
                 tool_parser_body = tool_parser_body + optional(id_parser + space() + literal(",") + space());
             }
@@ -620,7 +687,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
                 auto gen_id_parser = atomic(
                     literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() +
                     choice({
-                        literal("\"") + tool_id(json_string_content()) + literal("\""),
+                        literal("\"") + tool_id(string_content('"')) + literal("\""),
                         tool_id(json_number())
                     })
                 );
@@ -669,7 +736,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
             id_parser = atomic(
                 literal("\"" + call_id_key + "\"") + space() + literal(":") + space() +
                 choice({
-                    literal("\"") + tool_id(json_string_content()) + literal("\""),
+                    literal("\"") + tool_id(string_content('"')) + literal("\""),
                     tool_id(json_number())
                 })
             );
@@ -680,7 +747,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
             gen_id_parser = atomic(
                 literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() +
                 choice({
-                    literal("\"") + tool_id(json_string_content()) + literal("\""),
+                    literal("\"") + tool_id(string_content('"')) + literal("\""),
                     tool_id(json_number())
                 })
             );

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

@@ -129,7 +129,7 @@ json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const {
                 {"type", "function"},
                 {"function", {
                     {"name", tool_call.name},
-                    {"arguments", json::parse(tool_call.arguments)},
+                    {"arguments", json(tool_call.arguments)},
                 }},
             };
             if (!tool_call.id.empty()) {
@@ -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 +1440,12 @@ 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) {
         workaround::system_message_not_supported(params.messages);
     }
@@ -1420,6 +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;
@@ -1519,14 +1620,18 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
         build_chat_peg_parser([](common_chat_peg_builder & p) { return p.content(p.rest()) + p.end(); }) :
         src_parser;
 
-        if (src_parser.empty()) {
-        LOG_WRN("No parser definition detected, assuming pure content parser.");
+    if (src_parser.empty()) {
+        LOG_DBG("No parser definition detected, assuming pure content parser.");
     }
 
     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()) {
@@ -1539,7 +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);
             }
@@ -1555,7 +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/common.h

@@ -104,6 +104,7 @@ enum llama_example {
     LLAMA_EXAMPLE_DIFFUSION,
     LLAMA_EXAMPLE_FINETUNE,
     LLAMA_EXAMPLE_FIT_PARAMS,
+    LLAMA_EXAMPLE_RESULTS,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -456,6 +457,8 @@ struct common_params {
 
     bool   kl_divergence    = false; // compute KL divergence
 
+    bool check             = false; // check rather than generate results for llama-results
+
     bool usage             = false; // print usage
     bool completion        = false; // print source-able completion script
     bool use_color         = false; // use color to distinguish generations and inputs

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/json-schema-to-grammar.cpp

@@ -790,7 +790,7 @@ public:
                             } else if (target.is_array()) {
                                 size_t sel_index;
                                 try {
-                                    sel_index = std::stoul(sel);
+                                    sel_index = std::stoull(sel);
                                 } catch (const std::invalid_argument & e) {
                                     sel_index = target.size();
                                 }

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);
@@ -665,32 +658,23 @@ struct parser_executor {
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
     }
 
-    static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos) {
+    static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos, const char delimiter) {
         ++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);
         }
 
-        switch (ctx.input[pos]) {
-            case '"':
-            case '\'':
-            case '\\':
-            case '/':
-            case 'b':
-            case 'f':
-            case 'n':
-            case 'r':
-            case 't':
-                ++pos;
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
-            case 'u':
-                return handle_unicode_escape(ctx, start, pos);
-            default:
-                // Invalid escape sequence
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
+        char c = ctx.input[pos];
+        if (c == delimiter || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' || c == 't') {
+            ++pos;
+            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
+        } else if (c == 'u') {
+            return handle_unicode_escape(ctx, start, pos);
+        } else {
+            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
         }
     }
 
@@ -698,7 +682,7 @@ 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);
@@ -711,20 +695,20 @@ struct parser_executor {
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
     }
 
-    common_peg_parse_result operator()(const common_peg_json_string_parser & /* p */) {
+    common_peg_parse_result operator()(const common_peg_string_parser & p) {
         auto pos = start_pos;
 
         // Parse string content (without quotes)
         while (pos < ctx.input.size()) {
             char c = ctx.input[pos];
 
-            if (c == '"') {
-                // Found closing quote - success (don't consume it)
+            if (c == p.delimiter) {
+                // Found closing delimiter - success (don't consume it)
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
             }
 
             if (c == '\\') {
-                auto result = handle_escape_sequence(ctx, start_pos, pos);
+                auto result = handle_escape_sequence(ctx, start_pos, pos, p.delimiter);
                 if (!result.success()) {
                     return result;
                 }
@@ -732,7 +716,7 @@ 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);
@@ -747,49 +731,7 @@ struct parser_executor {
         }
 
         // Reached end without finding closing quote
-        if (!ctx.is_partial) {
-            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);
-    }
-
-    common_peg_parse_result operator()(const common_peg_python_dict_string_parser & /* p */) {
-        auto pos = start_pos;
-
-        // Parse string content (without quotes)
-        while (pos < ctx.input.size()) {
-            char c = ctx.input[pos];
-
-            if (c == '\'') {
-                // Found closing quote - success (don't consume it)
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
-            }
-
-            if (c == '\\') {
-                auto result = handle_escape_sequence(ctx, start_pos, pos);
-                if (!result.success()) {
-                    return result;
-                }
-            } else {
-                auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos);
-
-                if (utf8_result.status == utf8_parse_result::INCOMPLETE) {
-                    if (!ctx.is_partial) {
-                        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);
-                }
-
-                if (utf8_result.status == utf8_parse_result::INVALID) {
-                    return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
-                }
-
-                pos += utf8_result.bytes_consumed;
-            }
-        }
-
-        // 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 +749,7 @@ 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 +779,7 @@ 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 +818,7 @@ 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);
@@ -995,8 +937,7 @@ void common_peg_arena::resolve_refs() {
                                  std::is_same_v<T, common_peg_ref_parser> ||
                                  std::is_same_v<T, common_peg_until_parser> ||
                                  std::is_same_v<T, common_peg_literal_parser> ||
-                                 std::is_same_v<T, common_peg_json_string_parser> ||
-                                 std::is_same_v<T, common_peg_python_dict_string_parser> ||
+                                 std::is_same_v<T, common_peg_string_parser> ||
                                  std::is_same_v<T, common_peg_chars_parser> ||
                                  std::is_same_v<T, common_peg_any_parser> ||
                                  std::is_same_v<T, common_peg_space_parser>) {
@@ -1072,10 +1013,8 @@ std::string common_peg_arena::dump_impl(common_peg_parser_id
                 return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", unbounded)";
             }
             return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", " + std::to_string(p.max_count) + ")";
-        } else if constexpr (std::is_same_v<T, common_peg_json_string_parser>) {
-            return "JsonString()";
-        } else if constexpr (std::is_same_v<T, common_peg_python_dict_string_parser>) {
-            return "PythonDictString()";
+        } else if constexpr (std::is_same_v<T, common_peg_string_parser>) {
+            return "String(" + std::string(1, p.delimiter) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_until_parser>) {
             return "Until(" + string_join(p.delimiters, " | ") + ")";
         } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
@@ -1288,47 +1227,25 @@ common_peg_arena common_peg_parser_builder::build() {
 
 // String primitives
 
-common_peg_parser common_peg_parser_builder::json_string_content() {
-    return wrap(arena_.add_parser(common_peg_json_string_parser{}));
-}
-
-common_peg_parser common_peg_parser_builder::single_quoted_string_content() {
-    return wrap(arena_.add_parser(common_peg_python_dict_string_parser{}));
+common_peg_parser common_peg_parser_builder::string_content(char delimiter) {
+    return wrap(arena_.add_parser(common_peg_string_parser{delimiter}));
 }
 
 common_peg_parser common_peg_parser_builder::double_quoted_string() {
-    return rule("dq-string",
-                [this]() { return sequence({ literal("\""), json_string_content(), literal("\""), space() }); });
-}
-
-common_peg_parser common_peg_parser_builder::single_quoted_string() {
-    return rule("sq-string",
-                [this]() { return sequence({ literal("'"), single_quoted_string_content(), literal("'"), space() }); });
-}
-
-common_peg_parser common_peg_parser_builder::flexible_string() {
-    return rule("flexible-string", [this]() { return choice({ double_quoted_string(), single_quoted_string() }); });
-}
-
-// Generic helpers for object/array structure
-
-common_peg_parser common_peg_parser_builder::generic_object(const std::string &            name,
-                                                             const common_peg_parser & string_parser,
-                                                             const common_peg_parser & value_parser) {
-    return rule(name, [this, string_parser, value_parser]() {
-        auto ws      = space();
-        auto member  = sequence({ string_parser, ws, literal(":"), ws, value_parser });
-        auto members = sequence({ member, zero_or_more(sequence({ ws, literal(","), ws, member })) });
-        return sequence({ literal("{"), ws, choice({ literal("}"), sequence({ members, ws, literal("}") }) }) });
+    return rule("double-quoted-string", [this]() {
+        return sequence({literal("\""), string_content('"'), literal("\""), space()});
     });
 }
 
-common_peg_parser common_peg_parser_builder::generic_array(const std::string &            name,
-                                                            const common_peg_parser & value_parser) {
-    return rule(name, [this, value_parser]() {
-        auto ws       = space();
-        auto elements = sequence({ value_parser, zero_or_more(sequence({ literal(","), ws, value_parser })) });
-        return sequence({ literal("["), ws, choice({ literal("]"), sequence({ elements, ws, literal("]") }) }) });
+common_peg_parser common_peg_parser_builder::single_quoted_string() {
+    return rule("single-quoted-string", [this]() {
+        return sequence({literal("'"), string_content('\''), literal("'"), space()});
+    });
+}
+
+common_peg_parser common_peg_parser_builder::quoted_string() {
+    return rule("quoted-string", [this]() {
+        return choice({double_quoted_string(), single_quoted_string()});
     });
 }
 
@@ -1351,7 +1268,7 @@ common_peg_parser common_peg_parser_builder::json_number() {
 
 common_peg_parser common_peg_parser_builder::json_string() {
     return rule("json-string", [this]() {
-        return sequence({literal("\""), json_string_content(), literal("\""), space()});
+        return sequence({literal("\""), string_content('"'), literal("\""), space()});
     });
 }
 
@@ -1368,11 +1285,36 @@ common_peg_parser common_peg_parser_builder::json_null() {
 }
 
 common_peg_parser common_peg_parser_builder::json_object() {
-    return generic_object("json-object", json_string(), json());
+    return rule("json-object", [this]() {
+        auto ws = space();
+        auto member = sequence({json_string(), ws, literal(":"), ws, json()});
+        auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))});
+        return sequence({
+            literal("{"),
+            ws,
+            choice({
+                literal("}"),
+                sequence({members, ws, literal("}")})
+            }),
+            ws
+        });
+    });
 }
 
 common_peg_parser common_peg_parser_builder::json_array() {
-    return generic_array("json-array", json());
+    return rule("json-array", [this]() {
+        auto ws = space();
+        auto elements = sequence({json(), zero_or_more(sequence({literal(","), ws, json()}))});
+        return sequence({
+            literal("["),
+            ws,
+            choice({
+                literal("]"),
+                sequence({elements, ws, literal("]")})
+            }),
+            ws
+        });
+    });
 }
 
 common_peg_parser common_peg_parser_builder::json() {
@@ -1389,7 +1331,9 @@ common_peg_parser common_peg_parser_builder::json() {
 }
 
 common_peg_parser common_peg_parser_builder::python_string() {
-    return rule("python-string", [this]() { return choice({ double_quoted_string(), single_quoted_string() }); });
+    return rule("python-string", [this]() {
+        return choice({double_quoted_string(), single_quoted_string()});
+    });
 }
 
 common_peg_parser common_peg_parser_builder::python_number() {
@@ -1397,24 +1341,63 @@ common_peg_parser common_peg_parser_builder::python_number() {
 }
 
 common_peg_parser common_peg_parser_builder::python_bool() {
-    return rule("python-bool", [this]() { return sequence({ choice({ literal("True"), literal("False") }), space() }); });
+    return rule("python-bool", [this]() {
+        return sequence({
+            choice({literal("True"), literal("False")}),
+            space()
+        });
+    });
 }
 
 common_peg_parser common_peg_parser_builder::python_null() {
-    return rule("python-none", [this]() { return sequence({ literal("None"), space() }); });
+    return rule("python-none", [this]() {
+        return sequence({literal("None"), space()});
+    });
 }
 
 common_peg_parser common_peg_parser_builder::python_dict() {
-    return generic_object("python-dict", python_string(), python_value());
+    return rule("python-dict", [this]() {
+        auto ws = space();
+        auto member = sequence({python_string(), ws, literal(":"), ws, python_value()});
+        auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))});
+        return sequence({
+            literal("{"),
+            ws,
+            choice({
+                literal("}"),
+                sequence({members, ws, literal("}")})
+            }),
+            ws
+        });
+    });
 }
 
 common_peg_parser common_peg_parser_builder::python_array() {
-    return generic_array("python-array", python_value());
+    return rule("python-array", [this]() {
+        auto ws = space();
+        auto elements = sequence({python_value(), zero_or_more(sequence({literal(","), ws, python_value()}))});
+        return sequence({
+            literal("["),
+            ws,
+            choice({
+                literal("]"),
+                sequence({elements, ws, literal("]")})
+            }),
+            ws
+        });
+    });
 }
 
 common_peg_parser common_peg_parser_builder::python_value() {
     return rule("python-value", [this]() {
-        return choice({ python_dict(), python_array(), python_string(), python_number(), python_bool(), python_null() });
+        return choice({
+            python_dict(),
+            python_array(),
+            python_string(),
+            python_number(),
+            python_bool(),
+            python_null()
+        });
     });
 }
 
@@ -1535,8 +1518,7 @@ static std::unordered_set<std::string> collect_reachable_rules(
                           std::is_same_v<T, common_peg_chars_parser> ||
                           std::is_same_v<T, common_peg_space_parser> ||
                           std::is_same_v<T, common_peg_any_parser> ||
-                          std::is_same_v<T, common_peg_json_string_parser> ||
-                          std::is_same_v<T, common_peg_python_dict_string_parser>) {
+                          std::is_same_v<T, common_peg_string_parser>) {
                 // These parsers do not have any children
             } else if constexpr (std::is_same_v<T, common_peg_sequence_parser>) {
                 for (auto child : p.children) {
@@ -1672,10 +1654,9 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                     return result + "{" + std::to_string(p.min_count) + "}";
                 }
                 return result + "{" + std::to_string(p.min_count) + "," + std::to_string(p.max_count) + "}";
-            } else if constexpr (std::is_same_v<T, common_peg_json_string_parser>) {
-                return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)";
-            } else if constexpr (std::is_same_v<T, common_peg_python_dict_string_parser>) {
-                return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)";
+            } else if constexpr (std::is_same_v<T, common_peg_string_parser>) {
+                const std::string delim(1, p.delimiter);
+                return R"(( [^)" + delim + R"(\\] | "\\" ( [)" + delim + R"(\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)";
             } else if constexpr (std::is_same_v<T, common_peg_until_parser>) {
                 if (p.delimiters.empty()) {
                     return ".*";
@@ -1805,10 +1786,8 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
                 {"min_count", p.min_count},
                 {"max_count", p.max_count}
             };
-        } else if constexpr (std::is_same_v<T, common_peg_json_string_parser>) {
-            return json{{"type", "json_string"}};
-        } else if constexpr (std::is_same_v<T, common_peg_python_dict_string_parser>) {
-            return json{{ "type", "python_dict_string" }};
+        } else if constexpr (std::is_same_v<T, common_peg_string_parser>) {
+            return json{{"type", "string"}, {"delimiter", std::string(1, p.delimiter)}};
         } else if constexpr (std::is_same_v<T, common_peg_until_parser>) {
             return json{{"type", "until"}, {"delimiters", p.delimiters}};
         } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
@@ -1935,11 +1914,15 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
         }
         return parser;
     }
-    if (type == "json_string") {
-        return common_peg_json_string_parser{};
-    }
-    if (type == "python_dict_string") {
-        return common_peg_python_dict_string_parser{};
+    if (type == "string") {
+        if (!j.contains("delimiter")) {
+            throw std::runtime_error("string parser missing delimiter field.");
+        }
+        std::string delimiter = j["delimiter"];
+        if (delimiter.empty()) {
+            throw std::runtime_error("string parser delimiter is empty.");
+        }
+        return common_peg_string_parser{delimiter[0]};
     }
     if (type == "until") {
         if (!j.contains("delimiters") || !j["delimiters"].is_array()) {

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;
@@ -210,8 +231,9 @@ struct common_peg_chars_parser {
     int max_count;  // -1 for unbounded
 };
 
-struct common_peg_json_string_parser {};
-struct common_peg_python_dict_string_parser {};
+struct common_peg_string_parser {
+    char delimiter;
+};
 
 struct common_peg_until_parser {
     std::vector<std::string> delimiters;
@@ -259,8 +281,7 @@ using common_peg_parser_variant = std::variant<
     common_peg_any_parser,
     common_peg_space_parser,
     common_peg_chars_parser,
-    common_peg_json_string_parser,
-    common_peg_python_dict_string_parser,
+    common_peg_string_parser,
     common_peg_until_parser,
     common_peg_schema_parser,
     common_peg_rule_parser,
@@ -319,10 +340,6 @@ class common_peg_parser_builder {
     common_peg_parser wrap(common_peg_parser_id id) { return common_peg_parser(id, *this); }
     common_peg_parser add(const common_peg_parser_variant & p) { return wrap(arena_.add_parser(p)); }
 
-    // Generic helpers for building object/array structures with configurable string/value parsers.
-    common_peg_parser generic_object(const std::string & name, const common_peg_parser & string_parser, const common_peg_parser & value_parser);
-    common_peg_parser generic_array(const std::string & name, const common_peg_parser & value_parser);
-
   public:
     common_peg_parser_builder();
 
@@ -423,13 +440,10 @@ class common_peg_parser_builder {
     common_peg_parser single_quoted_string();
 
     // Matches a string that accepts both double-quoted and single-quoted styles.
-    common_peg_parser flexible_string();
+    common_peg_parser quoted_string();
 
-    // Matches double-quoted string content without the surrounding quotes.
-    common_peg_parser json_string_content();
-
-    // Matches single-quoted string content without the surrounding quotes.
-    common_peg_parser single_quoted_string_content();
+    // Matches string content without the surrounding delimiter.
+    common_peg_parser string_content(char delimiter);
 
     // Creates a complete JSON parser supporting objects, arrays, strings, numbers, booleans, and null.
     //   value -> object | array | string | number | true | false | null

docs/backend/SYCL.md

@@ -9,6 +9,7 @@
 - [Linux](#linux)
 - [Windows](#windows)
 - [Environment Variable](#environment-variable)
+- [Design Rule](#design-rule)
 - [Known Issue](#known-issues)
 - [Q&A](#qa)
 - [TODO](#todo)
@@ -41,6 +42,9 @@ The following releases are verified and recommended:
 
 ## News
 
+- 2026.03
+  - Support Flash-Attention: less memory usage, performance impact depends on LLM.
+
 - 2026.02
   - Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. User can't build up the software for Nvidia & AMD GPU.
 
@@ -685,18 +689,45 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | Name              | Value            | Function                                                                                                                  |
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
+| GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) |
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|
 
+## Design Rule
 
+- Open to all contributors.
+
+- All code change should be useful to user:
+    - Fix bug.
+    - Add new function.
+    - Improve the performance/usage.
+    - Make code be easy to maintain.
+    - ...
+
+- Don't accept the codes of following cases:
+    - Break legacy function.
+    - Reduce the performance of legacy case in default.
+    - Not completed work/the functionality cannot be demonstrated.
+
+- Encourage to use environment variable to control features to be opened/closed.
+    - User can evaluate the feature without rebuild the code.
+    - Recommend the best features to user by setting them be opened as default.
+
+- Design the code based on the published official releases of oneAPI packages: compiler, library, driver, OS kernel.
+
+- Developers need to maintain the code they submit.
 
 ## Known Issues
 
 - `Split-mode:[row]` is not supported.
 
+- Missed the AOT (Ahead-of-Time) in buiding.
+  - Good: build quickly, smaller size of binary file.
+  - Bad: The startup is slow (JIT) in first time, but subsequent performance is unaffected.
+
 ## Q&A
 
 - Error:  `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`.

docs/build.md

@@ -599,7 +599,13 @@ If KleidiAI is enabled, the output will contain a line similar to:
 ```
 load_tensors: CPU_KLEIDIAI model buffer size =  3474.00 MiB
 ```
-KleidiAI's microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm and SME. llama.cpp selects the most efficient kernel based on runtime CPU feature detection. However, on platforms that support SME, you must manually enable SME microkernels by setting the environment variable `GGML_KLEIDIAI_SME=1`.
+KleidiAI’s microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm, SVE, and SME. Llama.cpp selects the most efficient kernels at runtime based on detected CPU capabilities.
+On CPUs that support SME, SME microkernels are enabled automatically using runtime detection.
+The environment variable GGML_KLEIDIAI_SME can be used to control SME behavior:
+- Not set: enable SME automatically if supported and detected.
+- 0: disable SME.
+- <n> > 0: enable SME and assume <n> available SME units (override auto detection).
+If SME is not supported by the CPU, SME microkernels are always disabled.
 
 Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`.
 

docs/ops.md

@@ -37,16 +37,17 @@ Legend:
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                             DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
 |                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -54,7 +55,7 @@ Legend:
 |                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -75,7 +76,7 @@ Legend:
 |                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
 |                              PAD | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                          POOL_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -90,9 +91,9 @@ Legend:
 |                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -100,7 +101,7 @@ Legend:
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -116,5 +117,5 @@ Legend:
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |

examples/json_schema_to_grammar.py

@@ -633,7 +633,7 @@ class SchemaConverter:
             return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None))
 
         elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema):
-            items = schema.get('items') or schema['prefixItems']
+            items = schema.get('items', schema.get('prefixItems'))
             if isinstance(items, list):
                 return self._add_rule(
                     rule_name,

ggml/include/ggml-rpc.h

@@ -8,7 +8,12 @@ extern "C" {
 
 #define RPC_PROTO_MAJOR_VERSION    3
 #define RPC_PROTO_MINOR_VERSION    6
-#define RPC_PROTO_PATCH_VERSION    0
+#define RPC_PROTO_PATCH_VERSION    1
+
+#ifdef  __cplusplus
+static_assert(GGML_OP_COUNT == 96, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION");
+#endif
+
 #define GGML_RPC_MAX_SERVERS       16
 
 // backend API

ggml/include/ggml.h

@@ -556,6 +556,7 @@ extern "C" {
         GGML_OP_GATED_LINEAR_ATTN,
         GGML_OP_RWKV_WKV7,
         GGML_OP_SOLVE_TRI,
+        GGML_OP_GATED_DELTA_NET,
 
         GGML_OP_UNARY,
 
@@ -2463,6 +2464,15 @@ extern "C" {
         bool                  lower,
         bool                  uni);
 
+    GGML_API struct ggml_tensor * ggml_gated_delta_net(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * g,
+            struct ggml_tensor  * beta,
+            struct ggml_tensor  * state);
+
     // custom operators
 
     typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);

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

@@ -202,8 +202,9 @@
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
+#define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
-#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x1_generic ggml_quantize_mat_q8_K_4x1
 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0

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

@@ -2021,6 +2021,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_solve_tri(params, tensor);
             } break;
+        case GGML_OP_GATED_DELTA_NET:
+            {
+                ggml_compute_forward_gated_delta_net(params, tensor);
+            } break;
         case GGML_OP_MAP_CUSTOM1:
             {
                 ggml_compute_forward_map_custom1(params, tensor);
@@ -2200,6 +2204,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_COUNT_EQUAL:
         case GGML_OP_SOLVE_TRI:
+        case GGML_OP_GATED_DELTA_NET:
             {
                 n_tasks = n_threads;
             } break;
@@ -2905,6 +2910,11 @@ struct ggml_cplan ggml_graph_plan(
                     {
                         cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
                     } break;
+                case GGML_OP_GATED_DELTA_NET:
+                    {
+                        const int64_t S_v = node->src[2]->ne[0];
+                        cur = S_v * sizeof(float) * n_tasks;
+                    } break;
                 case GGML_OP_COUNT:
                     {
                         GGML_ABORT("fatal error");

ggml/src/ggml-cpu/kleidiai/kernels.cpp

@@ -520,7 +520,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .packed_stride_ex      = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
             /* .pack_func_ex          = */ &rhs_pack_fn12<kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
         },
-        /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
+        /* .required_cpu       = */ CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
         /* .rhs_type           = */ GGML_TYPE_Q4_0,
         /* .op_type            = */ GGML_TYPE_F32,
@@ -631,7 +631,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .packed_stride_ex      = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
             /* .pack_func_ex          = */ &rhs_pack_fn12<kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
         },
-        /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
+        /* .required_cpu       = */ CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
         /* .rhs_type           = */ GGML_TYPE_Q4_0,
         /* .op_type            = */ GGML_TYPE_F32,
@@ -801,7 +801,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = {
             /* .packed_stride_ex      = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
             /* .pack_func_ex          = */ &rhs_pack_scale_fn12<kai_run_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
         },
-        /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
+        /* .required_cpu       = */ CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
         /* .rhs_type           = */ GGML_TYPE_Q8_0,
         /* .op_type            = */ GGML_TYPE_F32,

ggml/src/ggml-cpu/ops.cpp

@@ -10380,6 +10380,190 @@ void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, s
     }
 }
 
+// ggml_compute_forward_gated_delta_net
+static void ggml_compute_forward_gated_delta_net_one_chunk(
+    const ggml_compute_params * params,
+    ggml_tensor * dst,
+    int64_t ir0,
+    int64_t ir1) {
+
+    ggml_tensor * src_q     = dst->src[0];
+    ggml_tensor * src_k     = dst->src[1];
+    ggml_tensor * src_v     = dst->src[2];
+    ggml_tensor * src_g     = dst->src[3];
+    ggml_tensor * src_beta  = dst->src[4];
+    ggml_tensor * src_state = dst->src[5];
+
+    const int64_t S_v      = src_v->ne[0];
+    const int64_t H        = src_v->ne[1];
+    const int64_t n_tokens = src_v->ne[2];
+    const int64_t n_seqs   = src_v->ne[3];
+
+    GGML_ASSERT(ggml_is_contiguous_rows(src_q));
+    GGML_ASSERT(ggml_is_contiguous_rows(src_k));
+    GGML_ASSERT(ggml_is_contiguous_rows(src_v));
+    GGML_ASSERT(ggml_is_contiguous(src_g));
+    GGML_ASSERT(ggml_is_contiguous(src_beta));
+    GGML_ASSERT(ggml_is_contiguous(src_state));
+
+    GGML_ASSERT(src_g->ne[0] == 1 || src_g->ne[0] == S_v);
+    GGML_ASSERT(src_beta->ne[0] == 1);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, src_q, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbq, src_q, nb);
+    GGML_TENSOR_LOCALS(int64_t, nek, src_k, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbk, src_k, nb);
+    GGML_TENSOR_LOCALS(int64_t, nev, src_v, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbv, src_v, nb);
+    GGML_TENSOR_LOCALS(int64_t, neg, src_g, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbg, src_g, nb);
+    GGML_TENSOR_LOCALS(size_t,  nbb, src_beta, nb);
+
+    const bool kda = (neg0 == S_v);
+
+    // scratch layout per thread: [delta(S_v)]
+    const int64_t scratch_per_thread = S_v;
+    const int ith = params->ith;
+
+    float * delta = (float *)params->wdata + ith * scratch_per_thread + CACHE_LINE_SIZE_F32;
+
+    // output layout: [attn_scores | new_states]
+    // attn_scores: S_v * H * n_tokens * n_seqs floats
+    // new_states:  S_v * S_v * H * n_seqs floats
+    const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
+    float * attn_out_base  = (float *)dst->data;
+    float * state_out_base = (float *)dst->data + attn_score_elems;
+
+    const float * state_in_base = (const float *)src_state->data;
+
+    const int64_t rq1 = nev1 / neq1;
+    const int64_t rk1 = nev1 / nek1;
+    const int64_t rq3 = nev3 / neq3;
+    const int64_t rk3 = nev3 / nek3;
+
+    const float scale = 1.0f / sqrtf((float) S_v);
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        const int64_t iv1 = ir % H; // head_index
+        const int64_t iv3 = ir / H; // sequence
+
+        const int64_t iq1 = iv1 / rq1;
+        const int64_t ik1 = iv1 / rk1;
+
+        const int64_t iq3 = iv3 / rq3;
+        const int64_t ik3 = iv3 / rk3;
+
+        float * s_out = state_out_base + (iv3 * H + iv1) * S_v * S_v;
+
+        // copy input state into output buffer and operate in-place
+        const float * s_in = state_in_base + (iv3 * H + iv1) * S_v * S_v;
+        memcpy(s_out, s_in, S_v * S_v * sizeof(float));
+
+        // attn output pointer for first token of this (head, seq)
+        float * attn_data = attn_out_base + (iv3 * n_tokens * H + iv1) * S_v;
+
+        for (int64_t t = 0; t < n_tokens; t++) {
+            const float * q_d = (const float *)((const char *)src_q->data + iq3 * nbq3 + t * nbq2 + iq1 * nbq1);
+            const float * k_d = (const float *)((const char *)src_k->data + ik3 * nbk3 + t * nbk2 + ik1 * nbk1);
+            const float * v_d = (const float *)((const char *)src_v->data + iv3 * nbv3 + t * nbv2 + iv1 * nbv1);
+
+            const float beta_val = *(const float *)((const char *)src_beta->data + iv3 * nbb3 + t * nbb2 + iv1 * nbb1);
+            const float * g_d   =  (const float *)((const char *)src_g->data    + iv3 * nbg3 + t * nbg2 + iv1 * nbg1);
+
+            if (kda) {
+                for (int64_t i = 0; i < S_v; ++i) {
+                    ggml_vec_scale_f32(S_v, &s_out[i * S_v], expf(g_d[i]));
+                }
+            } else {
+                ggml_vec_scale_f32(S_v * S_v, s_out, expf(g_d[0]));
+            }
+
+            // delta[j] = sum_i S[j][i] * k[i]
+            memset(delta, 0, S_v * sizeof(float));
+            for (int64_t i = 0; i < S_v; ++i) {
+                ggml_vec_mad_f32(S_v, delta, &s_out[i * S_v], k_d[i]);
+            }
+            for (int64_t j = 0; j < S_v; ++j) {
+                delta[j] = (v_d[j] - delta[j]) * beta_val;
+            }
+
+            // outer product: S[j][i] += k[i] * delta[j]
+            for (int64_t i = 0; i < S_v; ++i) {
+                ggml_vec_mad_f32(S_v, &s_out[i * S_v], delta, k_d[i]);
+            }
+
+            // attn_out[j] = sum_i S[j][i] * q[i]
+            memset(attn_data, 0, S_v * sizeof(float));
+            for (int64_t i = 0; i < S_v; ++i) {
+                ggml_vec_mad_f32(S_v, attn_data, &s_out[i * S_v], q_d[i]);
+            }
+            ggml_vec_scale_f32(S_v, attn_data, scale);
+
+            attn_data += S_v * H; // advance to next token
+        }
+
+    }
+}
+
+
+static void ggml_compute_forward_gated_delta_net_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    ggml_tensor * V = dst->src[2];
+    int64_t nr = V->ne[1] * V->ne[3];
+
+    // disable for NUMA
+    const bool disable_chunking = ggml_is_numa();
+
+    int nth = params->nth;
+    int ith = params->ith;
+
+    // 4x chunks per thread
+    int nth_scaled = nth * 4;
+    int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled;
+    int64_t nchunk     = (nr + chunk_size - 1) / chunk_size;
+
+    if (nth == 1 || nchunk < nth || disable_chunking) {
+      nchunk = nth;
+    }
+
+    if (ith == 0) {
+      ggml_threadpool_chunk_set(params->threadpool, nth);
+    }
+
+    ggml_barrier(params->threadpool);
+
+    const int64_t dr = (nr + nchunk - 1) / nchunk;
+
+    int current_chunk = ith;
+
+    while (current_chunk < nchunk) {
+        const int64_t ir0 = dr * current_chunk;
+        const int64_t ir1 = MIN(ir0 + dr, nr);
+
+        ggml_compute_forward_gated_delta_net_one_chunk(params, dst, ir0, ir1);
+        current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
+    }
+}
+
+void ggml_compute_forward_gated_delta_net(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_gated_delta_net_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_rwkv_wkv7
 
 static void ggml_compute_forward_rwkv_wkv7_f32(

ggml/src/ggml-cpu/ops.h

@@ -102,6 +102,7 @@ void ggml_compute_forward_rwkv_wkv6(const struct ggml_compute_params * params, s
 void ggml_compute_forward_rwkv_wkv7(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_gated_delta_net(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst);

ggml/src/ggml-cpu/repack.h

@@ -28,13 +28,17 @@ template <int K, int N> struct block {
 // control size
 static_assert(sizeof(block<4, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 2, "wrong block<4,4> size/padding");
 static_assert(sizeof(block<4, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<4,8> size/padding");
+static_assert(sizeof(block<4, 16>) == 16 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<4,16> size/padding");
 static_assert(sizeof(block<8, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<8,4> size/padding");
 static_assert(sizeof(block<8, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<8,8> size/padding");
+static_assert(sizeof(block<8, 16>) == 16 * sizeof(ggml_half) + QK8_0 * 16, "wrong block<8,16> size/padding");
 
 using block_q4_0x4 = block<4, 4>;
 using block_q4_0x8 = block<4, 8>;
+using block_q4_0x16 = block<4, 16>;
 using block_q8_0x4 = block<8, 4>;
 using block_q8_0x8 = block<8, 8>;
+using block_q8_0x16 = block<8, 16>;
 
 struct block_q4_Kx8 {
     ggml_half d[8];      // super-block scale for quantized scales
@@ -44,7 +48,14 @@ struct block_q4_Kx8 {
 };
 
 static_assert(sizeof(block_q4_Kx8) == sizeof(ggml_half) * 16 + K_SCALE_SIZE * 8 + QK_K * 4, "wrong q4_K block size/padding");
+struct block_q4_Kx16 {
+    ggml_half d[16];      // super-block scale for quantized scales
+    ggml_half dmin[16];   // super-block scale for quantized mins
+    uint8_t scales[192];  // scales and mins, quantized with 6 bits
+    uint8_t qs[2048];    // 4--bit quants
+};
 
+static_assert(sizeof(block_q4_Kx16) == sizeof(ggml_half) * 32 + K_SCALE_SIZE * 16 + QK_K * 8, "wrong q4_K block size/padding");
 struct block_q2_Kx8 {
     ggml_half d[8];      // super-block scale for quantized scales
     ggml_half dmin[8];   // super-block scale for quantized mins
@@ -53,6 +64,13 @@ struct block_q2_Kx8 {
 };
 
 static_assert(sizeof(block_q2_Kx8) == sizeof(ggml_half) * 16 + QK_K/2 + QK_K * 2, "wrong q2_K block size/padding");
+struct block_q2_Kx16 {
+    ggml_half d[16];       // Super-block scale for quantized scales
+    ggml_half dmin[16];    // Super-block scale for quantized mins
+    uint8_t   scales[256]; // Sub-block scales (16 cols * 16 sub-blocks)
+    uint8_t   qs[1024];    // Data (16 cols * 64 bytes per block)
+};
+static_assert(sizeof(block_q2_Kx16) == sizeof(ggml_half) * 32 + QK_K + QK_K * 4, "wrong q2_K block size/padding");
 
 struct block_q5_Kx8 {
     ggml_half d[8];              // super-block scale for quantized scales
@@ -97,6 +115,12 @@ struct block_iq4_nlx8 {
 
 static_assert(sizeof(block_iq4_nlx8) == 8 * sizeof(ggml_half) + QK4_NL * 4, "wrong iq4_nlx8 block size/padding");
 
+struct block_iq4_nlx16 {
+    ggml_half d[16];            // deltas for 16 iq4_nl blocks
+    uint8_t   qs[QK4_NL * 8];  // nibbles / quants for 16 iq4_nl blocks
+};
+
+static_assert(sizeof(block_iq4_nlx16) == 16 * sizeof(ggml_half) + QK4_NL * 8, "wrong iq4_nlx16 block size/padding");
 struct block_mxfp4x4 {
     uint8_t e[4];
     uint8_t qs[QK_MXFP4 * 2];
@@ -109,7 +133,6 @@ struct block_mxfp4x8 {
 };
 static_assert(sizeof(block_mxfp4x8) == 8 + QK_MXFP4 * 4, "wrong mxfp4x8 block size/padding");
 
-
 #if defined(__cplusplus)
 extern "C" {
 #endif
@@ -132,6 +155,8 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -146,10 +171,22 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+#if defined __riscv_zvfh
+void ggml_quantize_mat_q8_0_4x1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
+void ggml_quantize_mat_q8_K_4x1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
+void ggml_gemv_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+#endif
 
 // Native implementations
 void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@@ -170,6 +207,8 @@ void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
 void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -184,10 +223,22 @@ void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
 void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+#if defined __riscv_zvfh
+void ggml_quantize_mat_q8_0_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
+void ggml_quantize_mat_q8_K_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
+void ggml_gemv_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+#endif
 
 #if defined(__cplusplus)
 } // extern "C"

ggml/src/ggml-cuda/gated_delta_net.cu

@@ -0,0 +1,223 @@
+#include "gated_delta_net.cuh"
+#include "ggml-cuda/common.cuh"
+
+template <int S_v, bool KDA>
+__global__ void gated_delta_net_cuda(const float * q,
+                                     const float * k,
+                                     const float * v,
+                                     const float * g,
+                                     const float * beta,
+                                     const float * curr_state,
+                                     float *       dst,
+                                     int64_t       H,
+                                     int64_t       n_tokens,
+                                     int64_t       n_seqs,
+                                     int64_t       sq1,
+                                     int64_t       sq2,
+                                     int64_t       sq3,
+                                     int64_t       sv1,
+                                     int64_t       sv2,
+                                     int64_t       sv3,
+                                     int64_t       sb1,
+                                     int64_t       sb2,
+                                     int64_t       sb3,
+                                     int64_t       rq1,
+                                     int64_t       rq3,
+                                     float         scale) {
+    const int64_t h_idx    = blockIdx.x;
+    const int64_t sequence = blockIdx.y;
+    const int     col      = threadIdx.x;  // each thread owns one column
+
+    const int64_t iq1 = h_idx / rq1;
+    const int64_t iq3 = sequence / rq3;
+
+    const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
+    float *       attn_data        = dst;
+    float *       state            = dst + attn_score_elems;
+
+    const int64_t state_offset = (sequence * H + h_idx) * S_v * S_v;
+    state += state_offset;
+    curr_state += state_offset;
+    attn_data += (sequence * n_tokens * H + h_idx) * S_v;
+
+    // Load state column into registers
+    float s[S_v];
+#pragma unroll
+    for (int i = 0; i < S_v; i++) {
+        s[i] = curr_state[i * S_v + col];
+    }
+
+    for (int t = 0; t < n_tokens; t++) {
+        const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1;
+        const float * k_t = k + iq3 * sq3 + t * sq2 + iq1 * sq1;
+        const float * v_t = v + sequence * sv3 + t * sv2 + h_idx * sv1;
+
+        const int64_t gb_offset = sequence * sb3 + t * sb2 + h_idx * sb1;
+        const float * beta_t = beta + gb_offset;
+        const float * g_t    = g    + gb_offset * (KDA ? S_v : 1);
+
+        const float beta_val = *beta_t;
+
+        if constexpr (!KDA) {
+            const float g_val = expf(*g_t);
+
+            // kv[col] = (S^T @ k)[col] = sum_i S[i][col] * k[i]
+            float kv_col = 0.0f;
+#pragma unroll
+            for (int i = 0; i < S_v; i++) {
+                kv_col += s[i] * k_t[i];
+            }
+
+            // delta[col] = (v[col] - g * kv[col]) * beta
+            float delta_col = (v_t[col] - g_val * kv_col) * beta_val;
+
+            // fused: S[i][col] = g * S[i][col] + k[i] * delta[col]
+            // attn[col] = (S^T @ q)[col] = sum_i S[i][col] * q[i]
+            float attn_col = 0.0f;
+#pragma unroll
+            for (int i = 0; i < S_v; i++) {
+                s[i] = g_val * s[i] + k_t[i] * delta_col;
+                attn_col += s[i] * q_t[i];
+            }
+
+            attn_data[col] = attn_col * scale;
+        } else {
+            // kv[col] = sum_i g[i] * S[i][col] * k[i]
+            float kv_col = 0.0f;
+#pragma unroll
+            for (int i = 0; i < S_v; i++) {
+                kv_col += expf(g_t[i]) * s[i] * k_t[i];
+            }
+
+            // delta[col] = (v[col] - kv[col]) * beta
+            float delta_col = (v_t[col] - kv_col) * beta_val;
+
+            // fused: S[i][col] = g[i] * S[i][col] + k[i] * delta[col]
+            // attn[col] = (S^T @ q)[col] = sum_i S[i][col] * q[i]
+            float attn_col = 0.0f;
+#pragma unroll
+            for (int i = 0; i < S_v; i++) {
+                s[i] = expf(g_t[i]) * s[i] + k_t[i] * delta_col;
+                attn_col += s[i] * q_t[i];
+            }
+
+            attn_data[col] = attn_col * scale;
+        }
+
+        attn_data += S_v * H;
+    }
+
+    // Write state back to global memory
+#pragma unroll
+    for (int i = 0; i < S_v; i++) {
+        state[i * S_v + col] = s[i];
+    }
+}
+
+template <bool KDA>
+static void launch_gated_delta_net(
+        const float * q_d, const float * k_d, const float * v_d,
+        const float * g_d, const float * b_d, const float * s_d,
+        float * dst_d,
+        int64_t S_v, int64_t H, int64_t n_tokens, int64_t n_seqs,
+        int64_t sq1, int64_t sq2, int64_t sq3,
+        int64_t sv1, int64_t sv2, int64_t sv3,
+        int64_t sb1, int64_t sb2, int64_t sb3,
+        int64_t rq1, int64_t rq3,
+        float scale, cudaStream_t stream) {
+
+    dim3 grid_dims(H, n_seqs, 1);
+    dim3 block_dims(S_v, 1, 1);
+
+    switch (S_v) {
+        case 32:
+            gated_delta_net_cuda<32, KDA><<<grid_dims, block_dims, 0, stream>>>(
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, rq1, rq3, scale);
+            break;
+        case 64:
+            gated_delta_net_cuda<64, KDA><<<grid_dims, block_dims, 0, stream>>>(
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, rq1, rq3, scale);
+            break;
+        case 128:
+            gated_delta_net_cuda<128, KDA><<<grid_dims, block_dims, 0, stream>>>(
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, rq1, rq3, scale);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src_q     = dst->src[0];
+    ggml_tensor * src_k     = dst->src[1];
+    ggml_tensor * src_v     = dst->src[2];
+    ggml_tensor * src_g     = dst->src[3];
+    ggml_tensor * src_beta  = dst->src[4];
+    ggml_tensor * src_state = dst->src[5];
+
+    GGML_TENSOR_LOCALS(int64_t, neq, src_q, ne);
+    GGML_TENSOR_LOCALS(size_t, nbq, src_q, nb);
+    GGML_TENSOR_LOCALS(int64_t, nev, src_v, ne);
+    GGML_TENSOR_LOCALS(size_t, nbv, src_v, nb);
+    GGML_TENSOR_LOCALS(size_t, nbb, src_beta, nb);
+
+    const int64_t S_v      = nev0;
+    const int64_t H        = nev1;
+    const int64_t n_tokens = nev2;
+    const int64_t n_seqs   = nev3;
+
+    const bool kda = (src_g->ne[0] == S_v);
+
+    const int64_t rq1 = nev1 / neq1;
+    const int64_t rq3 = nev3 / neq3;
+
+    const float * q_d = (const float *) src_q->data;
+    const float * k_d = (const float *) src_k->data;
+    const float * v_d = (const float *) src_v->data;
+    const float * g_d = (const float *) src_g->data;
+    const float * b_d = (const float *) src_beta->data;
+
+    const float * s_d   = (const float *) src_state->data;
+    float *       dst_d = (float *) dst->data;
+
+    GGML_ASSERT(ggml_is_contiguous_rows(src_q));
+    GGML_ASSERT(ggml_is_contiguous_rows(src_k));
+    GGML_ASSERT(ggml_is_contiguous_rows(src_v));
+    GGML_ASSERT(ggml_are_same_stride(src_q, src_k));
+    GGML_ASSERT(src_g->ne[0] == 1 || kda);
+    GGML_ASSERT(ggml_is_contiguous(src_g));
+    GGML_ASSERT(ggml_is_contiguous(src_beta));
+    GGML_ASSERT(ggml_is_contiguous(src_state));
+
+    // strides in floats (beta strides used for both g and beta offset computation)
+    const int64_t sq1 = nbq1 / sizeof(float);
+    const int64_t sq2 = nbq2 / sizeof(float);
+    const int64_t sq3 = nbq3 / sizeof(float);
+    const int64_t sv1 = nbv1 / sizeof(float);
+    const int64_t sv2 = nbv2 / sizeof(float);
+    const int64_t sv3 = nbv3 / sizeof(float);
+    const int64_t sb1 = nbb1 / sizeof(float);
+    const int64_t sb2 = nbb2 / sizeof(float);
+    const int64_t sb3 = nbb3 / sizeof(float);
+
+    const float scale = 1.0f / sqrtf((float) S_v);
+
+    cudaStream_t stream = ctx.stream();
+
+    if (kda) {
+        launch_gated_delta_net<true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+            S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+            sb1, sb2, sb3, rq1, rq3, scale, stream);
+    } else {
+        launch_gated_delta_net<false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+            S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+            sb1, sb2, sb3, rq1, rq3, scale, stream);
+    }
+}

ggml/src/ggml-cuda/gated_delta_net.cuh

@@ -0,0 +1,4 @@
+#include "common.cuh"
+#include "ggml.h"
+
+void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

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

@@ -53,6 +53,7 @@
 #include "ggml-cuda/upscale.cuh"
 #include "ggml-cuda/wkv.cuh"
 #include "ggml-cuda/gla.cuh"
+#include "ggml-cuda/gated_delta_net.cuh"
 #include "ggml-cuda/set.cuh"
 #include "ggml-cuda/set-rows.cuh"
 #include "ggml-cuda/pad_reflect_1d.cuh"
@@ -204,7 +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) {
@@ -242,6 +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;
 
@@ -256,22 +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 });
@@ -2733,6 +2750,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_GATED_LINEAR_ATTN:
             ggml_cuda_op_gated_linear_attn(ctx, dst);
             break;
+        case GGML_OP_GATED_DELTA_NET:
+            ggml_cuda_op_gated_delta_net(ctx, dst);
+            break;
         case GGML_OP_RWKV_WKV7:
             ggml_cuda_op_rwkv_wkv7(ctx, dst);
             break;
@@ -4974,6 +4994,13 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_GATED_LINEAR_ATTN:
         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-context.m

@@ -75,6 +75,10 @@ struct ggml_metal {
     // abort ggml_metal_graph_compute if callback returns true
     ggml_abort_callback abort_callback;
     void *              abort_callback_data;
+
+    // error state - set when a command buffer fails during synchronize
+    // once set, graph_compute will return GGML_STATUS_FAILED until the backend is recreated
+    bool has_error;
 };
 
 ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
@@ -158,6 +162,8 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
     res->capture_started = false;
     res->capture_scope = nil;
 
+    res->has_error = false;
+
     res->gf = nil;
     res->encode_async = nil;
     for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) {
@@ -246,7 +252,8 @@ void ggml_metal_synchronize(ggml_metal_t ctx) {
                 if (status == MTLCommandBufferStatusError) {
                     GGML_LOG_ERROR("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
                 }
-                GGML_ABORT("fatal error");
+                ctx->has_error = true;
+                return;
             }
         }
     }
@@ -262,7 +269,15 @@ void ggml_metal_synchronize(ggml_metal_t ctx) {
                 if (status == MTLCommandBufferStatusError) {
                     GGML_LOG_ERROR("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
                 }
-                GGML_ABORT("fatal error");
+
+                // release this and all remaining command buffers before returning
+                for (size_t j = i; j < ctx->cmd_bufs_ext.count; ++j) {
+                    [ctx->cmd_bufs_ext[j] release];
+                }
+                [ctx->cmd_bufs_ext removeAllObjects];
+
+                ctx->has_error = true;
+                return;
             }
 
             [cmd_buf release];
@@ -414,6 +429,11 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con
 }
 
 enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) {
+    if (ctx->has_error) {
+        GGML_LOG_ERROR("%s: backend is in error state from a previous command buffer failure - recreate the backend to recover\n", __func__);
+        return GGML_STATUS_FAILED;
+    }
+
     // number of nodes encoded by the main thread (empirically determined)
     const int n_main = MAX(64, 0.1*gf->n_nodes);
 

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-opencl/CMakeLists.txt

@@ -116,6 +116,7 @@ set(GGML_OPENCL_KERNELS
     neg
     norm
     relu
+    l2_norm
     rms_norm
     rope
     scale

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

@@ -497,6 +497,7 @@ struct ggml_backend_opencl_context {
               kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16;
     cl_kernel kernel_norm, kernel_norm_mul_add;
     cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
+    cl_kernel kernel_l2_norm_f32;
     cl_kernel kernel_group_norm, kernel_group_norm_mul_add;
     cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
     cl_kernel kernel_diag_f32;
@@ -1585,6 +1586,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // l2_norm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "l2_norm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("l2_norm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_l2_norm_f32     = clCreateKernel(prog, "kernel_l2_norm_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // rope
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3689,6 +3707,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return true;
         case GGML_OP_RMS_NORM:
             return op->ne[0] % 4 == 0 && ggml_is_contiguous_rows(op->src[0]);
+        case GGML_OP_L2_NORM:
+            return ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_REPEAT:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
         case GGML_OP_PAD:
@@ -7554,6 +7574,64 @@ static void ggml_cl_group_norm(ggml_backend_t backend, const ggml_tensor * src0,
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
 }
 
+static void ggml_cl_l2_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    GGML_TENSOR_LOCALS(int,      ne0, src0, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
+
+    size_t sgs;
+    if (backend_ctx->gpu_family == ADRENO) {
+        sgs = 64;
+    } else if (backend_ctx->gpu_family == INTEL) {
+        sgs = 32;
+    } else {
+        GGML_ASSERT(false && "Unsupported GPU");
+    }
+
+    cl_kernel kernel = backend_ctx->kernel_l2_norm_f32;
+
+    int nth = sgs;
+    while (nth < ne00 && nth < (int)backend_ctx->get_kernel_workgroup_size(kernel)) {
+        nth *= 2;
+    }
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),    &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong),  &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),    &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong),  &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(int),       &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),       &ne01));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),       &ne02));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),       &ne03));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong),  &nb01));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong),  &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),  &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float),     &eps));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float)*nth/sgs,  NULL));
+
+    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
+    size_t local_work_size[] = {(size_t)nth, 1, 1};
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+}
+
 static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -12184,6 +12262,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
             }
             func = ggml_cl_rms_norm;
             break;
+        case GGML_OP_L2_NORM:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_l2_norm;
+            break;
         case GGML_OP_GROUP_NORM:
             if (!any_on_device) {
                 return false;

ggml/src/ggml-opencl/kernels/l2_norm.cl

@@ -0,0 +1,71 @@
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_32
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_l2_norm_f32(
+        global void * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        float eps,
+        local float * sum
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float * x = (global float *) ((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01);
+    global float * y = (global float *) (dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+
+    float sumf = 0;
+
+    // parallel sum
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        sumf += x[i00] * x[i00];
+    }
+    sumf = sub_group_reduce_add(sumf);
+
+    if (get_sub_group_local_id() == 0) {
+        sum[get_sub_group_id()] = sumf;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // broadcast
+    for (uint i = get_local_size(0) / get_max_sub_group_size() / 2; i > 0; i /= 2) {
+       if (get_local_id(0) < i) {
+           sum[get_local_id(0)] += sum[get_local_id(0) + i];
+       }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const float scale = 1.0f/sqrt(max(sum[0], eps));
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        y[i00] = x[i00] * scale;
+    }
+}

ggml/src/ggml-quants.c

@@ -3104,6 +3104,11 @@ static void quantize_row_iq2_xxs_impl(const float * GGML_RESTRICT x, void * GGML
             }
             float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight);
             float eff_max = scale*kMaxQ;
+            if (eff_max <= 0) {
+                scales[ib] = 0;
+                memset(L, 0, 32);
+                continue;
+            }
             float best = 0;
             for (int is = -6; is <= 6; ++is) {
                 float id = (2*kMaxQ-1+is*0.1f)/eff_max;
@@ -3273,9 +3278,9 @@ static void quantize_row_iq2_xs_impl(const float * GGML_RESTRICT x, void * GGML_
             }
             float max = xval[0];
             for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]);
+            memset(L, 0, 16);
             if (max < GROUP_MAX_EPS) {
                 scales[ib] = 0;
-                memset(L, 0, 16);
                 continue;
             }
             float best = 0;
@@ -3714,9 +3719,9 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * GGML_RESTRICT
             }
             float max = xval[0];
             for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]);
+            memset(L, 0, 32);
             if (max < GROUP_MAX_EPS_IQ3_XXS) {
                 scales[ib] = 0;
-                memset(L, 0, 32);
                 continue;
             }
             float best = 0;
@@ -3922,6 +3927,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * GGML_RESTRICT
             }
             float max = xval[0];
             for (int i = 1; i < block_size; ++i) max = MAX(max, xval[i]);
+            memset(L, 0, block_size);
             if (!max) {
                 scales[ib] = 0;
                 continue;
@@ -4245,6 +4251,7 @@ static void quantize_row_iq1_s_impl(const float * GGML_RESTRICT x, void * GGML_R
             for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
             if (max < GROUP_MAX_EPS_IQ1_S) {
                 scales[ib] = 0;
+                shifts[ib] = 1;
                 memset(L, 1, block_size);
                 continue;
             }
@@ -4285,7 +4292,12 @@ static void quantize_row_iq1_s_impl(const float * GGML_RESTRICT x, void * GGML_R
                     }
                 }
             }
-            GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0);
+            if (besti1 < 0 || besti2 < 0 || best_shift == 0) {
+                scales[ib] = 0;
+                shifts[ib] = 1;
+                memset(L, 1, block_size);
+                continue;
+            }
             for (int j =      0; j < besti1; ++j) L[idx[2*j]] = 0;
             for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
             for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
@@ -4429,6 +4441,7 @@ static void quantize_row_iq1_m_impl(const float * GGML_RESTRICT x, void * GGML_R
             for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
             if (max < GROUP_MAX_EPS_IQ1_M) {
                 scales[ib] = 0;
+                shifts[ib] = 0;
                 memset(L, 1, block_size);
                 continue;
             }
@@ -4527,7 +4540,12 @@ static void quantize_row_iq1_m_impl(const float * GGML_RESTRICT x, void * GGML_R
                     }
                 }
             }
-            GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0);
+            if (besti1 < 0 || besti2 < 0 || best_k < 0) {
+                scales[ib] = 0;
+                shifts[ib] = 0;
+                memset(L, 1, block_size);
+                continue;
+            }
             for (int j =      0; j < besti1; ++j) L[idx[2*j]] = 0;
             for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
             for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
@@ -4874,6 +4892,7 @@ static void quantize_row_iq2_s_impl(const float * GGML_RESTRICT x, void * GGML_R
             }
             float max = xval[0];
             for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]);
+            memset(L, 0, 16);
             if (max < GROUP_MAX_EPS_IQ2_S) {
                 scales[ib] = 0;
                 continue;

ggml/src/ggml-sycl/CMakeLists.txt

@@ -25,6 +25,11 @@ ggml_add_backend_library(ggml-sycl
 
 file(GLOB   GGML_HEADERS_SYCL "*.hpp")
 file(GLOB   GGML_SOURCES_SYCL "*.cpp")
+file(GLOB   SRCS "template-instances/fattn-tile*.cpp")
+list(APPEND GGML_SOURCES_SYCL ${SRCS})
+file(GLOB   SRCS "template-instances/fattn-vec*.cpp")
+list(APPEND GGML_SOURCES_SYCL ${SRCS})
+
 target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL})
 
 if (WIN32)
@@ -145,6 +150,7 @@ else()
 endif()
 
 if (GGML_SYCL_GRAPH)
+    message(STATUS "find GGML_SYCL_GRAPH")
     target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_GRAPH)
 endif()
 

ggml/src/ggml-sycl/backend.hpp

@@ -23,6 +23,7 @@
 #include "dequantize.hpp"
 #include "dmmv.hpp"
 #include "element_wise.hpp"
+#include "fattn.hpp"
 #include "gla.hpp"
 #include "im2col.hpp"
 #include "mmq.hpp"

ggml/src/ggml-sycl/common.hpp

@@ -19,10 +19,13 @@
 #include <string>
 
 #include "dpct/helper.hpp"
+#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-sycl.h"
 #include "presets.hpp"
 #include "sycl_hw.hpp"
 
+namespace syclexp = sycl::ext::oneapi::experimental;
 
 #if GGML_SYCL_DNNL
 #include "dnnl.hpp"
@@ -31,6 +34,9 @@
 
 #define GGML_COMMON_DECL_SYCL
 #define GGML_COMMON_IMPL_SYCL
+#define SYCL_FLASH_ATTN //remove it to disable FLASH_ATTENTION in building.
+#define SYCL_FAST_FP16  //don't change. remove it will break fattn-tile.hpp building
+
 /* suppress warning spam */
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wnested-anon-types"
@@ -45,6 +51,8 @@ void ggml_sycl_host_free(void* ptr);
 extern int g_ggml_sycl_debug;
 extern int g_ggml_sycl_disable_optimize;
 extern int g_ggml_sycl_prioritize_dmmv;
+extern int g_ggml_sycl_enable_flash_attention;
+
 
 #if defined(__clang__) && __has_builtin(__builtin_expect)
 // Hint the optimizer to pipeline the more likely following instruction in branches
@@ -170,6 +178,10 @@ static size_t g_scratch_offset = 0;
 
 int get_current_device_id();
 
+inline int ggml_sycl_get_device() {
+    return get_current_device_id();
+}
+
 inline dpct::err0 ggml_sycl_set_device(const int device) try {
   int current_device_id;
   SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id()));
@@ -194,11 +206,14 @@ struct optimize_feature {
 };
 
 struct sycl_device_info {
-    int     cc;                 // compute capability
+    int cc;  // compute capability
     int nsm; // number of streaming multiprocessors (CUDA) maps to the maximum
              // number of compute units on a SYCL device.
     // size_t  smpb;               // max. shared memory per block
     size_t  smpbo;              // max. shared memory per block (with opt-in)
+    int warp_size;     // max sub_group_size of SYCL
+    int max_wg_per_cu; // max work groups per compute unit - refer to
+                       // cudaOccupancyMaxActiveBlocksPerMultiprocessor
     bool    vmm;                // virtual memory support
     size_t  total_vram;
     //sycl_hw_info hw_info;     \\ device id and aarch, currently not used
@@ -435,13 +450,15 @@ warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) {
     return a;
 }
 
-template <int width = WARP_SIZE>
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
 static __dpct_inline__ int warp_reduce_sum(int x) {
   return sycl::reduce_over_group(
       sycl::ext::oneapi::this_work_item::get_sub_group(), x, sycl::plus<>());
 }
 
-template <int width = WARP_SIZE>
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
 static __dpct_inline__ float warp_reduce_sum(float x) {
 #pragma unroll
   for (int offset = width / 2; offset > 0; offset >>= 1) {
@@ -451,7 +468,19 @@ static __dpct_inline__ float warp_reduce_sum(float x) {
   return x;
 }
 
-template <int width = WARP_SIZE>
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
+static __dpct_inline__ float warp_reduce_sum(float x, const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+  for (int offset = width / 2; offset > 0; offset >>= 1) {
+    x += dpct::permute_sub_group_by_xor(
+        item_ct1.get_sub_group(), x, offset);
+  }
+  return x;
+}
+
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
 static __dpct_inline__ sycl::float2 warp_reduce_sum(sycl::float2 a) {
 #pragma unroll
   for (int offset = width / 2; offset > 0; offset >>= 1) {
@@ -465,7 +494,8 @@ static __dpct_inline__ sycl::float2 warp_reduce_sum(sycl::float2 a) {
   return a;
 }
 
-template <int width = WARP_SIZE>
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
 static __dpct_inline__ sycl::half2 warp_reduce_sum(sycl::half2 a) {
 #pragma unroll
   for (int offset = width / 2; offset > 0; offset >>= 1) {
@@ -481,7 +511,52 @@ static constexpr int ggml_sycl_get_physical_warp_size() {
   return WARP_SIZE;
 }
 
-template <int width = WARP_SIZE>
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
+static __dpct_inline__ int warp_reduce_all(int x) {
+    if (width == ggml_sycl_get_physical_warp_size()) {
+        return sycl::all_of_group(
+            sycl::ext::oneapi::this_work_item::get_sub_group(),
+            (~0xffffffff &
+             (0x1 << sycl::ext::oneapi::this_work_item::get_sub_group()
+                         .get_local_linear_id())) ||
+                x);
+    } else {
+#pragma unroll
+        for (int offset = width / 2; offset > 0; offset >>= 1) {
+            x = dpct::permute_sub_group_by_xor(
+                    sycl::ext::oneapi::this_work_item::get_sub_group(), x,
+                    offset, width) &&
+                x;
+        }
+        return x;
+    }
+}
+
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
+static __dpct_inline__ int warp_reduce_any(int x) {
+    if (width == ggml_sycl_get_physical_warp_size()) {
+        return sycl::any_of_group(
+            sycl::ext::oneapi::this_work_item::get_sub_group(),
+            (0xffffffff &
+             (0x1 << sycl::ext::oneapi::this_work_item::get_sub_group()
+                         .get_local_linear_id())) &&
+                x);
+    } else {
+#pragma unroll
+        for (int offset = width / 2; offset > 0; offset >>= 1) {
+            x = dpct::permute_sub_group_by_xor(
+                    sycl::ext::oneapi::this_work_item::get_sub_group(), x,
+                    offset, width) ||
+                x;
+        }
+        return x;
+    }
+}
+
+/* use WARP_SIZE or WARP_32_SIZE*/
+template <int width>
 static __dpct_inline__ float warp_reduce_max(float x) {
 #pragma unroll
   for (int offset = width / 2; offset > 0; offset >>= 1) {
@@ -629,6 +704,42 @@ static const sycl::uint3 init_fastdiv_values(uint32_t d) {
     return sycl::uint3(mp, L, d);
 }
 
+// Maximum number of bytes that can be copied in a single instruction.
+// Set by test result.
+static constexpr int ggml_sycl_get_max_cpy_bytes() {
+    return 16;
+}
+
+// Aligned memory transfers of 8/16 bytes can be faster than 2 transfers with 4 bytes.
+template <int nbytes, int alignment = 0>
+static __dpct_inline__ void ggml_sycl_memcpy_1(void * dst, const void * src) {
+    if constexpr (alignment != 0) {
+        static_assert(nbytes % alignment == 0, "bad alignment");
+    }
+    constexpr int nb_per_cpy = alignment == 0 ? nbytes : alignment;
+
+#pragma unroll
+    for (int i = 0; i < nbytes/nb_per_cpy; ++i) {
+        if constexpr (nb_per_cpy == 1) {
+            ((char *) dst)[i] = ((const char *) src)[i];
+        } else if constexpr (nb_per_cpy == 2) {
+            ((short *) dst)[i] = ((const short *) src)[i];
+        } else if constexpr (nb_per_cpy == 4) {
+            ((int *) dst)[i] = ((const int *) src)[i];
+        } else if constexpr (nb_per_cpy == 8) {
+            ((sycl::int2 *) dst)[i] = ((const sycl::int2 *) src)[i];
+        } else if constexpr (nb_per_cpy == 16) {
+            ((sycl::int4 *) dst)[i] = ((const sycl::int4 *) src)[i];
+        } else {
+            static_assert(nbytes == 0 && nbytes == -1, "bad nbytes");
+        }
+    }
+}
+template <typename T>
+sycl::half2 __dpct_inline__ make_half2( T x, T y) {
+    sycl::half2 res(static_cast<sycl::half>(x),static_cast<sycl::half>(y));
+    return res;
+}
 
 static __dpct_inline__ uint32_t fastdiv(uint32_t n, const sycl::uint3 fastdiv_values) {
     const uint32_t hi = sycl::mul_hi<unsigned>(n, fastdiv_values.x());
@@ -636,6 +747,17 @@ static __dpct_inline__ uint32_t fastdiv(uint32_t n, const sycl::uint3 fastdiv_va
 }
 
 
+template <typename T>
+sycl::float2 __dpct_inline__ make_float2( T x, T y) {
+    sycl::float2 res(static_cast<float>(x),static_cast<float>(y));
+    return res;
+}
+
+sycl::float2 __dpct_inline__ __half22float2(sycl::half2 &H) {
+    sycl::float2 float2_value(static_cast<float>(H.x()), static_cast<float>(H.y()));
+    return float2_value;
+}
+
 static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3 fastdiv_values) {
     const uint32_t div_val = fastdiv(n, fastdiv_values);
     const uint32_t mod_val = n - div_val * fastdiv_values.z();
@@ -659,5 +781,97 @@ static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) {
     return result;
 }
 
+sycl::float2 __dpct_inline__ __half22float2(const sycl::half2 &H) {
+    sycl::float2 float2_value(static_cast<float>(H.x()), static_cast<float>(H.y()));
+    return float2_value;
+}
+
+float __dpct_inline__ __half2float(sycl::half H) {
+    return static_cast<float>(H);
+}
+
+static __dpct_inline__ void ggml_sycl_mad(float & acc, const float v, const float u) {
+    acc += v*u;
+}
+
+static __dpct_inline__ void ggml_sycl_mad(float & acc, const sycl::float2 v, const sycl::float2 u) {
+    acc += v.x() * u.x();
+    acc += v.y() * u.y();
+}
+
+static __dpct_inline__ void ggml_sycl_mad(float & acc, const sycl::half2 v, const sycl::half2 u) {
+#ifdef GGML_SYCL_F16
+    const sycl::float2 tmp = (v * u).template convert<float, sycl::rounding_mode::automatic>();
+    acc += tmp.x() + tmp.y();
+#else
+    const sycl::float2 tmpv = __half22float2(v);
+    const sycl::float2 tmpu = __half22float2(u);
+    acc += tmpv.x() * tmpu.x();
+    acc += tmpv.y() * tmpu.y();
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void ggml_sycl_mad(sycl::half2 & acc, const sycl::half2 v, const sycl::half2 u) {
+#ifdef GGML_SYCL_F16
+    acc += v*u;
+#else
+    const sycl::float2 tmpv = __half22float2(v);
+    const sycl::float2 tmpu = __half22float2(u);
+    sycl::float2 tmpacc = __half22float2(acc);
+    // tmpacc.x += tmpv.x() * tmpu.x();
+    // tmpacc.y += tmpv.y() * tmpu.y();
+    sycl::float2 tmp1(tmpacc.x() + tmpv.x() * tmpu.x(), tmpacc.y() + tmpv.y() * tmpu.y());
+    acc = make_half2(tmp1.x(), tmp1.y());
+#endif // GGML_SYCL_F16
+}
+
+template <int n>
+struct ggml_sycl_unroll {
+    template <typename Func, typename... Args>
+    void operator()(const Func & f, Args... args) const {
+        f(n - 1, args...);
+        ggml_sycl_unroll<n - 1>{}(f, args...);
+    }
+};
+
+template <>
+struct ggml_sycl_unroll<1> {
+    template <typename Func, typename... Args>
+    void operator()(const Func & f, Args... args) const {
+        f(0, args...);
+    }
+};
+
+static __dpct_inline__ sycl::half2 ggml_sycl_hmax2(const sycl::half2 a, const sycl::half2 b) {
+    sycl::half2 ret;
+    reinterpret_cast<sycl::half &>(ret.x()) =
+        sycl::vec<float, 1>(sycl::fmax(a[0], b[0])).convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+    reinterpret_cast<sycl::half &>(ret.y()) =
+        sycl::vec<float, 1>(sycl::fmax(a[1], b[1])).convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+    return ret;
+}
+
+static __dpct_inline__ sycl::half ggml_sycl_hmax(const sycl::half a, const sycl::half b) {
+    return sycl::vec<float, 1>(
+               sycl::fmax(sycl::vec<sycl::half, 1>(a).convert<float, sycl::rounding_mode::automatic>()[0],
+                          sycl::vec<sycl::half, 1>(b).convert<float, sycl::rounding_mode::automatic>()[0]))
+        .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+}
+
+static __dpct_inline__ uint32_t __hgt2_mask(const sycl::half2 a, const sycl::half2 b) {
+    const uint32_t mask_low  = 0x0000FFFF * (float(a[0]) > float(b[0]));
+    const uint32_t mask_high = 0xFFFF0000 * (float(a[1]) > float(b[1]));
+    return mask_low | mask_high;
+}
+
+static __dpct_inline__ uint32_t fastmodulo(uint32_t n, const sycl::uint3 fastdiv_values) {
+    // expects  fastdiv_values to contain <mp, L, divisor> in <x, y, z> (see init_fastdiv_values)
+    return n - fastdiv(n, fastdiv_values) * fastdiv_values.z();
+}
+
+static bool fast_fp16_available(const int cc) {
+    GGML_UNUSED(cc);
+    return true;   //Intel GPUs always support FP16.
+}
 
 #endif // GGML_SYCL_COMMON_HPP

ggml/src/ggml-sycl/convert.cpp

@@ -482,6 +482,63 @@ static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t
         });
 }
 
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_nc(const void * __restrict__ vx, dst_t * __restrict__ y,
+        const int64_t ne00, const int64_t ne01, const int64_t ne02,
+        const int64_t s01, const int64_t s02, const int64_t s03) {
+    auto          item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    const int64_t i00 = 2 * (int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2));
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int64_t i01 = item_ct1.get_group(1);
+    const int64_t i02 = item_ct1.get_group(0) % ne02;
+    const int64_t i03 = item_ct1.get_group(0) / ne02;
+
+    const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01;
+
+    const int64_t ib = ibx0 + i00/qk; // block index
+    const int64_t iqs = (i00%qk)/qr; // quant index
+    const int64_t iybs = i00 - i00%qk; // y block start index
+    const int64_t y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    #ifdef GGML_SYCL_F16
+        sycl::half2 v;
+    #else
+        sycl::float2 v;
+    #endif
+
+    dequantize_kernel(vx, ib, iqs, v);
+
+    const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs;
+    y[iy0 + 0]        = ggml_sycl_cast<dst_t>(v.x());
+    y[iy0 + y_offset] = ggml_sycl_cast<dst_t>(v.y());
+}
+
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_nc_sycl(const void *    vx,
+                                  dst_t *         y,
+                                  const int64_t   ne00,
+                                  const int64_t   ne01,
+                                  const int64_t   ne02,
+                                  const int64_t   ne03,
+                                  const int64_t   s01,
+                                  const int64_t   s02,
+                                  const int64_t   s03,
+                                  dpct::queue_ptr stream) {
+    const dpct::dim3 num_blocks((ne00 + 2 * SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2 * SYCL_DEQUANTIZE_BLOCK_SIZE), ne01,
+                                ne02 * ne03);
+    stream->parallel_for(sycl::nd_range<3>(num_blocks * sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                                           sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             GGML_UNUSED(item_ct1);
+                             dequantize_block_nc<qk, qr, dequantize_kernel>(vx, y, ne00, ne01, ne02, s01, s02, s03);
+                         });
+}
 template <typename src_t, typename dst_t>
 static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
                           const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03,
@@ -662,7 +719,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
     }
 }
 
-to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type) {
+
+to_fp16_nc_sycl_t ggml_get_to_fp16_nc_sycl(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
             return convert_unary_nc_sycl<float>;
@@ -670,6 +728,16 @@ to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type) {
         case GGML_TYPE_BF16:
             return convert_unary_nc_sycl<sycl::ext::oneapi::bfloat16>;
 #endif
+        case GGML_TYPE_Q4_0:
+            return dequantize_block_nc_sycl<QK4_0, QR4_0, dequantize_q4_0>;
+        case GGML_TYPE_Q4_1:
+            return dequantize_block_nc_sycl<QK4_1, QR4_1, dequantize_q4_1>;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_nc_sycl<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_nc_sycl<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_nc_sycl<QK8_0, QR8_0, dequantize_q8_0>;
         default:
             return nullptr;
     }

ggml/src/ggml-sycl/convert.hpp

@@ -29,6 +29,21 @@ using to_t_nc_sycl_t = void (*)(const void * x, T * y, int64_t ne00, int64_t ne0
                                    int64_t s01, int64_t s02, int64_t s03, dpct::queue_ptr queue);
 
 typedef to_t_nc_sycl_t<sycl::half> to_fp16_nc_sycl_t;
-to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type);
+to_fp16_nc_sycl_t ggml_get_to_fp16_nc_sycl(ggml_type type);
+
+template<typename dst_t, typename src_t>
+ inline dst_t ggml_sycl_cast(src_t x) {
+    if constexpr (std::is_same_v<dst_t, src_t>) {
+        return x;
+    } else if constexpr (std::is_same_v<dst_t, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::bfloat16(float(x));
+    } else if constexpr (std::is_same_v<src_t, sycl::ext::oneapi::bfloat16>) {
+        return static_cast<float>(x);
+    } else if constexpr(std::is_same_v<dst_t, int32_t>) {
+        return int32_t(x);
+    } else {
+        return float(x);
+    }
+}
 
 #endif  // GGML_SYCL_CONVERT_HPP

ggml/src/ggml-sycl/count-equal.cpp

@@ -18,7 +18,7 @@ static void count_equal(const T *__restrict__ x, const T *__restrict__ y,
         nequal += xi == yi;
     }
 
-    nequal = warp_reduce_sum(nequal);
+    nequal = warp_reduce_sum<WARP_SIZE>(nequal);
 
     if (item_ct1.get_local_id(2) != 0) {
         return;

ggml/src/ggml-sycl/dpct/helper.hpp

@@ -2997,6 +2997,778 @@ namespace dpct
       return 0;
     }
 
+    template <int n_nondefault_params, int n_default_params, typename T>
+    class args_selector;
+
+    /// args_selector is a helper class for extracting arguments from an
+    /// array of pointers to arguments or buffer of arguments to pass to a
+    /// kernel function.
+    ///
+    /// \param R(Ts...) The type of the kernel
+    /// \param n_nondefault_params The number of nondefault parameters of the
+    /// kernel (excluding parameters that like sycl::nd_item, etc.) \param
+    /// n_default_params The number of default parameters of the kernel
+    ///
+    /// Example usage:
+    /// With the following kernel:
+    ///   void foo(sycl::float2 *x, int n, sycl::nd_item<3> item_ct1, float
+    ///   f=.1) {}
+    /// and with the declaration:
+    ///   args_selector<2, 1, decltype(foo)> selector(kernelParams, extra);
+    /// we have:
+    ///   selector.get<0>() returns a reference to sycl::float*,
+    ///   selector.get<1>() returns a reference to int,
+    ///   selector.get<2>() returns a reference to float
+    template <int n_nondefault_params, int n_default_params, typename R,
+              typename... Ts>
+    class args_selector<n_nondefault_params, n_default_params, R(Ts...)> {
+      private:
+        void **kernel_params;
+        char *args_buffer;
+
+        template <int i> static constexpr int account_for_default_params() {
+            constexpr int n_total_params = sizeof...(Ts);
+            if constexpr (i >= n_nondefault_params) {
+                return n_total_params - n_default_params +
+                       (i - n_nondefault_params);
+            } else {
+                return i;
+            }
+        }
+
+      public:
+        /// Get the type of the ith argument of R(Ts...)
+        /// \param [in] i Index of parameter to get
+        /// \returns Type of ith parameter
+        template <int i>
+        using arg_type = std::tuple_element_t<account_for_default_params<i>(),
+                                              std::tuple<Ts...>>;
+        static constexpr int params_num = sizeof...(Ts);
+
+      private:
+        template <int i> static constexpr int get_offset() {
+            if constexpr (i == 0) {
+                // we can assume args_buffer is properly aligned to the
+                // first argument
+                return 0;
+            } else {
+                constexpr int prev_off = get_offset<i - 1>();
+                constexpr int prev_past_end =
+                    prev_off + sizeof(arg_type<i - 1>);
+                using T = arg_type<i>;
+                // is the past-the-end of the i-1st element properly aligned
+                // with the ith element's alignment?
+                if constexpr (prev_past_end % alignof(T) == 0) {
+                    return prev_past_end;
+                }
+                // otherwise bump prev_past_end to match alignment
+                else {
+                    return prev_past_end +
+                           (alignof(T) - (prev_past_end % alignof(T)));
+                }
+            }
+        }
+
+        static char *get_args_buffer(void **extra) {
+            if (!extra)
+                return nullptr;
+            for (; (std::size_t)*extra != 0; ++extra) {
+                if ((std::size_t)*extra == 1) {
+                    return static_cast<char *>(*(extra + 1));
+                }
+            }
+            return nullptr;
+        }
+
+      public:
+        /// If kernel_params is nonnull, then args_selector will
+        /// extract arguments from kernel_params. Otherwise, it
+        /// will extract them from extra.
+        /// \param [in] kernel_params Array of pointers to arguments
+        /// a or null pointer.
+        /// \param [in] extra Array containing pointer to argument buffer.
+        args_selector(void **kernel_params, void **extra)
+            : kernel_params(kernel_params),
+              args_buffer(get_args_buffer(extra)) {}
+
+        /// Get a reference to the ith argument extracted from kernel_params
+        /// or extra.
+        /// \param [in] i Index of argument to get
+        /// \returns Reference to the ith argument
+        template <int i> arg_type<i> &get() {
+            if (kernel_params) {
+                return *static_cast<arg_type<i> *>(kernel_params[i]);
+            } else {
+                return *reinterpret_cast<arg_type<i> *>(args_buffer +
+                                                        get_offset<i>());
+            }
+        }
+    }; // COPY from DPCT head file
+       // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/util.hpp
+
+    /// Utility class for launching SYCL kernels through kernel
+    /// function wrapper.
+    /// For example:
+    /// A SYCL kernel function:
+    ///   void kernel_func(int *ptr, sycl::nd_item<3> item);
+    /// Kernel function wrapper:
+    ///   void kernel_func_wrapper(int *ptr) {
+    ///     sycl::queue queue = *dpct::kernel_launcher::_que;
+    ///     unsigned int localMemSize = dpct::kernel_launcher::_local_mem_size;
+    ///     sycl::nd_range<3> nr = dpct::kernel_launcher::_nr;
+    ///     queue.parallel_for(
+    ///       nr,
+    ///       [=](sycl::nd_item<3> item_ct1) {
+    ///         kernel_func(ptr, item_ct1);
+    ///       });
+    ///   }
+    /// Then launch the kernel through wrapper like:
+    ///   typedef void(*fpt)(int *);
+    ///   fpt fp = kernel_func_wrapper;
+    ///   dpct::kernel_launcher::launch(fp, dpct::dim3(1), dpct::dim3(1), 0, 0,
+    ///   device_ptr);
+    /// If the origin function type is erased, then need to register it first:
+    ///   void *fp = (void *)wrapper_register(&kernel_func_wrapper).get();
+    ///   dpct::kernel_launcher::launch(fp, dpct::dim3(1), dpct::dim3(1), args,
+    ///   0, 0);
+    class kernel_launcher {
+        template <typename FuncT, typename ArgSelector, std::size_t... Index>
+        static void launch_helper(FuncT &&func, ArgSelector &selector,
+                                  std::index_sequence<Index...>) {
+            func(selector.template get<Index>()...);
+        }
+        static void set_execution_config(dim3 group_range, dim3 local_range,
+                                         unsigned int local_mem_size,
+                                         queue_ptr que) {
+            if (que) {
+                _que = que;
+            } else {
+                _que = &get_default_queue();
+            }
+            _nr = sycl::nd_range<3>(
+                static_cast<sycl::range<3>>(group_range * local_range),
+                static_cast<sycl::range<3>>(local_range));
+            _local_mem_size = local_mem_size;
+
+
+        };
+        static inline std::mutex kernel_function_ptr_map_mutex;
+
+      public:
+        /// Variables for storing execution configuration.
+        static inline thread_local sycl::queue *_que = nullptr;
+        static inline thread_local sycl::nd_range<3> _nr = sycl::nd_range<3>();
+        static inline thread_local unsigned int _local_mem_size = 0;
+        /// Map for retrieving launchable functor from a raw pointer.
+        static inline std::map<
+            const void *,
+            std::function<void(dim3, dim3, void **, unsigned int, queue_ptr)>>
+            kernel_function_ptr_map = {};
+
+        /// Registers a kernel function pointer with a corresponding launchable
+        /// functor.
+        /// \param [in] func Pointer to the kernel function.
+        /// \param [in] launcher Functor to handle kernel invocation.
+        static void register_kernel_ptr(
+            const void *func,
+            std::function<void(dim3, dim3, void **, unsigned int, queue_ptr)>
+                launcher) {
+            std::lock_guard<std::mutex> lock(kernel_function_ptr_map_mutex);
+            kernel_function_ptr_map[func] = std::move(launcher);
+        }
+        /// Launches a kernel function with arguments provided directly through
+        /// kernel function wrapper.
+        /// \tparam FuncT Type of the kernel function wrapper.
+        /// \tparam ArgsT Types of kernel arguments.
+        /// \param [in] func Pointer to the kernel function wrapper.
+        /// \param [in] group_range SYCL group range.
+        /// \param [in] local_range SYCL local range.
+        /// \param [in] local_mem_size The size of local memory required by the
+        /// kernel function. \param [in] que SYCL queue used to execute kernel.
+        /// \param [in] args Kernel arguments.
+        template <typename FuncT, typename... ArgsT>
+        static std::enable_if_t<std::is_invocable_v<FuncT *, ArgsT...>, void>
+        launch(FuncT *func, dim3 group_range, dim3 local_range,
+               unsigned int local_mem_size, queue_ptr que, ArgsT... args) {
+            set_execution_config(group_range, local_range, local_mem_size, que);
+            func(args...);
+        }
+        /// Launches a kernel function through registered kernel function
+        /// wrapper. \param [in] func Pointer to the registered kernel function
+        /// wrapper. \param [in] group_range SYCL group range. \param [in]
+        /// local_range SYCL local range. \param [in] args Array of pointers to
+        /// kernel arguments. \param [in] local_mem_size The size of local
+        /// memory required by the kernel function. \param [in] que SYCL queue
+        /// used to execute kernel.
+        static void launch(const void *func, dim3 group_range, dim3 local_range,
+                           void **args, unsigned int local_mem_size,
+                           queue_ptr que) {
+            std::lock_guard<std::mutex> lock(kernel_function_ptr_map_mutex);
+            auto Iter = kernel_function_ptr_map.find(func);
+            if (Iter == kernel_function_ptr_map.end()) {
+                throw std::runtime_error("dpct::launch() : no registered "
+                                         "kernel function wrapper found.");
+            }
+            (Iter->second)(group_range, local_range, args, local_mem_size, que);
+        }
+        /// Launches a kernel function with packed arguments through kernel
+        /// function wrapper.
+        /// \tparam FuncT Type of the kernel function wrapper.
+        /// \param [in] func Pointer to the kernel function wrapper.
+        /// \param [in] group_range SYCL group range.
+        /// \param [in] local_range SYCL local range.
+        /// \param [in] args Array of pointers to kernel arguments.
+        /// \param [in] local_mem_size The size of local memory required by the
+        /// kernel function. \param [in] que SYCL queue used to execute kernel.
+        template <typename FuncT>
+        static std::enable_if_t<std::is_function_v<FuncT>, void>
+        launch(FuncT *func, dim3 group_range, dim3 local_range, void **args,
+               unsigned int local_mem_size, queue_ptr que) {
+            constexpr size_t p_num = args_selector<0, 0, FuncT>::params_num;
+            set_execution_config(group_range, local_range, local_mem_size, que);
+            args_selector<p_num, p_num, FuncT> selector(args, nullptr);
+            launch_helper(func, selector, std::make_index_sequence<p_num>{});
+        }
+    }; // COPY from DPCT head file
+       // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/kernel.hpp
+
+    // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/util.hpp
+    template <typename T>
+    T select_from_sub_group(
+        sycl::sub_group g,
+        T x,
+        int remote_local_id,
+        int logical_sub_group_size = 32) {
+      unsigned int start_index = g.get_local_linear_id() /
+                                 logical_sub_group_size *
+                                 logical_sub_group_size;
+      return sycl::select_from_group(
+          g, x, start_index + remote_local_id % logical_sub_group_size);
+    }
+
+    // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/math.hpp
+    template <typename T>
+    void ldmatrix(uintptr_t addr, T* m, bool trans = false, unsigned mat = 0) {
+      auto sg = sycl::ext::oneapi::this_work_item::get_sub_group();
+      int lane = sg.get_local_linear_id();
+
+      int lane_group8_row = lane / 8;
+      int lane_group8_col = lane % 8;
+
+      if (!trans) {
+        // calculate the source lane
+        int src_lane = 2 * lane_group8_row;
+        if (lane_group8_col >= 4)
+          src_lane += 1;
+
+        // Broadcast the address from the source lane
+        auto recv_addr_uintp =
+            dpct::select_from_sub_group(sg, addr, mat * 8 + src_lane);
+
+        // Cast the received address from uintptr_t to the type of 'm'
+        auto recv_addr = reinterpret_cast<T*>(recv_addr_uintp);
+
+        // Non-transposed load
+        *m = recv_addr[lane_group8_col % 4];
+      } else {
+        // calculate the source lane
+        int src_lane = (lane % 4) * 2;
+
+        // Broadcast the address from the source lane
+        auto recv_addr_uintp_1 =
+            dpct::select_from_sub_group(sg, addr, mat * 8 + src_lane);
+        auto recv_addr_uintp_2 =
+            dpct::select_from_sub_group(sg, addr, mat * 8 + src_lane + 1);
+
+        // Cast the received address from uintptr_t to 'half *'
+        auto recv_addr_1 = reinterpret_cast<sycl::half*>(recv_addr_uintp_1);
+        auto recv_addr_2 = reinterpret_cast<sycl::half*>(recv_addr_uintp_2);
+
+        // Transposed load
+        int index = lane / 4;
+        sycl::half val0 = recv_addr_1[index];
+        sycl::half val1 = recv_addr_2[index];
+
+        // Combine the two 16-bits into one 32-bit value
+        sycl::half2 val = sycl::half2(val0, val1);
+        *m = *reinterpret_cast<T*>(&val);
+      }
+    }
+
+    template <typename T>
+    void ldmatrix(uintptr_t addr, T* m1, T* m2, bool trans = false) {
+      // Load 1st matrix
+      ldmatrix(addr, m1, trans, 0);
+      // Load 2nd matrix
+      ldmatrix(addr, m2, trans, 1);
+    }
+
+    template <typename T>
+    void ldmatrix(
+        uintptr_t addr, T* m1, T* m2, T* m3, T* m4, bool trans = false) {
+      // Load 1st matrix
+      ldmatrix(addr, m1, trans, 0);
+      // Load 2nd matrix
+      ldmatrix(addr, m2, trans, 1);
+      // Load 3rd matrix
+      ldmatrix(addr, m3, trans, 2);
+      // Load 4th matrix
+      ldmatrix(addr, m4, trans, 3);
+    }
+
+    // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/math.hpp
+
+    /// A helper struct that defines the pack type for the input matrix
+    /// fragments
+    /// of mma() function based on the type of input matrix fragments.
+    /// The MMAType struct is specialized for different types of input matrices.
+    /// Currently, the specialization for f16, bf16 and s8 types is defined
+    /// below. \tparam [in] T The type of the input matrix fragments
+    template <typename T>
+    struct MMAType {
+      using PackType = uint32_t;
+    };
+
+    /// Each work item of a sub-group (limited to size 32) calling this function
+    /// calculates a subset fragment for the output matrix D using MAD operation
+    /// on A, B & C matrix fragments (D = A * B + C). Current supported shapes &
+    /// types:
+    /// - m8n8k4 (f32.f16.f16.f32)
+    /// - m8n8k16 (s32.s8.s8.s32)
+    /// - m16n8k8 (f32.f16.f16.f32 & f32.bf16.bf16.f32)
+    /// - m16n8k16 (f32.f16.f16.f32 & s32.s8.s8.s32)
+    /// - m16n8k32 (s32.s8.s8.s32)
+    /// Here, m, n & k define the shapes of A, B & C matrices respectively
+    /// (A = [m x k], B = [k x n], C = [m x n]).
+    /// \tparam [in] M The rows of A, C & D matrices
+    /// \tparam [in] N The columns of B, C, D matrices
+    /// \tparam [in] K The columns & rows of A & B matrices respectively
+    /// \tparam [in] ABType The type of the input matrix (A & B) fragment
+    /// \tparam [in] CDType The type of the output matrix (C & D) fragment
+    /// \param [out] d_mat_frag The fragment of the output matrix D to store the
+    /// result of A * B + C
+    /// \param [in] a_mat_frag The fragment of the input matrix A to be
+    /// multiplied with B matrix fragment \param [in] b_mat_frag The fragment of
+    /// the input matrix B to be multiplied with A matrix fragment \param [in]
+    /// c_mat_frag The fragment of the input matrix C to be added with the
+    /// result of A * B fragments
+    template <int M, int N, int K, typename ABType, typename CDType>
+    void mma(
+        volatile void** d_mat_frag,
+        void* a_mat_frag,
+        void* b_mat_frag,
+        void* c_mat_frag) {
+      auto d = reinterpret_cast<volatile CDType**>(d_mat_frag);
+      auto a =
+          reinterpret_cast<typename MMAType<ABType>::PackType*>(a_mat_frag);
+      auto b =
+          reinterpret_cast<typename MMAType<ABType>::PackType*>(b_mat_frag);
+      auto c = reinterpret_cast<CDType*>(c_mat_frag);
+
+      auto sg = sycl::ext::oneapi::this_work_item::get_sub_group();
+      int lane = sg.get_local_linear_id();
+
+      static_assert(
+          (M == 8 && N == 8 && K == 4) || (M == 8 && N == 8 && K == 16) ||
+              (M == 16 && N == 8 && K == 8) || (M == 16 && N == 8 && K == 16) ||
+              (M == 16 && N == 8 && K == 32),
+          "Unsupported MMA shape!");
+
+      short row_load_offset = 4 * (lane >> 2);
+      short col_load_offset = 8 * (lane % 4);
+
+      if constexpr (M == 8 && N == 8 && K == 4) {
+        if constexpr (std::is_floating_point_v<CDType>) {
+          col_load_offset = row_load_offset % 16;
+
+          // Init D matrix with fragments of C matrix
+          *d[0] = c[0];
+          *d[1] = c[1];
+          *d[2] = c[2];
+          *d[3] = c[3];
+          *d[4] = c[4];
+          *d[5] = c[5];
+          *d[6] = c[6];
+          *d[7] = c[7];
+
+          // Calculate the row and col offset indices to iterate through the row
+          // & col fragments of A & B matrices
+          int r_ind = (lane % 2) ? 1 : 0;
+          int c_ind = ((lane % 4) / 2) ? 2 : 0;
+
+          // Each sub-group is responsible for computing a fragment size of 8*8
+          // elements of matrix D for each of 4 MMA computations.
+          // Each work item computes 8 elements of matrix D by gathering
+          // their corresponding col & row matrix fragments of length k (4)
+          // from A & B matrices respectively using below mapping logic:
+          // row0 = (i % 4) if (lane < 16) else (i % 4) + 4
+          // col0 = (lane % 4)
+          // As each row & col fragment of A & B matrices is distributed across
+          // 4 work items, each iteration of below loop loads a partial fragment
+          // of matrix A (row) and matrix B (col) using the row & col offsets.
+          typename MMAType<ABType>::PackType recv_a[2], recv_b[2];
+
+          for (int i = 0; i < 4; i++) {
+            // Load partial fragment from col0 of matrix A ({a0, a1})
+            recv_a[0] =
+                dpct::select_from_sub_group(sg, a[0], row_load_offset + i);
+            // Load partial fragment from col0 of matrix A ({a2, a3})
+            recv_a[1] =
+                dpct::select_from_sub_group(sg, a[1], row_load_offset + i);
+
+            // Load partial fragment from row0 of matrix B ({b0, b1})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i);
+            // Load partial fragment from row0 of matrix B ({b2, b3})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[1], col_load_offset + i);
+
+            auto ra = reinterpret_cast<ABType*>(recv_a);
+            auto rb = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment (for
+            // even work item indices) d0 += col0{ a0 } * row0{ b0 } d1 += col0{
+            // a0 } * row0{ b1 } d2 += col1{ a2 } * row0{ b0 } d3 += col1{ a2 }
+            // * row0{ b1 } (for odd work item indices) d0 += col0{ a1 } * row0{
+            // b2 } d1 += col0{ a1 } * row0{ b3 } d2 += col1{ a3 } * row0{ b2 }
+            // d3 += col1{ a3 } * row0{ b3 }
+            *d[0] +=
+                static_cast<float>(ra[r_ind]) * static_cast<float>(rb[c_ind]);
+            *d[1] += static_cast<float>(ra[r_ind]) *
+                     static_cast<float>(rb[c_ind + 1]);
+            *d[2] += static_cast<float>(ra[r_ind + 2]) *
+                     static_cast<float>(rb[c_ind]);
+            *d[3] += static_cast<float>(ra[r_ind + 2]) *
+                     static_cast<float>(rb[c_ind + 1]);
+
+            // Load partial fragment from row1 of matrix B ({b0, b1})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 16);
+            // Load partial fragment from row1 of matrix B ({b2, b3})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[1], col_load_offset + i + 16);
+
+            // (for even work item indices)
+            // d0 += col0{ a0 } * row1{ b0 }
+            // d1 += col0{ a0 } * row1{ b1 }
+            // d2 += col1{ a2 } * row1{ b0 }
+            // d3 += col1{ a2 } * row1{ b1 }
+            // (for odd work item indices)
+            // d0 += col0{ a1 } * row1{ b2 }
+            // d1 += col0{ a1 } * row1{ b3 }
+            // d2 += col1{ a3 } * row1{ b2 }
+            // d3 += col1{ a3 } * row1{ b3 }
+            *d[4] +=
+                static_cast<float>(ra[r_ind]) * static_cast<float>(rb[c_ind]);
+            *d[5] += static_cast<float>(ra[r_ind]) *
+                     static_cast<float>(rb[c_ind + 1]);
+            *d[6] += static_cast<float>(ra[r_ind + 2]) *
+                     static_cast<float>(rb[c_ind]);
+            *d[7] += static_cast<float>(ra[r_ind + 2]) *
+                     static_cast<float>(rb[c_ind + 1]);
+          }
+        }
+      } else if constexpr (M == 8 && N == 8 && K == 16) {
+        if constexpr (std::is_integral_v<ABType>) {
+          // Init D matrix with fragments of C matrix
+          *d[0] = c[0];
+          *d[1] = c[1];
+
+          // Each sub-group is responsible for computing a fragment size of 16*8
+          // elements of matrix D.
+          // Each work item computes 2 elements of matrix D by gathering
+          // their corresponding row & col matrix fragments of length k (16)
+          // from A & B matrices respectively using below mapping logic:
+          // row0 = ((lane % 4) * 4) + i
+          // col0 = (lane >> 2)
+          // As each row & col fragment of A & B matrices is distributed across
+          // 4 work items, each iteration of below loop loads a partial fragment
+          // of matrix A (row) and matrix B (col) using the row & col offsets.
+          for (int i = 0; i < 4; i++) {
+            typename MMAType<ABType>::PackType recv_a, recv_b[2];
+
+            // Load partial fragment from row0 of matrix A ({a0, a1, a2, a3})
+            recv_a = dpct::select_from_sub_group(sg, a[0], row_load_offset + i);
+            // Load partial fragment from col0 of matrix B ({b0, b1, b2, b3})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i);
+            // Load partial fragment from col1 of matrix B ({b0, b1, b2, b3})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4);
+
+            auto a = reinterpret_cast<ABType*>(&recv_a);
+            auto b = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment d0
+            // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{
+            // a0, a1, a2, a3 } * col1{ b0, b1, b2, b3 } d2 += row0{ a0, a1, a2,
+            // a3 } * col0{ b0, b1, b2, b3 } d3 += row0{ a0, a1, a2, a3 } *
+            // col1{ b0, b1, b2, b3 }
+            for (int j = 0; j < 4; j++) {
+              *d[0] += a[j] * b[j];
+              *d[1] += a[j] * b[j + 4];
+            }
+          }
+        }
+      } else if constexpr (M == 16 && N == 8 && K == 8) {
+        if constexpr (std::is_floating_point_v<CDType>) {
+          // Init D matrix fragment with C matrix fragment
+          *d[0] = c[0];
+          *d[1] = c[1];
+          *d[2] = c[2];
+          *d[3] = c[3];
+
+          // Each sub-group is responsible for computing a fragment size of 16*8
+          // elements of matrix D.
+          // Each work item computes 4 elements of matrix D by gathering
+          // their corresponding row & col matrix fragments of length k (8)
+          // from A & B matrices respectively using below mapping logic:
+          // row0 = (lane >> 2) & row1 = (lane >> 2) + 8
+          // col0 = (lane % 4) * 2 + (i & 0x1)
+          // As each row & col fragment of A & B matrices is distributed across
+          // 4 work items, each iteration of below loop loads a partial fragment
+          // of matrix A (row) and matrix B (col) using the row & col offsets.
+          for (int i = 0; i < 4; i++) {
+            typename MMAType<ABType>::PackType recv_a[2], recv_b[2];
+
+            // Load partial fragment from row0 of matrix A ({a0, a1})
+            recv_a[0] =
+                dpct::select_from_sub_group(sg, a[0], row_load_offset + i);
+            // Load partial fragment from row1 of matrix A ({a2, a3})
+            recv_a[1] =
+                dpct::select_from_sub_group(sg, a[1], row_load_offset + i);
+            // Load partial fragment from col0 of matrix B ({b0, b1})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i);
+            // Load partial fragment from col1 of matrix B ({b0, b1})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4);
+
+            auto ra = reinterpret_cast<ABType*>(recv_a);
+            auto rb = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment d0
+            // += row0{ a0, a1 } * col0{ b0, b1 } d1 += row0{ a0, a1 } * col1{
+            // b0, b1 } d2 += row1{ a2, a3 } * col0{ b0, b1 } d3 += row1{ a2, a3
+            // } * col1{ b0, b1 }
+            for (int j = 0; j < 2; j++) {
+              *d[0] += static_cast<float>(ra[j]) * static_cast<float>(rb[j]);
+              *d[1] +=
+                  static_cast<float>(ra[j]) * static_cast<float>(rb[j + 2]);
+              *d[2] +=
+                  static_cast<float>(ra[j + 2]) * static_cast<float>(rb[j]);
+              *d[3] +=
+                  static_cast<float>(ra[j + 2]) * static_cast<float>(rb[j + 2]);
+            }
+          }
+        }
+      } else if constexpr (M == 16 && N == 8 && K == 16) {
+        if constexpr (std::is_floating_point_v<CDType>) {
+          // Init D matrix fragment with C matrix fragment
+          *d[0] = c[0];
+          *d[1] = c[1];
+          *d[2] = c[2];
+          *d[3] = c[3];
+
+          // Each sub-group is responsible for computing a fragment size of 16*8
+          // elements of matrix D.
+          // Each work item computes 4 elements of matrix D by gathering
+          // their corresponding row & col matrix fragments of length k (8)
+          // from A & B matrices respectively using below mapping logic:
+          // row0 = (lane >> 2)    & row1 = (lane >> 2) + 8
+          // col0 = (lane % 4) * 2 & col1 = (lane % 4) * 2 + 1
+          // As each row & col fragment of A & B matrices is distributed across
+          // 4 work items, each iteration of below loop loads a partial fragment
+          // of matrix A (row) and matrix B (col) using the row & col offsets.
+          for (int i = 0; i < 4; i++) {
+            typename MMAType<ABType>::PackType recv_a[4], recv_b[4];
+
+            // Load partial fragment from row0 of matrix A ({a0, a1})
+            recv_a[0] =
+                dpct::select_from_sub_group(sg, a[0], row_load_offset + i);
+            // Load partial fragment from row0 of matrix A ({a2, a3})
+            recv_a[1] =
+                dpct::select_from_sub_group(sg, a[2], row_load_offset + i);
+            // Load partial fragment from row1 of matrix A ({a0, a1})
+            recv_a[2] =
+                dpct::select_from_sub_group(sg, a[1], row_load_offset + i);
+            // Load partial fragment from row1 of matrix A ({a2, a3})
+            recv_a[3] =
+                dpct::select_from_sub_group(sg, a[3], row_load_offset + i);
+
+            // Load partial fragment from col0 of matrix B ({b0, b1})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i);
+            // Load partial fragment from col0 of matrix B ({b2, b3})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[1], col_load_offset + i);
+            // Load partial fragment from col1 of matrix B ({b0, b1})
+            recv_b[2] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + 4 + i);
+            // Load partial fragment from col1 of matrix B ({b2, b3})
+            recv_b[3] =
+                dpct::select_from_sub_group(sg, b[1], col_load_offset + 4 + i);
+
+            auto ra = reinterpret_cast<ABType*>(recv_a);
+            auto rb = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment d0
+            // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{
+            // a0, a1, a2, a3 } * col1{ b0, b1, b2, b3 } d2 += row1{ a0, a1, a2,
+            // a3 } * col0{ b0, b1, b2, b3 } d3 += row1{ a0, a1, a2, a3 } *
+            // col1{ b0, b1, b2, b3 }
+            for (int j = 0; j < 4; j++) {
+              *d[0] += static_cast<CDType>(ra[j]) * static_cast<CDType>(rb[j]);
+              *d[1] +=
+                  static_cast<CDType>(ra[j]) * static_cast<CDType>(rb[j + 4]);
+              *d[2] +=
+                  static_cast<CDType>(ra[j + 4]) * static_cast<CDType>(rb[j]);
+              *d[3] += static_cast<CDType>(ra[j + 4]) *
+                       static_cast<CDType>(rb[j + 4]);
+            }
+          }
+        } else if constexpr (std::is_integral_v<ABType>) {
+          // Init D matrix with fragments of C matrix
+          *d[0] = c[0];
+          *d[1] = c[1];
+          *d[2] = c[2];
+          *d[3] = c[3];
+
+          // Each sub-group is responsible for computing a fragment size of 16*8
+          // elements of matrix D.
+          // Each work item computes 4 elements of matrix D by gathering
+          // their corresponding row & col matrix fragments of length k (8)
+          // from A & B matrices respectively using below mapping logic:
+          // row0 = (lane >> 2)    & row1 = (lane >> 2) + 8
+          // col0 = (lane % 4) * 2 & col1 = (lane % 4) * 2 + 1
+          // As each row & col fragment of A & B matrices is distributed across
+          // 4 work items, each iteration of below loop loads a partial fragment
+          // of matrix A (row) and matrix B (col) using the row & col offsets.
+          for (int i = 0; i < 4; i++) {
+            typename MMAType<ABType>::PackType recv_a[2], recv_b[2];
+
+            // Load partial fragment from row0 of matrix A ({a0, a1, a2, a3})
+            recv_a[0] =
+                dpct::select_from_sub_group(sg, a[0], row_load_offset + i);
+            // Load partial fragment from row1 of matrix A ({a4, a5, a6, a7})
+            recv_a[1] =
+                dpct::select_from_sub_group(sg, a[1], row_load_offset + i);
+            // Load partial fragment from col0 of matrix B ({b0, b1, b2, b3})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i);
+            // Load partial fragment from col1 of matrix B ({b4, b5, b6, b7})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4);
+
+            auto ra = reinterpret_cast<ABType*>(recv_a);
+            auto rb = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment d0
+            // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{
+            // a0, a1, a2, a3 } * col1{ b4, b5, b6, b7 } d2 += row1{ a4, a5, a6,
+            // a7 } * col0{ b0, b1, b2, b3 } d3 += row1{ a4, a5, a6, a7 } *
+            // col1{ b4, b5, b6, b7 }
+            for (int i = 0; i < 4; i++) {
+              *d[0] += ra[i] * rb[i];
+              *d[1] += ra[i] * rb[i + 4];
+              *d[2] += ra[i + 4] * rb[i];
+              *d[3] += ra[i + 4] * rb[i + 4];
+            }
+          }
+        }
+      } else if constexpr (M == 16 && N == 8 && K == 32) {
+        if constexpr (std::is_integral_v<ABType>) {
+          // Init D matrix with fragments of C matrix
+          *d[0] = c[0];
+          *d[1] = c[1];
+          *d[2] = c[2];
+          *d[3] = c[3];
+
+          // Each sub-group is responsible for computing a fragment size of 16*8
+          // elements of matrix D.
+          // Each work item computes 4 elements of matrix D by gathering
+          // their corresponding row & col matrix fragments of length k (32)
+          // from A & B matrices respectively using below mapping logic:
+          // row0 = (lane >> 2)    & row1 = (lane >> 2) + 8
+          // col0 = ((lane % 4) * 4) + (i & 0x3) & col1 = ((lane % 4) * 4) + (i
+          // & 0x3) As each row & col fragment of A & B matrices is distributed
+          // across 4 work items, each iteration of below loop loads a partial
+          // fragment of matrix A (row) and matrix B (col) using the row & col
+          // offsets.
+          for (int i = 0; i < 4; i++) {
+            typename MMAType<ABType>::PackType recv_a[2], recv_b[2];
+
+            // Load partial fragment from row0 of matrix A ({a0, a1, a2, a3})
+            recv_a[0] =
+                dpct::select_from_sub_group(sg, a[0], row_load_offset + i);
+            // Load partial fragment from row1 of matrix A ({a4, a5, a6, a7})
+            recv_a[1] =
+                dpct::select_from_sub_group(sg, a[1], row_load_offset + i);
+            // Load partial fragment from col0 of matrix B ({b0, b1, b2, b3})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i);
+            // Load partial fragment from col1 of matrix B ({b0, b1, b2, b3})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4);
+
+            auto a = reinterpret_cast<ABType*>(recv_a);
+            auto b = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment d0
+            // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{
+            // a0, a1, a2, a3 } * col1{ b0, b1, b2, b3 } d2 += row1{ a4, a5, a6,
+            // a7 } * col0{ b0, b1, b2, b3 } d3 += row1{ a4, a5, a6, a7 } *
+            // col1{ b0, b1, b2, b3 }
+            for (int j = 0; j < 4; j++) {
+              *d[0] += a[j] * b[j];
+              *d[1] += a[j] * b[j + 4];
+              *d[2] += a[j + 4] * b[j];
+              *d[3] += a[j + 4] * b[j + 4];
+            }
+          }
+
+          for (int i = 0; i < 4; i++) {
+            typename MMAType<ABType>::PackType recv_a[2], recv_b[2];
+
+            // Load partial fragment from row0 of matrix A ({a8, a9, a10, a11})
+            recv_a[0] =
+                dpct::select_from_sub_group(sg, a[2], row_load_offset + i);
+            // Load partial fragment from row1 of matrix A ({a12, a13, a14,
+            // a15})
+            recv_a[1] =
+                dpct::select_from_sub_group(sg, a[3], row_load_offset + i);
+            // Load partial fragment from col0 of matrix B ({b4, b5, b6, b7})
+            recv_b[0] =
+                dpct::select_from_sub_group(sg, b[1], col_load_offset + i);
+            // Load partial fragment from col1 of matrix B ({b4, b5, b6, b7})
+            recv_b[1] =
+                dpct::select_from_sub_group(sg, b[1], col_load_offset + i + 4);
+
+            auto a = reinterpret_cast<ABType*>(recv_a);
+            auto b = reinterpret_cast<ABType*>(recv_b);
+
+            // Each work item calculates a partial product of A & B matrix
+            // fragments and adds it to the corresponding D matrix fragment d0
+            // += row0{ a8, a9, a10, a11 } * col0{ b4, b5, b6, b7 } d1 += row0{
+            // a8, a9, a10, a11 } * col1{ b4, b5, b6, b7 } d2 += row1{ a12, a13,
+            // a14, a15 } * col0{ b4, b5, b6, b7 } d3 += row1{ a12, a13, a14,
+            // a15 } * col1{ b4, b5, b6, b7 }
+            for (int j = 0; j < 4; j++) {
+              *d[0] += a[j] * b[j];
+              *d[1] += a[j] * b[j + 4];
+              *d[2] += a[j + 4] * b[j];
+              *d[3] += a[j + 4] * b[j + 4];
+            }
+          }
+        }
+      }
+    }
 } // COPY from DPCT head files
 
 #endif // GGML_SYCL_DPCT_HELPER_HPP

ggml/src/ggml-sycl/fattn-tile.cpp

@@ -0,0 +1,55 @@
+#include <sycl/sycl.hpp>
+#include <sycl/ext/oneapi/work_group_static.hpp>
+#include "dpct/helper.hpp"
+#include "common.hpp"
+#include "fattn-common.hpp"
+#include "fattn-tile.hpp"
+#include <cmath>
+#include <float.h>
+namespace syclex = sycl::ext::oneapi::experimental;
+
+void ggml_sycl_flash_attn_ext_tile(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * K = dst->src[1];
+    const ggml_tensor * V = dst->src[2];
+    switch (K->ne[0]) {
+        case  40: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case< 40,  40>(ctx, dst);
+        } break;
+        case  64: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case< 64,  64>(ctx, dst);
+        } break;
+        case  72: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case< 72,  72>(ctx, dst);
+        } break;
+        case  80: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case< 80,  80>(ctx, dst);
+        } break;
+        case  96: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case< 96,  96>(ctx, dst);
+        } break;
+        case 112: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case<112, 112>(ctx, dst);
+        } break;
+        case 128: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case<128, 128>(ctx, dst);
+        } break;
+        case 256: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_sycl_flash_attn_ext_tile_case<256, 256>(ctx, dst);
+        } break;
+        case 576: {
+            GGML_ASSERT(V->ne[0] == 512);
+            ggml_sycl_flash_attn_ext_tile_case<576, 512>(ctx, dst);
+        } break;
+        default: {
+            GGML_ABORT("Unsupported head size");
+        } break;
+    }
+}

ggml/src/ggml-sycl/fattn-vec.hpp

@@ -0,0 +1,667 @@
+#ifndef GGML_SYCL_FATTN_VEC_HPP
+#define GGML_SYCL_FATTN_VEC_HPP
+
+#include <sycl/sycl.hpp>
+#include <sycl/ext/oneapi/work_group_static.hpp>
+#include <iostream>
+#include <iomanip>
+
+#include "dpct/helper.hpp"
+#include "common.hpp"
+#include "ggml.h"
+#include "fattn-common.hpp"
+#include <cmath>
+#include <float.h>
+
+namespace syclex = sycl::ext::oneapi::experimental;
+
+static int ggml_sycl_fattn_vec_get_nthreads_host(const int cc) {
+    return 128;
+    GGML_UNUSED(cc);
+}
+
+static constexpr int ggml_sycl_fattn_vec_get_nthreads_device() {
+    return 128;
+}
+
+// Currenlty llvm with the amdgcn target dose not support unrolling loops
+// that contain a break that can not be resolved at compile time.
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpass-failed"
+#endif // __clang__
+
+template <int D,
+          int ncols,
+          int type_K,
+          int type_V,
+          bool use_logit_softcap,
+          int warp_size>  // D == head size
+static void flash_attn_ext_vec(const char* __restrict__ Q,
+                        const char* __restrict__ K,
+                        const char* __restrict__ V,
+                        const char* __restrict__ mask,
+                        const char* __restrict__ sinks,
+                        const int* __restrict__ KV_max,
+                        float* __restrict__ dst,
+                        sycl::float2* __restrict__ dst_meta,
+                        const float scale,
+                        const float max_bias,
+                        const float m0,
+                        const float m1,
+                        const uint32_t n_head_log2,
+                        const float logit_softcap,
+                        const int32_t ne00,
+                        const sycl::uint3 ne01,
+                        const int32_t ne02,
+                        const int32_t ne03,
+                        const int32_t nb01,
+                        const int32_t nb02,
+                        const int32_t nb03,
+                        const int32_t ne10,
+                        const int32_t ne11,
+                        const int32_t ne12,
+                        const int32_t ne13,
+                        const int32_t nb11,
+                        const int32_t nb12,
+                        const int64_t nb13,
+                        const int32_t nb21,
+                        const int32_t nb22,
+                        const int64_t nb23,
+                        const int32_t ne31,
+                        const int32_t ne32,
+                        const int32_t ne33,
+                        const int32_t nb31,
+                        const int32_t nb32,
+                        const int64_t nb33) {
+#ifdef SYCL_FLASH_ATTN
+    // Skip unused kernel variants for faster compilation:
+
+    auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+            max_bias, m0, m1, n_head_log2, logit_softcap,
+            ne00, ne01, ne02, ne03,
+                  nb01, nb02, nb03,
+            ne10, ne11, ne12, ne13,
+                  nb11, nb12, nb13,
+                  nb21, nb22, nb23,
+                  ne31, ne32, ne33,
+                  nb31, nb32, nb33);
+        return;
+    }
+
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes();
+    constexpr int cpy_ne = cpy_nb / 4;
+
+    constexpr int nthreads_KQ_q = (D/4 < warp_size ? D/4 : warp_size);
+    constexpr int nthreads_V_q  = (D/4 < warp_size ? D/4 : warp_size);
+
+    constexpr int nthreads    = ggml_sycl_fattn_vec_get_nthreads_device();
+    constexpr int nthreads_KQ = type_K == GGML_TYPE_F16 ? 128 / cpy_nb : nthreads_KQ_q;
+    constexpr int nthreads_V  = type_V == GGML_TYPE_F16 ? 128 / cpy_nb : nthreads_V_q;
+
+    static_assert(warp_size % nthreads_KQ == 0, "bad nthreads_K");
+    static_assert(warp_size % nthreads_V  == 0, "bad nthreads_V");
+
+    constexpr int V_rows_per_thread = type_V == GGML_TYPE_F16 ? 2*cpy_ne : 4;
+    constexpr int V_cols_per_iter   = warp_size / nthreads_V;
+
+    constexpr vec_dot_KQ_t vec_dot_KQ = get_vec_dot_KQ<type_K, D, nthreads_KQ, warp_size>();
+    constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
+#ifdef GGML_SYCL_F16
+    constexpr dequantize_V_t dequantize_V = get_dequantize_V<type_V, sycl::half, V_rows_per_thread>();
+#else
+    constexpr dequantize_V_t dequantize_V = get_dequantize_V<type_V, float, V_rows_per_thread>();
+#endif // GGML_SYCL_F16
+
+    const int ic0 = item_ct1.get_group(2) * ncols;  // Index of the Q/QKV column to work on.
+
+    const int sequence  = item_ct1.get_group(0) / ne02;
+    const int head      = item_ct1.get_group(0) - sequence * ne02;
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    Q += nb03*sequence + nb02* head              + nb01*ic0;
+    K += nb13*sequence + nb12*(head / gqa_ratio);
+    V += nb23*sequence + nb22*(head / gqa_ratio);
+
+    const sycl::half * maskh = (const sycl::half *) (mask + nb33 * (sequence % ne33) + nb31 * ic0);
+
+    const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
+
+    static_assert(D % (2*warp_size) == 0, "D not divisible by 2*warp_size == 64.");
+    constexpr int nwarps = nthreads / warp_size;
+    const int     tid    = warp_size * item_ct1.get_local_id(1) + item_ct1.get_local_id(2);
+    __builtin_assume(tid < nthreads);
+
+    constexpr int ne_KQ      = ncols*D;
+    constexpr int ne_combine = nwarps*V_cols_per_iter*D;
+
+    constexpr size_t lsm_size1 = ncols * warp_size;
+    constexpr size_t lsm_size2 = ncols * warp_size;
+#ifdef GGML_SYCL_F16
+    sycl::half2 VKQ[ncols][(D / 2) / nthreads_V] = { { { 0.0f, 0.0f } } };
+    constexpr size_t lsm_size3 = (ne_KQ > ne_combine ? ne_KQ : ne_combine);
+    constexpr size_t local_share_mem_size = (lsm_size1 + lsm_size2)*sizeof(float) + lsm_size3*sizeof(sycl::half);
+
+    syclex::work_group_static<char[local_share_mem_size]> lsm;
+
+    float *KQ_max_shared = (float *)&lsm;
+    float *KQ_sum_shared = KQ_max_shared+lsm_size1;
+    sycl::half* KQ = (sycl::half*)(KQ_sum_shared + lsm_size2);
+
+
+#else
+    sycl::float2 VKQ[ncols][(D/2)/nthreads_V] = {{{0.0f, 0.0f}}};
+
+    constexpr size_t lsm_size3 = (ne_KQ > ne_combine ? ne_KQ : ne_combine);
+    constexpr size_t local_share_mem_size = (lsm_size1 + lsm_size2 + lsm_size3)*sizeof(float);
+
+
+    syclex::work_group_static<char[local_share_mem_size]> lsm;
+    float *KQ_max_shared = (float *)&lsm;
+    float *KQ_sum_shared = KQ_max_shared+lsm_size1;
+    float* KQ = KQ_sum_shared + lsm_size2;
+
+#endif // GGML_SYCL_F16
+
+    float KQ_max[ncols];
+    float KQ_sum[ncols];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        KQ_max[j] = -FLT_MAX/2.0f;
+        KQ_sum[j] = 0.0f;
+    }
+
+    // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
+#ifdef GGML_SYCL_F16
+    sycl::half2 Q_reg[ncols][(D / 2) / nthreads_KQ] = {{{0.0f, 0.0f}}};  // Will be initialized completely.
+#else
+    sycl::float2 Q_reg[ncols][(D/2)/nthreads_KQ] = {{{0.0f, 0.0f}}}; // May be only partially initialized.
+#endif // GGML_SYCL_F16
+    int    Q_i32[ncols][1 > D/(sizeof(int)*nthreads_KQ) ? 1 : D/(sizeof(int)*nthreads_KQ)];
+    sycl::float2 Q_ds[ncols][1 > D / (sizeof(int) * nthreads_KQ) ? 1 : D / (sizeof(int) * nthreads_KQ)];
+    if constexpr (Q_q8_1) {
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + item_ct1.get_local_id(1);
+
+            if (j0 + nwarps > ncols && j >= ncols) {
+                break;
+            }
+
+            // Reuse KQ as temporary storage for converting Q to q8_1:
+            int    * tmp_q_i32 = (int    *) &KQ[j*D];
+            sycl::float2 * tmp_q_ds  = (sycl::float2 *) (tmp_q_i32 + D / sizeof(int));
+
+            // Set memory to zero if out of bounds:
+            if (ncols > 1 && ic0 + j >= int(ne01.z())) {
+#pragma unroll
+                for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += warp_size) {
+                    const int i = i0 + item_ct1.get_local_id(2);
+
+                    if (i0 + warp_size <= int(D/sizeof(int)) || i < int(D/sizeof(int))) {
+                        tmp_q_i32[i] = 0;
+                    }
+                }
+                if (item_ct1.get_local_id(2) < D/QK8_1) {
+                    tmp_q_ds[item_ct1.get_local_id(2)] = sycl::float2(0.0f, 0.0f);
+                }
+            } else {
+                const float * Q_f = (const float *) (Q + j*nb01);
+                constexpr int nthreads_quantize = D/sizeof(int) < warp_size ? D/sizeof(int) : warp_size;
+#pragma unroll
+                for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += nthreads_quantize) {
+                    quantize_q8_1_to_shared<sycl::float2, nthreads_quantize, warp_size>
+                        (Q_f + i0*sizeof(int), scale, tmp_q_i32 + i0, tmp_q_ds + i0/QI8_1);
+                }
+            }
+        }
+
+
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            int    * tmp_q_i32 = (int    *) &KQ[j*D];
+            sycl::float2 * tmp_q_ds  = (sycl::float2 *) (tmp_q_i32 + D / sizeof(int));
+
+#pragma unroll
+            for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += nthreads_KQ) {
+                const int i =
+                    i0 + (nthreads_KQ == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_KQ);
+
+                Q_i32[j][i0/nthreads_KQ] = tmp_q_i32[i];
+                Q_ds[j][i0/nthreads_KQ]  = tmp_q_ds[i/QI8_1];
+            }
+        }
+
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+    } else {
+#ifdef GGML_SYCL_F16
+        const sycl::half2 scale_h2 = sycl::half2(scale, scale);
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            const sycl::float2 * Q_j = (const sycl::float2 *) (Q + j * nb01);
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += nthreads_KQ*cpy_ne) {
+                const int i = i0 + (nthreads_KQ == warp_size ? item_ct1.get_local_id(2) :
+                                                               item_ct1.get_local_id(2) % nthreads_KQ) *
+                                       cpy_ne;
+
+                sycl::float2 tmp[cpy_ne] = {
+                    { 0.0f, 0.0f }
+                };
+                if (ncols == 1 || ic0 + j < int(ne01.z())) {
+                    ggml_sycl_memcpy_1<cpy_nb>(tmp,            &Q_j[i]);
+                    ggml_sycl_memcpy_1<cpy_nb>(tmp + cpy_ne/2, &Q_j[i + cpy_ne/2]);
+                }
+#pragma unroll
+                for (int i1 = 0; i1 < cpy_ne; ++i1) {
+                    Q_reg[j][i0 / nthreads_KQ + i1] = sycl::half2(tmp[i1].x(), tmp[i1].y());
+                }
+            }
+#pragma unroll
+            for (int k = 0; k < (D/2)/nthreads_KQ; ++k) {
+                Q_reg[j][k] *= scale_h2;
+            }
+        }
+#else
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            const sycl::float2 * Q_j = (const sycl::float2 *) (Q + j*nb01);
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += nthreads_KQ*cpy_ne) {
+                const int i = i0 + (nthreads_KQ == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_KQ)*cpy_ne;
+                if (ncols == 1 || ic0 + j < int(ne01.z())) {
+                    ggml_sycl_memcpy_1<cpy_nb>(&Q_reg[j][i0/nthreads_KQ],            &Q_j[i]);
+                    ggml_sycl_memcpy_1<cpy_nb>(&Q_reg[j][i0/nthreads_KQ + cpy_ne/2], &Q_j[i + cpy_ne/2]);
+                }
+            }
+#pragma unroll
+            for (int k = 0; k < (D/2)/nthreads_KQ; ++k) {
+                Q_reg[j][k].x() *= scale;
+                Q_reg[j][k].y() *= scale;
+            }
+        }
+#endif // GGML_SYCL_F16
+    }
+
+    const int k_VKQ_max = KV_max ? KV_max[sequence * item_ct1.get_group_range(2) + item_ct1.get_group(2)] : ne11;
+    K += item_ct1.get_group(1) * nthreads * nb11;
+    V += item_ct1.get_group(1) * nthreads * nb21;
+    maskh += item_ct1.get_group(1) * nthreads;
+    for (int k_VKQ_0 = item_ct1.get_group(1) * nthreads; k_VKQ_0 < k_VKQ_max;
+         k_VKQ_0 += item_ct1.get_group_range(1) * nthreads,
+             // Increment pointers after each loop:
+         K += item_ct1.get_group_range(1) * nthreads * nb11, V += item_ct1.get_group_range(1) * nthreads * nb21,
+             maskh += item_ct1.get_group_range(1) * nthreads) {
+        // Calculate KQ tile and keep track of new maximum KQ values:
+        float KQ_reg[ncols]={}; // KQ in registers.
+        float KQ_max_new[ncols]={};
+
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            KQ_max_new[j] = KQ_max[j];
+        }
+
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < nthreads_KQ; ++i_KQ_0) {
+            const int i_KQ = item_ct1.get_local_id(1) * warp_size +
+                             (nthreads_KQ == warp_size ? 0 : (item_ct1.get_local_id(2) & ~(nthreads_KQ - 1))) + i_KQ_0;
+
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                float sum = vec_dot_KQ(K + i_KQ*nb11, Q_reg[j], Q_i32[j], Q_ds[j]);
+                sum = warp_reduce_sum<nthreads_KQ>(sum);
+
+                if (use_logit_softcap) {
+                    sum = logit_softcap * sycl::tanh(sum);
+                }
+                if (mask) {
+                    sum += slope * sycl::vec<sycl::half, 1>(maskh[j * ne11 + i_KQ])
+                                       .convert<float, sycl::rounding_mode::automatic>()[0];
+                }
+
+                KQ_max_new[j] = sycl::fmax((float) KQ_max_new[j], sum);
+
+                if (int(nthreads_KQ == warp_size ? item_ct1.get_local_id(2)
+                                                 : item_ct1.get_local_id(2) %
+                                                       nthreads_KQ) == i_KQ_0) {
+                  KQ_reg[j] = sum;
+                }
+            }
+        }
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+            for (int offset = nthreads_KQ; offset < warp_size; offset <<= 1) {
+               KQ_max_new[j] = sycl::fmax(
+                  (float)KQ_max_new[j],
+                  (float)dpct::permute_sub_group_by_xor(
+                      sycl::ext::oneapi::this_work_item::get_sub_group(),
+                      KQ_max_new[j],
+                      offset,
+                      warp_size));
+            }
+            const float KQ_max_scale = sycl::native::exp((float) (KQ_max[j] - KQ_max_new[j]));
+            KQ_max[j] = KQ_max_new[j];
+
+            KQ_reg[j]            = sycl::native::exp((float) (KQ_reg[j] - KQ_max[j]));
+            KQ_sum[j] = KQ_sum[j]*KQ_max_scale + KQ_reg[j];
+            KQ[j*nthreads + tid] = KQ_reg[j];
+
+#ifdef GGML_SYCL_F16
+            const sycl::half2 KQ_max_scale_h2 = sycl::half2(KQ_max_scale, KQ_max_scale);
+#pragma unroll
+            for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
+                VKQ[j][i_VKQ_0/nthreads_V] *= KQ_max_scale_h2;
+            }
+#else
+#pragma unroll
+            for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
+                VKQ[j][i_VKQ_0/nthreads_V].x() *= KQ_max_scale;
+                VKQ[j][i_VKQ_0/nthreads_V].y() *= KQ_max_scale;
+            }
+#endif // GGML_SYCL_F16
+        }
+
+        sycl::group_barrier(sycl::ext::oneapi::this_work_item::get_sub_group());
+
+#pragma unroll
+        for (int k0 = 0; k0 < warp_size; k0 += V_cols_per_iter) {
+            const int k = item_ct1.get_local_id(1) * warp_size + k0 +
+                          (nthreads_V == warp_size ? 0 : item_ct1.get_local_id(2) / nthreads_V);
+
+#ifdef GGML_SYCL_F16
+            sycl::half2 KQ_k[ncols];
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                KQ_k[j] = sycl::half2(KQ[j * nthreads + k]);
+            }
+#pragma unroll
+            for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) {
+                sycl::half2 tmp[V_rows_per_thread / 2];
+                dequantize_V(V + k * nb21, tmp,
+                             2 * i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) :
+                                                                      item_ct1.get_local_id(2) % nthreads_V) *
+                                               V_rows_per_thread);
+#pragma unroll
+                for (int i_VKQ_1 = 0; i_VKQ_1 < V_rows_per_thread/2; ++i_VKQ_1) {
+#pragma unroll
+                    for (int j = 0; j < ncols; ++j) {
+                        VKQ[j][i_VKQ_0/nthreads_V + i_VKQ_1] += tmp[i_VKQ_1]*KQ_k[j];
+                    }
+                }
+            }
+#else
+            float KQ_k[ncols];
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                KQ_k[j] = KQ[j*nthreads + k];
+            }
+#pragma unroll
+            for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) {
+                sycl::float2 tmp[V_rows_per_thread/2];
+                dequantize_V(V + k*nb21, tmp,
+                    2*i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_V)*V_rows_per_thread);
+#pragma unroll
+                for (int i_VKQ_1 = 0; i_VKQ_1 < V_rows_per_thread/2; ++i_VKQ_1) {
+#pragma unroll
+                    for (int j = 0; j < ncols; ++j) {
+                        VKQ[j][i_VKQ_0/nthreads_V + i_VKQ_1].x() += tmp[i_VKQ_1].x()*KQ_k[j];
+                        VKQ[j][i_VKQ_0/nthreads_V + i_VKQ_1].y() += tmp[i_VKQ_1].y()*KQ_k[j];
+                    }
+                }
+            }
+#endif // GGML_SYCL_F16
+        }
+    }
+
+    if (sinks && item_ct1.get_group(1) == 0) {
+        const float sink = ((const float *) sinks)[head];
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + item_ct1.get_local_id(1);
+
+            if (j0 + nwarps > ncols && j >= ncols) {
+                break;
+            }
+            const float kqmax_new_j  = sycl::fmax(sink, (float) KQ_max[j]);
+            const float KQ_max_scale = sycl::native::exp((float) (KQ_max[j] - kqmax_new_j));
+            KQ_max[j] = kqmax_new_j;
+
+            KQ_sum[j] = KQ_sum[j] * KQ_max_scale +
+                        (item_ct1.get_local_id(2) == 0 ? sycl::native::exp((float) (sink - KQ_max[j])) : 0.0f);
+#ifdef GGML_SYCL_F16
+            const sycl::half2 KQ_max_scale_h2 = sycl::half2(KQ_max_scale, KQ_max_scale);
+#pragma unroll
+            for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
+                VKQ[j][i_VKQ_0/nthreads_V] *= KQ_max_scale_h2;
+            }
+#else
+#pragma unroll
+            for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
+                VKQ[j][i_VKQ_0/nthreads_V].x() *= KQ_max_scale;
+                VKQ[j][i_VKQ_0/nthreads_V].y() *= KQ_max_scale;
+            }
+#endif // GGML_SYCL_F16
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (item_ct1.get_local_id(1) == 0) {
+            KQ_max_shared[j*warp_size+item_ct1.get_local_id(2)] = -FLT_MAX / 2.0f;
+            KQ_sum_shared[j*warp_size+item_ct1.get_local_id(2)] = 0.0f;
+        }
+    }
+
+    item_ct1.barrier(sycl::access::fence_space::local_space);
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (item_ct1.get_local_id(2) == 0) {
+            KQ_max_shared[j*warp_size+item_ct1.get_local_id(1)] = KQ_max[j];
+        }
+    }
+
+
+    item_ct1.barrier(sycl::access::fence_space::local_space);
+
+#pragma unroll
+    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
+        if (ncols > 1 && ic0 + j_VKQ >= int(ne01.z())) {
+            break;
+        }
+
+        float kqmax_new         = KQ_max_shared[j_VKQ*warp_size+item_ct1.get_local_id(2)];
+        kqmax_new = warp_reduce_max<warp_size>(kqmax_new);
+        const float kqmax_scale = sycl::native::exp((float) (KQ_max[j_VKQ] - kqmax_new));
+        KQ_max[j_VKQ] = kqmax_new;
+
+#ifdef GGML_SYCL_F16
+        sycl::half2 * VKQ_tmp = (sycl::half2 *) KQ + item_ct1.get_local_id(1) * (V_cols_per_iter * D / 2) +
+                                (nthreads_V == warp_size ? 0 : item_ct1.get_local_id(2) / nthreads_V) * (D / 2);
+
+        const sycl::half2 kqmax_scale_h2 = sycl::half2(kqmax_scale, kqmax_scale);
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
+            VKQ[j_VKQ][i_VKQ_0/nthreads_V] *= kqmax_scale_h2;
+        }
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) {
+            const int i_VKQ =
+                i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_V) *
+                              (V_rows_per_thread / 2);
+
+            ggml_sycl_memcpy_1<V_rows_per_thread * sizeof(sycl::half)>(VKQ_tmp + i_VKQ,
+                                                                       &VKQ[j_VKQ][i_VKQ_0 / nthreads_V]);
+        }
+#else
+        sycl::float2 * VKQ_tmp = (sycl::float2 *) KQ + item_ct1.get_local_id(1)*(V_cols_per_iter*D/2)
+            + (nthreads_V == warp_size ? 0 : item_ct1.get_local_id(2) / nthreads_V)*(D/2);
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
+            VKQ[j_VKQ][i_VKQ_0/nthreads_V].x() *= kqmax_scale;
+            VKQ[j_VKQ][i_VKQ_0/nthreads_V].y() *= kqmax_scale;
+        }
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) {
+            const int i_VKQ = i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_V)*(V_rows_per_thread/2);
+
+            ggml_sycl_memcpy_1<V_rows_per_thread/2*sizeof(float)>(VKQ_tmp + i_VKQ,                       &VKQ[j_VKQ][i_VKQ_0/nthreads_V]);
+            ggml_sycl_memcpy_1<V_rows_per_thread/2*sizeof(float)>(VKQ_tmp + i_VKQ + V_rows_per_thread/4, &VKQ[j_VKQ][i_VKQ_0/nthreads_V + V_rows_per_thread/4]);
+        }
+#endif // GGML_SYCL_F16
+
+        KQ_sum[j_VKQ] *= kqmax_scale;
+        KQ_sum[j_VKQ] = warp_reduce_sum<warp_size>(KQ_sum[j_VKQ]);
+        if (item_ct1.get_local_id(2) == 0) {
+            KQ_sum_shared[j_VKQ*warp_size+item_ct1.get_local_id(1)] = KQ_sum[j_VKQ];
+        }
+
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+
+        if (nthreads <= D || tid < D) {
+            KQ_sum[j_VKQ] = KQ_sum_shared[j_VKQ*warp_size+item_ct1.get_local_id(2)];
+            KQ_sum[j_VKQ] = warp_reduce_sum<warp_size>(KQ_sum[j_VKQ]);
+
+#pragma unroll
+            for (int i0 = 0; i0 < D; i0 += nthreads) {
+                float dst_val = 0;
+#pragma unroll
+                for (int w = 0; w < nwarps; ++w) {
+#pragma unroll
+                    for (int v = 0; v < V_cols_per_iter; ++v) {
+                        dst_val += float(KQ[w*V_cols_per_iter*D + v*D + i0 + tid]);
+                    }
+                }
+                if (item_ct1.get_group_range(1) == 1) {
+                    dst_val /= KQ_sum[j_VKQ];
+                }
+                dst[(((sequence * int(ne01.z()) + ic0 + j_VKQ) * ne02 + head) * item_ct1.get_group_range(1) +
+                     item_ct1.get_group(1)) *
+                        D +
+                    i0 + tid] = dst_val;
+            }
+        }
+
+        if (j_VKQ < ncols-1) {
+            item_ct1.barrier(sycl::access::fence_space::local_space);
+        }
+
+    }
+
+    if (item_ct1.get_group_range(1) != 1 && tid < ncols && (ncols == 1 || ic0 + tid < int(ne01.z()))) {
+        dst_meta[((sequence * int(ne01.z()) + ic0 + tid) * ne02 + head) * item_ct1.get_group_range(1) +
+                 item_ct1.get_group(1)] = make_float2(KQ_max[tid], KQ_sum[tid]);
+    }
+#else
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
+
+#endif // SYCL_FLASH_ATTN
+}
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif // __clang__
+
+
+template <int D, int cols_per_block, int type_K, int type_V, bool use_logit_softcap>
+void ggml_sycl_flash_attn_ext_vec_case_impl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+
+    const int warp_size = WARP_16_SIZE; //better performance than WARP_32_SIZE
+
+    const int cc = ggml_sycl_info().devices[ggml_sycl_get_device()].cc;
+
+    const int nthreads = ggml_sycl_fattn_vec_get_nthreads_host(cc);
+    const int nwarps   = nthreads / warp_size;
+
+    const bool need_f16_K = type_K == GGML_TYPE_F16;
+    const bool need_f16_V = type_V == GGML_TYPE_F16;
+    constexpr size_t nbytes_shared = 0;
+
+    launch_fattn<D, cols_per_block, 1,
+                 flash_attn_ext_vec<D, cols_per_block, type_K, type_V,
+                                    use_logit_softcap, warp_size>, warp_size>(
+        ctx, dst, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false);
+}
+
+template <int D, int type_K, int type_V>
+void ggml_sycl_flash_attn_ext_vec_case(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+
+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
+    if (Q->ne[1] == 1) {
+        constexpr int cols_per_block = 1;
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_sycl_flash_attn_ext_vec_case_impl<D, cols_per_block, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_sycl_flash_attn_ext_vec_case_impl<D, cols_per_block, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 2;
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        ggml_sycl_flash_attn_ext_vec_case_impl<D, cols_per_block, type_K, type_V, use_logit_softcap>(ctx, dst);
+    } else {
+        constexpr bool use_logit_softcap = true;
+        ggml_sycl_flash_attn_ext_vec_case_impl<D, cols_per_block, type_K, type_V, use_logit_softcap>(ctx, dst);
+    }
+}
+
+#define DECL_FATTN_VEC_CASE(D, type_K, type_V)                              \
+    template void ggml_sycl_flash_attn_ext_vec_case                         \
+    <D, type_K, type_V>(ggml_backend_sycl_context & ctx, ggml_tensor * dst) \
+
+#define EXTERN_DECL_FATTN_VEC_CASES(D, type_K)             \
+    extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_F16);  \
+    extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q4_0); \
+    extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q4_1); \
+    extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q5_0); \
+    extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q5_1); \
+    extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q8_0); \
+
+EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_F16)
+EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q4_0)
+EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q4_1)
+EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q5_0)
+EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q5_1)
+EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q8_0)
+
+EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_F16)
+EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q4_0)
+EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q4_1)
+EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q5_0)
+EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q5_1)
+EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q8_0)
+
+EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_F16)
+EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q4_0)
+EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q4_1)
+EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q5_0)
+EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q5_1)
+EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q8_0)
+
+#endif // GGML_SYCL_FATTN_VEC_HPP

ggml/src/ggml-sycl/fattn.cpp

@@ -0,0 +1,225 @@
+//
+// MIT license
+// Copyright (C) 2025 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+
+#include <sycl/sycl.hpp>
+#include "dpct/helper.hpp"
+#include "common.hpp"
+#include "fattn-common.hpp"
+#include "fattn-tile.hpp"
+#include "fattn-vec.hpp"
+#include "fattn.hpp"
+
+
+#define FATTN_VEC_CASE(D, type_K, type_V)                                                                        \
+    {                                                                                                            \
+        const bool type_K_okay = K->type == (type_K) || (K->type == GGML_TYPE_F32 && (type_K) == GGML_TYPE_F16); \
+        const bool type_V_okay = V->type == (type_V) || (V->type == GGML_TYPE_F32 && (type_V) == GGML_TYPE_F16); \
+        if (Q->ne[0] == (D) && type_K_okay && type_V_okay) {                                                     \
+            ggml_sycl_flash_attn_ext_vec_case<D, type_K, type_V>(ctx, dst);                                      \
+            return;                                                                                              \
+        }                                                                                                        \
+    }                                                                    \
+
+#define FATTN_VEC_CASES_ALL_D(type_K, type_V) \
+    FATTN_VEC_CASE( 64, type_K, type_V)       \
+    FATTN_VEC_CASE(128, type_K, type_V)       \
+    FATTN_VEC_CASE(256, type_K, type_V)       \
+
+static void ggml_sycl_flash_attn_ext_vec(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * Q = dst->src[0];
+    ggml_tensor * K = dst->src[1];
+    ggml_tensor * V = dst->src[2];
+
+#ifdef GGML_SYCL_FA_ALL_QUANTS
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_F16)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_F16)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_F16)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_F16)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_F16)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_F16)
+
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_Q4_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
+
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_Q4_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
+
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_Q5_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
+
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_Q5_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
+
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_Q8_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
+#else
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16,  GGML_TYPE_F16)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
+#endif // GGML_SYCL_FA_ALL_QUANTS
+
+    GGML_ABORT("Not match KV type in vec");
+}
+
+// Best FlashAttention kernel for a specific GPU:
+enum best_fattn_kernel {
+    BEST_FATTN_KERNEL_NONE     =   0,
+    BEST_FATTN_KERNEL_VEC      = 100,
+    BEST_FATTN_KERNEL_TILE     = 200,
+};
+
+static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const ggml_tensor * dst) {
+    GGML_UNUSED(device);
+#ifndef SYCL_FLASH_ATTN
+    GGML_UNUSED(dst);
+    return BEST_FATTN_KERNEL_NONE;
+#endif// SYCL_FLASH_ATTN
+
+    if(!g_ggml_sycl_enable_flash_attention) return BEST_FATTN_KERNEL_NONE;
+
+    const ggml_tensor * KQV   = dst;
+    const ggml_tensor * Q     = dst->src[0];
+    const ggml_tensor * K     = dst->src[1];
+    const ggml_tensor * V     = dst->src[2];
+    const ggml_tensor * mask  = dst->src[3];
+
+    const int gqa_ratio = Q->ne[2] / K->ne[2];
+    GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
+
+    float max_bias = 0.0f;
+    memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
+
+    bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    for (const ggml_tensor * t : {Q, K, V, mask}) {
+        if (t == nullptr || ggml_is_quantized(t->type)) {
+            continue;
+        }
+        for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
+            if (t->nb[i] % 16 != 0) {
+                gqa_opt_applies = false;
+                break;
+            }
+        }
+    }
+
+    switch (K->ne[0]) {
+        case  40:
+        case  64:
+        case  72:
+        case  80:
+        case  96:
+        case 128:
+        case 112:
+        case 256:
+            if (V->ne[0] != K->ne[0]) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
+        case 576:
+            if (V->ne[0] != 512) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            if (!gqa_opt_applies) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
+        default:
+            return BEST_FATTN_KERNEL_NONE;
+    }
+
+#ifndef GGML_SYCL_FA_ALL_QUANTS
+    if (K->type != V->type) {
+        return BEST_FATTN_KERNEL_NONE;
+    }
+#endif // GGML_SYCL_FA_ALL_QUANTS
+
+    switch (K->type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            break;
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+#ifndef GGML_SYCL_FA_ALL_QUANTS
+            return BEST_FATTN_KERNEL_NONE;
+#endif // GGML_SYCL_FA_ALL_QUANTS
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            break;
+        default:
+            return BEST_FATTN_KERNEL_NONE;
+    }
+
+    if (mask && mask->ne[2] != 1) {
+        return BEST_FATTN_KERNEL_NONE;
+    }
+
+    // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes:
+    const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0;
+
+    // Todo: Use the XMX kernel if possible:
+
+    // If there are no tensor cores available, use the generic tile kernel:
+    if (can_use_vector_kernel) {
+        if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {
+            if (Q->ne[1] == 1) {
+                if (!gqa_opt_applies) {
+                    return BEST_FATTN_KERNEL_VEC;
+                }
+            }
+        } else {
+            if (Q->ne[1] <= 2) {
+                return BEST_FATTN_KERNEL_VEC;
+            }
+        }
+    }
+    return BEST_FATTN_KERNEL_TILE;
+}
+
+void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_set_device(ctx.device);
+    switch (ggml_sycl_get_best_fattn_kernel(ggml_sycl_get_device(), dst)) {
+        case BEST_FATTN_KERNEL_NONE:
+            GGML_ABORT("Not support Flash-Attention");
+        case BEST_FATTN_KERNEL_TILE:
+            ggml_sycl_flash_attn_ext_tile(ctx, dst);
+            break;
+        case BEST_FATTN_KERNEL_VEC:
+            ggml_sycl_flash_attn_ext_vec(ctx, dst);
+            break;
+    }
+}
+
+bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst) {
+    return ggml_sycl_get_best_fattn_kernel(device, dst) != BEST_FATTN_KERNEL_NONE;
+}

ggml/src/ggml-sycl/fattn.hpp

@@ -0,0 +1,22 @@
+//
+// MIT license
+// Copyright (C) 2025 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_FATTN_HPP
+#define GGML_SYCL_FATTN_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst);
+
+#endif // GGML_SYCL_FATTN_HPP

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

@@ -62,6 +62,8 @@ int g_ggml_sycl_disable_graph = 0;
 int g_ggml_sycl_disable_dnn = 0;
 int g_ggml_sycl_prioritize_dmmv = 0;
 int g_ggml_sycl_use_async_mem_op = 0;
+int g_ggml_sycl_enable_flash_attention = 1;
+
 
 static ggml_sycl_device_info ggml_sycl_init() {
     ggml_sycl_device_info info = {};
@@ -94,11 +96,12 @@ static ggml_sycl_device_info ggml_sycl_init() {
 
         info.devices[i].cc =
             100 * prop.get_major_version() + 10 * prop.get_minor_version();
-        info.devices[i].nsm = prop.get_max_compute_units();
+        info.devices[i].nsm = prop.get_max_compute_units() / 16; //16: Number of Xe Cores
         info.devices[i].opt_feature.reorder = device.ext_oneapi_architecture_is(syclex::arch_category::intel_gpu);
         info.devices[i].smpbo = prop.get_local_mem_size();
-
         info.max_work_group_sizes[i] = prop.get_max_work_group_size();
+        info.devices[i].max_wg_per_cu = info.max_work_group_sizes[i] / prop.get_max_compute_units();
+
     }
 
     for (int id = 0; id < info.device_count; ++id) {
@@ -211,7 +214,37 @@ static void ggml_check_sycl() try {
         g_ggml_sycl_disable_graph = get_sycl_env("GGML_SYCL_DISABLE_GRAPH", 1);
         g_ggml_sycl_disable_dnn = get_sycl_env("GGML_SYCL_DISABLE_DNN", 0);
         g_ggml_sycl_prioritize_dmmv = get_sycl_env("GGML_SYCL_PRIORITIZE_DMMV", 0);
+
+#ifdef SYCL_FLASH_ATTN
+        g_ggml_sycl_enable_flash_attention = get_sycl_env("GGML_SYCL_ENABLE_FLASH_ATTN", 1);
+#else
+        g_ggml_sycl_enable_flash_attention = 0;
+#endif
+
         GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n");
+
+        GGML_LOG_INFO("Build with Macros:\n");
+#if defined(GGML_SYCL_FORCE_MMQ)
+        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: no\n");
+#endif
+#if defined(GGML_SYCL_F16)
+        GGML_LOG_INFO("  GGML_SYCL_F16: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_F16: no\n");
+#endif
+#if defined(GGML_SYCL_GRAPH)
+        GGML_LOG_INFO("  GGML_SYCL_GRAPH: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_GRAPH: no\n");
+#endif
+#if defined(GGML_SYCL_DNNL)
+        GGML_LOG_INFO("  GGML_SYCL_DNNL: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_DNNL: no\n");
+#endif
+
         GGML_LOG_INFO("Running with Environment Variables:\n");
         GGML_LOG_INFO("  GGML_SYCL_DEBUG: %d\n", g_ggml_sycl_debug);
         GGML_LOG_INFO("  GGML_SYCL_DISABLE_OPT: %d\n", g_ggml_sycl_disable_optimize);
@@ -226,16 +259,12 @@ static void ggml_check_sycl() try {
         GGML_LOG_INFO("  GGML_SYCL_DISABLE_DNN: DNN disabled by compile flag\n");
 #endif
         GGML_LOG_INFO("  GGML_SYCL_PRIORITIZE_DMMV: %d\n", g_ggml_sycl_prioritize_dmmv);
-        GGML_LOG_INFO("Build with Macros:\n");
-#if defined(GGML_SYCL_FORCE_MMQ)
-        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: yes\n");
+
+#ifdef SYCL_FLASH_ATTN
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_FLASH_ATTN: %d\n", g_ggml_sycl_enable_flash_attention);
 #else
-        GGML_LOG_INFO("  GGML_SYCL_FORCE_MMQ: no\n");
-#endif
-#if defined(GGML_SYCL_F16)
-        GGML_LOG_INFO("  GGML_SYCL_F16: yes\n");
-#else
-        GGML_LOG_INFO("  GGML_SYCL_F16: no\n");
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_FLASH_ATTN: %d disabled by compile flag\n",
+            g_ggml_sycl_enable_flash_attention);
 #endif
 
 /* NOT REMOVE, keep it for next optimize for XMX.
@@ -3012,7 +3041,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
 
         }
 #if GGML_SYCL_DNNL
-        // oneDNN handles strided data and does not need overhead of get_to_fp16_nc_sycl
+        // oneDNN handles strided data and does not need overhead of ggml_get_to_fp16_nc_sycl
         const int64_t ne_src1 = src1->nb[last_str] * src1->ne[last_dim] / type_size_src1;
         src1_f16_alloc.alloc(ne_src1);
         const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type, dst);
@@ -3021,7 +3050,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
 # else
         const int64_t ne_src1 = ggml_nelements(src1);
         src1_f16_alloc.alloc(ne_src1);
-        const to_fp16_nc_sycl_t to_fp16_nc_sycl = get_to_fp16_nc_sycl(src1->type);
+        const to_fp16_nc_sycl_t to_fp16_nc_sycl = ggml_get_to_fp16_nc_sycl(src1->type);
         GGML_ASSERT(to_fp16_nc_sycl != nullptr);
         to_fp16_nc_sycl(src1_f16, src1_f16_alloc.get(), ne10, ne11, ne12, ne13, s11, s12, s13, queue);
 #endif
@@ -4158,6 +4187,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_ARANGE:
             ggml_sycl_arange(ctx, dst);
             break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            ggml_sycl_flash_attn_ext(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -4862,6 +4894,8 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             return op->type == GGML_TYPE_F32;
         case GGML_OP_ARANGE:
             return op->type == GGML_TYPE_F32;
+        case GGML_OP_FLASH_ATTN_EXT:
+            return ggml_sycl_flash_attn_ext_supported(device, op);
         default:
             return false;
     }

ggml/src/ggml-sycl/presets.hpp

@@ -73,4 +73,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA
 #define MUL_MAT_SRC1_COL_STRIDE 128
 
 #define QK_WARP_SIZE 32
+#define WARP_32_SIZE 32
+#define WARP_16_SIZE 16
+
 #endif // GGML_SYCL_PRESETS_HPP

ggml/src/ggml-sycl/softmax.cpp

@@ -102,7 +102,7 @@ static void soft_max_f32(const float *         x,
         max_val   = sycl::max(max_val, val);
     }
     // find the max value in the block
-    max_val = warp_reduce_max(max_val);
+    max_val = warp_reduce_max<WARP_SIZE>(max_val);
 
     if (block_size > WARP_SIZE) {
         if (warp_id == 0) {
@@ -116,7 +116,7 @@ static void soft_max_f32(const float *         x,
         item_ct1.barrier();
 
         max_val = buf_iw[lane_id];
-        max_val = warp_reduce_max(max_val);
+        max_val = warp_reduce_max<WARP_SIZE>(max_val);
     }
     float tmp = 0.0f; // partial sum
 
@@ -133,7 +133,7 @@ static void soft_max_f32(const float *         x,
         vals[col] = val;
     }
     // find the sum of exps in the block
-    tmp = warp_reduce_sum(tmp);
+    tmp = warp_reduce_sum<WARP_SIZE>(tmp);
     if (block_size > WARP_SIZE) {
         item_ct1.barrier();
         if (warp_id == 0) {
@@ -153,7 +153,7 @@ static void soft_max_f32(const float *         x,
         for (size_t i = 1; i < nreduce; i += 1) {
             tmp += buf_iw[lane_id + i * WARP_SIZE];
         }
-        tmp = warp_reduce_sum(tmp);
+        tmp = warp_reduce_sum<WARP_SIZE>(tmp);
     }
     if (sinks) {
         tmp += sycl::native::exp(sinks[i02] - max_val);
@@ -191,7 +191,7 @@ static void soft_max_back_f32(const float *grad, const float *dstf, float *dst,
         dgf_dot += dstf[col]*grad[col];
     }
 
-    dgf_dot = warp_reduce_sum(dgf_dot);
+    dgf_dot = warp_reduce_sum<WARP_SIZE>(dgf_dot);
 
     for (int col = tid; col < ncols; col += WARP_SIZE) {
         dst[col] = scale * (grad[col] - dgf_dot) * dstf[col];

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq112-dv112.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(112, 112);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq128-dv128.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(128, 128);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq256-dv256.cpp

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

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq40-dv40.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(40, 40);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq576-dv512.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(576, 512);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq64-dv64.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(64, 64);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq72-dv72.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(72, 72);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq80-dv80.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(80, 80);

ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq96-dv96.cpp

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.hpp"
+
+DECL_FATTN_TILE_CASE(96, 96);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-f16.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q4_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q4_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q5_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q5_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q8_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q8_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-f16.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_F16);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q8_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-f16.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_F16);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q8_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-f16.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_F16);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q8_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-f16.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_F16);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_0.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);

ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_1.cpp

@@ -0,0 +1,7 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec.hpp"
+
+DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
+DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);