context : fix logits size overflow for huge batches

README.md

@@ -17,7 +17,6 @@ LLM inference in C/C++
 
 ## Hot topics
 
-- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
 - Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)
 - Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
 - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode

common/arg.cpp

@@ -2947,12 +2947,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
         "- none: leaves thoughts unparsed in `message.content`\n"
         "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
-        "(default: auto)",
+        "(default: deepseek)",
         [](common_params & params, const std::string & value) {
             /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
             else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
             else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
-            else if (value == "auto") {     params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; }
             else { throw std::invalid_argument("invalid value"); }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));

common/chat-parser.cpp

@@ -55,15 +55,7 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
 bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
     std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
     std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
-    std::string arguments = "";
-    if (tool_call.contains("arguments")) {
-        if (tool_call.at("arguments").is_object()) {
-            arguments = tool_call.at("arguments").dump();
-        } else {
-            arguments = tool_call.at("arguments");
-        }
-    }
-
+    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
     return add_tool_call(name, id, arguments);
 }
 

common/chat.cpp

@@ -126,8 +126,6 @@ std::vector<common_chat_msg_diff> common_chat_msg_diff::compute_diffs(const comm
 typedef minja::chat_template common_chat_template;
 
 struct common_chat_templates {
-    bool add_bos;
-    bool add_eos;
     bool has_explicit_template; // Model had builtin template or template overridde was specified.
     std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
     std::unique_ptr<common_chat_template> template_tool_use;
@@ -145,8 +143,6 @@ struct templates_params {
     bool enable_thinking = true;
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
     json extra_context;
-    bool add_bos;
-    bool add_eos;
 };
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
@@ -449,8 +445,6 @@ std::string common_chat_format_single(
 
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
-    inputs.add_bos = tmpls->add_bos;
-    inputs.add_eos = tmpls->add_eos;
 
     std::string fmt_past_msg;
     if (!past_msg.empty()) {
@@ -475,8 +469,6 @@ std::string common_chat_format_single(
 std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) {
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
-    inputs.add_bos = tmpls->add_bos;
-    inputs.add_eos = tmpls->add_eos;
     auto add_simple_msg = [&](auto role, auto content) {
         common_chat_msg msg;
         msg.role = role;
@@ -554,8 +546,6 @@ common_chat_templates_ptr common_chat_templates_init(
     }
     std::string token_bos = bos_token_override;
     std::string token_eos = eos_token_override;
-    bool add_bos = false;
-    bool add_eos = false;
     if (model) {
         const auto * vocab = llama_model_get_vocab(model);
         const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
@@ -570,13 +560,9 @@ common_chat_templates_ptr common_chat_templates_init(
         };
         token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
         token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
-        add_bos = llama_vocab_get_add_bos(vocab);
-        add_eos = llama_vocab_get_add_eos(vocab);
     }
     common_chat_templates_ptr tmpls(new common_chat_templates());
     tmpls->has_explicit_template = has_explicit_template;
-    tmpls->add_bos = add_bos;
-    tmpls->add_eos = add_eos;
     try {
         tmpls->template_default = std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos);
     } catch (const std::exception & e) {
@@ -606,8 +592,6 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
         case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
         case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
-        case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
-        case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -616,10 +600,8 @@ const char * common_chat_format_name(common_chat_format format) {
 const char * common_reasoning_format_name(common_reasoning_format format) {
     switch (format) {
         case COMMON_REASONING_FORMAT_NONE:     return "none";
-        case COMMON_REASONING_FORMAT_AUTO:     return "auto";
         case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
         case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
-        case COMMON_REASONING_FORMAT_GRANITE: return "granite";
         default:
             throw std::runtime_error("Unknown reasoning format");
     }
@@ -766,10 +748,10 @@ static std::string apply(
     // instead of using `chat_template_options.use_bos_token = false`, since these tokens
     // may be needed inside the template / between messages too.
     auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
-    if (inputs.add_bos && string_starts_with(result, tmpl.bos_token())) {
+    if (string_starts_with(result, tmpl.bos_token())) {
         result = result.substr(tmpl.bos_token().size());
     }
-    if (inputs.add_eos && string_ends_with(result, tmpl.eos_token())) {
+    if (string_ends_with(result, tmpl.eos_token())) {
         result = result.substr(0, result.size() - tmpl.eos_token().size());
     }
     return result;
@@ -1307,26 +1289,6 @@ static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
         tool_calls_end);
 }
 
-static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-    auto prompt = apply(tmpl, inputs);
-
-    data.prompt = prompt;
-    data.format = COMMON_CHAT_FORMAT_GPT_OSS;
-
-    // TODO: support tool calls in GPT-OSS?
-
-    return data;
-}
-static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
-    // TODO @ngxson : this won't work with --special enabled, we should fix that
-    builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|start|>assistant<|channel|>final<|message|>");
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-}
-
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
     LOG_DBG("%s\n", __func__);
     common_chat_params data;
@@ -1736,124 +1698,6 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
     builder.add_content(builder.consume_rest());
 }
 
-static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-
-    // Pass thinking context for Granite template
-    json additional_context = {
-        {"thinking", inputs.enable_thinking},
-    };
-
-    data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context);
-    data.format = COMMON_CHAT_FORMAT_GRANITE;
-
-    if (string_ends_with(data.prompt, "<think>\n") || string_ends_with(data.prompt, "<think>")) {
-        if (!inputs.enable_thinking) {
-            data.prompt += "</think>";
-        } else {
-            data.thinking_forced_open = true;
-        }
-    }
-
-    if (!inputs.tools.is_null()) {
-        // Granite uses <|tool_call|> followed by JSON list
-        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            std::vector<std::string> tool_rules;
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                std::string name = function.at("name");
-                auto parameters = function.at("parameters");
-                builder.resolve_refs(parameters);
-                tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name +
-"-args", {
-                    {"type", "object"},
-                    {"properties", {
-                        {"name", {{"const", name}}},
-                        {"arguments", parameters},
-                    }},
-                    {"required", json::array({"name", "arguments"})},
-                })));
-            });
-
-            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
-            auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\"");
-
-            if (data.thinking_forced_open) {
-                builder.add_rule("root", "\"</think>\" space \"<response>\" space [^<]* \"</response>\" space \"<|tool_call|>\" space " + tool_list);
-            } else {
-                builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list);
-            }
-
-            data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
-                "<|tool_call|>"
-            });
-
-            data.preserved_tokens = {
-                "<think>",
-                "</think>",
-                "<response>",
-                "</response>",
-                "<|tool_call|>",
-            };
-        });
-    } else {
-        // Handle thinking tags for non-tool responses
-        if (data.thinking_forced_open && inputs.enable_thinking) {
-            data.grammar_lazy = false;
-            data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-                builder.add_rule("root", "\"</think>\" space \"<response>\" space .* \"</response>\" space");
-            });
-            data.preserved_tokens = {
-                "<think>",
-                "</think>",
-                "<response>",
-                "</response>",
-            };
-        }
-    }
-
-    return data;
-}
-
-static void common_chat_parse_granite(common_chat_msg_parser & builder) {
-    // Parse thinking tags
-    builder.try_parse_reasoning("<think>", "</think>");
-
-    // Parse response tags using regex
-    static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
-    if (auto res = builder.try_find_regex(response_regex)) {
-        // Extract the content between the tags (capture group 1)
-        auto content = builder.str(res->groups[1]);
-        builder.add_content(content);
-        builder.move_to(res->groups[0].end);
-    }
-
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-
-    // Look for tool calls
-    static const common_regex tool_call_regex(regex_escape("<|tool_call|>"));
-    if (auto res = builder.try_find_regex(tool_call_regex)) {
-        builder.move_to(res->groups[0].end);
-
-        // Expect JSON array of tool calls
-        auto tool_calls_data = builder.consume_json();
-        if (tool_calls_data.json.is_array()) {
-            if (!builder.add_tool_calls(tool_calls_data.json)) {
-                builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
-            }
-        } else {
-            builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
-        }
-    } else {
-        builder.add_content(builder.consume_rest());
-    }
-}
-
 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     data.prompt = apply(tmpl, inputs);
@@ -1887,8 +1731,6 @@ static common_chat_params common_chat_templates_apply_jinja(
     params.enable_thinking = inputs.enable_thinking;
     params.grammar = inputs.grammar;
     params.now = inputs.now;
-    params.add_bos = inputs.add_bos;
-    params.add_eos = inputs.add_eos;
 
     params.extra_context = json::object();
     for (auto el : inputs.chat_template_kwargs) {
@@ -1925,21 +1767,11 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_command_r7b(tmpl, params);
     }
 
-    // Granite (IBM) - detects thinking / tools support
-    if (src.find("elif thinking") != std::string::npos && src.find("<|tool_call|>") != std::string::npos) {
-        return common_chat_params_init_granite(tmpl, params);
-    }
-
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
     if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);
     }
 
-    // GPT-OSS
-    if (src.find("<|channel|>") != std::string::npos && params.json_schema.is_null()) {
-        return common_chat_params_init_gpt_oss(tmpl, params);
-    }
-
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -1990,7 +1822,6 @@ static common_chat_params common_chat_templates_apply_legacy(
     int alloc_size = 0;
     std::vector<llama_chat_message> chat;
     std::vector<std::string> contents;
-
     for (const auto & msg : inputs.messages) {
         auto content = msg.content;
         for (const auto & part : msg.content_parts) {
@@ -2092,12 +1923,6 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_COMMAND_R7B:
             common_chat_parse_command_r7b(builder);
             break;
-        case COMMON_CHAT_FORMAT_GRANITE:
-            common_chat_parse_granite(builder);
-            break;
-        case COMMON_CHAT_FORMAT_GPT_OSS:
-            common_chat_parse_gpt_oss(builder);
-            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }

common/chat.h

@@ -109,8 +109,6 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
     COMMON_CHAT_FORMAT_COMMAND_R7B,
-    COMMON_CHAT_FORMAT_GRANITE,
-    COMMON_CHAT_FORMAT_GPT_OSS,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -129,8 +127,6 @@ struct common_chat_templates_inputs {
     bool enable_thinking = true;
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
     std::map<std::string, std::string> chat_template_kwargs;
-    bool add_bos = false;
-    bool add_eos = false;
 };
 
 struct common_chat_params {

common/common.h

@@ -236,10 +236,8 @@ struct common_params_diffusion {
 
 enum common_reasoning_format {
     COMMON_REASONING_FORMAT_NONE,
-    COMMON_REASONING_FORMAT_AUTO,
     COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
     COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
-    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };
 
 struct common_params {
@@ -396,7 +394,7 @@ struct common_params {
     std::string chat_template = "";                                                                         // NOLINT
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int reasoning_budget = -1;
     bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 

convert_hf_to_gguf.py

@@ -3328,13 +3328,7 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
 @ModelBase.register("InternVisionModel")
 class InternVisionModel(MmprojModel):
     def set_gguf_parameters(self):
-        assert self.hparams_vision is not None
-        if isinstance(self.hparams_vision['image_size'], list):
-            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
-        if isinstance(self.hparams_vision['patch_size'], list):
-            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
         super().set_gguf_parameters()
-
         hparams = self.hparams
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
         self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
@@ -3358,30 +3352,14 @@ class InternVisionModel(MmprojModel):
             return gguf.GGMLQuantizationType.F32
         return False
 
-    def _mapping_interns1_name(self, name):
-        names_map = {
-            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
-            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
-            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
-            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
-            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
-            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
-        }
-        if name in names_map:
-            name = names_map[name]
-        return name
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
-        vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
-        # deal with intern-s1 special case
-        name = self._mapping_interns1_name(name)
-        if any([name.startswith(prefix) for prefix in vision_prefix]):
+        if name.startswith("vision_model") or name.startswith("mlp"):
             # process visual tensors
             # correct name
             if name.startswith("vision_model"):
                 name = "vision_tower." + name
-            if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
+            if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"):
                 name += ".weight"
             # split QKV tensors if needed
             if ".qkv." in name:
@@ -3467,10 +3445,6 @@ class Qwen2MoeModel(TextModel):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # process the experts separately
-        name = name.replace("language_model.", "") # InternVL
-        if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
-            # skip visual tensors
-            return []
         if name.find("experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
@@ -3524,85 +3498,6 @@ class Qwen3Model(Qwen2Model):
 class Qwen3MoeModel(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3MOE
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        hparams = ModelBase.load_hparams(self.dir_model)
-        self.origin_hf_arch = hparams.get('architectures', [None])[0]
-
-    def set_vocab(self):
-        # deal with intern-s1
-        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
-            self._set_vocab_interns1()
-            return
-
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def _set_vocab_interns1(self):
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
-        vocab_size = self.hparams.get("vocab_size", len(vocab))
-        assert max(vocab.values()) < vocab_size
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()
-
-        added_tokens_decoder = tokenizer.added_tokens_decoder
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
-                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
-                    if not added_tokens_decoder[i].normalized:
-                        previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_tokens_map_file = self.dir_model / 'special_tokens_map.json'
-        additional_special_tokens = []
-        if special_tokens_map_file.is_file():
-            with open(special_tokens_map_file, encoding = 'utf-8') as f:
-                additional_special_tokens = json.load(f).get('additional_special_tokens', [])
-        tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json'
-        if tokenizer_cfg_file.is_file():
-            with open(tokenizer_cfg_file, encoding = 'utf-8') as f:
-                added_tokens_decoder = json.load(f).get('added_tokens_decoder', {})
-                token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']}
-                for token in additional_special_tokens:
-                    if token in token2ids_map:
-                        special_vocab._set_special_token(token, token2ids_map[token])
-        special_vocab._set_special_token('eos', 151645)
-        special_vocab._set_special_token("bos", 151643)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
 
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):
@@ -8055,130 +7950,6 @@ class SmolLM3Model(LlamaModel):
             self.gguf_writer.add_chat_template(chat_template)
 
 
-@ModelBase.register("GptOssForCausalLM")
-class GptOssModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GPT_OSS
-
-    def transform_nibble_layout(self, tensor):
-        assert tensor.dtype == torch.uint8
-        assert tensor.shape[-1] == 16
-        # swap nibbles
-        t_lo = tensor & 0x0F
-        t_hi = tensor & 0xF0
-        t_swapped = (t_lo << 4) | (t_hi >> 4)
-        tensor = t_swapped
-        # transform aaaa...bbbb... to abababab...
-        blk_a, blk_b = tensor.chunk(2, dim=-1)
-        # get a_
-        blk_a0 = (blk_a & 0xF0).view(-1, 1)
-        blk_a1 = (blk_a << 4).view(-1, 1)
-        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
-        # get _b
-        blk_b0 = (blk_b >> 4).view(-1, 1)
-        blk_b1 = (blk_b & 0x0F).view(-1, 1)
-        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
-        # swap once more
-        out = blk_a | blk_b
-        out_h = out & 0xF0
-        out_l = out & 0x0F
-        out = (out_h >> 4) | (out_l << 4)
-        return out
-
-    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
-        assert blocks.dtype == torch.uint8
-        assert scales.dtype == torch.uint8
-        scales = scales.unsqueeze(-1)
-        assert len(blocks.shape) == 4
-        assert len(scales.shape) == 4
-        blocks = self.transform_nibble_layout(blocks)
-        new_data = torch.concat((scales, blocks), dim=-1)
-        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
-        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
-        # flatten last dim
-        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
-        new_data = new_data.numpy()
-        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        blocks0: Tensor = torch.zeros(1)
-        blocks1: Tensor = torch.zeros(1)
-        # we assume that tensors are loaded in the correct order
-        for name, data_torch in self.get_tensors():
-            if "mlp.experts.down_proj_blocks" in name:
-                blocks0 = data_torch
-            elif "mlp.experts.down_proj_scales" in name:
-                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
-                self.repack_mxfp4(new_name, blocks0, data_torch)
-            elif "mlp.experts.gate_up_proj_blocks" in name:
-                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
-            elif "mlp.experts.gate_up_proj_scales" in name:
-                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
-                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
-                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
-                self.repack_mxfp4(new_name_gate, blocks0, scales0)
-                self.repack_mxfp4(new_name_up, blocks1, scales1)
-        return []
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        del bid  # unused
-
-        if "sinks" in name:
-            name += ".weight"
-
-        # correct naming for down_proj
-        if "down_proj" in name:
-            if name.endswith("_bias"):
-                name = name.replace("down_proj_bias", "down_proj.bias")
-            elif "_blocks" not in name and "_scales" not in name:
-                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
-                name = name.replace("down_proj", "down_proj.weight")
-                data_torch = data_torch.transpose(-1, -2)
-            else:
-                # otherwise, it should already be repacked to ggml MXFP4 format
-                return []
-
-        # split the gate_up into gate and up
-        if "gate_up_proj" in name:
-            if name.endswith("_bias"):
-                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
-                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
-                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
-                return [
-                    (self.map_tensor_name(name_gate), gate_proj_bias),
-                    (self.map_tensor_name(name_up), up_proj_bias)
-                ]
-            elif "_blocks" not in name and "_scales" not in name:
-                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
-                name_up = name.replace("gate_up_proj", "up_proj.weight")
-                name_gate = name.replace("gate_up_proj", "gate_proj.weight")
-                data_torch = data_torch.transpose(-1, -2)
-                gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :]
-                return [
-                    (self.map_tensor_name(name_gate), gate_proj_weight),
-                    (self.map_tensor_name(name_up), up_proj_weight)
-                ]
-            else:
-                # otherwise, it should already be repacked to ggml MXFP4 format
-                return []
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
-
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type"))
-        assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}"
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-        self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-        self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096))
-
-
 @ModelBase.register("Lfm2ForCausalLM")
 @ModelBase.register("LFM2ForCausalLM")
 class LFM2Model(TextModel):
@@ -8318,7 +8089,6 @@ class LazyTorchTensor(gguf.LazyBase):
     _dtype_map: dict[torch.dtype, type] = {
         torch.float16: np.float16,
         torch.float32: np.float32,
-        torch.uint8: np.uint8,
     }
 
     # used for safetensors slices

ggml/CMakeLists.txt

@@ -176,7 +176,6 @@ option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
 option(GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12   "ggml: enable rocWMMA FlashAttention on GFX12"    OFF)
 option(GGML_HIP_MMQ_MFMA                    "ggml: enable MFMA MMA for CDNA in MMQ"           ON)
-option(GGML_HIP_EXPORT_METRICS              "ggml: enable kernel perf metrics output"         OFF)
 option(GGML_MUSA_GRAPHS                     "ggml: use MUSA graph, experimental, unstable"    OFF)
 option(GGML_MUSA_MUDNN_COPY                 "ggml: enable muDNN for accelerated copy"         OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)

ggml/cmake/ggml-config.cmake.in

@@ -106,7 +106,7 @@ if(NOT TARGET ggml::ggml)
 
     find_library(GGML_LIBRARY ggml
         REQUIRED
-        HINTS ${GGML_LIB_DIR}
+        HINTS ${GGML_LIB_DIR} ${GGML_BACKEND_DIR}
         NO_CMAKE_FIND_ROOT_PATH)
 
     add_library(ggml::ggml UNKNOWN IMPORTED)
@@ -125,56 +125,54 @@ if(NOT TARGET ggml::ggml)
             IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
 
     set(_ggml_all_targets "")
-    if (NOT GGML_BACKEND_DL)
-        foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
-            string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
-            string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
+    foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
+        string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
+        string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
 
-            find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
-                REQUIRED
-                HINTS ${GGML_LIB_DIR}
-                NO_CMAKE_FIND_ROOT_PATH)
+        find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
+            REQUIRED
+            HINTS ${GGML_LIB_DIR}
+            NO_CMAKE_FIND_ROOT_PATH)
 
-            message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
+        message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
 
-            add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
+        add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
+        set_target_properties(ggml::${_ggml_backend}
+            PROPERTIES
+                INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
+                IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
+                IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
+                INTERFACE_COMPILE_FEATURES c_std_90
+                POSITION_INDEPENDENT_CODE ON)
+
+        string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
+        if(is_cpu_variant)
+            list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
             set_target_properties(ggml::${_ggml_backend}
-                PROPERTIES
-                    INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
-                    IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
-                    IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
-                    INTERFACE_COMPILE_FEATURES c_std_90
-                    POSITION_INDEPENDENT_CODE ON)
+            PROPERTIES
+                INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
 
-            string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
-            if(is_cpu_variant)
-                list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
-                set_target_properties(ggml::${_ggml_backend}
-                PROPERTIES
-                    INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
-
-                if(GGML_CPU_INTERFACE_LINK_OPTIONS)
-                    set_target_properties(ggml::${_ggml_backend}
-                        PROPERTIES
-                            INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
-                endif()
-
-            else()
-                list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+            if(GGML_CPU_INTERFACE_LINK_OPTIONS)
                 set_target_properties(ggml::${_ggml_backend}
                     PROPERTIES
-                        INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
-
-                if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
-                    set_target_properties(ggml::${_ggml_backend}
-                        PROPERTIES
-                            INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
-                endif()
+                        INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
             endif()
 
-            list(APPEND _ggml_all_targets ggml::${_ggml_backend})
-        endforeach()
-    endif()
+        else()
+            list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+            set_target_properties(ggml::${_ggml_backend}
+                PROPERTIES
+                    INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
+
+            if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
+                set_target_properties(ggml::${_ggml_backend}
+                    PROPERTIES
+                        INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
+            endif()
+        endif()
+
+        list(APPEND _ggml_all_targets ggml::${_ggml_backend})
+    endforeach()
 
     list(APPEND GGML_INTERFACE_LINK_LIBRARIES ggml::ggml-base "${_ggml_all_targets}")
     set_target_properties(ggml::ggml

ggml/include/ggml.h

@@ -304,16 +304,6 @@
     GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
     GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
 
-#define GGML_TENSOR_TERNARY_OP_LOCALS \
-    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
-    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
-    GGML_TENSOR_LOCALS(int64_t, ne2, src2, ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb2, src2, nb) \
-    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
-
 #define GGML_TENSOR_BINARY_OP_LOCALS01 \
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
     GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
@@ -405,8 +395,7 @@ extern "C" {
         // GGML_TYPE_IQ4_NL_4_4 = 36,
         // GGML_TYPE_IQ4_NL_4_8 = 37,
         // GGML_TYPE_IQ4_NL_8_8 = 38,
-        GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
-        GGML_TYPE_COUNT   = 40,
+        GGML_TYPE_COUNT   = 39,
     };
 
     // precision
@@ -441,7 +430,6 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
-        GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
     };
 
     // available tensor operations:
@@ -450,7 +438,6 @@ extern "C" {
 
         GGML_OP_DUP,
         GGML_OP_ADD,
-        GGML_OP_ADD_ID,
         GGML_OP_ADD1,
         GGML_OP_ACC,
         GGML_OP_SUB,
@@ -570,7 +557,6 @@ extern "C" {
         GGML_GLU_OP_REGLU,
         GGML_GLU_OP_GEGLU,
         GGML_GLU_OP_SWIGLU,
-        GGML_GLU_OP_SWIGLU_OAI,
         GGML_GLU_OP_GEGLU_ERF,
         GGML_GLU_OP_GEGLU_QUICK,
 
@@ -845,13 +831,6 @@ extern "C" {
             struct ggml_tensor  * b,
             enum   ggml_type      type);
 
-    // dst[i0, i1, i2] = a[i0, i1, i2] + b[i0, ids[i1, i2]]
-    GGML_API struct ggml_tensor * ggml_add_id(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            struct ggml_tensor  * ids);
-
     GGML_API struct ggml_tensor * ggml_add1(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1219,13 +1198,6 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
-    GGML_API struct ggml_tensor * ggml_swiglu_oai(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            float                 alpha,
-            float                 limit);
-
     // normalize along rows
     GGML_API struct ggml_tensor * ggml_norm(
             struct ggml_context * ctx,
@@ -1598,10 +1570,6 @@ extern "C" {
             float                 scale,
             float                 max_bias);
 
-    GGML_API void ggml_soft_max_add_sinks(
-            struct ggml_tensor * a,
-            struct ggml_tensor * sinks);
-
     GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -2084,10 +2052,6 @@ extern "C" {
     GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
             const struct ggml_tensor * a);
 
-    GGML_API void ggml_flash_attn_ext_add_sinks(
-            struct ggml_tensor * a,
-            struct ggml_tensor * sinks);
-
     // TODO: needs to be adapted to ggml_flash_attn_ext
     GGML_API struct ggml_tensor * ggml_flash_attn_back(
            struct ggml_context * ctx,

ggml/src/ggml-alloc.c

@@ -29,7 +29,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_DIAG_MASK_ZERO:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_ADD:
-        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
         case GGML_OP_MUL:

ggml/src/ggml-backend.cpp

@@ -1071,11 +1071,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 }
             }
         }
-        // if the node is still unassigned, assign it to the first backend that supports it
-        for (int b = 0; b < sched->n_backends && *cur_backend_id == -1; b++) {
-            ggml_backend_sched_set_if_supported(sched, node, b, cur_backend_id);
-        }
-        GGML_ASSERT(*cur_backend_id != -1);
     }
 
     // pass 5: split graph, find tensors that need to be copied
@@ -1103,7 +1098,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             const int node_backend_id = tensor_backend_id(node);
 
-            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
+            assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
 
             // check if we should start a new split based on the sources of the current node
             bool need_new_split = false;
@@ -1161,7 +1156,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
                 size_t src_id = hash_id(src);
                 const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
-                GGML_ASSERT(src_backend_id != -1); // all inputs should be assigned by now
+                assert(src_backend_id != -1); // all inputs should be assigned by now
 
                 if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
                     if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {

ggml/src/ggml-cann/CMakeLists.txt

@@ -31,13 +31,6 @@ string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
 message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")
-option(USE_ACL_GRAPH "Enable CANN graph execution (ACL graph mode)" OFF)
-
-if(USE_ACL_GRAPH AND (SOC_TYPE_MAJOR_SN STREQUAL "310P" OR SOC_TYPE_COMPILE_OPTION STREQUAL "ASCEND_310P"))
-    message(FATAL_ERROR
-        "CANN Graph (ACL graph mode) is not supported on 310P devices. "
-        "Please build with -DUSE_ACL_GRAPH=OFF or use a supported SOC.")
-endif()
 
 if (CANN_INSTALL_DIR)
     # Only Support Linux.
@@ -75,13 +68,6 @@ if (CANN_INSTALL_DIR)
 
     target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
 
-    if (USE_ACL_GRAPH)
-        target_compile_definitions(ggml-cann PRIVATE USE_ACL_GRAPH)
-        message(STATUS "CANN: USE_ACL_GRAPH is enabled.")
-    else()
-        message(STATUS "CANN: USE_ACL_GRAPH is disabled.")
-    endif()
-
     message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
     message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
 else()

ggml/src/ggml-cann/common.h

@@ -337,29 +337,6 @@ private:
     int32_t device_;
 };
 
-#ifdef USE_ACL_GRAPH
-struct ggml_graph_node_properties {
-    void * node_address;
-    ggml_op node_op;
-    int64_t ne[GGML_MAX_DIMS];
-    size_t nb[GGML_MAX_DIMS];
-    void * src_address[GGML_MAX_SRC];
-    int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
-};
-
-struct ggml_cann_graph {
-    ~ggml_cann_graph() {
-        if (graph != nullptr) {
-            aclmdlRIDestroy(graph);
-        }
-    }
-
-    aclmdlRI graph = nullptr;
-
-    std::vector<ggml_graph_node_properties> ggml_graph_properties;
-};
-#endif  // USE_ACL_GRAPH
-
 /**
  * @brief Context for managing CANN backend operations.
  */
@@ -368,13 +345,8 @@ struct ggml_backend_cann_context {
     std::string name;                /**< Name of the device. */
     std::string description;         /**< Description of the device. */
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
-#ifdef USE_ACL_GRAPH
-    /// Cached CANN ACL graph used for executing the current ggml computation graph.
-    std::unique_ptr<ggml_cann_graph> cann_graph;
-#endif
     cann_task_queue task_queue;
     bool async_mode;
-    bool support_set_rows;
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
@@ -390,14 +362,6 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
-
-        support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
-        GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");
-
-        if (!support_set_rows) {
-            GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
-                    "Falling back to eager mode.\n", __func__);
-        }
     }
 
     /**

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

@@ -2075,160 +2075,6 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
     ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
 }
 
-#ifdef USE_ACL_GRAPH
-/**
- * @brief Populate the internal CANN graph node properties from the ggml computation graph.
- *
- * This function copies all node attributes (operation type, dimensions, strides, input sources,
- * and operation parameters) into the cached CANN graph structure for later reuse or comparison.
- *
- * @param cann_ctx  The CANN backend context.
- * @param cgraph    The ggml computational graph.
- */
-static void set_ggml_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
-    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
-        ggml_tensor * node = cgraph->nodes[node_idx];
-        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_address = node->data;
-        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_op = node->op;
-
-        for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
-            cann_ctx->cann_graph->ggml_graph_properties[node_idx].ne[dim] = node->ne[dim];
-            cann_ctx->cann_graph->ggml_graph_properties[node_idx].nb[dim] = node->nb[dim];
-        }
-        for (int src = 0; src < GGML_MAX_SRC; src++) {
-            cann_ctx->cann_graph->ggml_graph_properties[node_idx].src_address[src] =
-                node->src[src] ? node->src[src]->data : nullptr;
-        }
-        memcpy(cann_ctx->cann_graph->ggml_graph_properties[node_idx].op_params, node->op_params, GGML_MAX_OP_PARAMS);
-    }
-}
-
-/**
- * @brief Check if a ggml tensor node matches a previously captured CANN graph node.
- *
- * This function compares all relevant fields (address, op type, shape, source inputs, op params)
- * to determine whether the current node matches a previously recorded version.
- *
- * @param node                  The current ggml tensor node.
- * @param graph_node_properties The stored properties of a CANN graph node.
- * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise.
- */
-static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
-    if (node->data != graph_node_properties->node_address &&
-           node->op != GGML_OP_VIEW) {
-        return false;
-    }
-    if (node->op != graph_node_properties->node_op) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (node->ne[i] != graph_node_properties->ne[i]) {
-            return false;
-        }
-        if (node->nb[i] != graph_node_properties->nb[i]) {
-            return false;
-        }
-    }
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (node->src[i] &&
-            node->src[i]->data != graph_node_properties->src_address[i] &&
-            node->op != GGML_OP_VIEW
-        ) {
-            return false;
-        }
-    }
-    if (node->op == GGML_OP_SCALE &&
-        memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
-        return false;
-    }
-    return true;
-}
-
-/**
- * @brief Determine if the CANN graph needs to be rebuilt due to graph changes.
- *
- * This checks whether the number or properties of ggml graph nodes have changed
- * compared to the last captured CANN graph. If so, the CANN graph must be re-captured.
- *
- * @param cann_ctx  The CANN backend context.
- * @param cgraph    The current ggml computation graph.
- * @return true if an update is required; false otherwise.
- */
-static bool is_cann_graph_update_required(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
-    // The number of nodes is different, so the graph needs to be reconstructed.
-    if (cann_ctx->cann_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
-        cann_ctx->cann_graph->ggml_graph_properties.resize(cgraph->n_nodes);
-        return true;
-    }
-
-    // The number of nodes is the same; iterate over each node to check whether they match.
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        bool has_matching_properties = ggml_graph_node_has_matching_properties(
-            cgraph->nodes[i], &cann_ctx->cann_graph->ggml_graph_properties[i]);
-        if(!has_matching_properties) {
-            return true;
-        }
-    }
-    return false;
-}
-#endif  // USE_ACL_GRAPH
-
-/**
- * @brief Evaluate the computation graph and optionally capture or execute it using CANN graph API.
- *
- * If CANN graph execution is enabled and graph capture is required, this function begins
- * graph capture, runs the graph, ends capture, and stores the captured graph.
- *
- * Otherwise, it falls back to op-by-op execution using the CANN compute kernel dispatcher.
- *
- * @param cann_ctx                 The CANN backend context.
- * @param cgraph                   The ggml computation graph.
- * @param use_cann_graph           Whether to use CANN graph execution.
- * @param cann_graph_update_required Whether graph capture is needed due to graph changes.
- */
-static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph,
-    bool & use_cann_graph, bool & cann_graph_update_required) {
-#ifdef USE_ACL_GRAPH
-    if (use_cann_graph && cann_graph_update_required) {
-        if (cann_ctx->cann_graph->graph != nullptr) {
-            ACL_CHECK(aclmdlRIDestroy(cann_ctx->cann_graph->graph));
-            cann_ctx->cann_graph->graph = nullptr;
-        }
-        ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));
-    }
-#endif // USE_ACL_GRAPH
-
-    // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
-    // With the use of CANN graphs, the execution will be performed by the graph launch.
-    if (!use_cann_graph || cann_graph_update_required) {
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            ggml_tensor * node = cgraph->nodes[i];
-
-            if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
-                continue;
-            }
-
-            bool ok = ggml_cann_compute_forward(*cann_ctx, node);
-            if (!ok) {
-                GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
-            }
-            GGML_ASSERT(ok);
-        }
-    }
-
-#ifdef USE_ACL_GRAPH
-    if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture
-        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &cann_ctx->cann_graph->graph));
-    }
-
-    if (use_cann_graph) {
-        // Execute graph
-        ACL_CHECK(aclmdlRIExecuteAsync(cann_ctx->cann_graph->graph, cann_ctx->stream()));
-    }
-#endif // USE_ACL_GRAPH
-}
-
-
 /**
  * @brief Computes a computational graph using a CANN backend.
  *
@@ -2245,37 +2091,26 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     ggml_backend_t backend, ggml_cgraph* cgraph) {
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
+
     ggml_cann_set_device(cann_ctx->device);
+    //release temp buffer create by set tensor.
     release_nz_workspace();
-#ifdef USE_ACL_GRAPH
-    bool use_cann_graph = true;
-    bool cann_graph_update_required = false;
 
-    // check environment LLAMA_SET_ROWS
-    if (!cann_ctx->support_set_rows) {
-        use_cann_graph = false;
-    }
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor* node = cgraph->nodes[i];
 
-    if (use_cann_graph) {
-        if (cann_ctx->cann_graph == nullptr) {
-            cann_ctx->cann_graph.reset(new ggml_cann_graph());
-            cann_graph_update_required = true;
+        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
+            continue;
         }
 
-        cann_graph_update_required = is_cann_graph_update_required(cann_ctx, cgraph);
-        set_ggml_graph_node_properties(cann_ctx, cgraph);
-    }
-#else
-    bool use_cann_graph = false;
-    bool cann_graph_update_required = false;
-#endif  // USE_ACL_GRAPH
+        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
 
-    evaluate_and_capture_cann_graph(
-        cann_ctx,
-        cgraph,
-        use_cann_graph,
-        cann_graph_update_required
-    );
+        if (!ok) {
+            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
+                    node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
 
     return GGML_STATUS_SUCCESS;
 }
@@ -2391,6 +2226,12 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // only support F32 and F16.
                 return false;
             }
+
+            if (!ggml_are_same_shape(op, src) && !ggml_is_contiguous(op)) {
+                // unsupport dst is not contiguous.
+                return false;
+            }
+
             return true;
         } break;
         case GGML_OP_CONT: {
@@ -2499,10 +2340,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
             return bias == 0.0f; // TODO: support bias != 0.0f
         case GGML_OP_SOFT_MAX:
-            // TODO: support attention sinks [TAG_ATTN_SINKS]
-            if (op->src[2]) {
-                return false;
-            }
             // TODO: support broadcast
             // ref: https://github.com/ggml-org/llama.cpp/pull/14435
             return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
@@ -2517,10 +2354,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){
                 return false;
             }
-            // TODO: support attention sinks [TAG_ATTN_SINKS]
-            if (op->src[4]) {
-                return false;
-            }
             if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                 // different head sizes of K and V are not supported yet
                 return false;

ggml/src/ggml-common.h

@@ -99,9 +99,6 @@ typedef sycl::half2 ggml_half2;
 #define QI4_1 (QK4_1 / (4 * QR4_1))
 #define QR4_1 2
 
-#define QI_MXFP4 (QK_MXFP4 / (4 * QR_MXFP4))
-#define QR_MXFP4 2
-
 #define QI5_0 (QK5_0 / (4 * QR5_0))
 #define QR5_0 2
 
@@ -187,13 +184,6 @@ typedef struct {
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");
 
-#define QK_MXFP4 32
-typedef struct {
-    uint8_t e; // E8M0
-    uint8_t qs[QK_MXFP4/2];
-} block_mxfp4;
-static_assert(sizeof(block_mxfp4) == sizeof(uint8_t) + QK_MXFP4/2, "wrong mxfp4 block size/padding");
-
 #define QK5_0 32
 typedef struct {
     ggml_half d;           // delta
@@ -1084,17 +1074,10 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
     0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
 GGML_TABLE_END()
 
-// TODO: fix name to kvalues_iq4_nl
 GGML_TABLE_BEGIN(int8_t, kvalues_iq4nl, 16)
     -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113,
 GGML_TABLE_END()
 
-// e2m1 values (doubled)
-// ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
-GGML_TABLE_BEGIN(int8_t, kvalues_mxfp4, 16)
-    0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12,
-GGML_TABLE_END()
-
 #define NGRID_IQ1S 2048
 #define IQ1S_DELTA 0.125f
 #define IQ1M_DELTA 0.125f

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

@@ -13,7 +13,6 @@
 #define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
 #define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -69,7 +68,6 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -92,7 +90,6 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -123,7 +120,6 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #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_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -153,7 +149,6 @@
 #define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
 #define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -184,7 +179,6 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #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_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8

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

@@ -589,67 +589,6 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 }
 
-void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_MXFP4 == 0);
-    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
-
-    const block_mxfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    const int nb = n / QK_MXFP4;
-
-    int ib = 0;
-    float sumf = 0;
-
-#if defined __ARM_NEON
-    const int8x16_t values = vld1q_s8(kvalues_mxfp4);
-    const uint8x16_t m4b = vdupq_n_u8(0x0f);
-    uint8x16x2_t q4bits;
-    int8x16x4_t q4b;
-    int8x16x4_t q8b;
-    int32x4_t prod_1;
-    int32x4_t prod_2;
-
-    for (; ib + 1 < nb; ib += 2) {
-        q4bits.val[0] = vld1q_u8(x[ib + 0].qs);
-        q4bits.val[1] = vld1q_u8(x[ib + 1].qs);
-        q8b.val[0]    = vld1q_s8(y[ib + 0].qs);
-        q8b.val[1]    = vld1q_s8(y[ib + 0].qs + 16);
-        q8b.val[2]    = vld1q_s8(y[ib + 1].qs);
-        q8b.val[3]    = vld1q_s8(y[ib + 1].qs + 16);
-
-        q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
-        q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
-        q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
-        q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
-
-        prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
-        prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
-
-        sumf +=
-            GGML_E8M0_TO_FP32_HALF(x[ib + 0].e) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) +
-            GGML_E8M0_TO_FP32_HALF(x[ib + 1].e) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2);
-    }
-
-#endif
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
-        int sumi1 = 0;
-        int sumi2 = 0;
-        for (int j = 0; j < QK_MXFP4/2; ++j) {
-            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

ggml/src/ggml-cpu/arch/x86/quants.c

@@ -66,12 +66,6 @@ static inline int hsum_i32_4(const __m128i a) {
 }
 
 #if defined(__AVX2__) || defined(__AVX512F__)
-static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
-    const __m256i ax = _mm256_sign_epi8(x, x);
-    const __m256i sy = _mm256_sign_epi8(y, x);
-    return _mm256_maddubs_epi16(ax, sy);
-}
-
 // spread 32 bits to 32 bytes { 0x00, 0xFF }
 static inline __m256i bytes_from_bits_32(const uint8_t * x) {
     uint32_t x32;
@@ -267,11 +261,6 @@ static inline __m256 quad_fp16_delta_float(const float x0, const float y0, const
     return _mm256_set_m128(_mm_set1_ps(GGML_CPU_FP16_TO_FP32(x1) * GGML_CPU_FP16_TO_FP32(y1)),
                            _mm_set1_ps(GGML_CPU_FP16_TO_FP32(x0) * GGML_CPU_FP16_TO_FP32(y0)));
 }
-
-static inline __m256 quad_mx_delta_float(const int8_t x0, const float y0, const int8_t x1, const float y1) {
-    return _mm256_set_m128(_mm_set1_ps(GGML_E8M0_TO_FP32_HALF(x1) * GGML_CPU_FP16_TO_FP32(y1)),
-                           _mm_set1_ps(GGML_E8M0_TO_FP32_HALF(x0) * GGML_CPU_FP16_TO_FP32(y0)));
-}
 #endif
 #elif defined(__SSSE3__)
 // horizontally add 4x4 floats
@@ -757,91 +746,6 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }
 
-void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_MXFP4 == 0);
-    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
-
-    const block_mxfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    const int nb = n / QK_MXFP4;
-
-    int ib = 0;
-    float sumf = 0;
-
-#if defined __AVX2__
-
-    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_mxfp4);
-    const __m128i m4b  = _mm_set1_epi8(0x0f);
-    const __m256i mone = _mm256_set1_epi16(1);
-
-    __m256 accum1 = _mm256_setzero_ps();
-    __m256 accum2 = _mm256_setzero_ps();
-    for (; ib + 1 < nb; ib += 2) {
-        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs);
-        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs);
-        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs);
-        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs);
-        const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
-                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
-        const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
-                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
-        const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
-        const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
-        const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
-        const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
-        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 0].d)*GGML_E8M0_TO_FP32_HALF(x[ib + 0].e)),
-                _mm256_cvtepi32_ps(p_1), accum1);
-        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 1].d)*GGML_E8M0_TO_FP32_HALF(x[ib + 1].e)),
-                _mm256_cvtepi32_ps(p_2), accum2);
-    }
-
-    sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
-
-#elif defined __AVX__
-    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_mxfp4);
-    const __m128i m4b  = _mm_set1_epi8(0x0f);
-
-    __m256 accum = _mm256_setzero_ps();
-    for (; ib + 1 < nb; ib += 2) {
-        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs);
-        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs);
-        const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs);
-        const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1);
-        const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs);
-        const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1);
-
-        const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b));
-        const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b));
-        const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b));
-        const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b));
-
-        const __m256 p = mul_sum_i8_quad_float(q4b_1_0, q4b_1_1, q4b_2_0, q4b_2_1, q8b_1_0, q8b_1_1, q8b_2_0, q8b_2_1);
-        const __m256 deltas = quad_mx_delta_float(x[ib].e, y[ib].d, x[ib + 1].e, y[ib + 1].d);
-        accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum);
-    }
-
-    sumf = hsum_float_8(accum);
-
-#endif
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
-        int sumi1 = 0;
-        int sumi2 = 0;
-        for (int j = 0; j < QK_MXFP4/2; ++j) {
-            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -3302,6 +3206,14 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif
 }
 
+#if defined(__AVX2__)
+static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
+    const __m256i ax = _mm256_sign_epi8(x, x);
+    const __m256i sy = _mm256_sign_epi8(y, x);
+    return _mm256_maddubs_epi16(ax, sy);
+}
+#endif
+
 void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);

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

@@ -253,12 +253,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
     },
-    [GGML_TYPE_MXFP4] = {
-        .from_float               = quantize_row_mxfp4,
-        .vec_dot                  = ggml_vec_dot_mxfp4_q8_0,
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-        .nrows                    = 1,
-    },
     [GGML_TYPE_Q2_K] = {
         .from_float               = quantize_row_q2_K,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
@@ -1676,10 +1670,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_add(params, tensor);
             } break;
-        case GGML_OP_ADD_ID:
-            {
-                ggml_compute_forward_add_id(params, tensor);
-            } break;
         case GGML_OP_ADD1:
             {
                 ggml_compute_forward_add1(params, tensor);
@@ -1934,7 +1924,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_FLASH_ATTN_EXT:
             {
-                ggml_compute_forward_flash_attn_ext(params, tensor);
+                ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
             } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
@@ -2121,7 +2111,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_DUP:
         case GGML_OP_CONT:
         case GGML_OP_ADD:
-        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_ACC:
             {
@@ -2183,7 +2172,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     {
@@ -2685,7 +2673,6 @@ struct ggml_cplan ggml_graph_plan(
                         }
                     } break;
                 case GGML_OP_ADD:
-                case GGML_OP_ADD_ID:
                 case GGML_OP_ADD1:
                     {
                         if (ggml_is_quantized(node->src[0]->type)) {

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

@@ -35,7 +35,7 @@
 
 // ggml-backend interface
 
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types() {
+std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type() {
     static std::vector<ggml_backend_buffer_type_t> bufts = []() {
         std::vector<ggml_backend_buffer_type_t> bufts;
 
@@ -57,6 +57,8 @@ std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_type
         }
 #endif
 
+        bufts.push_back(NULL);
+
         return bufts;
     }();
 
@@ -64,20 +66,14 @@ std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_type
 }
 
 static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_type(ggml_backend_dev_t device) {
-    static std::vector<ggml_backend_buffer_type_t> extra_bufts = [] {
-        std::vector<ggml_backend_buffer_type_t> bufts = ggml_backend_cpu_get_extra_buffer_types();
-        bufts.push_back(nullptr);
-        return bufts;
-    }();
-
-    return extra_bufts.data();
+    return ggml_backend_cpu_get_extra_buffers_type().data();
 
     GGML_UNUSED(device);
 }
 
 static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) {
-    for (auto * extra : ggml_backend_cpu_get_extra_buffer_types()) {
-        if (extra == buft) {
+    for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) {
+        if (extra && extra == buft) {
             return true;
         }
     }
@@ -401,13 +397,20 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
         return true;
     }
 
-    // check extra buffer types
-    // note: only the first sources are checked for extra buffer types to reduce overhead, increase if necessary
-    for (int i = 0; i < 4; i++) {
-        if (op->src[i] && op->src[i]->buffer &&
-            ggml_backend_cpu_is_extra_buffer_type(op->src[i]->buffer->buft)) {
-            auto * buf_extra = (ggml::cpu::extra_buffer_type *) op->src[i]->buffer->buft->context;
-            return buf_extra->supports_op(dev, op);
+    // extra_buffer_op?
+    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+        if (extra) {
+            auto buf_extra = (ggml::cpu::extra_buffer_type*) extra->context;
+            if (buf_extra && buf_extra->supports_op(dev, op)) {
+                return true;
+            }
+        }
+    }
+
+    // the other case need host buffer.
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op->src[i] && op->src[i]->buffer && !ggml_backend_buft_is_host(op->src[i]->buffer->buft)) {
+            return false;
         }
     }
 

ggml/src/ggml-cpu/ops.cpp

@@ -8,7 +8,6 @@
 #include "vec.h"
 
 #include <float.h>
-#include <algorithm>
 
 // ggml_compute_forward_dup
 
@@ -1284,7 +1283,6 @@ void ggml_compute_forward_add(
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1311,77 +1309,6 @@ void ggml_compute_forward_add(
     }
 }
 
-// ggml_compute_forward_add_id
-
-static void ggml_compute_forward_add_id_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst) {
-
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
-
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(src2->type == GGML_TYPE_I32);
-
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-    GGML_ASSERT(src1->nb[0] == sizeof(float));
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int nr  = ggml_nrows(src0);
-
-    GGML_TENSOR_TERNARY_OP_LOCALS
-
-    GGML_ASSERT( nb0 == sizeof(float));
-    GGML_ASSERT(nb10 == sizeof(float));
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // src0 indices
-        const int i3 = ir/(ne2*ne1);
-        const int i2 = (ir - i3*ne2*ne1)/ne1;
-        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
-
-        // src1 indices
-        const int i11 = *(int32_t *) ((char *) src2->data + i1*nb20 + i2*nb21);
-
-        GGML_ASSERT(i11 >= 0 && i11 < ne11);
-
-        ggml_vec_add_f32(ne0,
-                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
-                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
-                (float *) ((char *) src1->data + i11*nb11));
-    }
-}
-
-void ggml_compute_forward_add_id(
-        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_add_id_f32(params, dst);
-            } break;
-        default:
-            {
-                GGML_ABORT("unsupported type for ggml_compute_forward_add_id: %s", ggml_type_name(src0->type));
-            }
-    }
-}
-
 // ggml_compute_forward_add1
 
 static void ggml_compute_forward_add1_f32(
@@ -1733,7 +1660,6 @@ void ggml_compute_forward_add1(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1861,7 +1787,6 @@ void ggml_compute_forward_acc(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -3689,93 +3614,6 @@ static void ggml_compute_forward_swiglu(
     }
 }
 
-// ggml_compute_forward_swiglu_oai
-
-static void ggml_compute_forward_swiglu_oai_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst) {
-
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    char * src0_d = (char *) src0->data;
-    char * src1_d = (char *) (src1 ? src1->data : src0->data);
-    const size_t src0_o = src0->nb[1];
-    const size_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
-
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_1(dst));
-
-    if (src1) {
-        GGML_ASSERT(ggml_is_contiguous_1(src1));
-        GGML_ASSERT(src0->type == src1->type);
-    }
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
-    const int nr = ggml_nrows(src0);
-
-    GGML_ASSERT(dst->ne[0] == nc);
-    GGML_ASSERT(ggml_nrows(dst) == nr);
-
-    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
-    const float alpha = ggml_get_op_params_f32(dst, 2);
-    const float limit = ggml_get_op_params_f32(dst, 3);
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int i1 = ir0; i1 < ir1; i1++) {
-        float * src0_p = (float *) (src0_d + i1*src0_o);
-        float * src1_p = (float *) (src1_d + i1*src1_o);
-        float * dst_p  = (float *) ((char *) dst->data + i1*(dst->nb[1]));
-
-        if (!src1) {
-            src0_p += swapped ? nc : 0;
-            src1_p += swapped ? 0 : nc;
-        }
-
-        for (int k = 0; k < nc; k++) {
-            const float x = std::min(src0_p[k], limit);
-            const float y = std::clamp(src1_p[k], -limit, limit);
-            const float out_glu = x / (1.f + expf(alpha * (-x)));
-            dst_p[k] = out_glu * (y + 1.f);
-        }
-
-#ifndef NDEBUG
-        for (int k = 0; k < nc; k++) {
-            const float x = dst_p[k];
-            GGML_UNUSED(x);
-            assert(!isnan(x));
-            assert(!isinf(x));
-        }
-#endif
-    }
-}
-
-static void ggml_compute_forward_swiglu_oai(
-        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_swiglu_oai_f32(params, dst);
-            } break;
-        default:
-            {
-                GGML_ABORT("fatal error");
-            }
-    }
-}
-
 // ggml_compute_forward_geglu_erf
 
 static void ggml_compute_forward_geglu_erf_f32(
@@ -4761,7 +4599,6 @@ void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5036,7 +4873,6 @@ void ggml_compute_forward_set(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5298,7 +5134,6 @@ void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5688,7 +5523,6 @@ static void ggml_compute_forward_soft_max_f32(
 
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
 
     assert(ggml_is_contiguous(dst));
     assert(ggml_are_same_shape(src0, dst));
@@ -5723,9 +5557,6 @@ static void ggml_compute_forward_soft_max_f32(
 
     const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
 
-    // sinks
-    const float * sk = src2 ? (float *)((char *) src2->data) : nullptr;
-
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
@@ -5768,18 +5599,9 @@ static void ggml_compute_forward_soft_max_f32(
                 float max = -INFINITY;
                 ggml_vec_max_f32(ne00, &max, wp);
 
-                // if we have sinks, make a correction as if they were included in the softmax
-                if (sk) {
-                    max = MAX(max, sk[i02]);
-                }
-
                 ggml_float sum = ggml_vec_soft_max_f32(ne00, dp, wp, max);
                 assert(sum > 0.0);
 
-                if (sk) {
-                    sum += (ggml_float) expf(sk[i02] - max);
-                }
-
                 sum = 1.0/sum;
                 ggml_vec_scale_f32(ne00, dp, sum);
 
@@ -6014,7 +5836,6 @@ void ggml_compute_forward_clamp(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -8168,14 +7989,12 @@ void ggml_compute_forward_argsort(
 
 static void ggml_compute_forward_flash_attn_ext_f16(
         const ggml_compute_params * params,
+        const ggml_tensor * q,
+        const ggml_tensor * k,
+        const ggml_tensor * v,
+        const ggml_tensor * mask,
         ggml_tensor * 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 ggml_tensor * sinks = dst->src[4];
-
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
     GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
     GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
@@ -8370,23 +8189,6 @@ static void ggml_compute_forward_flash_attn_ext_f16(
             }
         }
 
-        // sinks
-        if (sinks) {
-            const float s = ((float *)((char *) sinks->data))[h];
-
-            float ms = 1.0f;
-            float vs = 1.0f;
-
-            if (s > M) {
-                ms = expf(M - s);
-                ggml_vec_scale_f32(DV, VKQ32, ms);
-            } else {
-                vs = expf(s - M);
-            }
-
-            S = S*ms + vs;
-        }
-
         // V /= S
         const float S_inv = 1.0f/S;
         ggml_vec_scale_f32(DV, VKQ32, S_inv);
@@ -8406,13 +8208,17 @@ static void ggml_compute_forward_flash_attn_ext_f16(
 
 void ggml_compute_forward_flash_attn_ext(
         const ggml_compute_params * params,
+        const ggml_tensor * q,
+        const ggml_tensor * k,
+        const ggml_tensor * v,
+        const ggml_tensor * mask,
         ggml_tensor * dst) {
     switch (dst->op_params[3]) {
         case GGML_PREC_DEFAULT:
         case GGML_PREC_F32:
             {
                 // uses F32 accumulators
-                ggml_compute_forward_flash_attn_ext_f16(params, dst);
+                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
             } break;
         default:
             {
@@ -9274,10 +9080,6 @@ void ggml_compute_forward_glu(
             {
                 ggml_compute_forward_swiglu(params, dst);
             } break;
-        case GGML_GLU_OP_SWIGLU_OAI:
-            {
-                ggml_compute_forward_swiglu_oai(params, dst);
-            } break;
         case GGML_GLU_OP_GEGLU_ERF:
             {
                 ggml_compute_forward_geglu_erf(params, dst);

ggml/src/ggml-cpu/ops.h

@@ -29,7 +29,6 @@ extern "C" {
 
 void ggml_compute_forward_dup(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_add_id(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_acc(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_sum(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -83,7 +82,13 @@ void ggml_compute_forward_arange(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_flash_attn_ext(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_flash_attn_ext(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * q,
+    const struct ggml_tensor * k,
+    const struct ggml_tensor * v,
+    const struct ggml_tensor * mask,
+    struct ggml_tensor * dst);
 void ggml_compute_forward_flash_attn_back(
         const struct ggml_compute_params * params,
         const bool masked,

ggml/src/ggml-cpu/quants.c

@@ -46,10 +46,6 @@ void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRI
     quantize_row_q8_1_ref(x, y, k);
 }
 
-void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
-    quantize_row_mxfp4_ref(x, y, k);
-}
-
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -185,37 +181,6 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
     *s = sumf;
 }
 
-void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_MXFP4 == 0);
-    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
-
-    const block_mxfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    const int nb = n / QK_MXFP4;
-
-    int ib = 0;
-    float sumf = 0;
-
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
-
-        int sumi1 = 0;
-        int sumi2 = 0;
-        for (int j = 0; j < QK_MXFP4/2; ++j) {
-            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

ggml/src/ggml-cpu/quants.h

@@ -19,8 +19,6 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
 void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
-
 void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -41,8 +39,6 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
-void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -71,12 +67,8 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
-void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);

ggml/src/ggml-cpu/traits.cpp

@@ -10,7 +10,7 @@ extra_buffer_type::~extra_buffer_type() {}
 }  // namespace ggml::cpu
 
 bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);
@@ -23,7 +23,7 @@ bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct
 }
 
 bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);

ggml/src/ggml-cpu/traits.h

@@ -33,6 +33,6 @@ class extra_buffer_type {
 }  // namespace ggml::cpu
 
 // implemented in ggml-cpu.cpp.
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types();
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffers_type();
 
 #endif

ggml/src/ggml-cpu/vec.h

@@ -55,22 +55,7 @@ inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x)
 
 inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const ggml_fp16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-
-inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) {
-    int i = 0;
-#if defined(__AVX2__)
-    for (; i + 7 < n; i += 8) {
-        __m256 vx = _mm256_loadu_ps(x + i);
-        __m256 vy = _mm256_loadu_ps(y + i);
-        __m256 vz = _mm256_add_ps(vx, vy);
-        _mm256_storeu_ps(z + i, vz);
-    }
-#endif
-    for (; i < n; ++i) {
-        z[i] = x[i] + y[i];
-    }
-}
-
+inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
 inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
     for (int i = 0; i < n; ++i) {
         z[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(x[i]) + GGML_CPU_FP16_TO_FP32(y[i]));
@@ -1007,9 +992,9 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
 
 inline static void ggml_vec_swiglu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const ggml_fp16_t * g) {
     for (int i = 0; i < n; ++i) {
-        float xi = GGML_CPU_FP16_TO_FP32(x[i]);
-        float gi = GGML_CPU_FP16_TO_FP32(g[i]);
-        y[i] = GGML_CPU_FP32_TO_FP16((xi/(1.0f + expf(-xi))) * gi);
+        float v = GGML_CPU_FP16_TO_FP32(x[i]);
+        float w = GGML_CPU_FP16_TO_FP32(g[i]);
+        y[i] = GGML_CPU_FP32_TO_FP16((v/(1.0f + expf(-v))) * w);
     }
 }
 

ggml/src/ggml-cuda/add-id.cu

@@ -1,58 +0,0 @@
-#include "add-id.cuh"
-
-static __global__ void add_id_kernel(
-        const float * src0, const float * src1, const int32_t * src2, float * dst,
-        int64_t ne0, int64_t ne1,
-        size_t nb01, size_t nb02,
-        size_t nb11,
-        size_t nb21
-    ) {
-
-    const int64_t i1 = blockIdx.x;
-    const int64_t i2 = blockIdx.y;
-
-    const int i11 = *(int32_t *) ((char *) src2 + i1*sizeof(int32_t) + i2*nb21);
-
-    const size_t nb1 = ne0 * sizeof(float);
-    const size_t nb2 = ne1 * nb1;
-
-    float * dst_row = (float *)((char *)dst + i1*nb1 + i2*nb2);
-    const float * src0_row = (const float *)((char *)src0 +  i1*nb01 + i2*nb02);
-    const float * src1_row = (const float *)((char *)src1 + i11*nb11);
-
-    for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
-        dst_row[i0] = src0_row[i0] + src1_row[i0];
-    }
-}
-
-void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
-
-    GGML_TENSOR_TERNARY_OP_LOCALS
-
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(src2->type == GGML_TYPE_I32);
-
-    GGML_ASSERT(nb00 == sizeof(float));
-    GGML_ASSERT(nb10 == sizeof(float));
-    GGML_ASSERT(nb20 == sizeof(int32_t));
-
-    const float * src0_d = (const float *)src0->data;
-    const float * src1_d = (const float *)src1->data;
-    const int32_t * src2_d = (const int32_t *)src2->data;
-    float * dst_d = (float *)dst->data;
-
-    int threads = std::min((int)ne00, 768); // cols
-    dim3 blocks(ne01, ne02); // n_experts_used, n_tokens
-    add_id_kernel<<<blocks, threads, 0, ctx.stream()>>>(
-        src0_d, src1_d, src2_d, dst_d,
-        ne0, ne1,
-        nb01, nb02,
-        nb11,
-        nb21
-    );
-}

ggml/src/ggml-cuda/add-id.cuh

@@ -1,3 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/common.cuh

@@ -1,7 +1,6 @@
 #pragma once
 
 #include "ggml.h"
-#include "ggml-impl.h"
 #include "ggml-cuda.h"
 
 #include <cstdint>
@@ -233,13 +232,9 @@ typedef float2 dfloat2;
 #endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
 
 #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
-#define TURING_MMA_AVAILABLE
+#define NEW_MMA_AVAILABLE
 #endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
-#define AMPERE_MMA_AVAILABLE
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
-
 #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #define CP_ASYNC_AVAILABLE
 #endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
@@ -307,14 +302,10 @@ static bool amd_mfma_available(const int cc) {
 }
 
 // Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
-static bool turing_mma_available(const int cc) {
+static bool new_mma_available(const int cc) {
     return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
 }
 
-static bool ampere_mma_available(const int cc) {
-    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
-}
-
 static bool cp_async_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
 }
@@ -558,24 +549,6 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #endif // defined(GGML_USE_HIP)
 }
 
-static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
-#if CUDART_VERSION >= 12080
-    const nv_bfloat16 e = __nv_cvt_e8m0_to_bf16raw(x);
-    return (float) e;
-#else
-    uint32_t bits;
-    if (x == 0) {
-        bits = 0x00400000;
-    } else {
-        bits = (uint32_t) x << 23;
-    }
-
-    float result;
-    memcpy(&result, &bits, sizeof(float));
-    return result;
-#endif // CUDART_VERSION >= 12050
-}
-
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
 
 static __device__ __forceinline__ float get_alibi_slope(
@@ -634,13 +607,6 @@ struct ggml_cuda_type_traits<GGML_TYPE_Q8_0> {
     static constexpr int qi = QI8_0;
 };
 
-template<>
-struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
-    static constexpr int qk = QK_MXFP4;
-    static constexpr int qr = QR_MXFP4;
-    static constexpr int qi = QI_MXFP4;
-};
-
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
     static constexpr int qk = QK_K;

ggml/src/ggml-cuda/convert.cu

@@ -465,24 +465,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
     }
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int64_t i   = blockIdx.x;
-    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[ib].qs + 4*il;
-    const float d = ggml_cuda_e8m0_to_fp32(x[ib].e);
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
-        y[j+16] = d * kvalues_mxfp4[q4[j] >>  4]*0.5f;
-    }
-}
-
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block_cuda(const void * vx, dst_t * y,
         const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
@@ -606,12 +588,6 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t
     dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
 }
 
-template<typename dst_t>
-static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
-    const int nb = (k + QK_K - 1) / QK_K;
-    dequantize_block_mxfp4<<<nb, 32, 0, stream>>>(vx, y);
-}
-
 template <typename src_t, typename dst_t>
 static __global__ void convert_unary(
         const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02,
@@ -701,8 +677,6 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
             return dequantize_row_iq4_xs_cuda;
         case GGML_TYPE_IQ3_S:
             return dequantize_row_iq3_s_cuda;
-        case GGML_TYPE_MXFP4:
-            return dequantize_row_mxfp4_cuda;
         case GGML_TYPE_F32:
             return convert_unary_cont_cuda<float>;
         case GGML_TYPE_BF16:
@@ -752,8 +726,6 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
             return dequantize_row_iq4_xs_cuda;
         case GGML_TYPE_IQ3_S:
             return dequantize_row_iq3_s_cuda;
-        case GGML_TYPE_MXFP4:
-            return dequantize_row_mxfp4_cuda;
         case GGML_TYPE_F16:
             return convert_unary_cont_cuda<half>;
         case GGML_TYPE_BF16:

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

@@ -15,7 +15,6 @@ typedef void (* fattn_kernel_t)(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -737,8 +736,7 @@ void launch_fattn(
 
     GGML_ASSERT(V || is_mla);
 
-    const ggml_tensor * mask  = dst->src[3];
-    const ggml_tensor * sinks = dst->src[4];
+    const ggml_tensor * mask = dst->src[3];
 
     ggml_tensor * KQV = dst;
 
@@ -942,7 +940,6 @@ void launch_fattn(
         K_data,
         V_data,
         mask ? ((const char *) mask->data) : nullptr,
-        sinks ? ((const char *) sinks->data) : nullptr,
         KV_max.ptr,
         !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
         scale, max_bias, m0, m1, n_head_log2, logit_softcap,

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

@@ -418,7 +418,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         float        * const __restrict__ KQ_max,
         float        * const __restrict__ KQ_rowsum,
         const int kb0) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     typedef fattn_mma_f16_config<DKQ, DV> c;
 
 #ifdef CP_ASYNC_AVAILABLE
@@ -776,7 +776,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
     GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
     NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup>
@@ -800,7 +800,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int jt,
         const int kb0_start,
         const int kb0_stop) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     typedef fattn_mma_f16_config<DKQ, DV> c;
@@ -1196,7 +1196,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     GGML_UNUSED(stride_Q2); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
     GGML_UNUSED(jt); GGML_UNUSED(kb0_start); GGML_UNUSED(kb0_stop);
     NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla>
@@ -1206,7 +1206,6 @@ static __global__ void flash_attn_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -1223,7 +1222,7 @@ static __global__ void flash_attn_ext_f16(
                             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) {
-#if defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
+#if defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1268,7 +1267,6 @@ static __global__ void flash_attn_ext_f16(
     // kb0 == k start index when in the output tile.
     int kb0_start = kbc % iter_k;
     int kb0_stop  = min(iter_k, kb0_start + kbc_stop - kbc);
-
     while (kbc < kbc_stop && kb0_stop == iter_k) {
         const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
         const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
@@ -1342,7 +1340,7 @@ static __global__ void flash_attn_ext_f16(
         (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
          ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -1354,7 +1352,7 @@ static __global__ void flash_attn_ext_f16(
     GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
     GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
     NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
+#endif // defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>

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

@@ -13,7 +13,6 @@ static __global__ void flash_attn_tile_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -273,7 +272,7 @@ static __global__ void flash_attn_tile_ext_f16(
         }
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
     GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);

ggml/src/ggml-cuda/fattn-tile-f32.cu

@@ -13,7 +13,6 @@ static __global__ void flash_attn_tile_ext_f32(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -38,7 +37,7 @@ static __global__ void flash_attn_tile_ext_f32(
     return;
 #endif // FP16_MMA_AVAILABLE
     if (use_logit_softcap && !(D == 128 || D == 256)) {
-        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
         GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
         GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
         GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);

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

@@ -16,7 +16,6 @@ static __global__ void flash_attn_vec_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -62,8 +61,7 @@ static __global__ void flash_attn_vec_ext_f16(
     K += nb13*sequence + nb12*(head / gqa_ratio);
     V += nb23*sequence + nb22*(head / gqa_ratio);
 
-    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
-    const float * sinksf = (const float *) (sinks);
+    const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
 
     const float slopef = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
     const half  slopeh = __float2half(slopef);
@@ -77,12 +75,11 @@ static __global__ void flash_attn_vec_ext_f16(
     half2 * KQ2 = (half2 *) KQ;
 
     half kqmax[ncols];
-    half kqsum[ncols];
 #pragma unroll
     for (int j = 0; j < ncols; ++j) {
         kqmax[j] = -HALF_MAX_HALF;
-        kqsum[j] = 0.0f;
     }
+    half kqsum[ncols] = {0.0f};
 
     __shared__ half kqmax_shared[ncols][WARP_SIZE];
     __shared__ half kqsum_shared[ncols][WARP_SIZE];
@@ -286,39 +283,6 @@ static __global__ void flash_attn_vec_ext_f16(
         __syncthreads();
     }
 
-    if (sinksf && blockIdx.y == 0) {
-        const half sink = __float2half(sinksf[head]);
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            if (threadIdx.x == 0) {
-                kqmax_shared[j][threadIdx.y] = fmaxf(kqmax[j], sink);
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            half kqmax_new_j = kqmax_shared[j][threadIdx.x];
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-
-            const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
-            kqmax[j] = kqmax_new_j;
-
-            const half val = hexp(sink - kqmax[j]);
-            kqsum[j] = kqsum[j]*KQ_max_scale;
-
-            if (tid == 0) {
-                kqsum[j] += val;
-            }
-
-            VKQ[j] *= __half2half2(KQ_max_scale);
-        }
-
-        __syncthreads();
-    }
-
 #pragma unroll
     for (int j = 0; j < ncols; ++j) {
         kqsum[j] = warp_reduce_sum((float)kqsum[j]);
@@ -349,7 +313,7 @@ static __global__ void flash_attn_vec_ext_f16(
         dst_meta[((sequence*ne01 + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(kqmax[tid], kqsum[tid]);
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);

ggml/src/ggml-cuda/fattn-vec-f32.cuh

@@ -16,7 +16,6 @@ static __global__ void flash_attn_vec_ext_f32(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -73,8 +72,7 @@ static __global__ void flash_attn_vec_ext_f32(
     K += nb13*sequence + nb12*(head / gqa_ratio);
     V += nb23*sequence + nb22*(head / gqa_ratio);
 
-    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
-    const float * sinksf = (const float *) (sinks);
+    const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
 
     const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
 
@@ -90,12 +88,11 @@ static __global__ void flash_attn_vec_ext_f32(
     }
 
     float kqmax[ncols];
-    float kqsum[ncols];
 #pragma unroll
     for (int j = 0; j < ncols; ++j) {
         kqmax[j] = -FLT_MAX/2.0f;
-        kqsum[j] = 0.0f;
     }
+    float kqsum[ncols] = {0.0f};
 
     __shared__ float kqmax_shared[ncols][WARP_SIZE];
     __shared__ float kqsum_shared[ncols][WARP_SIZE];
@@ -282,39 +279,6 @@ static __global__ void flash_attn_vec_ext_f32(
         __syncthreads();
     }
 
-    if (sinksf && blockIdx.y == 0) {
-        const float sink = sinksf[head];
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            if (threadIdx.x == 0) {
-                kqmax_shared[j][threadIdx.y] = fmaxf(kqmax[j], sink);
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            float kqmax_new_j = kqmax_shared[j][threadIdx.x];
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-
-            const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
-            kqmax[j] = kqmax_new_j;
-
-            const float val = expf(sink - kqmax[j]);
-            kqsum[j] = kqsum[j]*KQ_max_scale;
-
-            if (tid == 0) {
-                kqsum[j] += val;
-            }
-
-            VKQ[j] *= KQ_max_scale;
-        }
-
-        __syncthreads();
-    }
-
 #pragma unroll
     for (int j = 0; j < ncols; ++j) {
         kqsum[j] = warp_reduce_sum(kqsum[j]);

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

@@ -29,7 +29,6 @@ static __global__ void flash_attn_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
         const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
@@ -424,7 +423,7 @@ static __global__ void flash_attn_ext_f16(
         dst_meta[j_dst_unrolled] = dst_meta_val;
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
     GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);

ggml/src/ggml-cuda/fattn.cu

@@ -269,28 +269,17 @@ static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, gg
 }
 
 void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    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 ggml_tensor * sinks = dst->src[4];
+    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];
 
     ggml_cuda_set_device(ctx.device);
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
-    // TODO: currently only vec implementation for sinks is supported [TAG_ATTN_SINKS]
-    if (sinks) {
-        if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
-            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-        }
-        return;
-    }
-
 #if defined(GGML_HIP_ROCWMMA_FATTN)
     if (GGML_CUDA_CC_IS_AMD(cc) && fp16_mma_available(cc)) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
@@ -327,7 +316,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations
     const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
     const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (Q->ne[2] > 4*K->ne[2] && K->ne[1] >= 8192);
-    const bool mma_faster_for_bs1 = turing_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
+    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
         (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
     const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % (2*warp_size) == 0;
     if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
@@ -340,7 +329,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     }
 
     // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
-    if (fp16_mma_available(cc) && !turing_mma_available(cc)) {
+    if (fp16_mma_available(cc) && !new_mma_available(cc)) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
         return;
     }

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

@@ -4,7 +4,6 @@
 
 #include "ggml-cuda/common.cuh"
 #include "ggml-cuda/acc.cuh"
-#include "ggml-cuda/add-id.cuh"
 #include "ggml-cuda/arange.cuh"
 #include "ggml-cuda/argmax.cuh"
 #include "ggml-cuda/argsort.cuh"
@@ -22,9 +21,8 @@
 #include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
 #include "ggml-cuda/im2col.cuh"
-#include "ggml-cuda/mmf.cuh"
 #include "ggml-cuda/mmq.cuh"
-#include "ggml-cuda/mmvf.cuh"
+#include "ggml-cuda/mmv.cuh"
 #include "ggml-cuda/mmvq.cuh"
 #include "ggml-cuda/norm.cuh"
 #include "ggml-cuda/opt-step-adamw.cuh"
@@ -2009,9 +2007,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     const bool bad_padding_clear = ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE
         && ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) && src0->view_src;
 
-    bool use_mul_mat_vec_f = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
-        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
-    bool use_mul_mat_f     = !ggml_is_quantized(src0->type)
+    bool use_mul_mat_vec   = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
     bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
@@ -2031,18 +2027,14 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
             }
 
             const int cc            = ggml_cuda_info().devices[id].cc;
-            const int warp_size     = ggml_cuda_info().devices[id].warp_size;
             use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
-            use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
-            use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
+            use_mul_mat_vec         = use_mul_mat_vec           && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
             any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
         }
     } else {
         const int cc            = ggml_cuda_info().devices[ctx.device].cc;
-        const int warp_size     = ggml_cuda_info().devices[ctx.device].warp_size;
         use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
-        use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
-        use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
+        use_mul_mat_vec         = use_mul_mat_vec           && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
         any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
     }
 
@@ -2055,17 +2047,15 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
     //TODO update for generic tensor parallelism
-    const int cc                 = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    const int cc                     = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     bool use_batched_cublas_f16  = src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16);
     bool use_batched_cublas_bf16 = src0->type == GGML_TYPE_BF16 && bf16_mma_hardware_available(cc);
     bool use_batched_cublas_f32  = src0->type == GGML_TYPE_F32;
 
-    if (!split && use_mul_mat_vec_f) {
+    if (!split && use_mul_mat_vec) {
         // the custom F16 vector kernel can be used over batched cuBLAS GEMM
         // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention)
-        ggml_cuda_mul_mat_vec_f(ctx, src0, src1, nullptr, dst);
-    } else if (!split && use_mul_mat_f) {
-        ggml_cuda_mul_mat_f(ctx, src0, src1, nullptr, dst);
+        ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_vec_q) {
         ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_q) {
@@ -2074,8 +2064,8 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
         && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
         // general KQ + KQV multi-batch without FlashAttention
         ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
-    } else if (use_mul_mat_vec_f) {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_f, nullptr);
+    } else if (use_mul_mat_vec) {
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec, nullptr);
     } else if (use_mul_mat_vec_q) {
         ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda);
     } else if (use_mul_mat_q) {
@@ -2103,7 +2093,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
             if (ggml_is_quantized(src0->type)) {
                 ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
             } else {
-                ggml_cuda_mul_mat_vec_f(ctx, src0, src1, ids, dst);
+                ggml_cuda_mul_mat_vec(ctx, src0, src1, ids, dst);
             }
             return;
         }
@@ -2269,9 +2259,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_ADD1: // TODO: more efficient implementation
             ggml_cuda_op_add(ctx, dst);
             break;
-        case GGML_OP_ADD_ID:
-            ggml_cuda_op_add_id(ctx, dst);
-            break;
         case GGML_OP_SUB:
             ggml_cuda_op_sub(ctx, dst);
             break;
@@ -2346,9 +2333,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_GLU_OP_SWIGLU:
                     ggml_cuda_op_swiglu(ctx, dst);
                     break;
-                case GGML_GLU_OP_SWIGLU_OAI:
-                    ggml_cuda_op_swiglu_oai(ctx, dst);
-                    break;
                 case GGML_GLU_OP_GEGLU_ERF:
                     ggml_cuda_op_geglu_erf(ctx, dst);
                     break;
@@ -2623,9 +2607,6 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
 
     const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected";
     const std::string gemma3n_per_layer_proj_src1_name = "per_layer_proj";
-    const std::string ffn_moe_gate_bias_prefix = "ffn_moe_gate_biased";
-    const std::string ffn_moe_up_bias_prefix = "ffn_moe_up_biased";
-    const std::string ffn_moe_down_bias_prefix = "ffn_moe_down_biased";
 
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
@@ -2648,13 +2629,7 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
 #endif
         }
 
-        if (node->op == GGML_OP_ADD &&
-            node->src[1] && node->src[1]->ne[1] > 1 &&
-            (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) &&
-            (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true) &&
-            strncmp(node->name, ffn_moe_gate_bias_prefix.c_str(), ffn_moe_gate_bias_prefix.size()) != 0 &&
-            strncmp(node->name, ffn_moe_up_bias_prefix.c_str(), ffn_moe_up_bias_prefix.size()) != 0 &&
-            strncmp(node->name, ffn_moe_down_bias_prefix.c_str(), ffn_moe_down_bias_prefix.size()) != 0) {
+        if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1 && (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) && (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true)) {
             // disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation
             // by means of matching node names. See
             // https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and
@@ -3252,7 +3227,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous_1(op->src[0]);
@@ -3303,7 +3277,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
-                    case GGML_TYPE_MXFP4:
                     case GGML_TYPE_Q2_K:
                     case GGML_TYPE_Q3_K:
                     case GGML_TYPE_Q4_K:
@@ -3450,7 +3423,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
         case GGML_OP_ADD:
-        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
@@ -3525,16 +3497,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
 #endif // FLASH_ATTN_AVAILABLE
             if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                 const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
-                if (!turing_mma_available(cc)) {
+                if (!new_mma_available(cc)) {
                     return false;
                 }
                 const int gqa_ratio = op->src[0]->ne[2] / op->src[1]->ne[2];
                 return op->src[1]->ne[0] == 576 && op->src[2]->ne[0] == 512 && op->src[3] && gqa_ratio % 16 == 0;
             }
-            // TODO: more general-purpose attention sink support [TAG_ATTN_SINKS]
-            if (op->src[4] && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 128) { // currently only sinks for head_size 64 and 128 are supported
-                return false;
-            }
             if (op->src[0]->ne[0] == 192) {
                 return false;
             }

ggml/src/ggml-cuda/im2col.cu

@@ -1,5 +1,7 @@
 #include "im2col.cuh"
 
+#define MIN(a, b) (a) < (b) ? (a) : (b)
+
 #define MAX_GRIDDIM_Z 65535
 
 template <typename T>
@@ -36,9 +38,6 @@ static  __global__ void im2col_kernel(
             dst[offset_dst] = x[offset_src + iih * IW + iiw];
         }
     }
-
-    GGML_UNUSED(IC);
-    GGML_UNUSED(KH);
 }
 
 // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]

ggml/src/ggml-cuda/mma.cuh

@@ -23,13 +23,13 @@
 static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
     int ret = 0;
 
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
         : "=r"(ret) : "r"(x));
 #else
     GGML_UNUSED(x);
     NO_DEVICE_CODE;
-#endif // defined(TURING_MMA_AVAILABLE)
+#endif // defined(NEW_MMA_AVAILABLE)
     return ret;
 }
 
@@ -167,38 +167,6 @@ namespace ggml_cuda_mma {
         }
     };
 
-    template <int I_, int J_>
-    struct tile<I_, J_, nv_bfloat162> {
-        static constexpr int I  = I_;
-        static constexpr int J  = J_;
-        static constexpr int ne = I * J / WARP_SIZE;
-        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
-
-        static __device__ __forceinline__ int get_i(const int l) {
-            if constexpr (I == 8 && J == 8) {
-                return threadIdx.x / 4;
-            } else if constexpr (I == 16 && J == 4) {
-                return l * 8 + threadIdx.x / 4;
-            } else if constexpr (I == 16 && J == 8) {
-                return (l % 2) * 8 + threadIdx.x / 4;
-            } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
-            }
-        }
-
-        static __device__ __forceinline__ int get_j(const int l) {
-            if constexpr (I == 8 && J == 8) {
-                return l * 4 + threadIdx.x % 4;
-            } else if constexpr (I == 16 && J == 4) {
-                return threadIdx.x % 4;
-            } else if constexpr (I == 16 && J == 8) {
-                return (l / 2) * 4 + threadIdx.x % 4;
-            } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
-            }
-        }
-    };
-
     template <int I, int J>
     static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
         tile<I, J/2, half2> ret;
@@ -241,7 +209,7 @@ namespace ggml_cuda_mma {
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<8, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + ((threadIdx.x / t.I) * (t.J / 2)) % t.J;
         asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
@@ -249,13 +217,13 @@ namespace ggml_cuda_mma {
             : "l"(xs));
 #else
         load_generic(t, xs0, stride);
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<16, 4, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride;
         asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
@@ -264,13 +232,13 @@ namespace ggml_cuda_mma {
 #else
         load_generic(xs0, stride);
         GGML_UNUSED(t);
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#if defined(TURING_MMA_AVAILABLE)
+#if defined(NEW_MMA_AVAILABLE)
         int * xi = (int * ) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
@@ -278,13 +246,13 @@ namespace ggml_cuda_mma {
             : "l"(xs));
 #else
         load_generic(t, xs0, stride);
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix_trans(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         int * xi = (int * ) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
@@ -295,12 +263,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(xs0);
         GGML_UNUSED(stride);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, int> & D, const tile<16, 4, int> & A, const tile<8, 4, int> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
             : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
@@ -319,12 +287,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, int> & D, const tile<16, 8, int> & A, const tile<8, 8, int> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
             : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
@@ -349,12 +317,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 4, half2> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -376,12 +344,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, half2> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -412,29 +380,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
-    }
-
-    static __device__ __forceinline__ void mma(
-            tile<16, 8, float> & D, const tile<16, 8, float> & A, const tile<8, 8, float> & B) {
-#ifdef AMPERE_MMA_AVAILABLE
-        const int * Axi = (const int *) A.x;
-        const int * Bxi = (const int *) B.x;
-        int       * Dxi = (int       *) D.x;
-        asm("mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
-            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
-            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
-#else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
-        NO_DEVICE_CODE;
-#endif // AMPERE_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, float> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -456,29 +407,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
-    }
-
-    static __device__ __forceinline__ void mma(
-            tile<16, 8, float> & D, const tile<16, 8, nv_bfloat162> & A, const tile<8, 8, nv_bfloat162> & B) {
-#ifdef AMPERE_MMA_AVAILABLE
-        const int * Axi = (const int *) A.x;
-        const int * Bxi = (const int *) B.x;
-        int       * Dxi = (int       *) D.x;
-        asm("mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
-            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
-            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
-#else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
-        NO_DEVICE_CODE;
-#endif // AMPERE_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 16, float> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -509,7 +443,7 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(

ggml/src/ggml-cuda/mmf.cu

@@ -1,431 +0,0 @@
-#include "ggml.h"
-#include "common.cuh"
-#include "mma.cuh"
-#include "mmf.cuh"
-
-using namespace ggml_cuda_mma;
-
-#define MMF_ROWS_PER_BLOCK 32
-
-template <typename T, int rows_per_block, int cols_per_block, int nwarps>
-__launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1)
-static __global__ void mul_mat_f(
-        const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
-        const int ncols, const int nchannels_y, const int stride_row, const int stride_col_y, const int stride_col_dst,
-        const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
-        const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    typedef tile<16, 8, T>     tile_A;
-    typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
-
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
-    constexpr int tile_k_padded = warp_size + 4;
-    constexpr int ntA = rows_per_block / tile_A::I;
-    constexpr int ntB = (cols_per_block + tile_B::I - 1) / tile_B::I;
-
-    const int row0        = blockIdx.x * rows_per_block;
-    const int channel_dst = blockIdx.y;
-    const int channel_x   = channel_dst / channel_ratio;
-    const int channel_y   = channel_dst;
-    const int sample_dst  = blockIdx.z;
-    const int sample_x    = sample_dst / sample_ratio;
-    const int sample_y    = sample_dst;
-
-    x   += int64_t(sample_x)  *stride_sample_x   + channel_x  *stride_channel_x   + row0*stride_row ;
-    y   += int64_t(sample_y)  *stride_sample_y   + channel_y  *stride_channel_y;
-    dst += int64_t(sample_dst)*stride_sample_dst + channel_dst*stride_channel_dst;
-
-    const float2 * y2 = (const float2 *) y;
-
-    extern __shared__ char data_mmv[];
-
-    tile_C C[ntA][ntB];
-
-    T * tile_xy = (T *) data_mmv + threadIdx.y*(tile_A::I * tile_k_padded);
-
-    for (int col = threadIdx.y*warp_size + threadIdx.x; col < ncols; col += nwarps*warp_size) {
-        tile_A A[ntA][warp_size / tile_A::J];
-#pragma unroll
-        for (int itA = 0; itA < ntA; ++itA) {
-#pragma unroll
-            for (int i = 0; i < tile_A::I; ++i) {
-                tile_xy[i*tile_k_padded + threadIdx.x] = x[(itA*tile_A::I + i)*stride_row  + col];
-            }
-#pragma unroll
-            for (int k0 = 0; k0 < warp_size; k0 += tile_A::J) {
-                load_ldmatrix(A[itA][k0/tile_A::J], tile_xy + k0, tile_k_padded);
-            }
-        }
-
-#pragma unroll
-        for (int itB = 0; itB < ntB; ++itB) {
-            if constexpr (std::is_same_v<T, float>) {
-#pragma unroll
-                for (int j0 = 0; j0 < tile_B::I; ++j0) {
-                    const int j = j0 + itB*tile_B::I;
-
-                    tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[j*stride_col_y + col] : 0.0f;
-                }
-            } else if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
-#pragma unroll
-                for (int j0 = 0; j0 < tile_B::I; ++j0) {
-                    const int j = j0 + itB*tile_B::I;
-
-                    const float2 tmp = j < cols_per_block ? y2[j*stride_col_y + col] : make_float2(0.0f, 0.0f);
-                    tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
-                }
-            } else {
-                static_assert(std::is_same_v<T, void>, "unsupported type");
-            }
-#pragma unroll
-            for (int k0 = 0; k0 < warp_size; k0 += tile_B::J) {
-                tile_B B;
-                load_ldmatrix(B, tile_xy + k0, tile_k_padded);
-#pragma unroll
-                for (int itA = 0; itA < ntA; ++itA) {
-                    mma(C[itA][itB], A[itA][k0/tile_B::J], B);
-                }
-            }
-        }
-    }
-
-    float * buf_iw = (float *) data_mmv;
-    constexpr int kiw = nwarps*rows_per_block + 4;
-
-    if (nwarps > 1) {
-        __syncthreads();
-    }
-#pragma unroll
-    for (int itB = 0; itB < ntB; ++itB) {
-#pragma unroll
-        for (int itA = 0; itA < ntA; ++itA) {
-#pragma unroll
-            for (int l = 0; l < tile_C::ne; ++l) {
-                const int i = threadIdx.y*rows_per_block + itA*tile_C::I + tile_C::get_i(l);
-                const int j = itB*tile_C::J + tile_C::get_j(l);
-                buf_iw[j*kiw + i] = C[itA][itB].x[l];
-            }
-        }
-    }
-
-    if (nwarps > 1) {
-        __syncthreads();
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < cols_per_block; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
-
-        if (j0 + nwarps > cols_per_block && j >= cols_per_block) {
-            return;
-        }
-
-        float sum = 0.0f;
-        static_assert(rows_per_block == warp_size, "need loop/check");
-#pragma unroll
-        for (int i0 = 0; i0 < nwarps*rows_per_block; i0 += rows_per_block) {
-            const int i = i0 + threadIdx.x;
-
-            sum += buf_iw[j*kiw + i];
-        }
-        dst[j*stride_col_dst + row0 + threadIdx.x] = sum;
-    }
-#else
-    NO_DEVICE_CODE;
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(ids); GGML_UNUSED(dst);
-    GGML_UNUSED(ncols); GGML_UNUSED(nchannels_y); GGML_UNUSED(stride_row); GGML_UNUSED(stride_col_y); GGML_UNUSED(stride_col_dst);
-    GGML_UNUSED(channel_ratio); GGML_UNUSED(stride_channel_x); GGML_UNUSED(stride_channel_y); GGML_UNUSED(stride_channel_dst);
-    GGML_UNUSED(sample_ratio); GGML_UNUSED(stride_sample_x); GGML_UNUSED(stride_sample_y); GGML_UNUSED(stride_sample_dst);
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-}
-
-template <typename T, int cols_per_block>
-static void mul_mat_f_cuda(
-        const T * x, const float * y, const int32_t * ids, float * dst,
-        const int64_t ncols_x, const int64_t nrows_x,
-        const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
-        const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
-        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
-        const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
-        cudaStream_t stream) {
-    typedef tile<16, 8, T>     tile_A;
-    typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
-
-    GGML_ASSERT(!ids && "mul_mat_id not implemented");
-
-    GGML_ASSERT(ncols_x      % 2 == 0);
-    GGML_ASSERT(stride_row   % 2 == 0);
-    GGML_ASSERT(stride_col_y % 2 == 0);
-    GGML_ASSERT(ids || nchannels_dst % nchannels_x == 0);
-    GGML_ASSERT(       nsamples_dst  % nsamples_x  == 0);
-    const int64_t channel_ratio = nchannels_dst / nchannels_x;
-    const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
-
-    const int device = ggml_cuda_get_device();
-    const int warp_size = ggml_cuda_info().devices[device].warp_size;
-
-    int64_t nwarps_best     = 1;
-    int64_t niter_best      = (ncols_x + warp_size*2 - 1) / (warp_size*2);
-    int64_t max_block_size  = 256;
-    for (int64_t nwarps = 2; nwarps <= max_block_size/warp_size; nwarps++) {
-        const int64_t niter = (ncols_x + nwarps*warp_size*2 - 1) / (nwarps*warp_size*2);
-        if (niter < niter_best) {
-            niter_best  = niter;
-            nwarps_best = nwarps;
-        }
-    }
-
-    constexpr int rows_per_block = MMF_ROWS_PER_BLOCK;
-    const int nbytes_shared_iter = nwarps_best * tile_A::I * (warp_size + 4) * 4;
-    const int nbytes_shared_combine = GGML_PAD(cols_per_block, tile_B::I) * (nwarps_best*rows_per_block + 4) * 4;
-    const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine);
-    const dim3 block_nums(nrows_x/rows_per_block, nchannels_dst, nsamples_dst);
-    const dim3 block_dims(warp_size, nwarps_best, 1);
-    switch (nwarps_best) {
-        case 1: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 1><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 2: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 2><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 3: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 3><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 4: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 4><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 5: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 5><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 6: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 6><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 7: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 7><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 8: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 8><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        default: {
-            GGML_ABORT("fatal error");
-        } break;
-    }
-}
-
-template <typename T>
-static void mul_mat_f_switch_cols_per_block(
-        const T * x, const float * y, const int32_t * ids, float * dst,
-        const int64_t ncols_x, const int64_t nrows_x, const int64_t ncols_dst,
-        const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
-        const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
-        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
-        const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
-        cudaStream_t stream) {
-    switch (ncols_dst) {
-        case  1: {
-            mul_mat_f_cuda<T,  1>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  2: {
-            mul_mat_f_cuda<T,  2>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  3: {
-            mul_mat_f_cuda<T,  3>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  4: {
-            mul_mat_f_cuda<T,  4>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  5: {
-            mul_mat_f_cuda<T,  5>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  6: {
-            mul_mat_f_cuda<T,  6>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  7: {
-            mul_mat_f_cuda<T,  7>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  8: {
-            mul_mat_f_cuda<T,  8>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  9: {
-            mul_mat_f_cuda<T,  9>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 10: {
-            mul_mat_f_cuda<T, 10>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 11: {
-            mul_mat_f_cuda<T, 11>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 12: {
-            mul_mat_f_cuda<T, 12>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 13: {
-            mul_mat_f_cuda<T, 13>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 14: {
-            mul_mat_f_cuda<T, 14>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 15: {
-            mul_mat_f_cuda<T, 15>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 16: {
-            mul_mat_f_cuda<T, 16>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        default: {
-            GGML_ABORT("fatal error");
-        } break;
-    }
-}
-
-void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
-    GGML_ASSERT(        src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(!ids ||  ids->type == GGML_TYPE_I32);
-    GGML_ASSERT(         dst->type == GGML_TYPE_F32);
-
-    GGML_TENSOR_BINARY_OP_LOCALS;
-
-    const size_t ts_src0 = ggml_type_size(src0->type);
-    const size_t ts_src1 = ggml_type_size(src1->type);
-    const size_t ts_dst  = ggml_type_size(dst->type);
-
-    GGML_ASSERT(ne13 == ne3);
-
-    GGML_ASSERT(        nb00       == ts_src0);
-    GGML_ASSERT(        nb10       == ts_src1);
-    GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
-    GGML_ASSERT(        nb0        == ts_dst);
-
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
-
-    const float   * src1_d =       (const float   *) src1->data;
-    const int32_t *  ids_d = ids ? (const int32_t *)  ids->data : nullptr;
-    float         *  dst_d =       (float         *)  dst->data;
-
-    const int64_t s01 = src0->nb[1] / ts_src0;
-    const int64_t s11 = src1->nb[1] / ts_src1;
-    const int64_t s1  =  dst->nb[1] / ts_dst;
-    const int64_t s02 = src0->nb[2] / ts_src0;
-    const int64_t s12 = src1->nb[2] / ts_src1;
-    const int64_t s2  =  dst->nb[2] / ts_dst;
-    const int64_t s03 = src0->nb[3] / ts_src0;
-    const int64_t s13 = src1->nb[3] / ts_src1;
-    const int64_t s3  =  dst->nb[3] / ts_dst;
-
-    // For MUL_MAT_ID the memory layout is different than for MUL_MAT:
-    const int64_t ncols_dst          = ids ? ne2  : ne1;
-    const int64_t nchannels_y        = ids ? ne11 : ne12;
-    const int64_t nchannels_dst      = ids ? ne1  : ne2;
-    const int64_t stride_channel_dst = ids ? s1   : s2;
-    const int64_t stride_channel_y   = ids ? s11  : s12;
-
-    GGML_ASSERT(!ids || ncols_dst == 1);
-
-    switch (src0->type) {
-        case GGML_TYPE_F32: {
-            const float * src0_d = (const float *) src0->data;
-            constexpr int vals_per_T = 1;
-            mul_mat_f_switch_cols_per_block(
-                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
-                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
-        } break;
-        case GGML_TYPE_F16: {
-            const half2 * src0_d = (const half2 *) src0->data;
-            constexpr int vals_per_T = 2;
-            mul_mat_f_switch_cols_per_block(
-                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
-                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
-        } break;
-        case GGML_TYPE_BF16: {
-            const nv_bfloat162 * src0_d = (const nv_bfloat162 *) src0->data;
-            constexpr int vals_per_T = 2;
-            mul_mat_f_switch_cols_per_block(
-                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
-                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
-        } break;
-        default:
-            GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
-    }
-}
-
-bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne, int64_t ne11) {
-    if (src0_ne[0] % (warp_size * (4/ggml_type_size(type))) != 0) {
-        return false;
-    }
-    if (src0_ne[1] % MMF_ROWS_PER_BLOCK != 0) {
-        return false;
-    }
-    if (ne11 > 16) {
-        return false;
-    }
-    switch (type) {
-        case GGML_TYPE_F32:
-            return ampere_mma_available(cc);
-        case GGML_TYPE_F16:
-            return turing_mma_available(cc);
-        case GGML_TYPE_BF16:
-            return ampere_mma_available(cc);
-        default:
-            return false;
-    }
-}

ggml/src/ggml-cuda/mmf.cuh

@@ -1,5 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
-
-bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, int64_t ne11);

ggml/src/ggml-cuda/mmq.cu

@@ -20,9 +20,6 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
         case GGML_TYPE_Q8_0:
             mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
             break;
-        case GGML_TYPE_MXFP4:
-            mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
-            break;
         case GGML_TYPE_Q2_K:
             mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
             break;
@@ -285,7 +282,6 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -310,7 +306,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         return false;
     }
 
-    if (turing_mma_available(cc)) {
+    if (new_mma_available(cc)) {
         return true;
     }
 

ggml/src/ggml-cuda/mmv.cu

@@ -1,9 +1,9 @@
 #include "ggml.h"
 #include "common.cuh"
-#include "mmvf.cuh"
+#include "mmv.cuh"
 
 template <typename T, typename type_acc, int ncols_dst, int block_size>
-static __global__ void mul_mat_vec_f(
+static __global__ void mul_mat_vec(
         const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
         const int ncols2, const int nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst,
         const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
@@ -37,7 +37,7 @@ static __global__ void mul_mat_vec_f(
 
     float sumf[ncols_dst] = {0.0f};
 
-    if constexpr (std::is_same_v<T, float>) {
+    if constexpr (std::is_same<T, float>::value) {
         const float2 * x2 = (const float2 *) x;
 
         for (int col2 = tid; col2 < ncols2; col2 += block_size) {
@@ -50,10 +50,10 @@ static __global__ void mul_mat_vec_f(
                 sumf[j] += tmpx.y*tmpy.y;
             }
         }
-    } else if constexpr (std::is_same_v<T, half>) {
+    } else if constexpr (std::is_same<T, half>::value) {
         const half2 * x2 = (const half2 *) x;
 
-        if (std::is_same_v<type_acc, float>) {
+        if (std::is_same<type_acc, float>::value) {
             for (int col2 = tid; col2 < ncols2; col2 += block_size) {
                 const float2 tmpx = __half22float2(x2[col2]);
 
@@ -86,7 +86,7 @@ static __global__ void mul_mat_vec_f(
             NO_DEVICE_CODE;
 #endif // FP16_AVAILABLE
         }
-    } else if constexpr (std::is_same_v<T, nv_bfloat16>) {
+    } else if constexpr (std::is_same<T, nv_bfloat16>::value) {
         const int * x2 = (const int *) x;
         for (int col2 = tid; col2 < ncols2; col2 += block_size) {
             const int tmpx = x2[col2];
@@ -98,7 +98,7 @@ static __global__ void mul_mat_vec_f(
             }
         }
     } else {
-        static_assert(std::is_same_v<T, void>, "unsupported type");
+        static_assert(std::is_same<T, void>::value, "unsupported type");
     }
 
 #pragma unroll
@@ -126,7 +126,7 @@ static __global__ void mul_mat_vec_f(
 }
 
 template <typename T, typename type_acc, int ncols_dst>
-static void launch_mul_mat_vec_f_cuda(
+static void launch_mul_mat_vec_cuda(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
@@ -141,9 +141,11 @@ static void launch_mul_mat_vec_f_cuda(
     GGML_ASSERT(       nsamples_dst  % nsamples_x  == 0);
     const int64_t channel_ratio = nchannels_dst / nchannels_x;
     const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
+    int device;
+    int warp_size;
 
-    const int device = ggml_cuda_get_device();
-    const int warp_size = ggml_cuda_info().devices[device].warp_size;
+    CUDA_CHECK(cudaGetDevice(&device));
+    warp_size = ggml_cuda_info().devices[device].warp_size;
 
     int64_t block_size_best = warp_size;
     int64_t niter_best      = (ncols + 2*warp_size - 1) / (2*warp_size);
@@ -159,54 +161,54 @@ static void launch_mul_mat_vec_f_cuda(
         }
     }
 
-    const int nbytes_shared = warp_size*sizeof(float);
+    const int smem = warp_size*sizeof(float);
     const dim3 block_nums(nrows, nchannels_dst, nsamples_dst);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
         case   32: {
-            mul_mat_vec_f<T, type_acc, ncols_dst,  32><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst,  32><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   64: {
-            mul_mat_vec_f<T, type_acc, ncols_dst,  64><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst,  64><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   96: {
-            mul_mat_vec_f<T, type_acc, ncols_dst,  96><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst,  96><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  128: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  160: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  192: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  224: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  256: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
@@ -218,7 +220,7 @@ static void launch_mul_mat_vec_f_cuda(
 }
 
 template <typename T, typename type_acc>
-static void mul_mat_vec_f_cuda_switch_ncols_dst(
+static void mul_mat_vec_cuda_switch_ncols_dst(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
@@ -228,49 +230,49 @@ static void mul_mat_vec_f_cuda_switch_ncols_dst(
         cudaStream_t stream) {
     switch (ncols_dst) {
         case 1:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 1>
+            launch_mul_mat_vec_cuda<T, type_acc, 1>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 2:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 2>
+            launch_mul_mat_vec_cuda<T, type_acc, 2>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 3:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 3>
+            launch_mul_mat_vec_cuda<T, type_acc, 3>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 4:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 4>
+            launch_mul_mat_vec_cuda<T, type_acc, 4>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 5:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 5>
+            launch_mul_mat_vec_cuda<T, type_acc, 5>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 6:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 6>
+            launch_mul_mat_vec_cuda<T, type_acc, 6>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 7:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 7>
+            launch_mul_mat_vec_cuda<T, type_acc, 7>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 8:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 8>
+            launch_mul_mat_vec_cuda<T, type_acc, 8>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
@@ -282,7 +284,7 @@ static void mul_mat_vec_f_cuda_switch_ncols_dst(
 }
 
 template<typename T>
-static void mul_mat_vec_f_cuda(
+static void mul_mat_vec_cuda(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
         const int64_t stride_row, const int64_t stride_col_y, const int stride_col_dst,
@@ -290,22 +292,22 @@ static void mul_mat_vec_f_cuda(
         const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
         const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
         enum ggml_prec prec, cudaStream_t stream) {
-    if constexpr(std::is_same_v<T, half>) {
+    if constexpr(std::is_same<T, half>::value) {
         if (prec == GGML_PREC_DEFAULT) {
-            mul_mat_vec_f_cuda_switch_ncols_dst<T, half>
+            mul_mat_vec_cuda_switch_ncols_dst<T, half>
                 (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             return;
         }
     }
-    mul_mat_vec_f_cuda_switch_ncols_dst<T, float>
+    mul_mat_vec_cuda_switch_ncols_dst<T, float>
         (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
          nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
          stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
 }
 
-void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
     GGML_ASSERT(        src1->type == GGML_TYPE_F32);
     GGML_ASSERT(!ids ||  ids->type == GGML_TYPE_I32);
     GGML_ASSERT(         dst->type == GGML_TYPE_F32);
@@ -353,19 +355,19 @@ void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
@@ -374,7 +376,7 @@ void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor
     }
 }
 
-void ggml_cuda_op_mul_mat_vec_f(
+void ggml_cuda_op_mul_mat_vec(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
@@ -412,19 +414,19 @@ void ggml_cuda_op_mul_mat_vec_f(
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0_dd_i;
-            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0_dd_i;
-            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
-            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
@@ -440,15 +442,15 @@ void ggml_cuda_op_mul_mat_vec_f(
     GGML_UNUSED(src1_padded_row_size);
 }
 
-bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
+bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
     if (src0_ne[0] % 2 != 0) {
         return false;
     }
     switch (type) {
         case GGML_TYPE_F32:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
-                if (ampere_mma_available(cc)) {
-                    return ne11 <= 3;
+                if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
+                    return ne11 <= 8;
                 }
                 if (cc >= GGML_CUDA_CC_TURING) {
                     return ne11 <= 4;
@@ -464,9 +466,6 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
         case GGML_TYPE_F16:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
                 const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
-                if (ampere_mma_available(cc)) {
-                    return src0_small && ne11 == 1;
-                }
                 if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                     return src0_small && ne11 <= 4;
                 }
@@ -487,9 +486,6 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
         case GGML_TYPE_BF16:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
                 const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
-                if (ampere_mma_available(cc)) {
-                    return src0_small && ne11 == 1;
-                }
                 if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                     return src0_small && ne11 <= 4;
                 }

ggml/src/ggml-cuda/mmv.cuh

@@ -1,11 +1,11 @@
 #include "common.cuh"
 
-void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
+void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
 
-void ggml_cuda_op_mul_mat_vec_f(
+void ggml_cuda_op_mul_mat_vec(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream);
 
-bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);
+bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);

ggml/src/ggml-cuda/mmvq.cu

@@ -13,7 +13,6 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
         case GGML_TYPE_Q5_0:    return vec_dot_q5_0_q8_1;
         case GGML_TYPE_Q5_1:    return vec_dot_q5_1_q8_1;
         case GGML_TYPE_Q8_0:    return vec_dot_q8_0_q8_1;
-        case GGML_TYPE_MXFP4:   return vec_dot_mxfp4_q8_1;
         case GGML_TYPE_Q2_K:    return vec_dot_q2_K_q8_1;
         case GGML_TYPE_Q3_K:    return vec_dot_q3_K_q8_1;
         case GGML_TYPE_Q4_K:    return vec_dot_q4_K_q8_1;
@@ -39,7 +38,6 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
         case GGML_TYPE_Q5_0:    return VDR_Q5_0_Q8_1_MMVQ;
         case GGML_TYPE_Q5_1:    return VDR_Q5_1_Q8_1_MMVQ;
         case GGML_TYPE_Q8_0:    return VDR_Q8_0_Q8_1_MMVQ;
-        case GGML_TYPE_MXFP4:   return VDR_MXFP4_Q8_1_MMVQ;
         case GGML_TYPE_Q2_K:    return VDR_Q2_K_Q8_1_MMVQ;
         case GGML_TYPE_Q3_K:    return VDR_Q3_K_Q8_1_MMVQ;
         case GGML_TYPE_Q4_K:    return VDR_Q4_K_Q8_1_MMVQ;
@@ -386,13 +384,6 @@ static void mul_mat_vec_q_switch_type(
                  nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
                  stream);
             break;
-        case GGML_TYPE_MXFP4:
-            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_MXFP4>
-                (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
-                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 stream);
-            break;
         case GGML_TYPE_Q2_K:
             mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
                 (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,

ggml/src/ggml-cuda/softmax.cu

@@ -45,7 +45,7 @@ struct soft_max_params {
 #endif // __clang__
 template <bool use_shared, int ncols_template, int block_size_template, typename T>
 static __global__ void soft_max_f32(
-        const float * x, const T * mask, const float * sinks, float * dst, const soft_max_params p) {
+        const float * x, const T * mask, float * dst, const soft_max_params p) {
     const int ncols = ncols_template == 0 ? p.ncols : ncols_template;
 
     const int tid  = threadIdx.x;
@@ -77,7 +77,7 @@ static __global__ void soft_max_f32(
     // shared memory buffer to cache values between iterations:
     float * vals = use_shared ? buf_iw + WARP_SIZE : dst;
 
-    float max_val = sinks ? sinks[i02] : -INFINITY;
+    float max_val = -INFINITY;
 
 #pragma unroll
     for (int col0 = 0; col0 < ncols; col0 += block_size) {
@@ -143,10 +143,6 @@ static __global__ void soft_max_f32(
         tmp = warp_reduce_sum(tmp);
     }
 
-    if (sinks) {
-        tmp += expf(sinks[i02] - max_val);
-    }
-
     const float inv_sum = 1.0f / tmp;
 
 #pragma unroll
@@ -187,7 +183,7 @@ static __global__ void soft_max_back_f32(
 }
 
 template<int... Ns, typename T>
-static void launch_soft_max_kernels(const float * x, const T * mask, const float * sinks, float * dst,
+static void launch_soft_max_kernels(const float * x, const T * mask, float * dst,
                              const soft_max_params & p, cudaStream_t stream, dim3 block_dims, dim3 block_nums, size_t nbytes_shared)
 {
     const int id       = ggml_cuda_get_device();
@@ -200,7 +196,7 @@ static void launch_soft_max_kernels(const float * x, const T * mask, const float
         if (p.ncols == ncols) {
             CUDA_SET_SHARED_MEMORY_LIMIT((soft_max_f32<true, ncols, block, T>), smpbo);
             soft_max_f32<true, ncols, block><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, mask, sinks, dst, p);
+                (x, mask, dst, p);
             return true;
         }
         return false;
@@ -213,12 +209,12 @@ static void launch_soft_max_kernels(const float * x, const T * mask, const float
 
     //default case
     CUDA_SET_SHARED_MEMORY_LIMIT((soft_max_f32<true, 0, 0, T>), smpbo);
-    soft_max_f32<true, 0, 0><<<block_nums, block_dims, nbytes_shared, stream>>>(x, mask, sinks, dst, p);
+    soft_max_f32<true, 0, 0><<<block_nums, block_dims, nbytes_shared, stream>>>(x, mask, dst, p);
 }
 
 
 template<typename T>
-static void soft_max_f32_cuda(const float * x, const T * mask, const float * sinks, float * dst, const soft_max_params & params, cudaStream_t stream) {
+static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const soft_max_params & params, cudaStream_t stream) {
     int nth = WARP_SIZE;
     const int64_t ncols_x = params.ncols;
 
@@ -234,10 +230,10 @@ static void soft_max_f32_cuda(const float * x, const T * mask, const float * sin
 
 
     if (nbytes_shared <= smpbo) {
-        launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(x, mask, sinks, dst, params, stream, block_dims, block_nums, nbytes_shared);
+        launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(x, mask, dst, params, stream, block_dims, block_nums, nbytes_shared);
     } else {
         const size_t nbytes_shared_low = WARP_SIZE*sizeof(float);
-        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, sinks, dst, params);
+        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, dst, params);
     }
 }
 
@@ -253,11 +249,9 @@ static void soft_max_back_f32_cuda(
 void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
 
     const float * src0_d = (const float *) src0->data;
     const void  * src1_d = src1 ? (const void *) src1->data : nullptr;
-    const void  * src2_d = src2 ? (const void *) src2->data : nullptr;
     float       *  dst_d = (float *) dst->data;
 
     cudaStream_t stream = ctx.stream();
@@ -315,9 +309,9 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     params.m1 = m1;
 
     if (use_f16) {
-        soft_max_f32_cuda(src0_d, (const half  *) src1_d, (const float *) src2_d, dst_d, params, stream);
+        soft_max_f32_cuda(src0_d, (const half  *) src1_d, dst_d, params, stream);
     } else {
-        soft_max_f32_cuda(src0_d, (const float *) src1_d, (const float *) src2_d, dst_d, params, stream);
+        soft_max_f32_cuda(src0_d, (const float *) src1_d, dst_d, params, stream);
     }
 }
 

ggml/src/ggml-cuda/template-instances/mmq-instance-mxfp4.cu

@@ -1,5 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../mmq.cuh"
-
-DECL_MMQ_CASE(GGML_TYPE_MXFP4);

ggml/src/ggml-cuda/unary.cu

@@ -300,81 +300,6 @@ void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     ggml_cuda_op_unary_gated<op_gelu_quick>(ctx, dst);
 }
 
-// swiglu_oai
-
-template <typename T>
-static __global__ void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, float alpha, float limit) {
-    const int64_t i = int64_t(blockDim.x)*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    // perform base op and multiply with gate (either offset in same tensor or a separate one)
-    const int64_t j0 = (i / n) * o0 + (i % n);
-    const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
-
-    float xi = x[j0];
-    float gi = g[j1];
-    xi = fminf(xi, limit);
-    gi = fmaxf(fminf(gi, limit), -limit);
-
-    float out_glu = xi / (1.0f + expf(-xi * alpha));
-    out_glu = out_glu * (1.0f + gi);
-
-    dst[i] = out_glu;
-}
-
-template <typename T>
-static void swiglu_oai_cuda(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, const float alpha, const float limit, cudaStream_t stream) {
-    const int64_t num_blocks = (k + CUDA_GLU_BLOCK_SIZE - 1) / CUDA_GLU_BLOCK_SIZE;
-    swiglu_oai_kernel<<<num_blocks, CUDA_GLU_BLOCK_SIZE, 0, stream>>>(x, g, dst, k, n, o0, o1, alpha, limit);
-}
-
-void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    void * src0_d = src0->data;
-    void * src1_d = src1 ? src1->data : src0->data;
-    const int64_t src0_o = src0->nb[1];
-    const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
-    void * dst_d = dst->data;
-    const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
-    cudaStream_t stream = ctx.stream();
-
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(src0->nb[0] == ggml_element_size(src0));
-    GGML_ASSERT(ggml_is_contiguous(dst));
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->type == dst->type);
-    GGML_ASSERT(dst->ne[0] == nc);
-    GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0));
-
-    if (src1) {
-        GGML_ASSERT(ggml_is_contiguous_1(src1));
-        GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
-        GGML_ASSERT(src1->ne[0] == nc);
-        GGML_ASSERT(src0->type == src1->type);
-    }
-
-    //const int32_t swapped = ((const int32_t *) dst->op_params)[1];
-    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
-    const float alpha = ggml_get_op_params_f32(dst, 2);
-    const float limit = ggml_get_op_params_f32(dst, 3);
-
-    float * src0_p = (float *) src0_d;
-    float * src1_p = (float *) src1_d;
-
-    if (!src1) {
-        src0_p += swapped ? nc : 0;
-        src1_p += swapped ? 0 : nc;
-    }
-
-    swiglu_oai_cuda(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
-}
-
 /* silu_back */
 
 static __device__ __forceinline__ float op_silu_back(float grad, float x) {

ggml/src/ggml-cuda/unary.cuh

@@ -67,8 +67,6 @@ void ggml_cuda_op_geglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_swiglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
-void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
-
 void ggml_cuda_op_geglu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/vecdotq.cuh

@@ -1,20 +1,8 @@
 #pragma once
 
 #include "common.cuh"
-
 #include <cstdint>
 
-static __device__ __forceinline__ int get_int_b1(const void * x, const int & i32) {
-    const uint8_t * x8 = (const uint8_t *) x;
-
-    int x32  = x8[4*i32 + 0] <<  0;
-    x32     |= x8[4*i32 + 1] <<  8;
-    x32     |= x8[4*i32 + 2] << 16;
-    x32     |= x8[4*i32 + 3] << 24;
-
-    return x32;
-}
-
 static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
     const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment
 
@@ -28,20 +16,6 @@ static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32
     return ((const int *) x)[i32]; // assume at least 4 byte alignment
 }
 
-static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, const int8_t * table) {
-    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
-    const int8_t * q0_8   = (const int8_t *) &q0_32;
-    const char4    val0_8 = make_char4(
-        table[q0_8[0]], table[q0_8[1]], table[q0_8[2]], table[q0_8[3]]);
-
-    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
-    const int8_t * q1_8   = (const int8_t *) &q1_32;
-    const char4    val1_8 = make_char4(
-        table[q1_8[0]], table[q1_8[1]], table[q1_8[2]], table[q1_8[3]]);
-
-    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
-}
-
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
 
@@ -237,30 +211,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_16_q8_1_
     return d8_1*sumf;
 }
 
-#define VDR_MXFP4_Q8_1_MMVQ 2
-#define VDR_MXFP4_Q8_1_MMQ  4
-
-static __device__ __forceinline__ float vec_dot_mxfp4_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
-
-    const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq + kbx;
-
-    const int * q8 = (const int *) bq8_1->qs + iqs;
-
-    int sumi = 0;
-#pragma unroll
-    for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
-        const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
-        const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
-
-        sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
-        sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
-    }
-
-    const float d = ggml_cuda_e8m0_to_fp32(bq4->e) * 0.5f * __low2float(bq8_1->ds);
-    return d * sumi;
-}
-
 #define VDR_Q2_K_Q8_1_MMVQ 1
 #define VDR_Q2_K_Q8_1_MMQ  4
 
@@ -1118,6 +1068,20 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
     return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
 }
 
+static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) {
+    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
+    const int8_t * q0_8   = (const int8_t *) &q0_32;
+    const char4    val0_8 = make_char4(
+        kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]);
+
+    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
+    const int8_t * q1_8   = (const int8_t *) &q1_32;
+    const char4    val1_8 = make_char4(
+        kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]);
+
+    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
+}
+
 #define VDR_IQ4_NL_Q8_1_MMVQ 2
 #define VDR_IQ4_NL_Q8_1_MMQ  4
 
@@ -1132,7 +1096,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
 #pragma unroll
     for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
         const int aux_q4 = get_int_b2(bq4->qs, iqs + l);
-        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
+        const int2 v = get_int_from_table_16(aux_q4);
 
         sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
         sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
@@ -1154,7 +1118,7 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
 #pragma unroll
     for (int j = 0; j < 4; ++j) {
         const int aux_q4 = get_int_b4(bq4->qs, iqs + j);
-        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
+        const int2 v = get_int_from_table_16(aux_q4);
 
         const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0);
         const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4);

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

@@ -6,10 +6,6 @@
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
 
-#if CUDART_VERSION >= 12050
-#include <cuda_fp8.h>
-#endif // CUDART_VERSION >= 12050
-
 #if CUDART_VERSION < 11020
 #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
 #define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH

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

@@ -200,7 +200,6 @@
 #endif
 
 typedef hip_bfloat16 nv_bfloat16;
-typedef short2 nv_bfloat162; // FIXME there is no 2x BF16 type being defined in bfloat16.h, ad-hoc compilation fix
 
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));

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

@@ -137,5 +137,4 @@
 #define cudaStreamEndCapture musaStreamEndCapture
 #define cudaOccupancyMaxActiveBlocksPerMultiprocessor musaOccupancyMaxActiveBlocksPerMultiprocessor
 
-typedef __mt_bfloat16 nv_bfloat16;
-typedef __mt_bfloat162 nv_bfloat162;
+typedef mt_bfloat16 nv_bfloat16;

ggml/src/ggml-hip/CMakeLists.txt

@@ -121,10 +121,6 @@ if (GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12 OR ${hip_VERSION} VERSION_GREATER_EQUAL 7
     add_compile_definitions(GGML_HIP_ROCWMMA_FATTN_GFX12)
 endif()
 
-if (GGML_HIP_EXPORT_METRICS)
-    set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -Rpass-analysis=kernel-resource-usage --save-temps")
-endif()
-
 if (NOT GGML_CUDA_FA)
     add_compile_definitions(GGML_CUDA_NO_FA)
 endif()

ggml/src/ggml-impl.h

@@ -410,67 +410,6 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 #define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
 #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
 
-static inline float ggml_e8m0_to_fp32(uint8_t x) {
-    uint32_t bits;  // Stores the raw bit representation of the float
-
-    // Handle special case for minimum exponent (denormalized float)
-    if (x == 0) {
-        // Bit pattern for 2^(-127):
-        // - Sign bit: 0 (positive)
-        // - Exponent: 0 (denormalized number)
-        // - Mantissa: 0x400000 (0.5 in fractional form)
-        // Value = 0.5 * 2^(-126) = 2^(-127)
-        bits = 0x00400000;
-    }
-    // note: disabled as we don't need to handle NaNs
-    //// Handle special case for NaN (all bits set)
-    //else if (x == 0xFF) {
-    //    // Standard quiet NaN pattern:
-    //    // - Sign bit: 0
-    //    // - Exponent: all 1s (0xFF)
-    //    // - Mantissa: 0x400000 (quiet NaN flag)
-    //    bits = 0x7FC00000;
-    //}
-    // Normalized values (most common case)
-    else {
-        // Construct normalized float by shifting exponent into position:
-        // - Exponent field: 8 bits (positions 30-23)
-        // - Mantissa: 0 (implicit leading 1)
-        // Value = 2^(x - 127)
-        bits = (uint32_t) x << 23;
-    }
-
-    float result;  // Final float value
-                   // Safely reinterpret bit pattern as float without type-punning issues
-    memcpy(&result, &bits, sizeof(float));
-    return result;
-}
-
-// Equal to ggml_e8m0_to_fp32/2
-// Useful with MXFP4 quantization since the E0M2 values are doubled
-static inline float ggml_e8m0_to_fp32_half(uint8_t x) {
-    uint32_t bits;
-
-    // For x < 2: use precomputed denormal patterns
-    if (x < 2) {
-        // 0x00200000 = 2^(-128), 0x00400000 = 2^(-127)
-        bits = 0x00200000 << x;
-    }
-    // For x >= 2: normalized exponent adjustment
-    else {
-        // 0.5 * 2^(x-127) = 2^(x-128) = normalized with exponent (x-1)
-        bits = (uint32_t)(x - 1) << 23;
-    }
-    // Note: NaNs are not handled here
-
-    float result;
-    memcpy(&result, &bits, sizeof(float));
-    return result;
-}
-
-#define GGML_E8M0_TO_FP32(x) ggml_e8m0_to_fp32(x)
-#define GGML_E8M0_TO_FP32_HALF(x) ggml_e8m0_to_fp32_half(x)
-
 /**
  * Converts brain16 to float32.
  *

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

@@ -23,9 +23,6 @@
 #define N_R0_Q8_0 4
 #define N_SG_Q8_0 2
 
-#define N_R0_MXFP4 2
-#define N_SG_MXFP4 2
-
 #define N_R0_Q2_K 4
 #define N_SG_Q2_K 2
 
@@ -132,15 +129,6 @@ typedef struct {
     uint64_t o1[8];
 } ggml_metal_kargs_bin;
 
-typedef struct {
-    int64_t ne0;
-    int64_t ne1;
-    size_t nb01;
-    size_t nb02;
-    size_t nb11;
-    size_t nb21;
-} ggml_metal_kargs_add_id;
-
 typedef struct {
     int32_t  ne00;
     int32_t  ne01;
@@ -456,8 +444,6 @@ typedef struct{
     uint64_t nb1;
     int32_t  i00;
     int32_t  i10;
-    float    alpha;
-    float    limit;
 } ggml_metal_kargs_glu;
 
 typedef struct {

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

@@ -195,7 +195,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_ROW_C4,
     GGML_METAL_KERNEL_TYPE_DIV,
     GGML_METAL_KERNEL_TYPE_DIV_ROW_C4,
-    GGML_METAL_KERNEL_TYPE_ADD_ID,
     GGML_METAL_KERNEL_TYPE_REPEAT_F32,
     GGML_METAL_KERNEL_TYPE_REPEAT_F16,
     GGML_METAL_KERNEL_TYPE_REPEAT_I32,
@@ -235,7 +234,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,
-    GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,
     GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,
@@ -288,7 +286,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,
-    GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,
@@ -313,10 +310,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5,
-    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2,
-    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3,
-    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4,
-    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4,
@@ -358,7 +351,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,
-    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,
@@ -381,7 +373,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,
-    GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,
@@ -406,7 +397,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16,
-    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16,
@@ -589,7 +579,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_REGLU,
     GGML_METAL_KERNEL_TYPE_GEGLU,
     GGML_METAL_KERNEL_TYPE_SWIGLU,
-    GGML_METAL_KERNEL_TYPE_SWIGLU_OAI,
     GGML_METAL_KERNEL_TYPE_GEGLU_ERF,
     GGML_METAL_KERNEL_TYPE_GEGLU_QUICK,
     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
@@ -1210,7 +1199,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW_C4,                      mul_row_c4,                      true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                             div,                             true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW_C4,                      div_row_c4,                      true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ID,                          add_id,                          true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32,                      repeat_f32,                      true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16,                      repeat_f16,                      true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32,                      repeat_i32,                      true);
@@ -1250,7 +1238,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,                   get_rows_q5_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,                   get_rows_q5_1,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,                   get_rows_q8_0,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4,                  get_rows_mxfp4,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,                   get_rows_q2_K,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,                   get_rows_q3_K,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,                   get_rows_q4_K,                   true);
@@ -1303,7 +1290,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,                 mul_mv_q5_0_f32,                 has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,                 mul_mv_q5_1_f32,                 has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,                 mul_mv_q8_0_f32,                 has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,                mul_mv_mxfp4_f32,                has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,         mul_mv_ext_f16_f32_r1_2,         has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,         mul_mv_ext_f16_f32_r1_3,         has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,         mul_mv_ext_f16_f32_r1_4,         has_simdgroup_reduction);
@@ -1328,10 +1314,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3,        mul_mv_ext_q8_0_f32_r1_3,        has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4,        mul_mv_ext_q8_0_f32_r1_4,        has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5,        mul_mv_ext_q8_0_f32_r1_5,        has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2,       mul_mv_ext_mxfp4_f32_r1_2,       has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3,       mul_mv_ext_mxfp4_f32_r1_3,       has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4,       mul_mv_ext_mxfp4_f32_r1_4,       has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5,       mul_mv_ext_mxfp4_f32_r1_5,       has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2,        mul_mv_ext_q4_K_f32_r1_2,        has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3,        mul_mv_ext_q4_K_f32_r1_3,        has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4,        mul_mv_ext_q4_K_f32_r1_4,        has_simdgroup_reduction);
@@ -1373,7 +1355,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,              mul_mv_id_q5_0_f32,              has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,              mul_mv_id_q5_1_f32,              has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,              mul_mv_id_q8_0_f32,              has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32,             mul_mv_id_mxfp4_f32,             has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,              mul_mv_id_q2_K_f32,              has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,              mul_mv_id_q3_K_f32,              has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,              mul_mv_id_q4_K_f32,              has_simdgroup_reduction);
@@ -1396,8 +1377,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,                 mul_mm_q5_0_f32,                 has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,                 mul_mm_q5_1_f32,                 has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,                 mul_mm_q8_0_f32,                 has_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,                mul_mm_mxfp4_f32,                has_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,                mul_mm_mxfp4_f32,                has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,                 mul_mm_q2_K_f32,                 has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,                 mul_mm_q3_K_f32,                 has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,                 mul_mm_q4_K_f32,                 has_simdgroup_mm);
@@ -1422,7 +1401,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16,              mul_mm_id_q5_0_f16,              has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16,              mul_mm_id_q5_1_f16,              has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16,              mul_mm_id_q8_0_f16,              has_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16,             mul_mm_id_mxfp4_f16,             has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16,              mul_mm_id_q2_K_f16,              has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16,              mul_mm_id_q3_K_f16,              has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16,              mul_mm_id_q4_K_f16,              has_simdgroup_mm);
@@ -1605,7 +1583,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REGLU,                           reglu,                           true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU,                           geglu,                           true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SWIGLU,                          swiglu,                          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SWIGLU_OAI,                      swiglu_oai,                      true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU_ERF,                       geglu_erf,                       true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU_QUICK,                     geglu_quick,                     true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                        sum_rows,                        true);
@@ -1797,7 +1774,6 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous_1(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
@@ -1815,7 +1791,6 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
-        case GGML_OP_ADD_ID:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ACC:
         case GGML_OP_REPEAT:
@@ -2067,7 +2042,6 @@ static int ggml_metal_encode_node(
 
     const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
     const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
-    const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT;
     const enum ggml_type dstt  = dst  ? dst->type  : GGML_TYPE_COUNT;
 
     size_t offs_src0 = 0;
@@ -2317,38 +2291,6 @@ static int ggml_metal_encode_node(
                     [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                 }
             } break;
-        case GGML_OP_ADD_ID:
-            {
-                GGML_ASSERT(src0t == GGML_TYPE_F32);
-                GGML_ASSERT(src1t == GGML_TYPE_F32);
-                GGML_ASSERT(src2t == GGML_TYPE_I32);
-                GGML_ASSERT(dstt  == GGML_TYPE_F32);
-
-                GGML_ASSERT(ggml_is_contiguous_rows(src0));
-
-                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ID].pipeline;
-
-                ggml_metal_kargs_add_id args = {
-                    /*.ne0  =*/ ne0,
-                    /*.ne1  =*/ ne1,
-                    /*.nb01 =*/ nb01,
-                    /*.nb02 =*/ nb02,
-                    /*.nb11 =*/ nb11,
-                    /*.nb21 =*/ nb21,
-
-                };
-
-                [encoder setComputePipelineState:pipeline];
-                [encoder setBytes:&args length:sizeof(args) atIndex:0];
-                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
-                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
-                [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
-                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:4];
-
-                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);
-
-                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-            } break;
         case GGML_OP_REPEAT:
             {
                 id<MTLComputePipelineState> pipeline;
@@ -2768,9 +2710,6 @@ static int ggml_metal_encode_node(
                     case GGML_GLU_OP_SWIGLU:
                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SWIGLU].pipeline;
                         break;
-                    case GGML_GLU_OP_SWIGLU_OAI:
-                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SWIGLU_OAI].pipeline;
-                        break;
                     case GGML_GLU_OP_GEGLU_ERF:
                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GEGLU_ERF].pipeline;
                         break;
@@ -2781,9 +2720,7 @@ static int ggml_metal_encode_node(
                         GGML_ABORT("fatal error");
                 }
 
-                const int32_t swp = ggml_get_op_params_i32(dst, 1);
-                const float alpha = ggml_get_op_params_f32(dst, 2);
-                const float limit = ggml_get_op_params_f32(dst, 3);
+                const int32_t swp = ((const int32_t *) dst->op_params)[1];
 
                 const int32_t i00 = swp ? ne0 : 0;
                 const int32_t i10 = swp ? 0 : ne0;
@@ -2797,8 +2734,6 @@ static int ggml_metal_encode_node(
                     /*.nb1  =*/ nb1,
                     /*.i00  =*/ src1 ? 0 : i00,
                     /*.i10  =*/ src1 ? 0 : i10,
-                    /*.alpha=*/ alpha,
-                    /*.limit=*/ limit
                 };
 
                 [encoder setComputePipelineState:pipeline];
@@ -3057,13 +2992,8 @@ static int ggml_metal_encode_node(
                 } else {
                     [encoder setBuffer:h_src0 offset:offs_src0  atIndex:1];
                 }
-                if (id_src2) {
-                    [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
-                } else {
-                    [encoder setBuffer:h_src0 offset:offs_src0  atIndex:2];
-                }
-                [encoder setBuffer:id_dst offset:offs_dst       atIndex:3];
-                [encoder setBytes:&args   length:sizeof(args)   atIndex:4];
+                [encoder setBuffer:id_dst offset:offs_dst       atIndex:2];
+                [encoder setBytes:&args   length:sizeof(args)   atIndex:3];
 
                 [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
 
@@ -3361,7 +3291,6 @@ static int ggml_metal_encode_node(
                        src0t == GGML_TYPE_Q5_0 ||
                        src0t == GGML_TYPE_Q5_1 ||
                        src0t == GGML_TYPE_Q8_0 ||
-                       src0t == GGML_TYPE_MXFP4 ||
                        src0t == GGML_TYPE_IQ4_NL ||
                        false) && (ne11 >= 2 && ne11 <= 8)
                      ) ||
@@ -3454,14 +3383,6 @@ static int ggml_metal_encode_node(
                                 case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5].pipeline; break;
                                 default: GGML_ABORT("not implemented");
                             } break;
-                        case GGML_TYPE_MXFP4:
-                            switch (r1ptg) {
-                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2].pipeline; break;
-                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3].pipeline; break;
-                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4].pipeline; break;
-                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5].pipeline; break;
-                                default: GGML_ABORT("not implemented");
-                            } break;
                         case GGML_TYPE_Q4_K:
                             switch (r1ptg) {
                                 case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2].pipeline; break;
@@ -3560,7 +3481,6 @@ static int ggml_metal_encode_node(
                         case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32   ].pipeline; break;
                         case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32   ].pipeline; break;
                         case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32   ].pipeline; break;
-                        case GGML_TYPE_MXFP4:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32   ].pipeline; break;
                         case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32   ].pipeline; break;
                         case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32   ].pipeline; break;
                         case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32   ].pipeline; break;
@@ -3703,13 +3623,6 @@ static int ggml_metal_encode_node(
                                 nr0 = N_R0_Q8_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline;
                             } break;
-                        case GGML_TYPE_MXFP4:
-                            {
-                                nsg = N_SG_MXFP4;
-                                nr0 = N_R0_MXFP4;
-                                smem = 32*sizeof(float);
-                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32].pipeline;
-                            } break;
                         case GGML_TYPE_Q2_K:
                             {
                                 nsg = N_SG_Q2_K;
@@ -3843,6 +3756,8 @@ static int ggml_metal_encode_node(
         case GGML_OP_MUL_MAT_ID:
             {
                 // src2 = ids
+                const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t);
+
                 GGML_ASSERT(src2t == GGML_TYPE_I32);
 
                 GGML_ASSERT(!ggml_is_transposed(src0));
@@ -3968,7 +3883,6 @@ static int ggml_metal_encode_node(
                             case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16   ].pipeline; break;
                             case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16   ].pipeline; break;
                             case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16   ].pipeline; break;
-                            case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16  ].pipeline; break;
                             case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16   ].pipeline; break;
                             case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16   ].pipeline; break;
                             case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16   ].pipeline; break;
@@ -4104,13 +4018,6 @@ static int ggml_metal_encode_node(
                                 nr0 = N_R0_Q8_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline;
                             } break;
-                        case GGML_TYPE_MXFP4:
-                            {
-                                nsg = N_SG_MXFP4;
-                                nr0 = N_R0_MXFP4;
-                                smem = 32*sizeof(float);
-                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32].pipeline;
-                            } break;
                         case GGML_TYPE_Q2_K:
                             {
                                 nsg = N_SG_Q2_K;
@@ -4263,7 +4170,6 @@ static int ggml_metal_encode_node(
                     case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0   ].pipeline; break;
                     case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1   ].pipeline; break;
                     case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0   ].pipeline; break;
-                    case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4  ].pipeline; break;
                     case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K   ].pipeline; break;
                     case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K   ].pipeline; break;
                     case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K   ].pipeline; break;
@@ -5074,14 +4980,11 @@ static int ggml_metal_encode_node(
                 GGML_ASSERT(ne11 == ne21);
                 GGML_ASSERT(ne12 == ne22);
 
-                struct ggml_tensor * src3 = node->src[3]; // mask
-                struct ggml_tensor * src4 = node->src[4]; // sinks
+                struct ggml_tensor * src3 = node->src[3];
 
                 size_t offs_src3 = 0;
-                size_t offs_src4 = 0;
 
                 id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
-                id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil;
 
                 GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
                 GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
@@ -5097,6 +5000,8 @@ static int ggml_metal_encode_node(
                 const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
                 const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);
 
+                const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
+
                 float scale;
                 float max_bias;
                 float logit_softcap;
@@ -5484,12 +5389,7 @@ static int ggml_metal_encode_node(
                 } else {
                     [encoder setBuffer:id_src0 offset:offs_src0 atIndex:4];
                 }
-                if (id_src4) {
-                    [encoder setBuffer:id_src4 offset:offs_src4 atIndex:5];
-                } else {
-                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:5];
-                }
-                [encoder setBuffer:id_dst offset:offs_dst       atIndex:6];
+                [encoder setBuffer:id_dst offset:offs_dst       atIndex:5];
 
                 if (!use_vec_kernel) {
                     // half8x8 kernel

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

@@ -35,10 +35,6 @@ constexpr constant static float kvalues_iq4nl_f[16] = {
     -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
 };
 
-constexpr constant static float kvalues_mxfp4_f[16] = {
-    0, .5f, 1.f, 1.5f, 2.f, 3.f, 4.f, 6.f, -0, -.5f, -1.f, -1.5f, -2.f, -3.f, -4.f, -6.f
-};
-
 static inline int best_index_int8(int n, constant float * val, float x) {
     if (x <= val[0]) return 0;
     if (x >= val[n-1]) return n-1;
@@ -50,18 +46,6 @@ static inline int best_index_int8(int n, constant float * val, float x) {
     return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
 }
 
-static inline float e8m0_to_fp32(uint8_t x) {
-    uint32_t bits;
-
-    if (x == 0) {
-        bits = 0x00400000;
-    } else {
-        bits = (uint32_t) x << 23;
-    }
-
-    return as_type<float>(bits);
-}
-
 // NOTE: this is not dequantizing - we are simply fitting the template
 template <typename type4x4>
 void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
@@ -258,27 +242,6 @@ void quantize_q5_1(device const float * src, device block_q5_1 & dst) {
     }
 }
 
-void quantize_q8_0(device const float * src, device block_q8_0 & dst) {
-#pragma METAL fp math_mode(safe)
-    float amax = 0.0f; // absolute max
-
-    for (int j = 0; j < QK8_0; j++) {
-        const float v = src[j];
-        amax = MAX(amax, fabs(v));
-    }
-
-    const float d = amax / ((1 << 7) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dst.d = d;
-
-    for (int j = 0; j < QK8_0; ++j) {
-        const float x0 = src[j]*id;
-
-        dst.qs[j] = round(x0);
-    }
-}
-
 void quantize_iq4_nl(device const float * src, device block_iq4_nl & dst) {
 #pragma METAL fp math_mode(safe)
     float amax = 0.0f; // absolute max
@@ -499,34 +462,25 @@ void dequantize_q8_0_t4(device const block_q8_0 *xb, short il, thread type4 & re
     }
 }
 
-template <typename type4x4>
-void dequantize_mxfp4(device const block_mxfp4 * xb, short il, thread type4x4 & reg) {
-    device const uint8_t * q2 = (device const uint8_t *)xb->qs;
+void quantize_q8_0(device const float * src, device block_q8_0 & dst) {
+#pragma METAL fp math_mode(safe)
+    float amax = 0.0f; // absolute max
 
-    const float d = e8m0_to_fp32(xb->e);
-    const uint8_t shr = il >= 1 ? 4 : 0;
-
-    for (int i = 0; i < 4; ++i) {
-        reg[i][0] = d * kvalues_mxfp4_f[(q2[4*i + 0] >> shr) & 0x0F];
-        reg[i][1] = d * kvalues_mxfp4_f[(q2[4*i + 1] >> shr) & 0x0F];
-        reg[i][2] = d * kvalues_mxfp4_f[(q2[4*i + 2] >> shr) & 0x0F];
-        reg[i][3] = d * kvalues_mxfp4_f[(q2[4*i + 3] >> shr) & 0x0F];
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = src[j];
+        amax = MAX(amax, fabs(v));
     }
-}
 
-template <typename type4>
-void dequantize_mxfp4_t4(device const block_mxfp4 * xb, short il, thread type4 & reg) {
-    device const uint8_t * q2 = (device const uint8_t *)xb->qs;
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
 
-    const float d = e8m0_to_fp32(xb->e);
-    const short il4 = il%4;
+    dst.d = d;
 
-    const uint8_t shr = il >= 4 ? 4 : 0;
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = src[j]*id;
 
-    reg[0] = d * kvalues_mxfp4_f[(q2[4*il4 + 0] >> shr) & 0x0F];
-    reg[1] = d * kvalues_mxfp4_f[(q2[4*il4 + 1] >> shr) & 0x0F];
-    reg[2] = d * kvalues_mxfp4_f[(q2[4*il4 + 2] >> shr) & 0x0F];
-    reg[3] = d * kvalues_mxfp4_f[(q2[4*il4 + 3] >> shr) & 0x0F];
+        dst.qs[j] = round(x0);
+    }
 }
 
 template <typename type4x4>
@@ -1006,32 +960,6 @@ kernel void kernel_div(
     }
 }
 
-kernel void kernel_add_id(
-        constant ggml_metal_kargs_add_id & args,
-        device const char * src0,
-        device const char * src1,
-        device const char * src2,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i1 = tgpig.x;
-    const int i2 = tgpig.y;
-
-    const int i11 = *((device const int32_t *) (src2 + i1*sizeof(int32_t) + i2*args.nb21));
-
-    const size_t nb1 = args.ne0 * sizeof(float);
-    const size_t nb2 = args.ne1 * nb1;
-
-    device       float * dst_row  = (device       float *)((device char *)dst + i1*nb1 + i2*nb2);
-    device const float * src0_row = (device const float *)((device char *)src0 +  i1*args.nb01 + i2*args.nb02);
-    device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
-
-    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-        dst_row[i0] = src0_row[i0] + src1_row[i0];
-    }
-}
-
 template<typename T>
 kernel void kernel_repeat(
         constant ggml_metal_kargs_repeat & args,
@@ -1503,32 +1431,6 @@ kernel void kernel_swiglu(
     }
 }
 
-kernel void kernel_swiglu_oai(
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        constant ggml_metal_kargs_glu & args,
-        uint tgpig[[threadgroup_position_in_grid]],
-        uint tpitg[[thread_position_in_threadgroup]],
-        uint   ntg[[threads_per_threadgroup]]) {
-    device const float * src0_row = (device const float *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
-    device const float * src1_row = (device const float *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
-    device       float * dst_row  = (device       float *) ((device       char *) dst  + tgpig*args.nb1);
-
-    for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
-        float x0 = src0_row[i0];
-        float x1 = src1_row[i0];
-
-        x0 = min(x0, args.limit);
-        x1 = max(min(x1, args.limit), -args.limit);
-
-        float out_glu = x0 / (1.0f + exp(-x0 * args.alpha));
-        out_glu = out_glu * (1.0f + x1);
-
-        dst_row[i0] = out_glu;
-    }
-}
-
 kernel void kernel_geglu_erf(
         device const char * src0,
         device const char * src1,
@@ -1632,7 +1534,6 @@ template<typename T>
 kernel void kernel_soft_max(
         device const  char * src0,
         device const  char * src1,
-        device const  char * src2,
         device        char * dst,
         constant ggml_metal_kargs_soft_max & args,
         threadgroup  float * buf [[threadgroup(0)]],
@@ -1651,7 +1552,6 @@ kernel void kernel_soft_max(
 
     device const float * psrc0 =                (device const float *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
     device const     T * pmask = src1 != src0 ? (device const T *    ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
-    device const float * psrc2 = src2 != src0 ? (device const float *) (src2)                                                 : nullptr;
     device       float * pdst  =                (device       float *) (dst  + i01*args.nb1  + i02*args.nb2  + i03*args.nb3);
 
     float slope = 1.0f;
@@ -1667,7 +1567,7 @@ kernel void kernel_soft_max(
     }
 
     // parallel max
-    float lmax = psrc2 ? psrc2[i02] : -INFINITY;
+    float lmax = -INFINITY;
 
     for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
         lmax = MAX(lmax, psrc0[i00]*args.scale + (pmask ? slope*pmask[i00] : 0.0f));
@@ -1723,10 +1623,6 @@ kernel void kernel_soft_max(
         sum = simd_sum(sum);
     }
 
-    if (psrc2) {
-        sum += exp(psrc2[i02] - max_val);
-    }
-
     const float inv_sum = 1.0f/sum;
 
     for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
@@ -1738,7 +1634,6 @@ template<typename T>
 kernel void kernel_soft_max_4(
         device const  char * src0,
         device const  char * src1,
-        device const  char * src2,
         device        char * dst,
         constant ggml_metal_kargs_soft_max & args,
         threadgroup  float * buf [[threadgroup(0)]],
@@ -1757,7 +1652,6 @@ kernel void kernel_soft_max_4(
 
     device const float4 * psrc4 =                (device const float4 *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
     device const      T * pmask = src1 != src0 ? (device const T *     ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
-    device const float *  psrc2 = src2 != src0 ? (device const float * ) (src2)                                                 : nullptr;
     device       float4 * pdst4 =                (device       float4 *) (dst  + i01*args.nb1  + i02*args.nb2  + i03*args.nb3);
 
     float slope = 1.0f;
@@ -1772,7 +1666,7 @@ kernel void kernel_soft_max_4(
     }
 
     // parallel max
-    float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
+    float4 lmax4 = -INFINITY;
 
     for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
         lmax4 = fmax(lmax4, psrc4[i00]*args.scale + (float4)((pmask ? slope*pmask[i00] : 0.0f)));
@@ -1831,10 +1725,6 @@ kernel void kernel_soft_max_4(
         sum = simd_sum(sum);
     }
 
-    if (psrc2) {
-        sum += exp(psrc2[i02] - max_val);
-    }
-
     const float inv_sum = 1.0f/sum;
 
     for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
@@ -3216,11 +3106,6 @@ template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_3")]]   kernel mul_mv_ext_q4
 template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_4")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q8_0,   32, dequantize_q8_0_t4>;
 template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_5")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q8_0,   32, dequantize_q8_0_t4>;
 
-template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_2")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_mxfp4,  32, dequantize_mxfp4_t4>;
-template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_3")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_mxfp4,  32, dequantize_mxfp4_t4>;
-template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_4")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_mxfp4,  32, dequantize_mxfp4_t4>;
-template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_5")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_mxfp4,  32, dequantize_mxfp4_t4>;
-
 template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_iq4_nl, 32, dequantize_iq4_nl_t4>;
 template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_iq4_nl, 32, dequantize_iq4_nl_t4>;
 template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_iq4_nl, 32, dequantize_iq4_nl_t4>;
@@ -4207,7 +4092,6 @@ kernel void kernel_flash_attn_ext(
         device const char * k,
         device const char * v,
         device const char * mask,
-        device const char * sinks,
         device       char * dst,
         threadgroup  half * shmem_f16 [[threadgroup(0)]],
         uint3   tgpig[[threadgroup_position_in_grid]],
@@ -4523,35 +4407,6 @@ kernel void kernel_flash_attn_ext(
             }
         }
 
-        if (sinks != q && sgitg == 0) {
-            for (ushort j = 0; j < Q; ++j) {
-                const float m = M[j];
-                const float s = tiisg == 0 ? ((device const float *) sinks)[iq2] : -FLT_MAX/2;
-
-                M[j] = simd_max(max(M[j], s));
-
-                const float ms = exp(m - M[j]);
-                const float vs = exp(s - M[j]);
-
-                S[j] = S[j]*ms + simd_sum(vs);
-
-                if (tiisg == j) {
-                    ss[j*TS + 2*C + j] = ms;
-                }
-            }
-
-            // O = diag(ms)*O
-            {
-                s8x8_t ms;
-                simdgroup_load(ms, ss + 2*C, TS, 0, false);
-
-                #pragma unroll(DV8)
-                for (short i = 0; i < DV8; ++i) {
-                    simdgroup_multiply(lo[i], ms, lo[i]);
-                }
-            }
-        }
-
         // these are needed for reducing the results from the simdgroups (reuse the ss buffer)
         for (short j = tiisg; j < Q; j += NW) {
             ss[j*TS + 0] = S[j];
@@ -4763,7 +4618,6 @@ kernel void kernel_flash_attn_ext_vec(
         device const char * k,
         device const char * v,
         device const char * mask,
-        device const char * sinks,
         device       char * dst,
         threadgroup  half * shmem_f16 [[threadgroup(0)]],
         uint3   tgpig[[threadgroup_position_in_grid]],
@@ -4981,23 +4835,6 @@ kernel void kernel_flash_attn_ext_vec(
             }
         }
 
-        if (sinks != q && sgitg == 0) {
-            const float m = M;
-            const float s = tiisg == 0 ? ((device const float *) sinks)[iq2] : -FLT_MAX/2;
-
-            M = simd_max(max(M, s));
-
-            const float ms = exp(m - M);
-            const float vs = exp(s - M);
-
-            S = S*ms + simd_sum(vs);
-
-#pragma unroll(DV4/NL)
-            for (short ii = 0; ii < DV4; ii += NL) {
-                lo[ii/NL] *= ms;
-            }
-        }
-
         // these are needed for reducing the results from the simdgroups (reuse the ss buffer)
         if (tiisg == 0) {
             ss[0] = (s_t) S;
@@ -7103,95 +6940,6 @@ kernel void kernel_mul_mv_iq4_xs_f32(
     kernel_mul_mv_iq4_xs_f32_impl<N_R0_IQ4_XS, N_SG_IQ4_XS, N_SIMDWIDTH, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg);
 }
 
-template<int nr0, int nsg, int nw, typename args_t>
-void kernel_mul_mv_mxfp4_f32_impl(
-        args_t args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        threadgroup  char * shmem,
-        uint3  tgpig,
-        ushort tiisg,
-        ushort sgitg) {
-
-    threadgroup float * shmem_f32 = (threadgroup float *) shmem;
-    const int nb = args.ne00/QK_MXFP4;
-
-    const int r0 = tgpig.x;
-    const int r1 = tgpig.y;
-    const int im = tgpig.z;
-
-    const int first_row = (r0 * nsg + sgitg) * nr0;
-
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
-
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
-    const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
-
-    device const block_mxfp4 * x = (device const block_mxfp4 *) (src0 + offset0);
-    device const float       * y = (device const float       *) (src1 + offset1);
-
-    const short ix = tiisg/2;  // 0...15
-    const short it = tiisg%2;  // 0 or 1
-
-    shmem_f32[tiisg] = kvalues_mxfp4_f[tiisg%16];
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    float4 yl[4];
-    float sumf[nr0]={0.f};
-
-    device const float * yb = y + ix * QK_MXFP4 + it * 8;
-
-    for (int ib = ix; ib < nb; ib += 16) {
-        device const float4 * y4 = (device const float4 *)yb;
-        yl[0] = y4[0];
-        yl[1] = y4[4];
-        yl[2] = y4[1];
-        yl[3] = y4[5];
-
-#pragma unroll(nr0)
-        for (short row = 0; row < nr0; row++) {
-            device const block_mxfp4 & xb = x[row*nb + ib];
-            device const uint8_t     * q2 = (device const uint8_t *)(xb.qs + 8*it);
-
-            float4 acc1 = yl[0]*float4(shmem_f32[q2[0] &  0x0F], shmem_f32[q2[1] &  0x0F], shmem_f32[q2[2] &  0x0F], shmem_f32[q2[3] &  0x0F]);
-            float4 acc2 = yl[1]*float4(shmem_f32[q2[0] >> 4   ], shmem_f32[q2[1] >> 4   ], shmem_f32[q2[2] >> 4   ], shmem_f32[q2[3] >> 4   ]);
-            float4 acc3 = yl[2]*float4(shmem_f32[q2[4] &  0x0F], shmem_f32[q2[5] &  0x0F], shmem_f32[q2[6] &  0x0F], shmem_f32[q2[7] &  0x0F]);
-            float4 acc4 = yl[3]*float4(shmem_f32[q2[4] >> 4   ], shmem_f32[q2[5] >> 4   ], shmem_f32[q2[6] >> 4   ], shmem_f32[q2[7] >> 4   ]);
-
-            acc1 = (acc1 + acc3) + (acc2 + acc4);
-
-            sumf[row] += e8m0_to_fp32(xb.e) * ((acc1[0] + acc1[1]) + (acc1[2] + acc1[3]));
-        }
-
-        yb += 16 * QK_MXFP4;
-    }
-
-    device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
-
-    for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) {
-        float sum_all = simd_sum(sumf[row]);
-        if (tiisg == 0) {
-            dst_f32[first_row + row] = sum_all;
-        }
-    }
-}
-
-[[host_name("kernel_mul_mv_mxfp4_f32")]]
-kernel void kernel_mul_mv_mxfp4_f32(
-        constant ggml_metal_kargs_mul_mv & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        threadgroup  char * shmem [[threadgroup(0)]],
-        uint3  tgpig[[threadgroup_position_in_grid]],
-        ushort tiisg[[thread_index_in_simdgroup]],
-        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
-
-    kernel_mul_mv_mxfp4_f32_impl<N_R0_MXFP4, N_SG_MXFP4, N_SIMDWIDTH, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg);
-}
-
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
 kernel void kernel_get_rows_q(
         constant ggml_metal_kargs_get_rows & args,
@@ -7727,7 +7475,6 @@ template [[host_name("kernel_get_rows_q4_1")]]    kernel get_rows_q_t kernel_get
 template [[host_name("kernel_get_rows_q5_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_0,    2, dequantize_q5_0>;
 template [[host_name("kernel_get_rows_q5_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_1,    2, dequantize_q5_1>;
 template [[host_name("kernel_get_rows_q8_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q8_0,    2, dequantize_q8_0>;
-template [[host_name("kernel_get_rows_mxfp4")]]   kernel get_rows_q_t kernel_get_rows_q<block_mxfp4,   2, dequantize_mxfp4>;
 template [[host_name("kernel_get_rows_q2_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q2_K,    QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_get_rows_q3_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q3_K,    QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_get_rows_q4_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_K,    QK_NL, dequantize_q4_K>;
@@ -7780,7 +7527,6 @@ template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mul_mm_t kernel_mul_m
 template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_0,    2,     dequantize_q5_0>;
 template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_1,    2,     dequantize_q5_1>;
 template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q8_0,    2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_mxfp4_f32")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_mxfp4,   2,     dequantize_mxfp4>;
 template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q2_K,    QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q3_K,    QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_K,    QK_NL, dequantize_q4_K>;
@@ -7812,7 +7558,6 @@ template [[host_name("kernel_mul_mm_id_q4_1_f16")]]    kernel mul_mm_id kernel_m
 template [[host_name("kernel_mul_mm_id_q5_0_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q5_0,    2,     dequantize_q5_0>;
 template [[host_name("kernel_mul_mm_id_q5_1_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q5_1,    2,     dequantize_q5_1>;
 template [[host_name("kernel_mul_mm_id_q8_0_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q8_0,    2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_id_mxfp4_f16")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_mxfp4,   2,     dequantize_mxfp4>;
 template [[host_name("kernel_mul_mm_id_q2_K_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q2_K,    QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_id_q3_K_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q3_K,    QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_mul_mm_id_q4_K_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q4_K,    QK_NL, dequantize_q4_K>;
@@ -7958,8 +7703,6 @@ template [[host_name("kernel_mul_mv_id_q4_1_f32")]]    kernel kernel_mul_mv_id_t
 template [[host_name("kernel_mul_mv_id_q5_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_R0_Q5_0, N_SG_Q5_0, N_SIMDWIDTH>>>;
 template [[host_name("kernel_mul_mv_id_q5_1_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_R0_Q5_1, N_SG_Q5_1, N_SIMDWIDTH>>>;
 
-template [[host_name("kernel_mul_mv_id_mxfp4_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_mxfp4_f32_impl<N_R0_MXFP4, N_SG_MXFP4, N_SIMDWIDTH>>>;
-
 template [[host_name("kernel_mul_mv_id_q2_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl   <N_R0_Q2_K,    N_SG_Q2_K,    N_SIMDWIDTH>>>;
 template [[host_name("kernel_mul_mv_id_q3_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl   <N_R0_Q3_K,    N_SG_Q3_K,    N_SIMDWIDTH>>>;
 template [[host_name("kernel_mul_mv_id_q4_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl   <N_R0_Q4_K,    N_SG_Q4_K,    N_SIMDWIDTH>>>;

ggml/src/ggml-opencl/CMakeLists.txt

@@ -55,7 +55,6 @@ endfunction()
 
 set(GGML_OPENCL_KERNELS
     add
-    add_id
     argsort
     clamp
     cpy

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

@@ -345,7 +345,6 @@ struct ggml_backend_opencl_context {
     cl_command_queue queue;
 
     cl_program program_add;
-    cl_program program_add_id;
     cl_program program_clamp;
     cl_program program_cpy;
     cl_program program_cvt;
@@ -405,7 +404,6 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul, kernel_mul_row, kernel_mul_f16, kernel_mul_row_f16;
     cl_kernel kernel_div, kernel_div_row, kernel_div_f16, kernel_div_row_f16;
     cl_kernel kernel_sub, kernel_sub_row, kernel_sub_f16, kernel_sub_row_f16;
-    cl_kernel kernel_add_id;
     cl_kernel kernel_scale;
     cl_kernel kernel_silu, kernel_silu_4;
     cl_kernel kernel_gelu, kernel_gelu_4;
@@ -414,7 +412,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_relu;
     cl_kernel kernel_sigmoid_f32, kernel_sigmoid_f16;
     cl_kernel kernel_clamp;
-    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_swiglu_oai, kernel_geglu_erf, kernel_geglu_quick,
+    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_geglu_erf, kernel_geglu_quick,
               kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16;
     cl_kernel kernel_norm;
     cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
@@ -602,7 +600,6 @@ struct ggml_backend_opencl_context {
         if (ref_count == 0) {
 #ifdef GGML_OPENCL_PROFILING
             write_profiling_info();
-            profiling_info.clear();
 #endif
         }
     }
@@ -684,22 +681,6 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
-    // add_id
-    {
-#ifdef GGML_OPENCL_EMBED_KERNELS
-        const std::string kernel_src {
-            #include "add_id.cl.h"
-        };
-#else
-        const std::string kernel_src = read_file("add_id.cl");
-#endif
-        backend_ctx->program_add_id =
-            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
-
-        CL_CHECK((backend_ctx->kernel_add_id = clCreateKernel(backend_ctx->program_add_id, "kernel_add_id", &err), err));
-        GGML_LOG_CONT(".");
-    }
-
     // clamp
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -806,7 +787,6 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_geglu           = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err));
         CL_CHECK((backend_ctx->kernel_reglu           = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err));
         CL_CHECK((backend_ctx->kernel_swiglu          = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu", &err), err));
-        CL_CHECK((backend_ctx->kernel_swiglu_oai      = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu_oai", &err), err));
         CL_CHECK((backend_ctx->kernel_geglu_erf       = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_erf", &err), err));
         CL_CHECK((backend_ctx->kernel_geglu_quick     = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_quick", &err), err));
         CL_CHECK((backend_ctx->kernel_geglu_f16       = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_f16", &err), err));
@@ -2487,8 +2467,6 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return (op->src[0]->type == op->src[1]->type) &&
                    (op->src[0]->type == op->type) &&
                    (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
-        case GGML_OP_ADD_ID:
-            return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_GELU:
@@ -2510,7 +2488,6 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous_1(op->src[0]) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
@@ -3845,75 +3822,6 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
     }
 }
 
-static void ggml_cl_add_id(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src0);
-    GGML_ASSERT(src0->extra);
-    GGML_ASSERT(src1);
-    GGML_ASSERT(src1->extra);
-    GGML_ASSERT(dst);
-    GGML_ASSERT(dst->extra);
-
-    const ggml_tensor * src2 = dst->src[2];
-    GGML_ASSERT(src2);
-    GGML_ASSERT(src2->extra);
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(src2->type == GGML_TYPE_I32);
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-
-    GGML_ASSERT(ggml_is_contiguous_rows(src0));
-
-    const int ne00 = src0->ne[0];
-    const int ne01 = src0->ne[1];
-    const int ne02 = src0->ne[2];
-
-    const cl_ulong nb01 = src0->nb[1];
-    const cl_ulong nb02 = src0->nb[2];
-
-    const cl_ulong nb11 = src1->nb[1];
-
-    const cl_ulong nb21 = src2->nb[1];
-
-    const int ne0 = dst->ne[0];
-    const int ne1 = dst->ne[1];
-
-    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 * extra1 = (ggml_tensor_extra_cl *)src1->extra;
-    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
-    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
-
-    cl_ulong offset0 = extra0->offset + src0->view_offs;
-    cl_ulong offset1 = extra1->offset + src1->view_offs;
-    cl_ulong offset2 = extra2->offset + src2->view_offs;
-    cl_ulong offsetd = extrad->offset + dst->view_offs;
-
-    cl_kernel kernel = backend_ctx->kernel_add_id;
-
-    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
-    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
-    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
-    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
-    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb01));
-    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb02));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb11));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb21));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne0));
-    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne1));
-
-    int nth = MIN(ne00, (int) backend_ctx->get_kernel_workgroup_size(kernel));
-    size_t global_work_size[] = { (size_t)ne01*nth, (size_t)ne02, 1 };
-    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_mul(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -6592,24 +6500,17 @@ static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, c
         GGML_ASSERT(src1->extra);
     }
 
-    const ggml_tensor * src2 = dst->src[2];
-    if (src2) {
-        GGML_ASSERT(src2->extra);
-    }
-
     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;
 
     ggml_tensor_extra_cl * extra1 = src1 ? (ggml_tensor_extra_cl *)src1->extra : nullptr;
-    ggml_tensor_extra_cl * extra2 = src2 ? (ggml_tensor_extra_cl *)src2->extra : nullptr;
 
     cl_ulong offset0 = extra0->offset + src0->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
 
     cl_ulong offset1 = extra1 ? extra1->offset + src1->view_offs : offset0;
-    cl_ulong offset2 = extra2 ? extra2->offset + src2->view_offs : offset0;
 
     const int ne00 = src0->ne[0];
     const int ne01 = src0->ne[1];
@@ -6677,27 +6578,25 @@ static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, c
     CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
     CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   extra1 ? &extra1->data_device : &extra0->data_device));
     CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
-    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   extra2 ? &extra2->data_device : &extra0->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
-    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
-    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
-    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne12));
-    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne13));
-    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
-    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
-    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb13));
-    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb1));
-    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb2));
-    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb3));
-    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(float),    &scale));
-    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(float),    &max_bias));
-    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(float),    &m0));
-    CL_CHECK(clSetKernelArg(kernel, 23, sizeof(float),    &m1));
-    CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &n_head_log2));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne13));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb12));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb13));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb1));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb2));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb3));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(float),    &scale));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(float),    &max_bias));
+    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(float),    &m0));
+    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(float),    &m1));
+    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &n_head_log2));
 
     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};
@@ -7104,9 +7003,6 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
                 kernel = backend_ctx->kernel_swiglu_f16;
             }
             break;
-        case GGML_GLU_OP_SWIGLU_OAI:
-            kernel = backend_ctx->kernel_swiglu_oai;
-            break;
         case GGML_GLU_OP_GEGLU_ERF:
             if (dst->type == GGML_TYPE_F32) {
                 kernel = backend_ctx->kernel_geglu_erf;
@@ -7142,10 +7038,7 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
 
     const cl_ulong nb1  = dst->nb[1];
 
-    const int   swp   = ggml_get_op_params_i32(dst, 1);
-    const float alpha = ggml_get_op_params_f32(dst, 2);
-    const float limit = ggml_get_op_params_f32(dst, 3);
-
+    const int swp = ((const int32_t *) dst->op_params)[1];
     const int ne00_off = src1 ? 0 : (swp ? ne0 : 0);
     const int ne10_off = src1 ? 0 : (swp ? 0 : ne0);
 
@@ -7162,11 +7055,6 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
     CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne00_off));
     CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10_off));
 
-    if (ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU_OAI) {
-        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float), &limit));
-        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(float), &alpha));
-    }
-
     const size_t nrows = ggml_nrows(src0);
     size_t nth = 512;
     size_t global_work_size[] = {nrows*nth, 1, 1};
@@ -7223,12 +7111,6 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
             }
             func = ggml_cl_add;
             break;
-        case GGML_OP_ADD_ID:
-            if (!any_on_device) {
-                return false;
-            }
-            func = ggml_cl_add_id;
-            break;
         case GGML_OP_MUL:
             if (!any_on_device) {
                 return false;

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

@@ -1,42 +0,0 @@
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-
-//------------------------------------------------------------------------------
-// add_id
-//------------------------------------------------------------------------------
-kernel void kernel_add_id(
-    global char * src0,
-    ulong         offset0,
-    global char * src1,
-    ulong         offset1,
-    global char * src2,
-    ulong         offset2,
-    global char * dst,
-    ulong         offsetd,
-    ulong         nb01,
-    ulong         nb02,
-    ulong         nb11,
-    ulong         nb21,
-    int           ne0,
-    int           ne1
-) {
-    src0 = (global char*)((global char*)src0 + offset0);
-    src1 = (global char*)((global char*)src1 + offset1);
-    src2 = (global char*)((global char*)src2 + offset2);
-    dst  = (global char*)((global char*)dst  + offsetd);
-
-    int i1 = get_group_id(0);
-    int i2 = get_group_id(1);
-
-    const int i11 = *((global const int *) (src2 + i1*sizeof(int) + i2*nb21));
-
-    const size_t nb1 = ne0 * sizeof(float);
-    const size_t nb2 = ne1 * nb1;
-
-    global float * dst_row  = (global float *)((global char *)dst  + i1*nb1 + i2*nb2);
-    global float * src0_row = (global float *)((global char *)src0 + i1*nb01 + i2*nb02);
-    global float * src1_row = (global float *)((global char *)src1 + i11*nb11);
-
-    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
-        dst_row[i0] = src0_row[i0] + src1_row[i0];
-    }
-}

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

@@ -202,47 +202,6 @@ kernel void kernel_swiglu_f16(
     }
 }
 
-//------------------------------------------------------------------------------
-// swiglu_oai
-//------------------------------------------------------------------------------
-kernel void kernel_swiglu_oai(
-    global char * src0,
-    ulong         offset0,
-    global char * src1,
-    ulong         offset1,
-    global char * dst,
-    ulong         offsetd,
-    ulong         nb01,
-    ulong         nb11,
-    int           ne0,
-    ulong         nb1,
-    int           ne00_off,
-    int           ne10_off,
-    float         limit,
-    float         alpha
-) {
-    src0 = (global char*)((global char*)src0 + offset0);
-    src1 = (global char*)((global char*)src1 + offset1);
-    dst  = (global char*)((global char*)dst  + offsetd);
-
-    global float * src0_row = (global float *) ((global char *) src0 + get_group_id(0)*nb01) + ne00_off;
-    global float * src1_row = (global float *) ((global char *) src1 + get_group_id(0)*nb11) + ne10_off;
-    global float * dst_row  = (global float *) ((global char *) dst  + get_group_id(0)*nb1);
-
-    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
-        float x0 = src0_row[i0];
-        float x1 = src1_row[i0];
-
-        x0 = min(x0, limit);
-        x1 = max(min(x1, limit), -limit);
-
-        float out_glu = x0 / (1.0f + exp(-x0 * alpha));
-        out_glu = out_glu * (1.0f + x1);
-
-        dst_row[i0] = out_glu;
-    }
-}
-
 //------------------------------------------------------------------------------
 // geglu_erf
 //------------------------------------------------------------------------------

ggml/src/ggml-opencl/kernels/softmax_4_f16.cl

@@ -26,8 +26,6 @@ kernel void kernel_soft_max_4_f16(
         ulong offset0,
         global char * src1,
         ulong offset1,
-        global char * src2,
-        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -50,7 +48,6 @@ kernel void kernel_soft_max_4_f16(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
-    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -63,7 +60,6 @@ kernel void kernel_soft_max_4_f16(
 
     global float4 * psrc4 = (global float4 *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global half4  * pmask = src1 != src0 ? (global half4 *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
-    global float  * psrc2 = src2 != src0 ? (global float *)(src2) : 0;
     global float4 * pdst4 = (global float4 *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -79,7 +75,7 @@ kernel void kernel_soft_max_4_f16(
     }
 
     // parallel max
-    float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
+    float4 lmax4 = -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         lmax4 = fmax(lmax4, psrc4[i00]*scale + slope*(pmask ? convert_float4(pmask[i00]) : 0.0f));
     }
@@ -96,11 +92,7 @@ kernel void kernel_soft_max_4_f16(
     }
     float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
 
-    float sum = sub_group_reduce_add(lsum);
-
-    if (psrc2) {
-        sum += exp(psrc2[i02] - max);
-    }
+    const float sum = sub_group_reduce_add(lsum);
 
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         pdst4[i00] /= sum;

ggml/src/ggml-opencl/kernels/softmax_4_f32.cl

@@ -26,8 +26,6 @@ kernel void kernel_soft_max_4(
         ulong offset0,
         global char * src1,
         ulong offset1,
-        global char * src2,
-        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -50,7 +48,6 @@ kernel void kernel_soft_max_4(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
-    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -63,7 +60,6 @@ kernel void kernel_soft_max_4(
 
     global float4 * psrc4 = (global float4 *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global float4 * pmask = src1 != src0 ? (global float4 *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
-    global float  * psrc2 = src2 != src0 ? (global float  *)(src2) : 0;
     global float4 * pdst4 = (global float4 *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -79,7 +75,7 @@ kernel void kernel_soft_max_4(
     }
 
     // parallel max
-    float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
+    float4 lmax4 = -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
@@ -96,11 +92,7 @@ kernel void kernel_soft_max_4(
     }
     float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
 
-    float sum = sub_group_reduce_add(lsum);
-
-    if (psrc2) {
-        sum += exp(psrc2[i02] - max);
-    }
+    const float sum = sub_group_reduce_add(lsum);
 
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         pdst4[i00] /= sum;

ggml/src/ggml-opencl/kernels/softmax_f16.cl

@@ -26,8 +26,6 @@ kernel void kernel_soft_max_f16(
         ulong offset0,
         global char * src1,
         ulong offset1,
-        global char * src2,
-        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -50,7 +48,6 @@ kernel void kernel_soft_max_f16(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
-    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -63,7 +60,6 @@ kernel void kernel_soft_max_f16(
 
     global float * psrc0 = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global half  * pmask = src1 != src0 ? (global half *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
-    global float * psrc2 = src2 != src0 ? (global float *)(src2) : 0;
     global float * pdst  = (global float *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -79,7 +75,7 @@ kernel void kernel_soft_max_f16(
     }
 
     // parallel max
-    float lmax = psrc2 ? psrc2[i02] : -INFINITY;
+    float lmax = -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
@@ -95,11 +91,7 @@ kernel void kernel_soft_max_f16(
         pdst[i00] = exp_psrc0;
     }
 
-    float sum = sub_group_reduce_add(lsum);
-
-    if (psrc2) {
-        sum += exp(psrc2[i02] - max);
-    }
+    const float sum = sub_group_reduce_add(lsum);
 
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         pdst[i00] /= sum;

ggml/src/ggml-opencl/kernels/softmax_f32.cl

@@ -26,8 +26,6 @@ kernel void kernel_soft_max(
         ulong offset0,
         global char * src1,
         ulong offset1,
-        global char * src2,
-        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -50,7 +48,6 @@ kernel void kernel_soft_max(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
-    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -63,7 +60,6 @@ kernel void kernel_soft_max(
 
     global float * psrc0 = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global float * pmask = src1 != src0 ? (global float *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
-    global float * psrc2 = src2 != src0 ? (global float *)(src2) : 0;
     global float * pdst  = (global float *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -79,7 +75,7 @@ kernel void kernel_soft_max(
     }
 
     // parallel max
-    float lmax = psrc2 ? psrc2[i02] : -INFINITY;
+    float lmax = -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
@@ -95,11 +91,7 @@ kernel void kernel_soft_max(
         pdst[i00] = exp_psrc0;
     }
 
-    float sum = sub_group_reduce_add(lsum);
-
-    if (psrc2) {
-        sum += exp(psrc2[i02] - max);
-    }
+    const float sum = sub_group_reduce_add(lsum);
 
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         pdst[i00] /= sum;

ggml/src/ggml-quants.c

@@ -21,17 +21,6 @@
 
 #define UNUSED GGML_UNUSED
 
-static inline int best_index_int8(int n, const int8_t * val, float x) {
-    if (x <= val[0]) return 0;
-    if (x >= val[n-1]) return n-1;
-    int ml = 0, mu = n-1;
-    while (mu-ml > 1) {
-        int mav = (ml+mu)/2;
-        if (x < val[mav]) mu = mav; else ml = mav;
-    }
-    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
-}
-
 // reference implementation for deterministic creation of model files
 void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k) {
     static const int qk = QK4_0;
@@ -257,53 +246,6 @@ void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_REST
     }
 }
 
-static inline int best_index_mxfp4(float x, float e) {
-    int best_index = 0;
-    float best_err = fabsf(kvalues_mxfp4[0]*e - x);
-    for (int i = 1; i < 16; i++) {
-        float err = fabsf(kvalues_mxfp4[i]*e - x);
-        if (err < best_err) {
-            best_index = i;
-            best_err = err;
-        }
-    }
-    return best_index;
-}
-
-void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RESTRICT y, int64_t k) {
-    static const int qk = QK_MXFP4;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f; // absolute max
-
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i*qk + j];
-
-            if (amax < fabsf(v)) {
-                amax = fabsf(v);
-            }
-        }
-
-        const uint8_t e = (uint8_t) (floorf(log2f(amax)) - 2 + 127);
-
-        const float d = GGML_E8M0_TO_FP32_HALF(e);
-
-        y[i].e = e;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t x0 = best_index_mxfp4(x[i*qk + 0    + j], d);
-            const uint8_t x1 = best_index_mxfp4(x[i*qk + qk/2 + j], d);
-
-            y[i].qs[j]  = x0;
-            y[i].qs[j] |= x1 << 4;
-        }
-    }
-}
-
 void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
     static const int qk = QK4_0;
 
@@ -414,26 +356,6 @@ void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRI
     }
 }
 
-void dequantize_row_mxfp4(const block_mxfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
-    static const int qk = QK_MXFP4;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        const float d = GGML_E8M0_TO_FP32_HALF(x[i].e);
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int8_t x0 = kvalues_mxfp4[x[i].qs[j] & 0x0F];
-            const int8_t x1 = kvalues_mxfp4[x[i].qs[j] >>   4];
-
-            y[i*qk + j + 0   ] = x0*d;
-            y[i*qk + j + qk/2] = x1*d;
-        }
-    }
-}
-
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -2092,12 +2014,6 @@ size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,
     return nrow * row_size;
 }
 
-size_t quantize_mxfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    GGML_UNUSED(quant_weights);
-    quantize_row_mxfp4_ref(src, dst, (int64_t)nrow*n_per_row);
-    return nrow * ggml_row_size(GGML_TYPE_MXFP4, n_per_row);
-}
-
 // ====================== Ternary (de)-quantization (BitNet b1.58 and TriLMs)
 
 void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k) {
@@ -4635,6 +4551,17 @@ size_t quantize_iq1_m(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,
 
 // ============================ 4-bit non-linear quants
 
+static inline int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
 static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x,
         ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l,
         float * scales, float * weight, uint8_t * L,
@@ -5034,15 +4961,6 @@ static bool validate_fp16(ggml_fp16_t f, size_t i) {
     return true;
 }
 
-static bool validate_e_e8m0(uint8_t e, size_t i) {
-    if (e == 0xff) {
-        fprintf(stderr, "ggml_validate_row_data: found invalid e value %d at block %zu\n", e, i);
-        return false;
-    }
-
-    return true;
-}
-
 #define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
     const type * q = (const type *) (data); \
     for (size_t i = 0; i < (nb); ++i) { \
@@ -5059,14 +4977,6 @@ static bool validate_e_e8m0(uint8_t e, size_t i) {
         } \
     }
 
-#define VALIDATE_ROW_DATA_E_E8M0_IMPL(type, data, nb) \
-    const type * q = (const type *) (data); \
-    for (size_t i = 0; i < (nb); ++i) { \
-        if (!validate_e_e8m0(q[i].e, i)) { \
-            return false; \
-        } \
-    }
-
 #define VALIDATE_ROW_DATA_DVEC_F16_IMPL(type, data, nb, nr) \
     const type * q = (const type *) (data); \
     for (size_t i = 0; i < (nb); ++i) { \
@@ -5220,10 +5130,6 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
             {
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
             } break;
-        case GGML_TYPE_MXFP4:
-            {
-                VALIDATE_ROW_DATA_E_E8M0_IMPL(block_mxfp4, data, nb);
-            } break;
         case GGML_TYPE_Q2_K:
             {
                 VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);

ggml/src/ggml-quants.h

@@ -21,8 +21,6 @@ GGML_API void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 *
 GGML_API void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
 
-GGML_API void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RESTRICT y, int64_t k);
-
 GGML_API void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
@@ -47,8 +45,6 @@ GGML_API void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GG
 GGML_API void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 //GGML_API void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 
-GGML_API void dequantize_row_mxfp4(const block_mxfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
-
 GGML_API void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
@@ -94,8 +90,6 @@ GGML_API size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTR
 GGML_API size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 
-GGML_API size_t quantize_mxfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-
 GGML_API void iq2xs_init_impl(enum ggml_type type);
 GGML_API void iq2xs_free_impl(enum ggml_type type);
 GGML_API void iq3xs_init_impl(int grid_size);

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

@@ -2609,8 +2609,6 @@ static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml
     GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->ne[1] == 1);
-    GGML_ASSERT(src1->ne[3] == 1);
 
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
@@ -3198,7 +3196,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
             // The kernel from the if path is faster for that specific case, but does not support all mul mats.
             ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst);
         }
-    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1 && src1->ne[3] == 1) {
+    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
         ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst);
     } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2] * src1->ne[3] > 1) {
@@ -4193,9 +4191,15 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             {
-                struct ggml_tensor * a = op->src[0];
-                struct ggml_tensor * b = op->src[1];
-
+                struct ggml_tensor * a;
+                struct ggml_tensor * b;
+                if (op->op == GGML_OP_MUL_MAT) {
+                    a = op->src[0];
+                    b = op->src[1];
+                } else {
+                    a = op->src[2];
+                    b = op->src[1];
+                }
                 if (a->ne[3] != b->ne[3]) {
                     return false;
                 }
@@ -4210,9 +4214,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                     }
                 }
                 ggml_type src0_type = op->src[0]->type;
-                if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) {
-                    // TODO: support MXFP4
-                    // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added
+                if (src0_type == GGML_TYPE_BF16) {
                     return false;
                 }
                 return true;
@@ -4357,10 +4359,6 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             if (op->src[0]->ne[3] != 1) {
                 return false;
             }
-            // TODO: support attention sinks [TAG_ATTN_SINKS]
-            if (op->src[2]) {
-                return false;
-            }
             // TODO: support broadcast
             // ref: https://github.com/ggml-org/llama.cpp/pull/14435
             return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);

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

@@ -449,8 +449,6 @@ struct vk_device_struct {
     vk_pipeline pipeline_div[2][2][2];
     vk_pipeline pipeline_div_norepeat[2][2][2];
 
-    vk_pipeline pipeline_add_id_f32;
-
     vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32;
     vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bilinear_ac_f32;
     vk_pipeline pipeline_scale_f32;
@@ -485,7 +483,6 @@ struct vk_device_struct {
     vk_pipeline pipeline_geglu[2];
     vk_pipeline pipeline_reglu[2];
     vk_pipeline pipeline_swiglu[2];
-    vk_pipeline pipeline_swiglu_oai[2];
     vk_pipeline pipeline_geglu_erf[2];
     vk_pipeline pipeline_geglu_quick[2];
 
@@ -534,7 +531,6 @@ struct vk_device_struct {
     ggml_backend_buffer_type buffer_type;
 
     bool disable_fusion;
-    bool disable_host_visible_vidmem;
 
 #ifdef GGML_VULKAN_MEMORY_DEBUG
     std::unique_ptr<vk_memory_logger> memory_logger;
@@ -709,8 +705,6 @@ struct vk_op_glu_push_constants {
     uint32_t ne00;
     uint32_t ne20;
     uint32_t mode;  // 0: default, 1: swapped, 2: split
-    float alpha; // for swiglu_oai
-    float limit;
 };
 
 struct vk_op_unary_push_constants {
@@ -800,15 +794,6 @@ struct vk_op_binary_push_constants {
     float param1; float param2; int32_t param3;
 };
 
-struct vk_op_add_id_push_constants {
-    uint32_t ne0;
-    uint32_t ne1;
-    uint32_t s01;
-    uint32_t s02;
-    uint32_t s11;
-    uint32_t s21;
-};
-
 struct vk_op_diag_mask_push_constants {
     uint32_t ncols;
     uint32_t rows_per_channel;
@@ -850,7 +835,6 @@ struct vk_op_soft_max_push_constants {
     float m1;
     uint32_t n_head_log2;
     uint32_t nrows_x;
-    uint32_t has_sinks;
 };
 
 struct vk_op_argsort_push_constants {
@@ -1805,8 +1789,6 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
         } else if (device->uma) {
             // Fall back to host memory type
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-        } else if (device->disable_host_visible_vidmem) {
-            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eDeviceLocal);
         } else {
             // use rebar if available, otherwise fallback to device only visible memory
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
@@ -1995,7 +1977,6 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
         break;
     case GGML_TYPE_IQ4_NL:
     case GGML_TYPE_IQ4_XS:
-    case GGML_TYPE_MXFP4:
         lut_size = 4*16;
         break;
     default:
@@ -2286,14 +2267,14 @@ static void ggml_vk_load_shaders(vk_device& device) {
     };
 
 #define CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, HSK, HSV, HEAD_SIZES) \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
 
 #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
         CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 64, 64, 64) \
@@ -2372,7 +2353,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_S],   matmul_iq3_s_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_MXFP4],   matmul_mxfp4_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
 
         CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
 #if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
@@ -2399,7 +2379,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
     } else
@@ -2461,7 +2440,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         } else {
             CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
@@ -2483,7 +2461,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         }
 
         CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
@@ -2516,7 +2493,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         } else {
             CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
@@ -2538,7 +2514,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
 #undef CREATE_MM2
 #undef CREATE_MM
@@ -2606,7 +2581,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
         if (device->integer_dot_product) {
@@ -2644,7 +2618,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM2
 #undef CREATE_MMQ
 #undef CREATE_MM
@@ -2699,7 +2672,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
         if (device->integer_dot_product) {
@@ -2737,7 +2709,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
     }
     // reusing CREATE_MM from the fp32 path
     if ((device->coopmat2 || device->coopmat_support)
@@ -2796,7 +2767,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f32_f32_len,   mul_mat_vec_iq3_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f32_f32_len,  mul_mat_vec_iq4_xs_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f32_f32_len,  mul_mat_vec_iq4_nl_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32_"+std::to_string(i+1),   mul_mat_vec_mxfp4_f32_f32_len,   mul_mat_vec_mxfp4_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
 
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
@@ -2820,7 +2790,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f16_f32_len,   mul_mat_vec_iq3_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f16_f32_len,  mul_mat_vec_iq4_xs_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f16_f32_len,  mul_mat_vec_iq4_nl_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32_"+std::to_string(i+1),   mul_mat_vec_mxfp4_f16_f32_len,   mul_mat_vec_mxfp4_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
     }
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -2845,7 +2814,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   mul_mat_vec_id_iq3_s_f32_len,   mul_mat_vec_id_iq3_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  mul_mat_vec_id_iq4_xs_f32_len,  mul_mat_vec_id_iq4_xs_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  mul_mat_vec_id_iq4_nl_f32_len,  mul_mat_vec_id_iq4_nl_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_MXFP4],   "mul_mat_vec_id_mxfp4_f32",   mul_mat_vec_id_mxfp4_f32_len,   mul_mat_vec_id_mxfp4_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -2868,7 +2836,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_S],   "dequant_iq3_s",   dequant_iq3_s_len,   dequant_iq3_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_XS],  "dequant_iq4_xs",  dequant_iq4_xs_len,  dequant_iq4_xs_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL],  "dequant_iq4_nl",  dequant_iq4_nl_len,  dequant_iq4_nl_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_MXFP4],   "dequant_mxfp4",   dequant_mxfp4_len,   dequant_mxfp4_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
 
     // get_rows
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -2888,7 +2855,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_S],   "get_rows_iq3_s",   get_rows_iq3_s_len,   get_rows_iq3_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs",  get_rows_iq4_xs_len,  get_rows_iq4_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4],   "get_rows_mxfp4",   get_rows_mxfp4_len,   get_rows_mxfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -2907,10 +2873,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_S],   "get_rows_iq3_s_f32",   get_rows_iq3_s_f32_len,   get_rows_iq3_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs_f32",  get_rows_iq4_xs_f32_len,  get_rows_iq4_xs_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4],   "get_rows_mxfp4_f32",   get_rows_mxfp4_f32_len,   get_rows_mxfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, 5 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 2, 4 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1, "quantize_q8_1", quantize_q8_1_len, quantize_q8_1_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1);
 
     for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) {
@@ -3011,8 +2976,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     CREATE_BINARY(div, _norepeat, {1})
 #undef CREATE_BINARY
 
-    ggml_vk_create_pipeline(device, device->pipeline_add_id_f32, "add_id_f32", add_id_f32_len, add_id_f32_data, "main", 4, sizeof(vk_op_add_id_push_constants), {1, 1, 1}, {}, 1);
-
     ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
@@ -3063,7 +3026,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     CREATE_GLU(geglu)
     CREATE_GLU(reglu)
     CREATE_GLU(swiglu)
-    CREATE_GLU(swiglu_oai)
     CREATE_GLU(geglu_erf)
     CREATE_GLU(geglu_quick)
 #undef CREATE_GLU
@@ -3073,10 +3035,10 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true);
 
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
@@ -3268,9 +3230,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
         const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY");
         device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr;
 
-        const char* GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM = getenv("GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM");
-        device->disable_host_visible_vidmem = GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM != nullptr;
-
         bool fp16_storage = false;
         bool fp16_compute = false;
         bool maintenance4_support = false;
@@ -4285,7 +4244,6 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_MXFP4:
             break;
         default:
             return nullptr;
@@ -4356,7 +4314,6 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_MXFP4:
             break;
         default:
             return nullptr;
@@ -4400,7 +4357,6 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_MXFP4:
             break;
         default:
             return nullptr;
@@ -4455,7 +4411,6 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_MXFP4:
             break;
         default:
             return nullptr;
@@ -4491,7 +4446,6 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_MXFP4:
             break;
         default:
             return nullptr;
@@ -4677,7 +4631,6 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context&
     std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
     GGML_ASSERT(ctx->descriptor_set_idx < ctx->descriptor_sets.size());
     GGML_ASSERT(descriptor_buffer_infos.size() <= MAX_PARAMETER_COUNT);
-    GGML_ASSERT(pipeline->parameter_count == descriptor_buffer_infos.size());
 
     vk::DescriptorSet& descriptor_set = ctx->descriptor_sets[ctx->descriptor_set_idx++];
     vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() };
@@ -6507,14 +6460,11 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
     return supported;
 }
 
-static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, ggml_tensor * dst, bool dryrun = false) {
     VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3];
     std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3];
     std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3];
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
-    if (sinks) {
-        std::cerr << "), (" << sinks << ", name=" << sinks->name << ", type=" << sinks->type << ", ne0=" << sinks->ne[0] << ", ne1=" << sinks->ne[1] << ", ne2=" << sinks->ne[2] << ", ne3=" << sinks->ne[3] << ", nb0=" << sinks->nb[0] << ", nb1=" << sinks->nb[1] << ", nb2=" << sinks->nb[2] << ", nb3=" << sinks->nb[3];
-    }
     std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
 
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
@@ -6713,10 +6663,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
-    vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr, d_S = nullptr;
-    size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0, s_buf_offset = 0;
+    vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr;
+    size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0;
 
-    bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false, S_uma = false;
+    bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false;
 
     if (ctx->device->uma) {
         ggml_vk_host_get(ctx->device, q->data, d_Q, q_buf_offset);
@@ -6731,10 +6681,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
             ggml_vk_host_get(ctx->device, mask->data, d_M, m_buf_offset);
             M_uma = d_M != nullptr;
         }
-        if (sinks) {
-            ggml_vk_host_get(ctx->device, sinks->data, d_S, s_buf_offset);
-            S_uma = d_S != nullptr;
-        }
     }
 
 
@@ -6770,17 +6716,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         }
     }
 
-    if (!S_uma) {
-        d_S = d_Q;
-        s_buf_offset = q_buf_offset;
-        if (sinks) {
-            ggml_backend_vk_buffer_context * s_buf_ctx = (ggml_backend_vk_buffer_context*)sinks->buffer->context;
-            d_S = s_buf_ctx->dev_buffer;
-            s_buf_offset = vk_tensor_offset(sinks) + sinks->view_offs;
-        }
-    }
-
-    uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | ((mask != nullptr) << 16) | n_head_log2;
+    uint32_t mask_n_head_log2 = ((mask != nullptr) << 16) | n_head_log2;
 
     const vk_flash_attn_push_constants pc = { N, KV,
                                               (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
@@ -6804,7 +6740,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                         vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE},
-                                        vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE},
                                     },
                                     // We only use split_k when group query attention is enabled, which means
@@ -6814,11 +6749,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                     pc, { workgroups_x * pipeline->wg_denoms[0], workgroups_y, workgroups_z });
 
         ggml_vk_sync_buffers(subctx);
-        const std::array<uint32_t, 5> pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k, (sinks != nullptr) };
+        const std::array<uint32_t, 4> pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k };
         ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce,
                                     {
                                         vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE},
-                                        vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
                                     },
                                     pc2, { (uint32_t)ne1, HSV, (uint32_t)ne3 });
@@ -6829,7 +6763,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                         vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE},
-                                        vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
                                     },
                                     pc, { workgroups_x, workgroups_y, workgroups_z });
@@ -6914,11 +6847,6 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             break;
         }
         return nullptr;
-    case GGML_OP_ADD_ID:
-        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && src2->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_add_id_f32;
-        }
-        return nullptr;
     case GGML_OP_CONCAT:
         if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_concat_f32;
@@ -7064,8 +6992,6 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
                 return ctx->device->pipeline_reglu[dst->type == GGML_TYPE_F16];
             case GGML_GLU_OP_SWIGLU:
                 return ctx->device->pipeline_swiglu[dst->type == GGML_TYPE_F16];
-            case GGML_GLU_OP_SWIGLU_OAI:
-                return ctx->device->pipeline_swiglu_oai[dst->type == GGML_TYPE_F16];
             case GGML_GLU_OP_GEGLU_ERF:
                 return ctx->device->pipeline_geglu_erf[dst->type == GGML_TYPE_F16];
             case GGML_GLU_OP_GEGLU_QUICK:
@@ -7081,7 +7007,6 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         return nullptr;
     case GGML_OP_SOFT_MAX:
         GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
-        GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32);
 
         if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
             return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32;
@@ -7252,7 +7177,6 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
     case GGML_OP_SUB:
     case GGML_OP_MUL:
     case GGML_OP_DIV:
-    case GGML_OP_ADD_ID:
     case GGML_OP_CONCAT:
     case GGML_OP_UPSCALE:
     case GGML_OP_SQR:
@@ -7599,10 +7523,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
                 elements = { ne, 1, 1 };
             }
         } break;
-    case GGML_OP_ADD_ID:
-        {
-            elements = { (uint32_t)ne01, (uint32_t)ne02, 1 };
-        } break;
     case GGML_OP_SET_ROWS:
         {
             uint32_t ne = ggml_nelements(src0);
@@ -7642,8 +7562,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
         }
     }
 
-    if (op == GGML_OP_GLU) {
-        // Empty src1 is possible in glu, but the shader needs a buffer
+    if (op == GGML_OP_SOFT_MAX || op == GGML_OP_GLU) {
+        // Empty src1 is possible in soft_max, but the shader needs a buffer
         vk_subbuffer subbuf_y;
         if (use_src1) {
             subbuf_y = { d_Y, y_buf_offset, y_sz };
@@ -7653,24 +7573,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
 
         ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
-    } else if (op == GGML_OP_SOFT_MAX) {
-        // Empty src1 and src2 is possible in soft_max, but the shader needs a buffer
-        vk_subbuffer subbuf_y;
-        if (use_src1) {
-            subbuf_y = { d_Y, y_buf_offset, y_sz };
-        } else {
-            subbuf_y = { d_X, 0, x_sz };
-        }
-
-        vk_subbuffer subbuf_z;
-        if (use_src2) {
-            subbuf_z = { d_Z, z_buf_offset, z_sz };
-        } else {
-            subbuf_z = { d_X, 0, x_sz };
-        }
-
-        ggml_vk_sync_buffers(subctx);
-        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (op == GGML_OP_ROPE || op == GGML_OP_ROPE_BACK) {
         // Empty src2 is possible in rope, but the shader needs a buffer
         vk_subbuffer subbuf_z;
@@ -7799,21 +7701,6 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const
     }, dryrun);
 }
 
-static void ggml_vk_add_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t src1_type_size = ggml_type_size(src1->type);
-    const uint32_t src2_type_size = ggml_type_size(src2->type);
-
-    ggml_vk_op_f32<vk_op_add_id_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ADD_ID, {
-        (uint32_t)dst->ne[0],
-        (uint32_t)dst->ne[1],
-        (uint32_t)src0->nb[1] / src0_type_size,
-        (uint32_t)src0->nb[2] / src0_type_size,
-        (uint32_t)src1->nb[1] / src1_type_size,
-        (uint32_t)src2->nb[1] / src2_type_size,
-    }, dryrun);
-}
-
 static void ggml_vk_op_f32_wkv(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, int version, bool dryrun = false) {
     GGML_ASSERT(version == 6 || version == 7);
     int num_srcs = version == 6 ? 6 : 7;
@@ -8232,12 +8119,8 @@ static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, con
 }
 
 static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
-    const float * op_params_f = (const float *)dst->op_params;
-
     const bool swapped = (bool)dst->op_params[1];
     const bool split = src1 != nullptr;
-    const float alpha = op_params_f[2];
-    const float limit = op_params_f[3];
 
     GGML_ASSERT(ggml_is_contiguous(src0));
 
@@ -8251,15 +8134,7 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
 
     const uint32_t mode = split ? 2 : (swapped ? 1 : 0);
 
-    ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GLU,
-        {
-            (uint32_t)ggml_nelements(dst),
-            (uint32_t)src0->ne[0],
-            (uint32_t)dst->ne[0],
-            mode,
-            alpha,
-            limit
-        }, dryrun);
+    ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GLU, { (uint32_t)ggml_nelements(dst), (uint32_t)src0->ne[0], (uint32_t)dst->ne[0], mode }, dryrun);
 }
 
 static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
@@ -8267,7 +8142,7 @@ static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& sub
     ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }, dryrun);
 }
 
-static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
     float * op_params = (float *)dst->op_params;
 
     float scale = op_params[0];
@@ -8289,7 +8164,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
-    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, {
+    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, {
         ncols,
         src1 != nullptr ? nrows_y : (uint32_t)0,
         (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],
@@ -8299,7 +8174,6 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
         m0, m1,
         n_head_log2,
         nrows_x,
-        src2 != nullptr
     }, dryrun);
 }
 
@@ -9539,7 +9413,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_GLU_OP_GEGLU:
         case GGML_GLU_OP_REGLU:
         case GGML_GLU_OP_SWIGLU:
-        case GGML_GLU_OP_SWIGLU_OAI:
         case GGML_GLU_OP_GEGLU_ERF:
         case GGML_GLU_OP_GEGLU_QUICK:
             break;
@@ -9551,7 +9424,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_REPEAT_BACK:
     case GGML_OP_GET_ROWS:
     case GGML_OP_ADD:
-    case GGML_OP_ADD_ID:
     case GGML_OP_ACC:
     case GGML_OP_SUB:
     case GGML_OP_MUL:
@@ -9706,10 +9578,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_DIV:
         ggml_vk_div(ctx, compute_ctx, src0, src1, node, dryrun);
 
-        break;
-    case GGML_OP_ADD_ID:
-        ggml_vk_add_id(ctx, compute_ctx, src0, src1, src2, node, dryrun);
-
         break;
     case GGML_OP_CONCAT:
         ggml_vk_concat(ctx, compute_ctx, src0, src1, node, dryrun);
@@ -9807,7 +9675,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_GLU_OP_GEGLU:
         case GGML_GLU_OP_REGLU:
         case GGML_GLU_OP_SWIGLU:
-        case GGML_GLU_OP_SWIGLU_OAI:
         case GGML_GLU_OP_GEGLU_ERF:
         case GGML_GLU_OP_GEGLU_QUICK:
             ggml_vk_glu(ctx, compute_ctx, src0, src1, node, dryrun);
@@ -9821,7 +9688,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
 
         break;
     case GGML_OP_SOFT_MAX:
-        ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node, dryrun);
+        ggml_vk_soft_max(ctx, compute_ctx, src0, src1, node, dryrun);
 
         break;
     case GGML_OP_SOFT_MAX_BACK:
@@ -9894,7 +9761,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         break;
 
     case GGML_OP_FLASH_ATTN_EXT:
-        ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node->src[4], node, dryrun);
+        ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
 
         break;
 
@@ -9967,7 +9834,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
     case GGML_OP_SUB:
     case GGML_OP_MUL:
     case GGML_OP_DIV:
-    case GGML_OP_ADD_ID:
     case GGML_OP_CONCAT:
     case GGML_OP_UPSCALE:
     case GGML_OP_SCALE:
@@ -10037,7 +9903,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
         case GGML_GLU_OP_GEGLU:
         case GGML_GLU_OP_REGLU:
         case GGML_GLU_OP_SWIGLU:
-        case GGML_GLU_OP_SWIGLU_OAI:
         case GGML_GLU_OP_GEGLU_ERF:
         case GGML_GLU_OP_GEGLU_QUICK:
             buf = tensor->buffer;
@@ -10887,7 +10752,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous(op->src[0]) &&
@@ -10933,7 +10797,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ3_S:
                     case GGML_TYPE_IQ4_XS:
                     case GGML_TYPE_IQ4_NL:
-                    case GGML_TYPE_MXFP4:
                         break;
                     default:
                         return false;
@@ -10971,9 +10834,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 if (head_sizes == FA_HEAD_SIZE_UNSUPPORTED) {
                     return false;
                 }
-                if (op->src[4] && op->src[4]->type != GGML_TYPE_F32) {
-                    return false;
-                }
                 if (op->src[0]->type != GGML_TYPE_F32) {
                     return false;
                 }
@@ -11046,7 +10906,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ3_S:
                     case GGML_TYPE_IQ4_XS:
                     case GGML_TYPE_IQ4_NL:
-                    case GGML_TYPE_MXFP4:
                         return true;
                     default:
                         return false;
@@ -11145,9 +11004,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
             return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                    (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) &&
                    (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
-        case GGML_OP_ADD_ID:
-            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->src[2]->type == GGML_TYPE_I32 &&
-                   op->type == GGML_TYPE_F32;
         case GGML_OP_SILU_BACK:
         case GGML_OP_RMS_NORM_BACK:
         case GGML_OP_SQR:
@@ -11566,9 +11422,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
     if (tensor->op == GGML_OP_FLASH_ATTN_EXT) {
         const float * params = (const float *)tensor->op_params;
         tensor_clone = ggml_flash_attn_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], params[0], params[1], params[2]);
-        if (src_clone[4]) {
-            ggml_flash_attn_ext_add_sinks(tensor_clone, src_clone[4]);
-        }
     } else if (tensor->op == GGML_OP_MUL_MAT) {
         tensor_clone = ggml_mul_mat(ggml_ctx, src_clone[0], src_clone[1]);
     } else if (tensor->op == GGML_OP_MUL_MAT_ID) {

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

@@ -1,42 +0,0 @@
-#version 450
-
-#extension GL_EXT_control_flow_attributes : require
-
-#include "types.comp"
-
-layout (push_constant) uniform parameter
-{
-    uint ne0;
-    uint ne1;
-    uint s01;
-    uint s02;
-    uint s11;
-    uint s21;
-} p;
-
-#define BLOCK_SIZE 512
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) readonly buffer Z {int32_t data_c[];};
-layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i1 = gl_WorkGroupID.x;
-    const uint i2 = gl_WorkGroupID.y;
-
-    const uint i11 = data_c[i1 + i2 * p.s21];
-
-    const uint s1 = p.ne0;
-    const uint s2 = p.ne0 * p.ne1;
-
-    const uint d0 = i1 * s1 + i2 * s2;
-    const uint a0 = i1 * p.s01 + i2 * p.s02;
-    const uint b0 = i11 * p.s11;
-
-    for (uint i0 = gl_LocalInvocationID.x; i0 < p.ne0; i0 += BLOCK_SIZE) {
-        data_d[d0 + i0] = data_a[a0 + i0] + data_b[b0 + i0];
-    }
-}

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

@@ -4,8 +4,8 @@
 #include "generic_unary_head.comp"
 #include "dequant_funcs.comp"
 
-#if defined(DATA_A_IQ4_NL) || defined(DATA_A_MXFP4)
-// 16 invocations needed for init_iq_shmem
+#if defined(DATA_A_IQ4_NL)
+// 16 invocations needed for init_iq4nl_shmem
 layout(local_size_x = 16, local_size_y = 1, local_size_z = 1) in;
 #else
 layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;

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

@@ -434,18 +434,6 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
 }
 #endif
 
-#if defined(DATA_A_MXFP4)
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return vec2(kvalues_mxfp4[vui & 0xF], kvalues_mxfp4[vui >> 4]);
-}
-vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    vec2 v0 = dequantize(ib, iqs, a_offset);
-    vec2 v1 = dequantize(ib, iqs + 1, a_offset);
-    return vec4(v0.x, v0.y, v1.x, v1.y);
-}
-#endif
-
 #if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16)
 vec2 get_dm(uint ib, uint a_offset) {
     return vec2(0, 0);
@@ -467,12 +455,6 @@ vec2 get_dm(uint ib, uint a_offset) {
 }
 #endif
 
-#if defined(DATA_A_MXFP4)
-vec2 get_dm(uint ib, uint a_offset) {
-    return vec2(e8m0_to_fp32(data_a[a_offset + ib].e), 0);
-}
-#endif
-
 #if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
 vec2 get_dm(uint ib, uint a_offset) {
     return vec2(float(data_a[a_offset + ib].d), float(data_a[a_offset + ib].m));

ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp

@@ -654,25 +654,6 @@ float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoor
 }
 #endif
 
-#if defined(DATA_A_MXFP4)
-layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufMXFP4 {
-   block_mxfp4 block;
-};
-
-float16_t dequantFuncMXFP4(const in decodeBufMXFP4 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
-{
-    const float d = e8m0_to_fp32(bl.block.e);
-    const uint idx = coordInBlock[1];
-    const uint iqs = idx & 0xF;
-    const uint shift = (idx & 0x10) >> 2;
-    uint32_t qs = bl.block.qs[iqs];
-    qs >>= shift;
-    qs &= 0xF;
-    float16_t ret = float16_t(kvalues_mxfp4[qs] * d);
-    return ret;
-}
-#endif
-
 #if defined(DATA_A_Q4_0)
 #define dequantFuncA dequantFuncQ4_0
 #elif defined(DATA_A_Q4_1)
@@ -715,6 +696,4 @@ float16_t dequantFuncMXFP4(const in decodeBufMXFP4 bl, const in uint blockCoords
 #define dequantFuncA dequantFuncIQ4_XS
 #elif defined(DATA_A_IQ4_NL)
 #define dequantFuncA dequantFuncIQ4_NL
-#elif defined(DATA_A_MXFP4)
-#define dequantFuncA dequantFuncMXFP4
 #endif

ggml/src/ggml-vulkan/vulkan-shaders/dequant_mxfp4.comp

@@ -1,32 +0,0 @@
-#version 450
-
-#include "dequant_head.comp"
-
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {block_mxfp4 data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
-
-    init_iq_shmem(gl_WorkGroupSize);
-
-    const uint tid = gl_LocalInvocationID.x % 64;
-    const uint il  = tid/32;
-    const uint ir  = tid%32;
-    const uint ib = 32*i + ir;
-    if (ib >= p.nel / 32) {
-        return;
-    }
-
-    const uint q_idx = 8*il;
-    const uint b_idx = 1024*i + 32*ir + q_idx;
-
-    const float d = e8m0_to_fp32(data_a[ib].e);
-
-    [[unroll]] for (uint l = 0; l < 8; ++l) {
-        data_b[b_idx + l +  0] = D_TYPE(d * kvalues_mxfp4[data_a[ib].qs[q_idx + l] & 0xF]);
-        data_b[b_idx + l + 16] = D_TYPE(d * kvalues_mxfp4[data_a[ib].qs[q_idx + l] >>  4]);
-    }
-}

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

@@ -305,27 +305,6 @@ void main() {
         return;
     }
 
-    if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
-        [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
-            float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2);
-
-            float ms = 1.0f;
-            float vs = 1.0f;
-
-            if (sink > Mf[r]) {
-                ms = exp(Mf[r] - sink);
-
-                [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
-                    Of[r][d] *= ms;
-                }
-            } else {
-                vs = exp(sink - Mf[r]);
-            }
-
-            Lf[r] = Lf[r]*ms + vs;
-        }
-    }
-
     float Lfrcp[Br];
     [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
         Lfrcp[r] = 1.0 / Lf[r];

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

@@ -50,13 +50,10 @@ layout (push_constant) uniform parameter {
     uint32_t k_num;
 } p;
 
-#define SINK_ENABLE_BIT (1<<24)
 #define MASK_ENABLE_BIT (1<<16)
 #define N_LOG2_MASK 0xFFFF
 
-layout (binding = 4) readonly buffer S {float data_s[];};
-
-layout (binding = 5) writeonly buffer O {D_TYPE data_o[];};
+layout (binding = 4) writeonly buffer O {D_TYPE data_o[];};
 
 #if defined(A_TYPE_PACKED16)
 #define BINDING_IDX_K 0
@@ -114,14 +111,6 @@ ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const i
     return ACC_TYPE(pow(base, ACC_TYPE(exph)));
 }
 
-// Load the sink value, indexed by Q's dimension 2.
-ACC_TYPE perElemOpGetSink(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2)
-{
-    const uint32_t h = iq2 + (r % p.gqa_ratio);
-
-    return ACC_TYPE(data_s[h]);
-}
-
 uint32_t i, N, KV, split_k_index, Tr, start_j, end_j,
          iq2, iq3, rk2, rk3, rv2, rv3, ik2, ik3, iv2, iv3,
          q_stride, k_stride, v_stride, m_stride;

ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp

@@ -329,27 +329,6 @@ void main() {
         return;
     }
 
-    if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
-        [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
-            float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2);
-
-            float ms = 1.0f;
-            float vs = 1.0f;
-
-            if (sink > Mf[r]) {
-                ms = exp(Mf[r] - sink);
-
-                [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
-                    Of[r][d] *= ACC_TYPE(ms);
-                }
-            } else {
-                vs = exp(sink - Mf[r]);
-            }
-
-            Lf[r] = Lf[r]*ms + vs;
-        }
-    }
-
     float Lfrcp[rows_per_thread];
     [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
         Lfrcp[r] = 1.0 / Lf[r];

ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp

@@ -248,34 +248,6 @@ void main() {
     // resize L by using smear/reduce
     coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce);
 
-    if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
-        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> S;
-        coopMatPerElementNV(S, S, perElemOpGetSink, iq2);
-
-        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> Mr;
-
-        // resize M by using smear/reduce
-        coopMatReduceNV(Mr, M, gl_CooperativeMatrixReduceRowNV, smearReduce);
-
-        // O, Ldiag, Mr all have the same type so all element locations match
-        [[unroll]] for (uint32_t i = 0; i < Ldiag.length(); ++i) {
-            ACC_TYPE sink = S[i];
-
-            ACC_TYPE ms = ACC_TYPE(1.0f);
-            ACC_TYPE vs = ACC_TYPE(1.0f);
-
-            if (sink > Mr[i]) {
-                ms = exp(Mr[i] - sink);
-
-                O[i] *= ms;
-            } else {
-                vs = exp(sink - Mr[i]);
-            }
-
-            Ldiag[i] = Ldiag[i]*ms + vs;
-        }
-    }
-
     [[unroll]]
     for (int k = 0; k < Ldiag.length(); ++k) {
         Ldiag[k] = ACC_TYPE(1.0) / Ldiag[k];

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

@@ -7,15 +7,13 @@ layout(constant_id = 0) const uint BLOCK_SIZE = 32;
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {float data_a[];};
-layout (binding = 1) readonly buffer B {float data_s[];};
-layout (binding = 2) writeonly buffer D {float data_d[];};
+layout (binding = 1) writeonly buffer D {float data_d[];};
 
 layout (push_constant) uniform parameter {
     uint D;
     uint N;
     uint ne3;
     uint k_num;
-    uint sinks;
 } p;
 
 shared float tmpsh[BLOCK_SIZE];
@@ -75,22 +73,6 @@ void main() {
     }
     L = tmpsh[0];
 
-    float sink;
-    if (p.sinks != 0) {
-        sink = data_s[n];
-
-        float ms = 1.0f;
-        float vs = 1.0f;
-
-        if (sink > m_max) {
-            ms = exp(m_max - sink);
-        } else {
-            vs = exp(sink - m_max);
-        }
-
-        L = L*ms + vs;
-    }
-
     L = 1.0 / L;
 
     // D dimension is split across workgroups in the y dimension
@@ -103,13 +85,6 @@ void main() {
             float m = data_a[m_offset + k * lm_stride];
             O += exp(m - m_max) * data_a[o_offset];
         }
-        if (p.sinks != 0) {
-            if (sink > m_max) {
-                float ms = 1.0f;
-                ms = exp(m_max - sink);
-                O *= ms;
-            }
-        }
         O *= L;
         data_d[iq3 * D * N + D * n + d] = O;
     }

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

@@ -14,6 +14,4 @@ layout (push_constant) uniform parameter
     uint ne00;
     uint ne20;
     uint mode;
-    float alpha;
-    float limit;
 } p;

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

@@ -747,21 +747,6 @@ void main() {
             buf_a[buf_idx + 1 ] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]) * d;
             buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)]) * d;
             buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_iq4nl[vui >> 12]) * d;
-#elif defined(DATA_A_MXFP4)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 2 * loadr_a;
-
-            const uint ib = idx / 8;
-            const uint iqs = (idx & 0x07) * 2;
-
-            const float d = e8m0_to_fp32(data_a[ib].e);
-            const uint vui = uint(data_a[ib].qs[iqs]);
-            const uint vui2 = uint(data_a[ib].qs[iqs+1]);
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(kvalues_mxfp4[vui & 0xF] * d);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_mxfp4[vui >>  4] * d);
-            buf_a[buf_idx +  1] = FLOAT_TYPE(kvalues_mxfp4[vui2 & 0xF] * d);
-            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_mxfp4[vui2 >>  4] * d);
 #endif
         }
         [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {

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

@@ -92,12 +92,6 @@ FLOAT_TYPE get_d(uint ib) {
 }
 #endif
 
-#if defined(DATA_A_MXFP4)
-FLOAT_TYPE get_d(uint ib) {
-    return FLOAT_TYPE(e8m0_to_fp32(data_a[ib].e));
-}
-#endif
-
 #if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
 FLOAT_TYPE_VEC2 get_dm(uint ib) {
     return FLOAT_TYPE_VEC2(data_a_packed32[ib].dm);

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

@@ -20,7 +20,6 @@ layout (push_constant) uniform parameter
     float m1;
     uint n_head_log2;
     uint nrows_x;
-    uint has_sinks;
 } p;
 
 #include "types.comp"
@@ -30,8 +29,7 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) readonly buffer Z {float data_c[];};
-layout (binding = 3) buffer D {D_TYPE data_d[];};
+layout (binding = 2) buffer D {D_TYPE data_d[];};
 
 shared FLOAT_TYPE vals[BLOCK_SIZE];
 
@@ -62,13 +60,13 @@ void soft_max(uint num_iters) {
         const uint h = (rowx / p.ne01) % p.ne02; // head index
 
         const float base = h < p.n_head_log2 ? p.m0 : p.m1;
-        const uint   exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
 
         slope = pow(base, exp);
     }
 
     // Find max
-    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+    FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
 
     // Cache values while we compute the max, so we don't need to read them
     // again when we're ready to compute exp(x-max).
@@ -150,10 +148,6 @@ void soft_max(uint num_iters) {
     }
     sum = vals[0];
 
-    if (p.has_sinks != 0) {
-        sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val));
-    }
-
     FLOAT_TYPE rcpdivisor = 1.0/sum;
 
     [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {

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

@@ -1,14 +0,0 @@
-#version 450
-
-#include "glu_head.comp"
-
-float op(float a, float b) {
-    float xi = min(a, p.limit);
-    float gi = max(min(b, p.limit), -p.limit);
-
-    float out_glu = xi / (1.0f + exp(-xi * p.alpha));
-    out_glu = out_glu * (1.0f + gi);
-    return out_glu;
-}
-
-#include "glu_main.comp"

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

@@ -1337,29 +1337,6 @@ struct block_iq4_nl_packed16
 #define A_TYPE_PACKED16 block_iq4_nl_packed16
 #endif
 
-#define QUANT_K_MXFP4 32
-#define QUANT_R_MXFP4 2
-
-struct block_mxfp4
-{
-    uint8_t e;
-    uint8_t qs[QUANT_K_MXFP4/2];
-};
-
-//struct block_mxfp4_packed16
-//{
-//    uint8_t e;
-//    uint16_t qs[QUANT_K_MXFP4/2/2];
-//};
-
-#if defined(DATA_A_MXFP4)
-#define QUANT_K QUANT_K_MXFP4
-#define QUANT_R QUANT_R_MXFP4
-#define QUANT_AUXF 1
-#define A_TYPE block_mxfp4
-//#define A_TYPE_PACKED16 block_mxfp4_packed16
-#endif
-
 #if defined(DATA_A_IQ4_NL) || defined(DATA_A_IQ4_XS)
 const int8_t kvalues_iq4nl_const[16] = {
     int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10),
@@ -1379,25 +1356,6 @@ void init_iq_shmem(uvec3 wgsize)
 }
 #endif
 
-#if defined(DATA_A_MXFP4)
-const FLOAT_TYPE kvalues_mxfp4_const[16] = {
-    FLOAT_TYPE(0.0f), FLOAT_TYPE(0.5f), FLOAT_TYPE(1.0f), FLOAT_TYPE(1.5f), FLOAT_TYPE(2.0f), FLOAT_TYPE(3.0f), FLOAT_TYPE(4.0f), FLOAT_TYPE(6.0f),
-    FLOAT_TYPE(-0.0f), FLOAT_TYPE(-0.5f), FLOAT_TYPE(-1.0f), FLOAT_TYPE(-1.5f), FLOAT_TYPE(-2.0f), FLOAT_TYPE(-3.0f), FLOAT_TYPE(-4.0f), FLOAT_TYPE(-6.0f)
-};
-
-shared FLOAT_TYPE kvalues_mxfp4[16];
-
-#define NEEDS_INIT_IQ_SHMEM
-void init_iq_shmem(uvec3 wgsize)
-{
-    // copy the table into shared memory and sync
-    for (uint i = gl_LocalInvocationIndex.x; i < kvalues_mxfp4.length(); i += wgsize.x) {
-        kvalues_mxfp4[i] = kvalues_mxfp4_const[i];
-    }
-    barrier();
-}
-#endif
-
 // returns the bfloat value in the low 16b.
 // See ggml_compute_fp32_to_bf16
 uint32_t fp32_to_bf16(float f)
@@ -1412,17 +1370,4 @@ float bf16_to_fp32(uint32_t u)
     return uintBitsToFloat(u << 16);
 }
 
-float e8m0_to_fp32(uint8_t x) {
-    uint32_t bits;
-
-    if (x == 0) {
-        bits = 0x00400000;
-    } else {
-        bits = x;
-        bits = bits << 23;
-    }
-
-    return uintBitsToFloat(bits);
-}
-
 #endif // !defined(GGML_TYPES_COMP)

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

@@ -64,7 +64,6 @@ const std::vector<std::string> type_names = {
     "iq3_s",
     "iq4_xs",
     "iq4_nl",
-    "mxfp4",
     "bf16",
 };
 
@@ -119,7 +118,7 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s
     CloseHandle(pi.hProcess);
     CloseHandle(pi.hThread);
 #else
-    int stdout_pipe[2];
+int stdout_pipe[2];
     int stderr_pipe[2];
 
     if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) {
@@ -363,7 +362,7 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool
         std::string load_vec_quant = "2";
         if ((tname == "q4_0") || (tname == "q4_1") || (tname == "iq1_s") || (tname == "iq1_m") || (tname == "iq2_xxs") || (tname == "iq2_xs") || (tname == "iq2_s"))
             load_vec_quant = "8";
-        else if ((tname == "q5_0") || (tname == "q5_1") || (tname == "q8_0") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_nl") || (tname == "mxfp4"))
+        else if ((tname == "q5_0") || (tname == "q5_1") || (tname == "q8_0") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_nl"))
             load_vec_quant = "4";
 
         if (tname == "bf16") {
@@ -603,8 +602,6 @@ void process_shaders() {
         string_to_spv("reglu_f32" + suffix,      "reglu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
         string_to_spv("swiglu_f16" + suffix,     "swiglu.comp",      {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
         string_to_spv("swiglu_f32" + suffix,     "swiglu.comp",      {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
-        string_to_spv("swiglu_oai_f16" + suffix, "swiglu_oai.comp",  {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
-        string_to_spv("swiglu_oai_f32" + suffix, "swiglu_oai.comp",  {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
         string_to_spv("geglu_erf_f16" + suffix,  "geglu_erf.comp",   {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
         string_to_spv("geglu_erf_f32" + suffix,  "geglu_erf.comp",   {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
         string_to_spv("geglu_quick_f16" + suffix,"geglu_quick.comp", {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
@@ -674,8 +671,6 @@ void process_shaders() {
 
     string_to_spv("roll_f32", "roll.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
 
-    string_to_spv("add_id_f32", "add_id.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
-
     for (auto &c : compiles) {
         c.wait();
     }

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

@@ -19,21 +19,18 @@
 #include <vector>
 
 #ifdef GGML_WEBGPU_DEBUG
-#    define WEBGPU_LOG_DEBUG(msg)  std::cout << msg << std::endl
-#    define WEBGPU_DEBUG_BUF_ELEMS 32
+#    define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl
 #else
 #    define WEBGPU_LOG_DEBUG(msg) ((void) 0)
 #endif  // GGML_WEBGPU_DEBUG
 
 /* Constants */
 
-#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE     16
-#define WEBGPU_MUL_MAT_WG_SIZE               64
-#define WEBGPU_NUM_PARAM_BUFS                100
-#define WEBGPU_PARAMS_BUF_SIZE_BYTES         128  // enough for 32 parameters
-#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS       32
-#define WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES 4
-#define WEBGPU_STORAGE_BUF_BINDING_MULT      4  // a storage buffer binding size must be a multiple of 4
+#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 16
+#define WEBGPU_MUL_MAT_WG_SIZE           64
+#define WEBGPU_NUM_PARAM_BUFS            100
+#define WEBGPU_PARAMS_BUF_SIZE_BYTES     256
+#define WEBGPU_STORAGE_BUF_BINDING_MULT  4  // a storage buffer binding size must be a multiple of 4
 
 /* End Constants */
 
@@ -57,42 +54,46 @@ static void ggml_webgpu_create_buffer(wgpu::Device &    device,
                                       wgpu::BufferUsage usage,
                                       const char *      label);
 
-struct webgpu_pool_bufs {
+struct webgpu_param_bufs {
     wgpu::Buffer host_buf;
     wgpu::Buffer dev_buf;
 };
 
 // Holds a pool of parameter buffers for WebGPU operations
-struct webgpu_buf_pool {
-    std::vector<webgpu_pool_bufs> free;
+struct webgpu_param_buf_pool {
+    std::vector<webgpu_param_bufs> free;
 
     std::mutex mutex;
 
     std::condition_variable cv;
 
-    void init(wgpu::Device      device,
-              int               num_bufs,
-              size_t            buf_size,
-              wgpu::BufferUsage dev_buf_usage,
-              wgpu::BufferUsage host_buf_usage) {
-        for (int i = 0; i < num_bufs; i++) {
+    void init(wgpu::Device device) {
+        for (int i = 0; i < WEBGPU_NUM_PARAM_BUFS; i++) {
             wgpu::Buffer host_buf;
             wgpu::Buffer dev_buf;
-            ggml_webgpu_create_buffer(device, host_buf, buf_size, host_buf_usage, "ggml_webgpu_host_pool_buf");
-            ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf");
+            ggml_webgpu_create_buffer(device,
+                                      host_buf,
+                                      WEBGPU_PARAMS_BUF_SIZE_BYTES,
+                                      wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite,
+                                      "ggml_webgpu_host_params_buf");
+            ggml_webgpu_create_buffer(device,
+                                      dev_buf,
+                                      WEBGPU_PARAMS_BUF_SIZE_BYTES,
+                                      wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
+                                      "ggml_webgpu_dev_params_buf");
             free.push_back({ host_buf, dev_buf });
         }
     }
 
-    webgpu_pool_bufs alloc_bufs() {
+    webgpu_param_bufs alloc_bufs() {
         std::unique_lock<std::mutex> lock(mutex);
         cv.wait(lock, [this] { return !free.empty(); });
-        webgpu_pool_bufs bufs = free.back();
+        webgpu_param_bufs bufs = free.back();
         free.pop_back();
         return bufs;
     }
 
-    void free_bufs(std::vector<webgpu_pool_bufs> bufs) {
+    void free_bufs(std::vector<webgpu_param_bufs> bufs) {
         std::lock_guard<std::mutex> lock(mutex);
         free.insert(free.end(), bufs.begin(), bufs.end());
         cv.notify_all();
@@ -117,15 +118,15 @@ struct webgpu_context_struct {
     wgpu::Limits   limits;
 
     std::recursive_mutex mutex;
+    std::mutex           get_tensor_mutex;
+    std::mutex           init_mutex;
 
     bool device_init = false;
 
-    webgpu_buf_pool param_buf_pool;
-    webgpu_buf_pool set_rows_error_buf_pool;
+    webgpu_param_buf_pool param_buf_pool;
 
     wgpu::ComputePipeline memset_pipeline;
     wgpu::ComputePipeline mul_mat_pipeline;
-    wgpu::ComputePipeline set_rows_pipeline;
     wgpu::ComputePipeline cpy_pipeline;
 
     size_t memset_bytes_per_thread;
@@ -137,16 +138,7 @@ struct webgpu_context_struct {
     std::vector<wgpu::CommandBuffer> staged_command_bufs;
 
     // Parameter buffers associated with the staged command buffers
-    std::vector<webgpu_pool_bufs> staged_param_bufs;
-    // Buffers associated with set_rows operations, used to store potential errors
-    std::vector<webgpu_pool_bufs> staged_set_row_error_bufs;
-
-    std::vector<wgpu::FutureWaitInfo> callback_futures;
-
-#ifdef GGML_WEBGPU_DEBUG
-    wgpu::Buffer debug_host_buf;
-    wgpu::Buffer debug_dev_buf;
-#endif
+    std::vector<webgpu_param_bufs> staged_param_bufs;
 };
 
 typedef std::shared_ptr<webgpu_context_struct> webgpu_context;
@@ -229,83 +221,33 @@ static void ggml_webgpu_create_buffer(wgpu::Device &    device,
 
 /** WebGPU Actions */
 
-// Wait for the queue to finish processing all submitted work
 static void ggml_backend_webgpu_wait_on_submission(webgpu_context & ctx) {
-    std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
-    if (ctx->callback_futures.empty()) {
-        // no existing callbacks, wait on queue submission
-        ctx->instance.WaitAny(ctx->queue.OnSubmittedWorkDone(
-                                  wgpu::CallbackMode::AllowSpontaneous,
-                                  [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
-                                      if (status != wgpu::QueueWorkDoneStatus::Success) {
-                                          GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
-                                      }
-                                  }),
-                              UINT64_MAX);
-    } else {
-        // existing callbacks, wait on them
-        ctx->instance.WaitAny(ctx->callback_futures.size(), ctx->callback_futures.data(), UINT64_MAX);
-        ctx->callback_futures.clear();
-    }
+    // Wait for the queue to finish processing all commands
+    ctx->instance.WaitAny(ctx->queue.OnSubmittedWorkDone(
+                              wgpu::CallbackMode::AllowSpontaneous,
+                              [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
+                                  if (status != wgpu::QueueWorkDoneStatus::Success) {
+                                      GGML_LOG_ERROR("ggml_webgpu: Failed to wait on queue: %s\n", message.data);
+                                  }
+                              }),
+                          UINT64_MAX);
 }
 
 static void ggml_backend_webgpu_submit_queue(webgpu_context & ctx) {
     std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
-    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_submit_queue()");
-    if (ctx->staged_command_bufs.empty()) {
-        // Nothing to submit
-        return;
-    }
     ctx->queue.Submit(ctx->staged_command_bufs.size(), ctx->staged_command_bufs.data());
-
-    // If there are SET_ROWS operations in this submission, copy their error buffers to the host.
-    if (ctx->staged_set_row_error_bufs.size() > 0) {
-        wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
-        for (auto & error_bufs : ctx->staged_set_row_error_bufs) {
-            // Copy the error buffer to the host buffer
-            encoder.CopyBufferToBuffer(error_bufs.dev_buf, 0, error_bufs.host_buf, 0, error_bufs.host_buf.GetSize());
-        }
-        wgpu::CommandBuffer commands = encoder.Finish();
-        ctx->queue.Submit(1, &commands);
-    }
-
     ctx->staged_command_bufs.clear();
-    std::vector<webgpu_pool_bufs> staged_param_bufs         = std::move(ctx->staged_param_bufs);
-    std::vector<webgpu_pool_bufs> staged_set_row_error_bufs = std::move(ctx->staged_set_row_error_bufs);
-
+    std::vector<webgpu_param_bufs> staged_param_bufs = std::move(ctx->staged_param_bufs);
     // Free the staged parameter buffers once the submission completes
-    wgpu::Future p_f = ctx->queue.OnSubmittedWorkDone(
+    ctx->queue.OnSubmittedWorkDone(
         wgpu::CallbackMode::AllowSpontaneous,
         [ctx, staged_param_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
             if (status != wgpu::QueueWorkDoneStatus::Success) {
                 GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
             }
-            // Free the staged buffers
+            // Free the staged parameter buffers
             ctx->param_buf_pool.free_bufs(staged_param_bufs);
         });
-    ctx->callback_futures.push_back({ p_f });
-
-    // Check for errrors in SET_ROWS operations
-    for (auto & error_bufs : staged_set_row_error_bufs) {
-        wgpu::Future f = error_bufs.host_buf.MapAsync(
-            wgpu::MapMode::Read,
-            0,
-            error_bufs.host_buf.GetSize(),
-            wgpu::CallbackMode::AllowSpontaneous,
-            [ctx, error_bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) {
-                if (status != wgpu::MapAsyncStatus::Success) {
-                    GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", message.data);
-                } else {
-                    const uint32_t * error_data = (const uint32_t *) error_bufs.host_buf.GetConstMappedRange();
-                    if (*error_data) {
-                        GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported.");
-                    }
-                    // We can't unmap in here due to WebGPU reentrancy limitations.
-                    ctx->set_rows_error_buf_pool.free_bufs({ error_bufs });
-                }
-            });
-        ctx->callback_futures.push_back({ f });
-    }
 }
 
 static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
@@ -326,34 +268,13 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
                           UINT64_MAX);
 }
 
-#ifdef GGML_WEBGPU_DEBUG
-// This function adds debugging information to shaders, as WebGPU does not support printing directly.
-// To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and
-// debug statements in the shader, and then call this function after encoding the commands and submitting them.
-static void ggml_backend_webgpu_debug(webgpu_context & ctx) {
-    wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
-    encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize());
-    wgpu::CommandBuffer commands = encoder.Finish();
-    ctx->queue.Submit(1, &commands);
-
-    ggml_backend_webgpu_map_buffer(ctx, ctx->debug_host_buf, wgpu::MapMode::Read, 0, ctx->debug_host_buf.GetSize());
-    const uint32_t * debug_data = (const uint32_t *) ctx->debug_host_buf.GetConstMappedRange();
-    std::cout << "debug data:";
-    for (size_t i = 0; i < WEBGPU_DEBUG_BUF_ELEMS; i++) {
-        std::cout << "  " << i << ": " << debug_data[i];
-    }
-    std::cout << "\n";
-    ctx->debug_host_buf.Unmap();
-}
-#endif
-
 static void ggml_backend_webgpu_build_and_enqueue(webgpu_context &                  ctx,
                                                   wgpu::ComputePipeline &           pipeline,
                                                   std::vector<uint32_t>             params,
                                                   std::vector<wgpu::BindGroupEntry> bind_group_entries,
                                                   uint32_t                          wg_x,
-                                                  bool                              submit_and_wait = false) {
-    webgpu_pool_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
+                                                  bool                              submit_imm = false) {
+    webgpu_param_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
 
     ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0, params_bufs.host_buf.GetSize());
     uint32_t * _params = (uint32_t *) params_bufs.host_buf.GetMappedRange();
@@ -383,18 +304,17 @@ static void ggml_backend_webgpu_build_and_enqueue(webgpu_context &
     pass.DispatchWorkgroups(wg_x, 1, 1);
     pass.End();
     wgpu::CommandBuffer commands = encoder.Finish();
-    if (submit_and_wait) {
-        // Submit and wait immediately
+    if (submit_imm) {
+        // Submit immediately
         ctx->queue.Submit(1, &commands);
-        ctx->instance.WaitAny(ctx->queue.OnSubmittedWorkDone(
-                                  wgpu::CallbackMode::AllowSpontaneous,
-                                  [ctx, params_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
-                                      if (status != wgpu::QueueWorkDoneStatus::Success) {
-                                          GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
-                                      }
-                                      ctx->param_buf_pool.free_bufs({ params_bufs });
-                                  }),
-                              UINT64_MAX);
+        ctx->queue.OnSubmittedWorkDone(wgpu::CallbackMode::AllowSpontaneous,
+                                       [ctx, params_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
+                                           if (status != wgpu::QueueWorkDoneStatus::Success) {
+                                               GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n",
+                                                              message.data);
+                                           }
+                                           ctx->param_buf_pool.free_bufs({ params_bufs });
+                                       });
     } else {
         // Lock the context mutex when pushing to the staging vectors.
         std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
@@ -493,76 +413,6 @@ static void ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor
     ggml_backend_webgpu_build_and_enqueue(ctx, ctx->cpy_pipeline, params, entries, wg_x);
 }
 
-static void ggml_webgpu_set_rows(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * idx, ggml_tensor * dst) {
-    // For set rows specifically, we need to check if src and idx are empty tensors.
-    if (ggml_is_empty(src) || ggml_is_empty(idx)) {
-        return;
-    }
-
-    webgpu_pool_bufs error_bufs = ctx->set_rows_error_buf_pool.alloc_bufs();
-    if (error_bufs.host_buf.GetMapState() == wgpu::BufferMapState::Mapped) {
-        error_bufs.host_buf.Unmap();
-    }
-
-    size_t src_offset       = ggml_backend_webgpu_tensor_offset(src);
-    // assumes power of 2 offset alignment
-    size_t src_misalignment = src_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    // align to minimum offset alignment
-    src_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    size_t idx_offset       = ggml_backend_webgpu_tensor_offset(idx);
-    size_t idx_misalignment = idx_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    idx_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    size_t dst_offset       = ggml_backend_webgpu_tensor_offset(dst);
-    size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-
-    std::vector<uint32_t> params = { (uint32_t) (src_misalignment / ggml_type_size(src->type)),
-                                     (uint32_t) (idx_misalignment / ggml_type_size(idx->type)),
-                                     (uint32_t) (dst_misalignment / ggml_type_size(dst->type)),
-                                     // Convert byte-strides to element-strides
-                                     (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
-                                     (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
-                                     (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
-                                     (uint32_t) (idx->nb[0] / ggml_type_size(idx->type)),
-                                     (uint32_t) (idx->nb[1] / ggml_type_size(idx->type)),
-                                     (uint32_t) (idx->nb[2] / ggml_type_size(idx->type)),
-                                     (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
-                                     (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
-                                     (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
-                                     // Shape of src
-                                     (uint32_t) src->ne[0],
-                                     (uint32_t) src->ne[1],
-                                     (uint32_t) src->ne[2],
-                                     (uint32_t) src->ne[3],
-                                     // Shape of idx
-                                     (uint32_t) (idx->ne[1]),
-                                     (uint32_t) (idx->ne[2]) };
-
-    std::vector<wgpu::BindGroupEntry> entries = {
-        { .binding = 0,
-         .buffer  = ggml_backend_webgpu_tensor_buf(src),
-         .offset  = ggml_backend_webgpu_tensor_offset(src),
-         .size    = ggml_nbytes(src)                                                                       },
-        { .binding = 1,
-         .buffer  = ggml_backend_webgpu_tensor_buf(idx),
-         .offset  = ggml_backend_webgpu_tensor_offset(idx),
-         .size    = ggml_nbytes(idx)                                                                       },
-        { .binding = 2,
-         .buffer  = ggml_backend_webgpu_tensor_buf(dst),
-         .offset  = ggml_backend_webgpu_tensor_offset(dst),
-         .size    = ggml_nbytes(dst)                                                                       },
-        { .binding = 3, .buffer = error_bufs.dev_buf,    .offset = 0, .size = error_bufs.dev_buf.GetSize() }
-    };
-
-    size_t   max_wg_size = ctx->limits.maxComputeWorkgroupSizeX;
-    uint32_t wg_x        = (src->ne[1] * src->ne[2] * src->ne[3] + max_wg_size - 1) / max_wg_size;
-
-    std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
-    ctx->staged_set_row_error_bufs.push_back(error_bufs);
-
-    ggml_backend_webgpu_build_and_enqueue(ctx, ctx->set_rows_pipeline, params, entries, wg_x);
-}
-
 static void ggml_webgpu_mul_mat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
     std::vector<uint32_t> params = {
         (uint32_t) dst->ne[1],                                  // number of rows in result (M)
@@ -621,11 +471,6 @@ static bool ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node) {
                 ggml_webgpu_cpy(ctx, src0, node);
                 break;
             }
-        case GGML_OP_SET_ROWS:
-            {
-                ggml_webgpu_set_rows(ctx, src0, src1, node);
-                break;
-            }
         case GGML_OP_MUL_MAT:
             {
                 ggml_webgpu_mul_mat(ctx, src0, src1, node);
@@ -734,9 +579,6 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer,
         // memset the remaining bytes
         ggml_backend_webgpu_buffer_memset(
             webgpu_ctx, buf_ctx->buffer, val32, total_offset + (size - remaining_size), remaining_size);
-    } else {
-        // wait for WriteBuffer to complete
-        ggml_backend_webgpu_wait_on_submission(webgpu_ctx);
     }
 }
 
@@ -760,7 +602,7 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer,
         final_size = size + (4 - (size % 4));
     }
 
-    std::lock_guard<std::recursive_mutex> lock(webgpu_ctx->mutex);
+    std::lock_guard<std::mutex> lock(webgpu_ctx->get_tensor_mutex);
 
     if (webgpu_ctx->get_tensor_staging_buf == nullptr || webgpu_ctx->get_tensor_staging_buf.GetSize() < final_size) {
         // Create a new staging buffer if it doesn't exist or is too small
@@ -910,14 +752,6 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
     ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline, wgsl_mul_mat, "mul_mat");
 }
 
-static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants(1);
-    constants[0].key   = "wg_size";
-    constants[0].value = webgpu_ctx->limits.maxComputeWorkgroupSizeX;
-    ggml_webgpu_create_pipeline(
-        webgpu_ctx->device, webgpu_ctx->set_rows_pipeline, wgsl_set_rows, "set_rows", constants);
-}
-
 static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
     std::vector<wgpu::ConstantEntry> constants(1);
     constants[0].key   = "wg_size";
@@ -934,11 +768,10 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co
     webgpu_context                       webgpu_ctx = dev_ctx->webgpu_ctx;
 
     // Multiple threads may try to initialize the device
-    std::lock_guard<std::recursive_mutex> lock(webgpu_ctx->mutex);
+    std::lock_guard<std::mutex> lock(webgpu_ctx->init_mutex);
     if (!webgpu_ctx->device_init) {
         // Initialize device
-        std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16,
-                                                             wgpu::FeatureName::ImplicitDeviceSynchronization };
+        std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16, wgpu::FeatureName::ImplicitDeviceSynchronization };
         wgpu::DeviceDescriptor         dev_desc;
         dev_desc.requiredLimits       = &webgpu_ctx->limits;
         dev_desc.requiredFeatures     = required_features.data();
@@ -974,35 +807,11 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co
         webgpu_ctx->queue = webgpu_ctx->device.GetQueue();
 
         // Create buffer pool for shader parameters
-        webgpu_ctx->param_buf_pool.init(webgpu_ctx->device,
-                                        WEBGPU_NUM_PARAM_BUFS,
-                                        WEBGPU_PARAMS_BUF_SIZE_BYTES,
-                                        wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
-                                        wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
-        webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->device,
-                                                 WEBGPU_NUM_SET_ROWS_ERROR_BUFS,
-                                                 WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
-                                                 wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage,
-                                                 wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead);
+        webgpu_ctx->param_buf_pool.init(webgpu_ctx->device);
 
         ggml_webgpu_init_memset_pipeline(webgpu_ctx);
         ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
-        ggml_webgpu_init_set_rows_pipeline(webgpu_ctx);
         ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
-
-#ifdef GGML_WEBGPU_DEBUG
-        // Initialize debug buffers
-        ggml_webgpu_create_buffer(webgpu_ctx->device,
-                                  webgpu_ctx->debug_host_buf,
-                                  WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
-                                  wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead,
-                                  "debug_host_buf");
-        ggml_webgpu_create_buffer(webgpu_ctx->device,
-                                  webgpu_ctx->debug_dev_buf,
-                                  WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
-                                  wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc,
-                                  "debug_dev_buf");
-#endif
         webgpu_ctx->device_init = true;
     }
 
@@ -1053,7 +862,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
             return true;
-        case GGML_OP_CPY | GGML_OP_SET_ROWS:
+        case GGML_OP_CPY:
             return op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_MUL_MAT:
             return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;

ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl

@@ -1,82 +0,0 @@
-enable f16;
-
-@group(0) @binding(0)
-var<storage, read_write> src: array<f32>;
-
-@group(0) @binding(1)
-var<storage, read_write> idx: array<u32>;
-
-@group(0) @binding(2)
-var<storage, read_write> dst: array<f16>;
-
-@group(0) @binding(3)
-var<storage, read_write> error: atomic<u32>;
-
-struct Params {
-    offset_src: u32, // in elements
-    offset_idx: u32, // in elements
-    offset_dst: u32, // in elements
-
-    // Strides (in elements)
-    stride_src1: u32,
-    stride_src2: u32,
-    stride_src3: u32,
-
-    stride_idx0: u32,
-    stride_idx1: u32,
-    stride_idx2: u32,
-
-    stride_dst1: u32,
-    stride_dst2: u32,
-    stride_dst3: u32,
-
-    // Shape of src
-    ne0: u32,
-    n_rows: u32,
-    ne2: u32,
-    ne3: u32,
-
-    // Shape of idx
-    idx1: u32,
-    idx2: u32,
-};
-
-@group(0) @binding(4)
-var<uniform> params: Params;
-
-override wg_size: u32;
-@compute @workgroup_size(wg_size)
-fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
-    if (gid.x >= params.n_rows * params.ne2 * params.ne3) {
-        return;
-    }
-    var i = gid.x;
-    let i_src3 = i / (params.ne2 * params.n_rows);
-    let i_dst3 = i / (params.ne2 * 3);
-
-    i = i % (params.ne2 * params.n_rows);
-    let i_src2 = i / params.n_rows;
-    let i_src1 = i % params.n_rows;
-
-    let i_idx2 = i_src3 % params.idx2;
-    let i_idx1 = i_src2 % params.idx1;
-    let i_idx0 = i_src1;
-
-    let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2;
-
-    let idx_high_val = idx[idx_high];
-    let idx_low_val = idx[idx_high + 1];
-
-    if (idx_low_val != 0) {
-        // Upper bits of index are not zero, output will be incorrect
-        atomicStore(&error, 1);
-        return;
-    }
-
-    let i_dst_row = params.offset_dst + idx_high_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3;
-    let i_src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3;
-
-    for (var i: u32 = 0; i < params.ne0; i++) {
-      dst[i_dst_row + i] = f16(src[i_src_row + i]);
-    }
-}

ggml/src/ggml.c

@@ -582,6 +582,9 @@ FILE * ggml_fopen(const char * fname, const char * mode) {
 #endif
 
 }
+static void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * GGML_RESTRICT x, size_t bx, const float * GGML_RESTRICT y, size_t by, int nrc);
+static void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc);
+static void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * GGML_RESTRICT x, size_t bx, ggml_bf16_t * GGML_RESTRICT y, size_t by, int nrc);
 
 static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = {
     [GGML_TYPE_I8] = {
@@ -687,14 +690,6 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .from_float_ref           = (ggml_from_float_t) quantize_row_q8_1_ref,
     },
-    [GGML_TYPE_MXFP4] = {
-        .type_name                = "mxfp4",
-        .blck_size                = QK_MXFP4,
-        .type_size                = sizeof(block_mxfp4),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_mxfp4,
-        .from_float_ref           = (ggml_from_float_t)quantize_row_mxfp4_ref,
-    },
     [GGML_TYPE_Q2_K] = {
         .type_name                = "q2_K",
         .blck_size                = QK_K,
@@ -922,7 +917,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
 
     "DUP",
     "ADD",
-    "ADD_ID",
     "ADD1",
     "ACC",
     "SUB",
@@ -1016,14 +1010,13 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "GLU",
 };
 
-static_assert(GGML_OP_COUNT == 87, "GGML_OP_COUNT != 87");
+static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
 
     "x",
     "x+y",
-    "x[i]+y",
     "x+y",
     "view(x,nb,offset)+=y->x",
     "x-y",
@@ -1117,7 +1110,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "glu(x)",
 };
 
-static_assert(GGML_OP_COUNT == 87, "GGML_OP_COUNT != 87");
+static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -1147,12 +1140,11 @@ static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = {
     "REGLU",
     "GEGLU",
     "SWIGLU",
-    "SWIGLU_OAI",
     "GEGLU_ERF",
     "GEGLU_QUICK",
 };
 
-static_assert(GGML_GLU_OP_COUNT == 6, "GGML_GLU_OP_COUNT != 6");
+static_assert(GGML_GLU_OP_COUNT == 5, "GGML_GLU_OP_COUNT != 5");
 
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
@@ -1320,7 +1312,6 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_Q5_0:          wtype = GGML_TYPE_Q5_0;  break;
         case GGML_FTYPE_MOSTLY_Q5_1:          wtype = GGML_TYPE_Q5_1;  break;
         case GGML_FTYPE_MOSTLY_Q8_0:          wtype = GGML_TYPE_Q8_0;  break;
-        case GGML_FTYPE_MOSTLY_MXFP4:         wtype = GGML_TYPE_MXFP4; break;
         case GGML_FTYPE_MOSTLY_Q2_K:          wtype = GGML_TYPE_Q2_K;  break;
         case GGML_FTYPE_MOSTLY_Q3_K:          wtype = GGML_TYPE_Q3_K;  break;
         case GGML_FTYPE_MOSTLY_Q4_K:          wtype = GGML_TYPE_Q4_K;  break;
@@ -1971,27 +1962,6 @@ struct ggml_tensor * ggml_add_cast(
     return ggml_add_cast_impl(ctx, a, b, type);
 }
 
-struct ggml_tensor * ggml_add_id(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            struct ggml_tensor  * ids) {
-
-    GGML_ASSERT(a->ne[0] == b->ne[0]);
-    GGML_ASSERT(a->ne[1] == ids->ne[0]);
-    GGML_ASSERT(a->ne[2] == ids->ne[1]);
-    GGML_ASSERT(ids->type == GGML_TYPE_I32);
-
-    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
-
-    result->op     = GGML_OP_ADD_ID;
-    result->src[0] = a;
-    result->src[1] = b;
-    result->src[2] = ids;
-
-    return result;
-}
-
 // ggml_add1
 
 static struct ggml_tensor * ggml_add1_impl(
@@ -2842,19 +2812,6 @@ struct ggml_tensor * ggml_geglu_quick_split(
     return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_GEGLU_QUICK, false);
 }
 
-struct ggml_tensor * ggml_swiglu_oai(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b,
-        float                 alpha,
-        float                 limit) {
-    struct ggml_tensor * result = ggml_glu_impl(ctx, a, b, GGML_GLU_OP_SWIGLU_OAI, false);
-    ggml_set_op_params_f32(result, 2, alpha);
-    ggml_set_op_params_f32(result, 3, limit);
-
-    return result;
-}
-
 // ggml_norm
 
 static struct ggml_tensor * ggml_norm_impl(
@@ -3822,22 +3779,6 @@ struct ggml_tensor * ggml_soft_max_ext(
     return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
 }
 
-void ggml_soft_max_add_sinks(
-        struct ggml_tensor * a,
-        struct ggml_tensor * sinks) {
-    if (!sinks) {
-        a->src[2] = NULL;
-        return;
-    }
-
-    GGML_ASSERT(a->op == GGML_OP_SOFT_MAX);
-    GGML_ASSERT(a->src[2] == NULL);
-    GGML_ASSERT(a->src[0]->ne[2] == sinks->ne[0]);
-    GGML_ASSERT(sinks->type == GGML_TYPE_F32);
-
-    a->src[2] = sinks;
-}
-
 // ggml_soft_max_ext_back
 
 static struct ggml_tensor * ggml_soft_max_ext_back_impl(
@@ -4871,22 +4812,6 @@ enum ggml_prec ggml_flash_attn_ext_get_prec(
     return (enum ggml_prec) prec_i32;
 }
 
-void ggml_flash_attn_ext_add_sinks(
-        struct ggml_tensor * a,
-        struct ggml_tensor * sinks) {
-    if (!sinks) {
-        a->src[4] = NULL;
-        return;
-    }
-
-    GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT);
-    GGML_ASSERT(a->src[4] == NULL);
-    GGML_ASSERT(a->src[0]->ne[2] == sinks->ne[0]);
-    GGML_ASSERT(sinks->type == GGML_TYPE_F32);
-
-    a->src[4] = sinks;
-}
-
 // ggml_flash_attn_back
 
 struct ggml_tensor * ggml_flash_attn_back(
@@ -6947,7 +6872,6 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_Q5_0:    result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q5_1:    result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q8_0:    result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_MXFP4:   result = quantize_mxfp4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q2_K:    result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q3_K:    result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q4_K:    result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

gguf-py/gguf/constants.py

@@ -380,7 +380,6 @@ class MODEL_ARCH(IntEnum):
     HUNYUAN_MOE      = auto()
     HUNYUAN_DENSE    = auto()
     SMOLLM3          = auto()
-    GPT_OSS          = auto()
     LFM2             = auto()
     DREAM            = auto()
     SMALLTHINKER     = auto()
@@ -417,7 +416,6 @@ class MODEL_TENSOR(IntEnum):
     ATTN_OUT_NORM        = auto()
     ATTN_POST_NORM       = auto()
     ATTN_ROT_EMBD        = auto()
-    ATTN_SINKS           = auto()
     FFN_GATE_INP         = auto()
     FFN_GATE_INP_SHEXP   = auto()
     FFN_NORM             = auto()
@@ -712,7 +710,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
     MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
     MODEL_ARCH.SMOLLM3:          "smollm3",
-    MODEL_ARCH.GPT_OSS:          "gpt-oss",
     MODEL_ARCH.LFM2:             "lfm2",
     MODEL_ARCH.DREAM:            "dream",
     MODEL_ARCH.SMALLTHINKER:     "smallthinker",
@@ -747,7 +744,6 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_V:                    "blk.{bid}.attn_v",
     MODEL_TENSOR.ATTN_OUT:                  "blk.{bid}.attn_output",
     MODEL_TENSOR.ATTN_ROT_EMBD:             "blk.{bid}.attn_rot_embd",
-    MODEL_TENSOR.ATTN_SINKS:                "blk.{bid}.attn_sinks",
     MODEL_TENSOR.ATTN_Q_NORM:               "blk.{bid}.attn_q_norm",
     MODEL_TENSOR.ATTN_K_NORM:               "blk.{bid}.attn_k_norm",
     MODEL_TENSOR.ATTN_OUT_NORM:             "blk.{bid}.attn_output_norm",
@@ -2557,22 +2553,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
-    MODEL_ARCH.GPT_OSS: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_POST_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.ATTN_SINKS,
-        MODEL_TENSOR.FFN_GATE_INP,
-        MODEL_TENSOR.FFN_GATE_EXP,
-        MODEL_TENSOR.FFN_DOWN_EXP,
-        MODEL_TENSOR.FFN_UP_EXP,
-    ],
     MODEL_ARCH.LFM2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.TOKEN_EMBD_NORM,
@@ -2727,7 +2707,6 @@ class GGMLQuantizationType(IntEnum):
     BF16    = 30
     TQ1_0   = 34
     TQ2_0   = 35
-    MXFP4   = 39
 
 
 class ExpertGatingFuncType(IntEnum):
@@ -2868,7 +2847,6 @@ GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
     GGMLQuantizationType.BF16:    (1, 2),
     GGMLQuantizationType.TQ1_0:   (256, 2 + 4 * 13),
     GGMLQuantizationType.TQ2_0:   (256, 2 + 64),
-    GGMLQuantizationType.MXFP4:   (32, 1 + 16),
 }
 
 

gguf-py/gguf/gguf_writer.py

@@ -138,9 +138,8 @@ class GGUFWriter:
                 size = prod(shape)
 
                 if "_exps." in name:
-                    expert_count = shape[-2 if ".bias" in name else -3]
-                    expert_params += (size // expert_count)
-                    expert_sum += expert_count
+                    expert_params += (size // shape[-3])
+                    expert_sum += shape[-3]
                     n_expert_tensors += 1
                 else:
                     shared_params += size

gguf-py/gguf/tensor_mapping.py

@@ -285,10 +285,6 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.rotary_emb.inv_freq",            # codeshell
         ),
 
-        MODEL_TENSOR.ATTN_SINKS: (
-            "model.layers.{bid}.self_attn.sinks", # openai-moe
-        ),
-
         # Feed-forward norm
         MODEL_TENSOR.FFN_NORM: (
             "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
@@ -336,7 +332,6 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
             "model.layers.{bid}.feed_forward.router",           # llama4 jamba
             "encoder.layers.{bid}.mlp.router.layer",            # nomic-bert-moe
-            "model.layers.{bid}.mlp.router",                    # openai-moe
             "model.layers.{bid}.mlp.gate.wg",                   # hunyuan
             "model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker
         ),
@@ -1110,13 +1105,11 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_CLS: (
             "vision_tower.vision_model.embeddings.class_embedding",
-            "model.vision_tower.embeddings.cls_token", # Intern-S1
             "vision_model.class_embedding", # llama 4
         ),
 
         MODEL_TENSOR.V_ENC_EMBD_PATCH: (
             "vision_tower.vision_model.embeddings.patch_embedding",
-            "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1
             "vpm.embeddings.patch_embedding",
             "model.vision_model.embeddings.patch_embedding", # SmolVLM
             "vision_tower.patch_conv", # pixtral
@@ -1126,7 +1119,6 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_POS: (
             "vision_tower.vision_model.embeddings.position_embedding",
-            "model.vision_tower.embeddings.position_embeddings", # Intern-S1
             "vpm.embeddings.position_embedding",
             "model.vision_model.embeddings.position_embedding", # SmolVLM
             "vision_model.positional_embedding_vlm", # llama 4
@@ -1134,7 +1126,6 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
-            "model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.q_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4
@@ -1144,12 +1135,10 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_K: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
-            "model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.k_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4
@@ -1159,12 +1148,10 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_V: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
-            "model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.v_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4
@@ -1175,7 +1162,6 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_INPUT_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
             "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm1",
             "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
             "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
@@ -1186,7 +1172,6 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_ATTN_O: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
             "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.out_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4
@@ -1197,7 +1182,6 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
             "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm2",
             "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
             "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4
@@ -1207,7 +1191,6 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_FFN_UP: (
             "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
-            "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc1",
             "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
             "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral
@@ -1223,7 +1206,6 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_FFN_DOWN: (
             "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
-            "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc2",
             "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
             "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral
@@ -1234,12 +1216,10 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_LAYER_SCALE_1: (
             "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.lambda_1", # Intern-S1
         ),
 
         MODEL_TENSOR.V_LAYER_SCALE_2: (
             "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL
-            "model.vision_tower.encoder.layer.{bid}.lambda_2", # Intern-S1
         ),
 
         MODEL_TENSOR.V_PRE_NORM: (

include/llama.h

@@ -152,7 +152,6 @@ extern "C" {
         //LLAMA_FTYPE_MOSTLY_Q4_0_8_8      = 35, // removed from gguf files, use Q4_0 and runtime repack
         LLAMA_FTYPE_MOSTLY_TQ1_0         = 36, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_TQ2_0         = 37, // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_MXFP4_MOE     = 38, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };

models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja

@@ -1,59 +0,0 @@
-{# Alias tools -> available_tools #}
-{%- if tools and not available_tools -%}
-    {%- set available_tools = tools -%}
-{%- endif -%}
-{%- if messages[0]['role'] == 'system' %}
-     {%- set system_message = messages[0]['content'] %}
-     {%- set loop_messages = messages[1:] %}
- {%- else %}
-     {%- set system_message = "Knowledge Cutoff Date: April 2024. Today's Date: " + strftime_now('%B %d, %Y') + ". You are Granite, developed by IBM." %}
-     {%- if available_tools and documents %}
-         {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
-     {%- elif available_tools %}
-         {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %}
-     {%- elif documents %}
-         {%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
-    {%- elif thinking %}
-    {%- set system_message = system_message + " You are a helpful AI assistant.
-Respond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between <think></think> and write your response between <response></response> for each user query." %}
-     {%- else %}
-         {%- set system_message = system_message + " You are a helpful AI assistant." %}
-     {%- endif %}
-     {%- if 'citations' in controls and documents %}
-         {%- set system_message = system_message + '
-Use the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}
-     {%- endif %}
-     {%- if 'hallucinations' in controls and documents %}
-         {%- set system_message = system_message + '
-Finally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %}
-     {%- endif %}
-     {%- set loop_messages = messages %}
- {%- endif %}
- {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>
-' }}
- {%- if available_tools %}
-     {{- '<|start_of_role|>available_tools<|end_of_role|>' }}
-     {{- available_tools | tojson(indent=4) }}
-     {{- '<|end_of_text|>
-' }}
- {%- endif %}
- {%- if documents %}
-     {%- for document in documents %}
-         {{- '<|start_of_role|>document {"document_id": "' + document['doc_id'] | string + '"}<|end_of_role|>
-' }}
-         {{- document['text'] }}
-         {{- '<|end_of_text|>
-' }}
-              {%- endfor %}
- {%- endif %}
- {%- for message in loop_messages %}
-     {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>
-' }}
-     {%- if loop.last and add_generation_prompt %}
-         {{- '<|start_of_role|>assistant' }}
-             {%- if controls %}
-                 {{- ' ' + controls | tojson()}}
-             {%- endif %}
-         {{- '<|end_of_role|>' }}
-     {%- endif %}
- {%- endfor %}