ui : fix llama-ui-embed crash when no asset dir is given (#24597)

* Add arch support for cohere2-MoE

* Removed redundant gating_func checks

* Changed ffn lookup to prefer prefix_dense_intermediate_size

* Renamed arch to cohere2moe

* Removed redundant lmhead check and chat template changes

* Removed lm_head.weight check from modify tensors, load output tensor not required, fallback to token_embd.weight

* Changed to (routed+shared)*0.5 for shared expert combined avg

* fixed sliding_window_pattern issue and pattern

* Fixed transformers crash 'first_k_dense_replace' error

* Remove comment

* Removed cohere2-moe as a tokenizer type and kept as tiny_aya.  Renamed North-Mini-Code-1.0.

* Fixed MTP fail, changed to use iSWA

* Fixed remaining todos: cohere2moe renamed, changed swa parsing to use get_key_or_arr, removed extra get_arr use

* Force metadata usage

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Remove Cohere2 checkpoint comment

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Remove MTP comment

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Regenerate cohere2moe tokenizer hash

* Add cohere2moe to Llama Model Saver supported list

* Check for zerobios tensors and add support for Command to use LayerNorm

* Map expert_selection_fn to sigmoid in base.py instead of command.py

* use bools for foundnorm/foundnormrms

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

common/jinja/runtime.cpp

@@ -761,9 +761,9 @@ value member_expression::execute_impl(context & ctx) {
 
         if (is_stmt<slice_expression>(this->property)) {
             auto s = cast_stmt<slice_expression>(this->property);
-            value start_val = s->start_expr ? s->start_expr->execute(ctx) : mk_val<value_int>(0);
-            value stop_val  = s->stop_expr  ? s->stop_expr->execute(ctx)  : mk_val<value_int>(arr_size);
             value step_val  = s->step_expr  ? s->step_expr->execute(ctx)  : mk_val<value_int>(1);
+            value start_val = s->start_expr ? s->start_expr->execute(ctx) : (step_val->as_int() < 0 ? mk_val<value_int>(arr_size - 1) : mk_val<value_int>(0));
+            value stop_val  = s->stop_expr  ? s->stop_expr->execute(ctx)  : (step_val->as_int() < 0 ? mk_val<value_int>(-1) : mk_val<value_int>(arr_size));
 
             // translate to function call: obj.slice(start, stop, step)
             JJ_DEBUG("Member expression is a slice: start %s, stop %s, step %s",

common/jinja/value.cpp

@@ -90,14 +90,14 @@ static T slice(const T & array, int64_t start, int64_t stop, int64_t step = 1) {
             stop_val = std::min(stop_val, len);
         }
     } else {
-        start_val = len - 1;
+        start_val = start;
         if (start_val < 0) {
-            start_val = std::max(len + start_val, (int64_t)-1);
+            start_val = std::max(len + start_val, (int64_t)0);
         } else {
             start_val = std::min(start_val, len - 1);
         }
 
-        stop_val = -1;
+        stop_val = stop;
         if (stop_val < -1) {
             stop_val = std::max(len + stop_val, (int64_t)-1);
         } else {
@@ -673,6 +673,9 @@ const func_builtins & value_string_t::get_builtins() const {
             std::string str = val_input->as_string().str();
             // FIXME: Support non-specified delimiter (split on consecutive (no leading or trailing) whitespace)
             std::string delim = (args.count() > 1) ? args.get_pos(1)->as_string().str() : " ";
+            if (delim.empty()) {
+                throw raised_exception("empty separator");
+            }
             int64_t maxsplit = (args.count() > 2) ? args.get_pos(2)->as_int() : -1;
             auto result = mk_val<value_array>();
             size_t pos = 0;
@@ -697,6 +700,9 @@ const func_builtins & value_string_t::get_builtins() const {
             std::string str = val_input->as_string().str();
             // FIXME: Support non-specified delimiter (split on consecutive (no leading or trailing) whitespace)
             std::string delim = (args.count() > 1) ? args.get_pos(1)->as_string().str() : " ";
+            if (delim.empty()) {
+                throw raised_exception("empty separator");
+            }
             int64_t maxsplit = (args.count() > 2) ? args.get_pos(2)->as_int() : -1;
             auto result = mk_val<value_array>();
             size_t pos = 0;
@@ -722,10 +728,23 @@ const func_builtins & value_string_t::get_builtins() const {
             if (count > 0) {
                 throw not_implemented_exception("String replace with count argument not implemented");
             }
-            size_t pos = 0;
-            while ((pos = str.find(old_str, pos)) != std::string::npos) {
-                str.replace(pos, old_str.length(), new_str);
-                pos += new_str.length();
+            if (old_str != new_str) {
+                size_t pos = 0;
+                if (old_str.empty()) {
+                    std::string new_res;
+                    new_res.reserve(str.length() + new_str.length() * (str.length() + 1));
+                    new_res += new_str;
+                    for (const char c : str) {
+                        new_res.push_back(c);
+                        new_res += new_str;
+                    }
+                    str = new_res;
+                } else {
+                    while ((pos = str.find(old_str, pos)) != std::string::npos) {
+                        str.replace(pos, old_str.length(), new_str);
+                        pos += new_str.length();
+                    }
+                }
             }
             auto res = mk_val<value_string>(str);
             res->val_str.mark_input_based_on(args.get_pos(0)->val_str);

conversion/__init__.py

@@ -40,6 +40,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "ChatGLMModel": "chatglm",
     "CodeShellForCausalLM": "codeshell",
     "CogVLMForCausalLM": "cogvlm",
+    "Cohere2MoeForCausalLM": "command_r",
     "Cohere2ForCausalLM": "command_r",
     "CohereForCausalLM": "command_r",
     "DbrxForCausalLM": "dbrx",

conversion/base.py

@@ -1195,7 +1195,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_embedding_length(n_embd)
             logger.info(f"gguf: embedding length = {n_embd}")
 
-        if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
+        if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
             self.gguf_writer.add_feed_forward_length(n_ff)
             logger.info(f"gguf: feed forward length = {n_ff}")
 
@@ -1280,7 +1280,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_expert_group_used_count(n_group_used)
             logger.info(f"gguf: expert groups used count = {n_group_used}")
 
-        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
+        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -1495,6 +1495,9 @@ class TextModel(ModelBase):
         if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1":
             # ref: https://huggingface.co/CohereLabs/tiny-aya-base
             res = "tiny_aya"
+        if chkhsh == "52df12b4c8d4176e7481aab4b6e8454d1fd0a210a04a574f6d4e067d10e23c3e":
+            # ref: https://huggingface.co/CohereLabs/North-Mini-Code-1.0
+            res = "cohere2moe"
         if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
             # ref: https://huggingface.co/Qwen/Qwen1.5-7B
             res = "qwen2"

conversion/command_r.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import re
 from typing import Iterable, TYPE_CHECKING
 
 import torch
@@ -55,3 +56,122 @@ class Cohere2Model(TextModel):
             return
 
         yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Cohere2MoeForCausalLM")
+class Cohere2MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.COHERE2MOE
+    _n_main_layers: int | None = None
+    _expert_tensor_re = re.compile(
+        r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(down_proj|gate_proj|up_proj)\.weight"
+    )
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if (n_nextn := int(self.hparams.get("num_nextn_predict_layers", 0) or 0)) > 0 and not self.no_mtp:
+            self.block_count += n_nextn
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+        self._experts: list[dict[str, Tensor]] = [{} for _ in range(self.block_count)]
+
+    def _set_vocab_gpt2(self) -> None:
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        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_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        expert_intermediate_size = hparams["intermediate_size"]
+        mlp_layer_types = hparams.get("mlp_layer_types")
+        n_dense_lead = hparams.get("first_k_dense_replace", 0)
+        if mlp_layer_types is not None:
+            n_dense_lead = next((i for i, t in enumerate(mlp_layer_types) if t != "dense"), len(mlp_layer_types))
+
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_logit_scale(hparams["logit_scale"])
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
+        self.gguf_writer.add_leading_dense_block_count(n_dense_lead)
+        self.gguf_writer.add_expert_weights_norm(hparams.get("norm_topk_prob", False))
+        if (num_shared_experts := hparams.get("num_shared_experts", 0)) > 0:
+            if hparams.get("shared_expert_combination_strategy", "average") != "average":
+                raise ValueError("Cohere2 MoE only supports average shared expert combination")
+            self.gguf_writer.add_expert_shared_count(num_shared_experts)
+            self.gguf_writer.add_expert_shared_feed_forward_length(expert_intermediate_size * num_shared_experts)
+        if (n_nextn := hparams.get("num_nextn_predict_layers", 0)) > 0 and not self.no_mtp:
+            self.gguf_writer.add_nextn_predict_layers(n_nextn)
+        self.gguf_writer.add_rope_dimension_count(hparams["head_dim"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        hparams = {**self.hparams, **self.hparams.get("text_config", {})}
+        self._n_main_layers = hparams.get("num_hidden_layers")
+        type(self)._n_main_layers = self._n_main_layers
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    @classmethod
+    def filter_tensors(cls, item):
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+
+        if cls._n_main_layers is not None:
+            is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
+            if is_mtp and cls.no_mtp:
+                return None
+            if cls.mtp_only and not is_mtp and name not in (
+                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+            ):
+                return None
+
+        return name, gen
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".bias"):
+            if torch.any(data_torch != 0):
+                raise ValueError(f"Bias tensor {name!r} is not zero.")
+            logger.debug(f"Skipping bias tensor {name!r}.")
+            return
+
+        if (m := self._expert_tensor_re.fullmatch(name)) is not None:
+            n_experts = self.hparams["num_experts"]
+            layer_idx = int(m.group(1))
+            assert bid is None or bid == layer_idx
+
+            self._experts[layer_idx][name] = data_torch
+
+            expected = {
+                f"model.layers.{layer_idx}.mlp.experts.{xid}.{w_name}.weight"
+                for xid in range(n_experts)
+                for w_name in ("down_proj", "gate_proj", "up_proj")
+            }
+            if expected.issubset(self._experts[layer_idx]):
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{layer_idx}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[layer_idx][ename])
+                        del self._experts[layer_idx][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{layer_idx}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, layer_idx)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        experts = [k for d in self._experts for k in d.keys()]
+        if len(experts) > 0:
+            raise ValueError(f"Unprocessed experts: {experts}")

convert_hf_to_gguf_update.py

@@ -100,6 +100,7 @@ models = [
     {"name": "refact",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
     {"name": "command-r",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
     {"name": "tiny_aya",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/tiny-aya-base", },
+    {"name": "cohere2moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", },
     {"name": "qwen2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
     {"name": "olmo",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
     {"name": "dbrx",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },

docs/ops.md

@@ -14,15 +14,15 @@ Legend:
 
 | Operation | BLAS | CANN | CPU | CUDA | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
 |-----------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
@@ -41,25 +41,25 @@ Legend:
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
 |                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -68,9 +68,9 @@ Legend:
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -79,27 +79,27 @@ Legend:
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROLL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SET | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
@@ -107,16 +107,16 @@ Legend:
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |

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

@@ -833,6 +833,7 @@ struct vk_device_struct {
 
     // [src/dst 0=fp32,1=fp16]
     vk_pipeline pipeline_exp[2];
+    vk_pipeline pipeline_expm1[2];
     vk_pipeline pipeline_elu[2];
     vk_pipeline pipeline_gelu[2];
     vk_pipeline pipeline_gelu_erf[2];
@@ -1202,30 +1203,35 @@ struct vk_op_glu_push_constants {
     uint32_t mode;  // 0: default, 1: swapped, 2: split
     float alpha; // for swiglu_oai
     float limit;
+    uint32_t nb00;
     uint32_t nb01;
     uint32_t nb02;
     uint32_t nb03;
-    uint32_t ne01;
-    uint32_t ne02;
+    uint32_t nb10;
     uint32_t nb11;
     uint32_t nb12;
     uint32_t nb13;
-    uint32_t ne11;
-    uint32_t ne12;
+    uint32_t nb20;
+    uint32_t nb21;
+    uint32_t nb22;
+    uint32_t nb23;
+    uint32_t ne21;
+    uint32_t ne22;
+    uint32_t misalign_offsets;
+    uint32_t ne2_012mp; uint32_t ne2_012L;
+    uint32_t ne2_01mp;  uint32_t ne2_01L;
+    uint32_t ne2_0mp;   uint32_t ne2_0L;
 };
+static_assert(sizeof(vk_op_glu_push_constants) <= 128, "sizeof(vk_op_glu_push_constants) must be <= 128");
 
 struct vk_op_unary_push_constants {
     uint32_t ne;
     uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
     uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
     uint32_t misalign_offsets;
-    float param1; float param2;
-    uint32_t ne0_012mp; uint32_t ne0_012L;
-    uint32_t ne0_01mp;  uint32_t ne0_01L;
-    uint32_t ne0_0mp;   uint32_t ne0_0L;
-    uint32_t ne1_012mp; uint32_t ne1_012L;
-    uint32_t ne1_01mp;  uint32_t ne1_01L;
-    uint32_t ne1_0mp;   uint32_t ne1_0L;
+    float param1; float param2; float param3; float param4;
+    uint32_t ne0_012mp; uint32_t ne0_01mp; uint32_t ne0_0mp; uint32_t ne0_Ls;
+    uint32_t ne1_012mp; uint32_t ne1_01mp; uint32_t ne1_0mp; uint32_t ne1_Ls;
 };
 static_assert(sizeof(vk_op_unary_push_constants) <= 128, "sizeof(vk_op_unary_push_constants) must be <= 128");
 
@@ -1330,6 +1336,10 @@ static void init_fastdiv_values(uint32_t d, uint32_t &mp, uint32_t &L)
     mp = (uint32_t)((uint64_t{1} << 32) * ((uint64_t{1} << L) - d) / d + 1);
 }
 
+static uint32_t pack_fastdiv_L(uint32_t L0, uint32_t L1, uint32_t L2) {
+    return L0 | (L1 << 8) | (L2 << 16);
+}
+
 template <typename T> void init_pushconst_fastdiv(T &p) {
     GGML_UNUSED(p);
     static_assert(!std::is_const<T>::value, "unexpected type");
@@ -1337,12 +1347,29 @@ template <typename T> void init_pushconst_fastdiv(T &p) {
 
 template <> void init_pushconst_fastdiv(vk_op_unary_push_constants &p) {
     // Compute magic values to divide by these six numbers.
-    init_fastdiv_values(p.ne02*p.ne01*p.ne00,  p.ne0_012mp,    p.ne0_012L);
-    init_fastdiv_values(p.ne01*p.ne00,         p.ne0_01mp,     p.ne0_01L);
-    init_fastdiv_values(p.ne00,                p.ne0_0mp,      p.ne0_0L);
-    init_fastdiv_values(p.ne12*p.ne11*p.ne10,  p.ne1_012mp,    p.ne1_012L);
-    init_fastdiv_values(p.ne11*p.ne10,         p.ne1_01mp,     p.ne1_01L);
-    init_fastdiv_values(p.ne10,                p.ne1_0mp,      p.ne1_0L);
+    uint32_t ne0_012L;
+    uint32_t ne0_01L;
+    uint32_t ne0_0L;
+    uint32_t ne1_012L;
+    uint32_t ne1_01L;
+    uint32_t ne1_0L;
+
+    init_fastdiv_values(p.ne02*p.ne01*p.ne00,  p.ne0_012mp,    ne0_012L);
+    init_fastdiv_values(p.ne01*p.ne00,         p.ne0_01mp,     ne0_01L);
+    init_fastdiv_values(p.ne00,                p.ne0_0mp,      ne0_0L);
+    init_fastdiv_values(p.ne12*p.ne11*p.ne10,  p.ne1_012mp,    ne1_012L);
+    init_fastdiv_values(p.ne11*p.ne10,         p.ne1_01mp,     ne1_01L);
+    init_fastdiv_values(p.ne10,                p.ne1_0mp,      ne1_0L);
+
+    p.ne0_Ls = pack_fastdiv_L(ne0_012L, ne0_01L, ne0_0L);
+    p.ne1_Ls = pack_fastdiv_L(ne1_012L, ne1_01L, ne1_0L);
+}
+
+template <> void init_pushconst_fastdiv(vk_op_glu_push_constants &p) {
+    // GLU linearizes over dst, then uses dst coordinates for src0/src1.
+    init_fastdiv_values(p.ne22*p.ne21*p.ne20,  p.ne2_012mp,    p.ne2_012L);
+    init_fastdiv_values(p.ne21*p.ne20,         p.ne2_01mp,     p.ne2_01L);
+    init_fastdiv_values(p.ne20,                p.ne2_0mp,      p.ne2_0L);
 }
 
 struct vk_op_binary_push_constants {
@@ -5006,8 +5033,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_repeat_i16, "repeat_i16", repeat_i16_len, repeat_i16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
 #define CREATE_UNARY(name)  \
-    ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);  \
-    ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);  \
+    ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
     CREATE_UNARY(elu)
     CREATE_UNARY(gelu)
@@ -5030,6 +5057,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     CREATE_UNARY(trunc)
     CREATE_UNARY(sgn)
     CREATE_UNARY(exp)
+    CREATE_UNARY(expm1)
 #undef CREATE_UNARY
 
     ggml_vk_create_pipeline(device, device->pipeline_add1_f16_f16, "add1_f16_f16", add1_f16_f16_len, add1_f16_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
@@ -8192,7 +8220,6 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const
 static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, const vk_subbuffer & in, const vk_subbuffer & out) {
     VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
     std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
-    const int tensor_type_size = ggml_type_size(tensor->type);
 
     const uint32_t ne = ggml_nelements(tensor);
     std::array<uint32_t, 3> elements;
@@ -8205,14 +8232,11 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context&
         elements = { ne, 1, 1 };
     }
 
-    vk_op_unary_push_constants pc = {
-        (uint32_t)ne,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
-        0,
-        0.0f, 0.0f,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    };
+    vk_op_unary_push_constants pc = vk_op_unary_push_constants_init(tensor, tensor, ne);
+    pc.nb10 = 1;
+    pc.nb11 = (uint32_t)tensor->ne[0];
+    pc.nb12 = (uint32_t)(tensor->ne[0] * tensor->ne[1]);
+    pc.nb13 = (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]);
     init_pushconst_fastdiv(pc);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
     ggml_vk_sync_buffers(ctx, subctx);
@@ -8226,7 +8250,6 @@ static void ggml_vk_cpy_to_strided(
         uint32_t nb10, uint32_t nb11, uint32_t nb12, uint32_t nb13) {
     VK_LOG_DEBUG("ggml_vk_cpy_to_strided((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
     std::cerr << "dst_nb=(" << nb10 << ", " << nb11 << ", " << nb12 << ", " << nb13 << "), buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
-    const int tensor_type_size = ggml_type_size(tensor->type);
 
     const uint32_t ne = ggml_nelements(tensor);
     std::array<uint32_t, 3> elements;
@@ -8239,14 +8262,11 @@ static void ggml_vk_cpy_to_strided(
         elements = { ne, 1, 1 };
     }
 
-    vk_op_unary_push_constants pc = {
-        (uint32_t)ne,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], nb10, nb11, nb12, nb13,
-        0,
-        0.0f, 0.0f,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    };
+    vk_op_unary_push_constants pc = vk_op_unary_push_constants_init(tensor, tensor, ne);
+    pc.nb10 = nb10;
+    pc.nb11 = nb11;
+    pc.nb12 = nb12;
+    pc.nb13 = nb13;
     init_pushconst_fastdiv(pc);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
     ggml_vk_sync_buffers(ctx, subctx);
@@ -10451,6 +10471,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         switch (ggml_get_unary_op(dst)) {
             case GGML_UNARY_OP_EXP:
                 return ctx->device->pipeline_exp[dst->type == GGML_TYPE_F16];
+            case GGML_UNARY_OP_EXPM1:
+                return ctx->device->pipeline_expm1[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_ELU:
                 return ctx->device->pipeline_elu[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_SILU:
@@ -10849,6 +10871,21 @@ template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk
     GGML_UNUSED(src3);
 }
 
+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_glu_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
+    const uint32_t b_offset = src1 ? get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type) : a_offset;
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
+
+    GGML_ASSERT(a_offset < (1u << 8));
+    GGML_ASSERT(b_offset < (1u << 8));
+    GGML_ASSERT(d_offset < (1u << 8));
+
+    p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
+
+    GGML_UNUSED(src2);
+    GGML_UNUSED(src3);
+}
+
 template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_sum_rows_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
     const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
     const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
@@ -12198,17 +12235,17 @@ static void ggml_vk_l2_norm(ggml_backend_vk_context * ctx, vk_context& subctx, c
 }
 
 static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f });
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, vk_op_unary_push_constants_init(src0, dst));
 }
 
 static void ggml_vk_xielu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY,
-        {
-            (uint32_t)ggml_nelements(src0), 0,
-            op_params[1], op_params[2], op_params[3], op_params[4]
-        }
-    );
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
+    p.param1 = op_params[1];
+    p.param2 = op_params[2];
+    p.param3 = op_params[3];
+    p.param4 = op_params[4];
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, std::move(p));
 }
 
 static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -12228,6 +12265,9 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
     }
 
     const uint32_t mode = split ? 2 : (swapped ? 1 : 0);
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = split ? ggml_type_size(src1->type) : src0_type_size;
+    const uint32_t dst_type_size  = ggml_type_size(dst->type);
 
     ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GLU,
         {
@@ -12237,16 +12277,22 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
             mode,
             alpha,
             limit,
-            (uint32_t)(src0->nb[1] / src0->nb[0]),
-            (uint32_t)(src0->nb[2] / src0->nb[0]),
-            (uint32_t)(src0->nb[3] / src0->nb[0]),
-            (uint32_t)src0->ne[1],
-            (uint32_t)src0->ne[2],
-            (uint32_t)(dst->nb[1] / dst->nb[0]),
-            (uint32_t)(dst->nb[2] / dst->nb[0]),
-            (uint32_t)(dst->nb[3] / dst->nb[0]),
+            (uint32_t)(src0->nb[0] / src0_type_size),
+            (uint32_t)(src0->nb[1] / src0_type_size),
+            (uint32_t)(src0->nb[2] / src0_type_size),
+            (uint32_t)(src0->nb[3] / src0_type_size),
+            (uint32_t)((split ? src1->nb[0] : src0->nb[0]) / src1_type_size),
+            (uint32_t)((split ? src1->nb[1] : src0->nb[1]) / src1_type_size),
+            (uint32_t)((split ? src1->nb[2] : src0->nb[2]) / src1_type_size),
+            (uint32_t)((split ? src1->nb[3] : src0->nb[3]) / src1_type_size),
+            (uint32_t)(dst->nb[0] / dst_type_size),
+            (uint32_t)(dst->nb[1] / dst_type_size),
+            (uint32_t)(dst->nb[2] / dst_type_size),
+            (uint32_t)(dst->nb[3] / dst_type_size),
             (uint32_t)dst->ne[1],
-            (uint32_t)dst->ne[2]
+            (uint32_t)dst->ne[2],
+            0,
+            0, 0, 0, 0, 0, 0,
         });
 }
 
@@ -14249,6 +14295,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         switch (ggml_get_unary_op(node)) {
         case GGML_UNARY_OP_ELU:
         case GGML_UNARY_OP_EXP:
+        case GGML_UNARY_OP_EXPM1:
         case GGML_UNARY_OP_SILU:
         case GGML_UNARY_OP_GELU:
         case GGML_UNARY_OP_GELU_ERF:
@@ -16638,6 +16685,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_EXP:
+                case GGML_UNARY_OP_EXPM1:
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_ERF:
@@ -16658,8 +16706,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_UNARY_OP_FLOOR:
                 case GGML_UNARY_OP_TRUNC:
                 case GGML_UNARY_OP_SGN:
-                    return ggml_is_contiguous(op->src[0]) &&
-                           (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                    return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
                            (op->src[0]->type == op->type);
                 default:
@@ -16675,7 +16722,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
-                           (op->src[0]->type == op->type);
+                           (op->src[0]->type == op->type) &&
+                           (!op->src[1] || op->src[1]->type == op->src[0]->type);
                 default:
                     return false;
             }
@@ -17805,6 +17853,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             case GGML_UNARY_OP_EXP:
                 tensor_clone = ggml_exp(ggml_ctx, src_clone[0]);
                 break;
+            case GGML_UNARY_OP_EXPM1:
+                tensor_clone = ggml_expm1(ggml_ctx, src_clone[0]);
+                break;
             case GGML_UNARY_OP_ELU:
                 tensor_clone = ggml_elu(ggml_ctx, src_clone[0]);
                 break;

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

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

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(ceil(x));
-}

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

@@ -12,11 +12,11 @@ void main() {
         return;
     }
 
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
 
     if (i10 == i11) {

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

@@ -1,27 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    float x = float(data_a[i]);
-
-    if (x < 0.0f) {
-        x = exp(x) - 1;
-    }
-
-    data_d[i] = D_TYPE(x);
-}

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(exp(float(data_a[i])));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(floor(x));
-}

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

@@ -1,25 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const float GELU_COEF_A    = 0.044715f;
-    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float xi = float(data_a[i]);
-    const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi);
-    data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1)));
-}

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

@@ -1,39 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    // based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
-    // ref: https://www.johndcook.com/blog/python_erf/
-    const float p_erf  = 0.3275911f;
-    const float a1_erf = 0.254829592f;
-    const float a2_erf = -0.284496736f;
-    const float a3_erf = 1.421413741f;
-    const float a4_erf = -1.453152027f;
-    const float a5_erf = 1.061405429f;
-
-    const float SQRT_2_INV = 0.70710678118654752440084436210484f;
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float a = float(data_a[i]);
-    const float a_div_sqr2 = a * SQRT_2_INV;
-    const float sign_x = sign(a_div_sqr2);
-    const float x = abs(a_div_sqr2);
-    const float t = 1.0f / (1.0f + p_erf * x);
-    const float y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
-    const float erf_approx = sign_x * y;
-
-    data_d[i] = D_TYPE(0.5f * a * (1.0f + erf_approx));
-}

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

@@ -1,23 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const float GELU_QUICK_COEF = -1.702f;
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x))));
-}

ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.glsl

@@ -7,14 +7,12 @@ layout (push_constant) uniform parameter
     uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
     uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
     uint misalign_offsets;
-    float param1; float param2;
+    float param1; float param2; float param3; float param4;
 
-    uint ne0_012mp; uint ne0_012L;
-    uint ne0_01mp;  uint ne0_01L;
-    uint ne0_0mp;   uint ne0_0L;
-    uint ne1_012mp; uint ne1_012L;
-    uint ne1_01mp;  uint ne1_01L;
-    uint ne1_0mp;   uint ne1_0L;
+    // The three L values are packed as bytes to keep this layout under the 128B
+    // push constant limit while still leaving room for four float parameters.
+    uint ne0_012mp; uint ne0_01mp;  uint ne0_0mp;  uint ne0_Ls;
+    uint ne1_012mp; uint ne1_01mp;  uint ne1_0mp;  uint ne1_Ls;
 } p;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
@@ -42,42 +40,46 @@ uint fastdiv(uint n, uint mp, uint L) {
     return (msbs + n) >> L;
 }
 
+uint fastdiv_L(uint packed, uint slot) {
+    return (packed >> (slot * 8)) & 0x3Fu;
+}
+
 uint src0_idx(uint idx) {
-    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0));
     const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1));
     const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2));
     const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
     return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
 }
 
 uint dst_idx(uint idx) {
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
 }
 
 uint src0_idx_quant(uint idx, uint qk) {
-    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0));
     const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1));
     const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2));
     const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
     return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + (i00/qk)*p.nb00;
 }
 
 uint dst_idx_quant(uint idx, uint qk) {
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + (i10/qk)*p.nb10;
 }

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

@@ -15,14 +15,33 @@ layout (push_constant) uniform parameter
     uint mode;
     float alpha;
     float limit;
+    uint nb00;
     uint nb01;
     uint nb02;
     uint nb03;
-    uint ne01;
-    uint ne02;
+    uint nb10;
     uint nb11;
     uint nb12;
     uint nb13;
-    uint ne11;
-    uint ne12;
+    uint nb20;
+    uint nb21;
+    uint nb22;
+    uint nb23;
+    uint ne21;
+    uint ne22;
+    uint misalign_offsets;
+    uint ne2_012mp; uint ne2_012L;
+    uint ne2_01mp;  uint ne2_01L;
+    uint ne2_0mp;   uint ne2_0L;
 } p;
+
+uint get_aoffset() { return p.misalign_offsets >> 16; }
+uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; }
+uint get_doffset() { return p.misalign_offsets & 0xFF; }
+
+// see init_fastdiv_values in ggml-vulkan.cpp
+uint fastdiv(uint n, uint mp, uint L) {
+    uint msbs, lsbs;
+    umulExtended(n, mp, msbs, lsbs);
+    return (msbs + n) >> L;
+}

ggml/src/ggml-vulkan/vulkan-shaders/glu_main.glsl

@@ -5,35 +5,31 @@ void main() {
         return;
     }
 
-    const uint row = i / p.ne20;
-    const uint col = i - row * p.ne20;
+    const uint i23 = fastdiv(i, p.ne2_012mp, p.ne2_012L);
+    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
+    const uint i22 = fastdiv(i - i23_offset, p.ne2_01mp, p.ne2_01L);
+    const uint i22_offset = i22*p.ne21*p.ne20;
+    const uint i21 = fastdiv(i - i23_offset - i22_offset, p.ne2_0mp, p.ne2_0L);
+    const uint i20 = i - i23_offset - i22_offset - i21*p.ne20;
 
-    const uint i3 = row / (p.ne01 * p.ne02);
-    const uint i2 = (row % (p.ne01 * p.ne02)) / p.ne01;
-    const uint i1 = row % p.ne01;
-    const uint src_idx = i3 * p.nb03 + i2 * p.nb02 + i1 * p.nb01 + col;
-
-    const uint dst_i3 = row / (p.ne11 * p.ne12);
-    const uint dst_i2 = (row % (p.ne11 * p.ne12)) / p.ne11;
-    const uint dst_i1 = row % p.ne11;
-    const uint dst_idx = dst_i3 * p.nb13 + dst_i2 * p.nb12 + dst_i1 * p.nb11 + col;
+    const uint src_idx_a = get_aoffset() + i23 * p.nb03 + i22 * p.nb02 + i21 * p.nb01 + i20 * p.nb00;
+    const uint src_idx_b = get_boffset() + i23 * p.nb13 + i22 * p.nb12 + i21 * p.nb11 + i20 * p.nb10;
+    const uint dst_idx = get_doffset() + i23 * p.nb23 + i22 * p.nb22 + i21 * p.nb21 + i20 * p.nb20;
 
     if (p.mode == 0) {
         // Default
-        const uint offset = p.ne00 / 2;
-        const uint idx = src_idx;
+        const uint offset = (p.ne00 / 2) * p.nb00;
+        const uint idx = src_idx_a;
 
         data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_a[idx + offset])));
     } else if (p.mode == 1) {
         // Swapped
-        const uint offset = p.ne00 / 2;
-        const uint idx = src_idx;
+        const uint offset = (p.ne00 / 2) * p.nb00;
+        const uint idx = src_idx_a;
 
         data_d[dst_idx] = D_TYPE(op(float(data_a[idx + offset]), float(data_a[idx])));
     } else {
         // Split
-        const uint idx = src_idx;
-
-        data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_b[idx])));
+        data_d[dst_idx] = D_TYPE(op(float(data_a[src_idx_a]), float(data_b[src_idx_b])));
     }
 }

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(x * min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)));
-}

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(-float(data_a[i]));
-}

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

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

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

@@ -13,11 +13,11 @@ void main() {
     }
 
     // Destination multi-index (inlined dst_idx)
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     const uint d_idx = i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
 

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

@@ -20,11 +20,11 @@ void main() {
         return;
     }
 
-    const uint i3 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i3 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10;
-    const uint i2 = fastdiv(idx - i3_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i2 = fastdiv(idx - i3_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i2_offset = i2*p.ne11*p.ne10;
-    const uint i1 = fastdiv(idx - i3_offset - i2_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i1 = fastdiv(idx - i3_offset - i2_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10;
 
     const uint p1 = floatBitsToUint(p.param1);

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

@@ -1,29 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    float result;
-    // Round halfway cases away from zero as roundf does.
-    if (x >= 0.0) {
-        result = floor(x + 0.5);
-    } else {
-        result = ceil(x - 0.5);
-    }
-    data_d[i] = D_TYPE(result);
-}

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

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

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(1. / (1 + exp(-1. * float(data_a[i]))));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float xi = float(data_a[i]);
-    data_d[i] = D_TYPE(xi / (1.0f + exp(-xi)));
-}

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

@@ -1,23 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    const float result = (x > 20.0f) ? x : log(1.0f + exp(x));
-    data_d[i] = D_TYPE(result);
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(x >= 0.0f ? 1.0f : 0.0f);
-}

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(1. - 2. / (exp(2.*float(data_a[i])) + 1.));
-}

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

@@ -17,11 +17,11 @@ void main() {
         return;
     }
 
-    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0));
     const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1));
     const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2));
     const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
 
     int param = floatBitsToInt(p.param1);

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(trunc(x));
-}

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

@@ -0,0 +1,144 @@
+#version 450
+
+#include "types.glsl"
+#include "generic_unary_head.glsl"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+float op_abs(float x) {
+    return abs(x);
+}
+
+float op_sgn(float x) {
+    return sign(x);
+}
+
+float op_neg(float x) {
+    return -x;
+}
+
+float op_step(float x) {
+    return x >= 0.0f ? 1.0f : 0.0f;
+}
+
+float op_tanh(float x) {
+    return 1.0f - 2.0f / (exp(2.0f*x) + 1.0f);
+}
+
+float op_elu(float x) {
+    return x < 0.0f ? exp(x) - 1.0f : x;
+}
+
+float op_relu(float x) {
+    return max(x, 0.0f);
+}
+
+float op_sigmoid(float x) {
+    return 1.0f / (1.0f + exp(-x));
+}
+
+float op_gelu(float x) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const float val = SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x);
+    return 0.5f*x*(2.0f - 2.0f / (exp(2.0f * val) + 1.0f));
+}
+
+float op_gelu_quick(float x) {
+    const float GELU_QUICK_COEF = -1.702f;
+    return x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x)));
+}
+
+float op_silu(float x) {
+    return x / (1.0f + exp(-x));
+}
+
+float op_hardswish(float x) {
+    return x * min(1.0f, max(0.0f, (x + 3.0f) / 6.0f));
+}
+
+float op_hardsigmoid(float x) {
+    return min(1.0f, max(0.0f, (x + 3.0f) / 6.0f));
+}
+
+float op_exp(float x) {
+    return exp(x);
+}
+
+float op_expm1(float x) {
+    // exp(x) - 1 loses many ulps to cancellation near zero.  Use a degree-6
+    // Taylor expansion for |x| <= 1/4: the omitted x^7/5040 term is < 1.3e-8,
+    // about 0.5 ulp at expm1(0.25), and a host-side f32 model stays within
+    // 2 ulps over the interval.  The first native exp(x)-1 values outside the
+    // cutoff are about 1 ulp for +0.25 and 2 ulps for -0.25.
+    if (abs(x) <= 0.25f) {
+        return x * (1.0f + x * (0.5f + x * ((1.0f/6.0f) + x * ((1.0f/24.0f) + x * ((1.0f/120.0f) + x * (1.0f/720.0f))))));
+    }
+    return exp(x) - 1.0f;
+}
+
+float op_softplus(float x) {
+    return (x > 20.0f) ? x : log(1.0f + exp(x));
+}
+
+float op_gelu_erf(float a) {
+    // based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
+    const float p_erf  = 0.3275911f;
+    const float a1_erf = 0.254829592f;
+    const float a2_erf = -0.284496736f;
+    const float a3_erf = 1.421413741f;
+    const float a4_erf = -1.453152027f;
+    const float a5_erf = 1.061405429f;
+
+    const float SQRT_2_INV = 0.70710678118654752440084436210484f;
+    const float a_div_sqr2 = a * SQRT_2_INV;
+    const float sign_x = sign(a_div_sqr2);
+    const float x = abs(a_div_sqr2);
+    const float t = 1.0f / (1.0f + p_erf * x);
+    const float y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
+    return 0.5f * a * (1.0f + sign_x * y);
+}
+
+float op_xielu(float x) {
+    const float alpha_n = p.param1;
+    const float alpha_p = p.param2;
+    const float beta = p.param3;
+    const float eps = p.param4;
+
+    if (x > 0.0f) {
+        return alpha_p * x * x + beta * x;
+    }
+
+    const float min_x_eps = min(x, eps);
+    return (op_expm1(min_x_eps) - x) * alpha_n + beta * x;
+}
+
+float op_floor(float x) {
+    return floor(x);
+}
+
+float op_ceil(float x) {
+    return ceil(x);
+}
+
+float op_round(float x) {
+    // Round halfway cases away from zero as roundf does.
+    return x >= 0.0f ? floor(x + 0.5f) : ceil(x - 0.5f);
+}
+
+float op_trunc(float x) {
+    return trunc(x);
+}
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint a_idx = get_aoffset() + src0_idx(idx);
+    const uint d_idx = get_doffset() + dst_idx(idx);
+
+    data_d[d_idx] = D_TYPE(OP(float(data_a[a_idx])));
+}

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

@@ -868,47 +868,49 @@ void process_shaders() {
 
     string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
 
-    string_to_spv("exp_f16",        "exp.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("exp_f32",        "exp.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("exp_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_exp"}});
+    string_to_spv("exp_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_exp"}});
+    string_to_spv("expm1_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_expm1"}});
+    string_to_spv("expm1_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_expm1"}});
 
     string_to_spv("log_f16",        "log.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("log_f32",        "log.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("gelu_f16",       "gelu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("gelu_f32",       "gelu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("gelu_erf_f16",   "gelu_erf.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("gelu_erf_f32",   "gelu_erf.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("gelu_quick_f16", "gelu_quick.comp",  {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("gelu_quick_f32", "gelu_quick.comp",  {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("silu_f16",       "silu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("silu_f32",       "silu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("relu_f16",       "relu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("relu_f32",       "relu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("neg_f16",        "neg.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("neg_f32",        "neg.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("tanh_f16",       "tanh.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("tanh_f32",       "tanh.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("sigmoid_f16",    "sigmoid.comp",     {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("sigmoid_f32",    "sigmoid.comp",     {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("hardsigmoid_f16","hardsigmoid.comp", {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("hardsigmoid_f32","hardsigmoid.comp", {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("hardswish_f16",  "hardswish.comp",   {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("hardswish_f32",  "hardswish.comp",   {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("abs_f16",        "abs.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("abs_f32",        "abs.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("elu_f16",        "elu.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("elu_f32",        "elu.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("xielu_f16",      "xielu.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("xielu_f32",      "xielu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("sgn_f16",        "sgn.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("sgn_f32",        "sgn.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("gelu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_gelu"}});
+    string_to_spv("gelu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_gelu"}});
+    string_to_spv("gelu_erf_f16",   "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_gelu_erf"}});
+    string_to_spv("gelu_erf_f32",   "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_gelu_erf"}});
+    string_to_spv("gelu_quick_f16", "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_gelu_quick"}});
+    string_to_spv("gelu_quick_f32", "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_gelu_quick"}});
+    string_to_spv("silu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_silu"}});
+    string_to_spv("silu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_silu"}});
+    string_to_spv("relu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_relu"}});
+    string_to_spv("relu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_relu"}});
+    string_to_spv("neg_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_neg"}});
+    string_to_spv("neg_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_neg"}});
+    string_to_spv("tanh_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_tanh"}});
+    string_to_spv("tanh_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_tanh"}});
+    string_to_spv("sigmoid_f16",    "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sigmoid"}});
+    string_to_spv("sigmoid_f32",    "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sigmoid"}});
+    string_to_spv("hardsigmoid_f16","unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_hardsigmoid"}});
+    string_to_spv("hardsigmoid_f32","unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_hardsigmoid"}});
+    string_to_spv("hardswish_f16",  "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_hardswish"}});
+    string_to_spv("hardswish_f32",  "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_hardswish"}});
+    string_to_spv("abs_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_abs"}});
+    string_to_spv("abs_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_abs"}});
+    string_to_spv("elu_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_elu"}});
+    string_to_spv("elu_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_elu"}});
+    string_to_spv("xielu_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_xielu"}});
+    string_to_spv("xielu_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_xielu"}});
+    string_to_spv("sgn_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sgn"}});
+    string_to_spv("sgn_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sgn"}});
 
     string_to_spv("tri_f16",        "tri.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("tri_f32",        "tri.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
     string_to_spv("diag_f16",       "diag.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("diag_f32",       "diag.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
-    string_to_spv("softplus_f16",   "softplus.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("softplus_f32",   "softplus.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("softplus_f16",   "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_softplus"}});
+    string_to_spv("softplus_f32",   "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_softplus"}});
 
     string_to_spv("add1_f16_f16",   "add1.comp",        {{"A_TYPE", "float16_t"},   {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
     string_to_spv("add1_f16_f32",   "add1.comp",        {{"A_TYPE", "float16_t"},   {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
@@ -916,16 +918,16 @@ void process_shaders() {
     string_to_spv("arange_f32",     "arange.comp",      {{"A_TYPE", "float"},       {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
     string_to_spv("fill_f32",       "fill.comp",        {{"D_TYPE", "float"},       {"FLOAT_TYPE", "float"}});
     string_to_spv("fill_f16",       "fill.comp",        {{"D_TYPE", "float16_t"},   {"FLOAT_TYPE", "float"}});
-    string_to_spv("step_f16",       "step.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("step_f32",       "step.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("round_f16",      "round.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("round_f32",      "round.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("ceil_f16",       "ceil.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("ceil_f32",       "ceil.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("floor_f16",      "floor.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("floor_f32",      "floor.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("trunc_f16",      "trunc.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("trunc_f32",      "trunc.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("step_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_step"}});
+    string_to_spv("step_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_step"}});
+    string_to_spv("round_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_round"}});
+    string_to_spv("round_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_round"}});
+    string_to_spv("ceil_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_ceil"}});
+    string_to_spv("ceil_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_ceil"}});
+    string_to_spv("floor_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_floor"}});
+    string_to_spv("floor_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_floor"}});
+    string_to_spv("trunc_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_trunc"}});
+    string_to_spv("trunc_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_trunc"}});
 
     string_to_spv("geglu_f16",      "geglu.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("geglu_f32",      "geglu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});

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

@@ -1,35 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    float x = float(data_a[i]);
-
-    float alpha_n = p.param1;
-    float alpha_p = p.param2;
-    float beta = p.param3;
-    float eps = p.param4;
-
-    if (x > 0.0f) {
-        x = alpha_p * x * x + beta * x;
-    } else {
-        const float min_x_eps = min(x, eps);
-        x = (exp(min_x_eps) - 1 - x) * alpha_n + beta * x;
-    }
-
-    data_d[i] = D_TYPE(x);
-}

gguf-py/gguf/constants.py

@@ -457,6 +457,7 @@ class MODEL_ARCH(IntEnum):
     XVERSE           = auto()
     COMMAND_R        = auto()
     COHERE2          = auto()
+    COHERE2MOE       = auto()
     DBRX             = auto()
     OLMO             = auto()
     OLMO2            = auto()
@@ -1012,6 +1013,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.XVERSE:           "xverse",
     MODEL_ARCH.COMMAND_R:        "command-r",
     MODEL_ARCH.COHERE2:          "cohere2",
+    MODEL_ARCH.COHERE2MOE:       "cohere2moe",
     MODEL_ARCH.DBRX:             "dbrx",
     MODEL_ARCH.OLMO:             "olmo",
     MODEL_ARCH.OLMO2:            "olmo2",
@@ -2872,6 +2874,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.COHERE2MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_GATE_UP_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
     MODEL_ARCH.DBRX: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

scripts/ui-assets.cmake

@@ -16,23 +16,7 @@ set(HF_VERSION        "" CACHE STRING "Version to download (empty = resolve from
 set(HF_ENABLED        "" CACHE STRING "Whether to allow HF Bucket download (ON/OFF)")
 set(BUILD_UI          "" CACHE STRING "Build UI via npm (ON/OFF)")
 set(LLAMA_UI_EMBED    "" CACHE STRING "Path to llama-ui-embed helper")
-
-# IMPORTANT: When adding PWA assets, sync:
-#   - tools/ui/src/lib/constants/pwa.ts   (APPLE_DEVICES, PWA_MANIFEST)
-#
-# The HTTP server registers routes and public endpoints for every embedded asset.
-set(REQUIRED_ASSETS
-    index.html
-    loading.html
-    manifest.webmanifest
-    sw.js
-    build.json
-    # post-build.js flattens and dehashes these to fixed names in the dist dir
-    bundle.js
-    bundle.css
-    workbox.js
-    version.json
-)
+set(LLAMA_UI_GZIP     "" CACHE STRING "Apply gzip compress to assets to save bandwidth")
 
 set(DIST_DIR     "${UI_BINARY_DIR}/dist")
 set(SRC_DIST_DIR "${UI_SOURCE_DIR}/dist")
@@ -40,22 +24,10 @@ set(STAMP_FILE   "${UI_BINARY_DIR}/.ui-stamp")
 set(UI_CPP       "${UI_BINARY_DIR}/ui.cpp")
 set(UI_H         "${UI_BINARY_DIR}/ui.h")
 
-function(assets_present dir out_var)
-    set(present TRUE)
-    foreach(asset ${REQUIRED_ASSETS})
-        if(NOT EXISTS "${dir}/${asset}")
-            set(present FALSE)
-            break()
-        endif()
-    endforeach()
-    set(${out_var} ${present} PARENT_SCOPE)
-endfunction()
-
 function(npm_build_should_skip out_var)
     set(${out_var} FALSE PARENT_SCOPE)
 
-    assets_present("${DIST_DIR}" present)
-    if(NOT present)
+    if(NOT EXISTS "${DIST_DIR}/index.html")
         return()
     endif()
 
@@ -162,8 +134,7 @@ function(npm_build out_var)
         return()
     endif()
 
-    assets_present("${DIST_DIR}" present)
-    if(NOT present)
+    if(NOT EXISTS "${DIST_DIR}/index.html")
         message(STATUS "UI: npm build finished but assets missing in ${DIST_DIR}")
         return()
     endif()
@@ -242,8 +213,7 @@ function(hf_download version out_var out_resolved)
 
         file(ARCHIVE_EXTRACT INPUT "${archive}" DESTINATION "${DIST_DIR}")
 
-        assets_present("${DIST_DIR}" present)
-        if(NOT present)
+        if(NOT EXISTS "${DIST_DIR}/index.html")
             message(STATUS "UI: archive from ${resolved} is missing required assets")
             continue()
         endif()
@@ -256,11 +226,35 @@ function(hf_download version out_var out_resolved)
 endfunction()
 
 function(emit_files dist_dir)
-    assets_present("${dist_dir}" present)
+    # If gzip is requested, compress every asset into a parallel _gzip/ tree
+    # the structure stays the same; for ex: /abc/def --> /_gzip/abc/def
+    # embed.cpp will check for _gzip and will pick it up
+    if(LLAMA_UI_GZIP AND EXISTS "${dist_dir}/index.html")
+        find_program(GZIP_EXECUTABLE gzip)
+        if(NOT GZIP_EXECUTABLE)
+            message(WARNING "UI: LLAMA_UI_GZIP requested but gzip not found, embedding uncompressed")
+        else()
+            set(gzip_dir "${dist_dir}/_gzip")
+            file(REMOVE_RECURSE "${gzip_dir}")
+            file(GLOB_RECURSE all_files RELATIVE "${dist_dir}" "${dist_dir}/*")
+            foreach(f ${all_files})
+                get_filename_component(dst_dir "${gzip_dir}/${f}" DIRECTORY)
+                file(MAKE_DIRECTORY "${dst_dir}")
+                execute_process(
+                    COMMAND "${GZIP_EXECUTABLE}" -c "${dist_dir}/${f}"
+                    OUTPUT_FILE "${gzip_dir}/${f}"
+                    RESULT_VARIABLE gz_rc
+                )
+                if(NOT gz_rc EQUAL 0)
+                    message(FATAL_ERROR "UI: gzip failed for ${f}")
+                endif()
+            endforeach()
+            message(STATUS "UI: gzip compression applied (${gzip_dir})")
+        endif()
+    endif()
 
     set(args "${UI_CPP}" "${UI_H}")
-    if(present)
-        # llama-ui-embed embeds every top-level file in dist_dir
+    if(EXISTS "${dist_dir}/index.html")
         list(APPEND args "${dist_dir}")
     endif()
 
@@ -276,8 +270,7 @@ endfunction()
 # ---------------------------------------------------------------------------
 # 1. Priority 1: pre-built assets supplied in tools/ui/dist
 # ---------------------------------------------------------------------------
-assets_present("${SRC_DIST_DIR}" SRC_OK)
-if(SRC_OK)
+if(EXISTS "${SRC_DIST_DIR}/index.html")
     message(STATUS "UI: using pre-built assets from ${SRC_DIST_DIR}")
     emit_files("${SRC_DIST_DIR}")
     return()
@@ -312,7 +305,10 @@ if(NOT provisioned AND HF_ENABLED)
         endif()
     endif()
 
-    assets_present("${DIST_DIR}" have_assets)
+    set(have_assets FALSE)
+    if(EXISTS "${DIST_DIR}/index.html")
+        set(have_assets TRUE)
+    endif()
     if(stamp_ok AND have_assets)
         message(STATUS "UI: HF stamp '${stamped}' matches version, skipping HF fetch")
         set(provisioned TRUE)
@@ -332,8 +328,7 @@ endif()
 # 4. Fallback: warn about stale or missing assets, then emit whatever we have
 # ---------------------------------------------------------------------------
 if(NOT provisioned)
-    assets_present("${DIST_DIR}" have_assets)
-    if(have_assets)
+    if(EXISTS "${DIST_DIR}/index.html")
         message(WARNING "UI: provisioning failed; embedding stale assets from ${DIST_DIR}")
     else()
         message(WARNING "UI: no assets available - building without an embedded UI. "

src/llama-arch.cpp

@@ -66,6 +66,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_XVERSE,           "xverse"           },
     { LLM_ARCH_COMMAND_R,        "command-r"        },
     { LLM_ARCH_COHERE2,          "cohere2"          },
+    { LLM_ARCH_COHERE2MOE,       "cohere2moe"       },
     { LLM_ARCH_DBRX,             "dbrx"             },
     { LLM_ARCH_OLMO,             "olmo"             },
     { LLM_ARCH_OLMO2,            "olmo2"            },

src/llama-arch.h

@@ -71,6 +71,7 @@ enum llm_arch {
     LLM_ARCH_XVERSE,
     LLM_ARCH_COMMAND_R,
     LLM_ARCH_COHERE2,
+    LLM_ARCH_COHERE2MOE,
     LLM_ARCH_DBRX,
     LLM_ARCH_OLMO,
     LLM_ARCH_OLMO2,

src/llama-model-saver.cpp

@@ -18,6 +18,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
         case LLM_ARCH_GEMMA3:
         case LLM_ARCH_GEMMA3N:
         case LLM_ARCH_COHERE2:
+        case LLM_ARCH_COHERE2MOE:
         case LLM_ARCH_OLMO2:
         case LLM_ARCH_BITNET:
         case LLM_ARCH_T5:

src/llama-model.cpp

@@ -157,6 +157,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_command_r(params);
         case LLM_ARCH_COHERE2:
             return new llama_model_cohere2(params);
+        case LLM_ARCH_COHERE2MOE:
+            return new llama_model_cohere2moe(params);
         case LLM_ARCH_DBRX:
             return new llama_model_dbrx(params);
         case LLM_ARCH_OLMO:
@@ -1467,9 +1469,12 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
     }
     ml.done_getting_tensors();
 
+    // Tied NVFP4 output is valid when no separate LM-head scale tensors are present.
+    // If sidecar scales exist, the output weight must be an actual output tensor.
     GGML_ASSERT(!(output && tok_embd &&
             strcmp(output->name, tok_embd->name) == 0 &&
-            output->type == GGML_TYPE_NVFP4));
+            output->type == GGML_TYPE_NVFP4 &&
+            (output_s || output_in_s)));
     // populate tensors_by_name
     for (auto & [_, ctx_ptr] : ml.ctx_map) {
         for (auto * cur = ggml_get_first_tensor(ctx_ptr.get()); cur != NULL; cur = ggml_get_next_tensor(ctx_ptr.get(), cur)) {
@@ -1844,6 +1849,7 @@ void llama_model::print_info() const {
         }
 
         if (arch == LLM_ARCH_MELLUM ||
+                arch == LLM_ARCH_COHERE2MOE ||
                 arch == LLM_ARCH_QWEN3MOE ||
                 arch == LLM_ARCH_OPENAI_MOE ||
                 arch == LLM_ARCH_QWEN3VLMOE ||
@@ -2389,6 +2395,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_XVERSE:
         case LLM_ARCH_COMMAND_R:
         case LLM_ARCH_COHERE2:
+        case LLM_ARCH_COHERE2MOE:
         case LLM_ARCH_OLMO:
         case LLM_ARCH_ARCTIC:
         case LLM_ARCH_DEEPSEEK:

src/models/cohere2.cpp

@@ -122,9 +122,9 @@ llama_model_cohere2::graph::graph(const llama_model & model, const llm_graph_par
         // feed-forward network
         {
             cur = build_ffn(ffn_inp,
-                    model.layers[il].ffn_up, NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
+                    model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
+                    model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
+                    model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
                     NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
             cb(cur, "ffn_out", il);
         }

src/models/cohere2moe.cpp

@@ -0,0 +1,443 @@
+#include "models.h"
+
+void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
+    const bool found_norm     = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,     hparams.f_norm_eps,     false);
+    const bool found_norm_rms = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
+    if (!found_norm && !found_norm_rms) {
+        throw std::runtime_error("missing Cohere2 MoE norm epsilon");
+    }
+    if (!found_norm_rms) {
+        hparams.f_norm_rms_eps = 0.0f;
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.n_layer_nextn, false);
+    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
+
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+    }
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    uint32_t swa_period = 4;
+    if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
+        hparams.set_swa_pattern(swa_period, true);
+    } else {
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+    }
+
+    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+
+    switch (hparams.n_layer()) {
+        case 49: type = LLM_TYPE_30B_A3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
+    LLAMA_LOAD_LOCALS;
+
+    const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
+    // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
+    // tensors live in a separate file. Mark MTP tensors NOT_REQUIRED so the
+    // trunk loads cleanly.
+    const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
+    const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
+    const int trunk_flags = mtp_only  ? TENSOR_NOT_REQUIRED : 0;
+    const int mtp_flags   = trunk_only ? TENSOR_NOT_REQUIRED : 0;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+    }
+
+    if (n_expert == 0) {
+        throw std::runtime_error("n_expert must be > 0 for Cohere2Moe");
+    }
+    if (n_expert_used == 0) {
+        throw std::runtime_error("n_expert_used must be > 0 for Cohere2Moe");
+    }
+
+    auto load_block_trunk = [&](int i, int flags) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
+
+        if (static_cast<uint32_t>(i) < hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, flags);
+        } else {
+            const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
+
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, flags);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
+            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
+
+            if (hparams.n_expert_shared > 0) {
+                const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), { n_embd, n_ff_shexp }, flags);
+            }
+        }
+    };
+
+    auto load_block_mtp = [&](int i, int flags) {
+        auto & layer = layers[i];
+
+        // MTP block looks like a full-attention Cohere2 MoE decoder block.
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
+
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
+
+        // Routed experts
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, flags);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
+        create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
+
+        if (hparams.n_expert_shared > 0) {
+            const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
+
+            // Shared experts
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), { n_embd, n_ff_shexp }, flags);
+        }
+
+        // NextN-specific tensors that define the MTP block.
+        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), { 2 * n_embd, n_embd }, flags);
+        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), { n_embd },              flags);
+        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), { n_embd },              flags);
+        layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
+        layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
+        layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd },              TENSOR_NOT_REQUIRED);
+    };
+
+    for (int i = 0; i < n_layer; ++i) {
+        load_block_trunk(i, trunk_flags);
+    }
+    // MTP/NextN layers are loaded as extra decoder blocks.
+    for (int i = n_layer; i < n_layer_all; ++i) {
+        load_block_mtp(i, mtp_flags);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_cohere2moe::build_arch_graph(const llm_graph_params & params) const {
+    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+        return std::make_unique<graph_mtp>(*this, params);
+    }
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_cohere2moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
+
+    const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
+    const float f_logit_scale = hparams.f_logit_scale;
+    ggml_tensor * cur;
+    ggml_tensor * inpL = build_inp_embd(model.tok_embd);
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv_iswa();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
+    for (int il = 0; il < n_layer; ++il) {
+        const bool is_swa = hparams.is_swa(il);
+        // Dense-prefix full-attention layers use RoPE; later layers follow the SWA pattern.
+        const bool force_rope = static_cast<uint32_t>(il) < hparams.n_layer_dense_lead;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, cohere2moe_norm_type, il);
+        cb(cur, "attn_norm", il);
+
+        ggml_tensor * ffn_inp = cur;
+
+        {
+            const auto & layer = model.layers[il];
+
+            auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
+                    n_embd_head, n_head, n_head_kv, il);
+
+            if (is_swa || force_rope) {
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+            }
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    layer.wo, layer.wo_b, layer.wo_s,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
+                    1.0f / sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+            cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL    = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+        }
+
+        ggml_tensor * attn_out = cur;
+
+        const auto & layer = model.layers[il];
+
+        if (layer.ffn_gate_inp == nullptr) {
+            cur = build_ffn(ffn_inp,
+                    layer.ffn_up,   nullptr, layer.ffn_up_s,
+                    layer.ffn_gate, nullptr, layer.ffn_gate_s,
+                    layer.ffn_down, nullptr, layer.ffn_down_s,
+                    nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = build_moe_ffn(ffn_inp,
+                    layer.ffn_gate_inp,
+                    layer.ffn_up_exps,
+                    layer.ffn_gate_exps,
+                    layer.ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, hparams.expert_weights_norm,
+                    hparams.expert_weights_scale,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il,
+                    nullptr, layer.ffn_gate_up_exps,
+                    layer.ffn_up_exps_s,
+                    layer.ffn_gate_exps_s,
+                    layer.ffn_down_exps_s);
+            cb(cur, "ffn_moe_out", il);
+
+            if (layer.ffn_up_shexp) {
+                ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+                        layer.ffn_up_shexp,   nullptr, layer.ffn_up_shexp_s,
+                        layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
+                        layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
+                        nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, cur, ffn_shexp);
+                cur = ggml_scale(ctx0, cur, 0.5f);
+                cb(cur, "ffn_out", il);
+            }
+        }
+
+        cur = ggml_add(ctx0, cur, inpL);
+        cur = ggml_add(ctx0, cur, attn_out);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+    cur = build_norm(cur, model.output_norm, nullptr, cohere2moe_norm_type, -1);
+
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    if (f_logit_scale) {
+        cur = ggml_scale(ctx0, cur, f_logit_scale);
+    }
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+llama_model_cohere2moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    GGML_ASSERT(hparams.n_layer_nextn > 0 && "COHERE2MOE MTP requires n_layer_nextn > 0");
+    GGML_ASSERT(hparams.n_layer_nextn == 1 && "COHERE2MOE MTP currently only supports a single MTP block");
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
+
+    const int il = hparams.n_layer();
+    const auto & layer = model.layers[il];
+    GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
+    GGML_ASSERT(layer.nextn.enorm   && "MTP block missing nextn.enorm");
+    GGML_ASSERT(layer.nextn.hnorm   && "MTP block missing nextn.hnorm");
+    GGML_ASSERT(layer.ffn_gate_inp  && "MTP block missing ffn_gate_inp");
+
+    const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
+
+    // TODO: extract in a common llm_graph_context::build_inp_embd_h()
+    auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
+    ggml_set_input(inp->embd);
+
+    // TODO: make static using `ggml_build_forward_select()`
+    //       see llm_graph_context::build_inp_embd() for reference
+    ggml_tensor * tok_embd;
+    if (ubatch.token) {
+        ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+        tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+    } else {
+        tok_embd = inp->embd;
+    }
+    cb(tok_embd, "mtp_tok_embd", il);
+
+    inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+    ggml_set_input(inp->h);
+    ggml_set_name(inp->h, "mtp_h_input");
+
+    ggml_tensor * h_embd = inp->h;
+
+    res->add_input(std::move(inp));
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+    auto * inp_attn = build_attn_inp_kv_iswa();
+
+    ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, cohere2moe_norm_type, il);
+    cb(h_norm, "mtp_hnorm", il);
+
+    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, cohere2moe_norm_type, il);
+    cb(e_norm, "mtp_enorm", il);
+
+    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
+    cb(concat, "mtp_concat", il);
+
+    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
+    cb(cur, "mtp_eh_proj", il);
+
+    ggml_tensor * inpL = cur;
+
+    cur = build_norm(cur, layer.attn_norm, nullptr, cohere2moe_norm_type, il);
+    cb(cur, "mtp_attn_norm", il);
+    ggml_tensor * ffn_inp = cur;
+
+    auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il);
+    ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+    Qcur = ggml_rope_ext(
+            ctx0, Qcur, inp_pos, rope_factors,
+            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    Kcur = ggml_rope_ext(
+            ctx0, Kcur, inp_pos, rope_factors,
+            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+
+    cb(Qcur, "mtp_Qcur", il);
+    cb(Kcur, "mtp_Kcur", il);
+    cb(Vcur, "mtp_Vcur", il);
+
+    cur = build_attn(inp_attn,
+            layer.wo, layer.wo_b, layer.wo_s,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
+            1.0f / sqrtf(float(n_embd_head)), il);
+    cb(cur, "mtp_attn_out", il);
+
+    ggml_tensor * attn_out = cur;
+
+    cur = build_moe_ffn(ffn_inp,
+            layer.ffn_gate_inp,
+            layer.ffn_up_exps,
+            layer.ffn_gate_exps,
+            layer.ffn_down_exps,
+            nullptr,
+            n_expert, n_expert_used,
+            LLM_FFN_SILU, hparams.expert_weights_norm,
+            hparams.expert_weights_scale,
+            (llama_expert_gating_func_type) hparams.expert_gating_func,
+            il,
+            nullptr, layer.ffn_gate_up_exps,
+            layer.ffn_up_exps_s,
+            layer.ffn_gate_exps_s,
+            layer.ffn_down_exps_s);
+    cb(cur, "mtp_ffn_moe_out", il);
+
+    if (layer.ffn_up_shexp) {
+        ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+                layer.ffn_up_shexp,   nullptr, layer.ffn_up_shexp_s,
+                layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
+                layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
+                nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "mtp_ffn_shexp", il);
+
+        cur = ggml_add(ctx0, cur, ffn_shexp);
+        cur = ggml_scale(ctx0, cur, 0.5f);
+        cb(cur, "mtp_ffn_out", il);
+    }
+
+    cur = ggml_add(ctx0, cur, inpL);
+    cur = ggml_add(ctx0, cur, attn_out);
+    cb(cur, "mtp_post_ffn", il);
+
+    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+            ? layer.nextn.shared_head_norm
+            : model.output_norm;
+    GGML_ASSERT(head_norm_w && "COHERE2MOE MTP: missing both nextn.shared_head_norm and output_norm");
+    cur = build_norm(cur, head_norm_w, nullptr, cohere2moe_norm_type, -1);
+
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    cb(cur, "mtp_shared_head_norm", -1);
+
+    ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
+    GGML_ASSERT(head_w && "COHERE2MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
+    cur = build_lora_mm(head_w, cur, layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : nullptr);
+
+    if (hparams.f_logit_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
+    }
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/models.h

@@ -937,6 +937,23 @@ struct llama_model_cohere2 : public llama_model_base {
 };
 
 
+struct llama_model_cohere2moe : public llama_model_base {
+    llama_model_cohere2moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    struct graph_mtp : public llm_graph_context {
+        graph_mtp(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_dbrx : public llama_model_base {
     llama_model_dbrx(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

tests/test-jinja.cpp

@@ -435,6 +435,24 @@ static void test_expressions(testing & t) {
         "('c', 'b', 'a')"
     );
 
+    test_template(t, "string slice negative step",
+        "{{ 'abcdef'[::-2] }}",
+        json::object(),
+        "fdb"
+    );
+
+    test_template(t, "string slice negative start and step",
+        "{{ 'abcdef'[-1:1:-1] }}",
+        json::object(),
+        "fedc"
+    );
+
+    test_template(t, "string slice negative start, stop and step",
+        "{{ 'abcdef'[-1:-5:-1] }}",
+        json::object(),
+        "fedc"
+    );
+
     test_template(t, "arithmetic",
         "{{ (a + b) * c }}",
         {{"a", 2}, {"b", 3}, {"c", 4}},
@@ -1320,6 +1338,12 @@ static void test_string_methods(testing & t) {
         "hello jinja"
     );
 
+    test_template(t, "string.replace() empty",
+        "{{ s.replace('', '.') }}",
+        {{"s", "hello world"}},
+        ".h.e.l.l.o. .w.o.r.l.d."
+    );
+
     test_template(t, "string.replace() with count",
         "{{ s.replace('a', 'X', 2) }}",
         {{"s", "banana"}},

tests/test-llama-archs.cpp

@@ -185,7 +185,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,              10000.0f);
         // SWA pattern: every 5th layer is full attention (matches E2B layer_types)
         ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5));
-    } else if (arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35) {
+    } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35) {
         std::vector<uint32_t> pattern;
         pattern.reserve(n_layer);
         for (uint32_t il = 0; il < n_layer; il++) {
@@ -322,6 +322,7 @@ static std::vector<float> get_logits(
 static bool moe_mandatory(const llm_arch arch) {
     switch (arch) {
         case LLM_ARCH_LLAMA4:
+        case LLM_ARCH_COHERE2MOE:
         case LLM_ARCH_GROK:
         case LLM_ARCH_QWEN2MOE:
         case LLM_ARCH_QWEN3MOE:

tools/server/server-http.cpp

@@ -26,52 +26,6 @@ server_http_context::server_http_context()
 
 server_http_context::~server_http_context() = default;
 
-// transform path --> asset name ; rules:
-// delete "_app/" prefix
-// delete hash, for ex: bundle.HCjcCZFH.css --> bundle.css
-//                      workbox-12bd46aa.js --> workbox.js
-static std::string asset_name_from_path(const std::string & path) {
-    // Strip leading slash
-    std::string s = (!path.empty() && path[0] == '/') ? path.substr(1) : path;
-    // Strip _app/ prefix
-    if (s.size() > 5 && s.compare(0, 5, "_app/") == 0) {
-        s = s.substr(5);
-    }
-    // Strip hash segment from filename:
-    //   bundle.HCjcCZFH.css -> bundle.css  (name.HASH.ext)
-    //   workbox-12bd46aa.js -> workbox.js  (name-HEXHASH.ext)
-    size_t slash = s.rfind('/');
-    std::string dir  = (slash != std::string::npos) ? s.substr(0, slash + 1) : "";
-    std::string file = (slash != std::string::npos) ? s.substr(slash + 1)    : s;
-
-    auto is_alnum_hash = [](const std::string & h) {
-        if (h.size() < 6 || h.size() > 16) return false;
-        for (char c : h) { if (!isalnum((unsigned char)c)) return false; }
-        return true;
-    };
-    auto is_hex_hash = [](const std::string & h) {
-        if (h.size() < 6 || h.size() > 16) return false;
-        for (char c : h) { if (!isxdigit((unsigned char)c)) return false; }
-        return true;
-    };
-
-    size_t dot1 = file.find('.');
-    if (dot1 != std::string::npos) {
-        size_t dot2 = file.find('.', dot1 + 1);
-        if (dot2 != std::string::npos && is_alnum_hash(file.substr(dot1 + 1, dot2 - dot1 - 1))) {
-            file = file.substr(0, dot1) + file.substr(dot2);
-        } else {
-            size_t dot = file.rfind('.');
-            size_t dash = file.rfind('-', dot);
-            if (dash != std::string::npos && is_hex_hash(file.substr(dash + 1, dot - dash - 1))) {
-                file = file.substr(0, dash) + file.substr(dot);
-            }
-        }
-    }
-
-    return dir + file;
-}
-
 static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
     // skip logging requests that are regularly sent, to avoid log spam
     if (req.path == "/health"
@@ -240,9 +194,8 @@ bool server_http_context::init(const common_params & params) {
             return true;
         }
 
-        // If path is public or a UI asset (including hashed paths like /_app/bundle.XXX.js),
-        // skip validation
-        if (get_public_endpoints.count("/" + asset_name_from_path(req.path))) {
+        // If path is public or a UI asset, skip validation
+        if (get_public_endpoints.count(req.path)) {
             return true;
         }
 
@@ -366,8 +319,26 @@ bool server_http_context::init(const common_params & params) {
             }
         } else {
 #if defined(LLAMA_UI_HAS_ASSETS)
+            static auto handle_gzip_header = [](const httplib::Request & req, httplib::Response & res) {
+                if (!llama_ui_use_gzip()) {
+                    // no gzip build, skip
+                    return true;
+                }
+                if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) {
+                    res.status = 415; // unsupported media type
+                    res.set_content("Error: gzip is not supported by this browser", "text/plain");
+                    return false;
+                } else {
+                    res.set_header("Content-Encoding", "gzip");
+                }
+                return true;
+            };
+
             auto serve_asset_cached = [](const std::string & name, bool isolation) {
                 return [name, isolation](const httplib::Request & req, httplib::Response & res) {
+                    if (!handle_gzip_header(req, res)) {
+                        return true; // returns error message
+                    }
                     const llama_ui_asset * a = llama_ui_find_asset(name);
                     if (!a) { res.status = 404; return false; }
                     res.set_header("ETag", a->etag);
@@ -387,7 +358,10 @@ bool server_http_context::init(const common_params & params) {
             };
 
             auto serve_asset_nocache = [](const std::string & name) {
-                return [name](const httplib::Request & /*req*/, httplib::Response & res) {
+                return [name](const httplib::Request & req, httplib::Response & res) {
+                    if (!handle_gzip_header(req, res)) {
+                        return true; // returns error message
+                    }
                     const llama_ui_asset * a = llama_ui_find_asset(name);
                     if (!a) {
                         res.status = 404;
@@ -399,17 +373,9 @@ bool server_http_context::init(const common_params & params) {
                 };
             };
 
-            // Hashed routes: browser requests contain the build hash, assets are stored without.
-            auto serve_hashed = [serve_asset_cached](const std::string & name) {
-                return serve_asset_cached(name, false);
-            };
-            srv->Get(params.api_prefix + R"(/_app/immutable/bundle\.[^/]+\.js)",         serve_hashed("bundle.js"));
-            srv->Get(params.api_prefix + R"(/_app/immutable/assets/bundle\.[^/]+\.css)", serve_hashed("bundle.css"));
-            srv->Get(params.api_prefix + R"(/workbox-[^/]+\.js)",                        serve_hashed("workbox.js"));
-
-            // SPA entry — also aliased at "/_app/version.json" (referenced by the service worker)
-            srv->Get(params.api_prefix + "/",                  serve_asset_cached ("index.html",   true));
-            srv->Get(params.api_prefix + "/_app/version.json", serve_asset_nocache("version.json"));
+            // main index file
+            srv->Get(params.api_prefix + "/",           serve_asset_cached("index.html", true));
+            srv->Get(params.api_prefix + "/index.html", serve_asset_cached("index.html", true));
 
             // All remaining assets registered directly from the embedded asset table.
             // PWA revalidation files (sw.js, manifest, version.json) use no-cache;
@@ -417,15 +383,14 @@ bool server_http_context::init(const common_params & params) {
             static const std::unordered_set<std::string> no_cache_names = {
                 "sw.js",
                 "manifest.webmanifest",
-                "version.json",
+                "_app/version.json",
                 "build.json"
             };
-            // index.html also accessible at /index.html (with the same isolation headers as /)
-            srv->Get(params.api_prefix + "/index.html", serve_asset_cached("index.html", true));
 
             for (const auto & a : llama_ui_get_assets()) {
                 if (a.name == "index.html") continue;  // served at "/" and "/index.html" above
                 if (no_cache_names.count(a.name)) {
+                    SRV_DBG("serve nocache for %s\n", a.name.c_str());
                     srv->Get(params.api_prefix + "/" + a.name, serve_asset_nocache(a.name));
                 } else {
                     srv->Get(params.api_prefix + "/" + a.name, serve_asset_cached(a.name, false));

tools/ui/CMakeLists.txt

@@ -1,6 +1,7 @@
 set(TARGET llama-ui)
 
 set(LLAMA_UI_HF_BUCKET "ggml-org/llama-ui" CACHE STRING "Hugging Face bucket name for prebuilt UI assets")
+set(LLAMA_UI_GZIP      ON                  CACHE BOOL   "Apply gzip compress to assets to save bandwidth")
 
 # Backward compat: forward old var to new one
 if(DEFINED LLAMA_BUILD_WEBUI)
@@ -83,6 +84,7 @@ add_custom_target(llama-ui-assets ALL
         "-DHF_ENABLED=${LLAMA_USE_PREBUILT_UI}"
         "-DBUILD_UI=${LLAMA_BUILD_UI}"
         "-DLLAMA_UI_EMBED=${LLAMA_UI_EMBED_EXE}"
+        "-DLLAMA_UI_GZIP=${LLAMA_UI_GZIP}"
         -P "${PROJECT_SOURCE_DIR}/scripts/ui-assets.cmake"
     COMMENT "Provisioning UI assets"
     VERBATIM

tools/ui/embed.cpp

@@ -3,7 +3,8 @@
 // Usage:
 //   llama-ui-embed <out_cpp> <out_h> [<asset_dir>]
 //
-// Embeds every regular file directly under <asset_dir> (non-recursive).
+// Recursively embeds every regular file under <asset_dir>.
+// Asset names are relative paths from <asset_dir> (e.g. "_app/immutable/bundle.HASH.js").
 // Without <asset_dir>, emits an empty asset table.
 
 #include <inttypes.h>
@@ -15,6 +16,7 @@
 #include <algorithm>
 #include <filesystem>
 #include <fstream>
+#include <functional>
 #include <string>
 #include <vector>
 
@@ -103,7 +105,24 @@ static bool write_if_different(const std::string & path, const std::string & con
     if (!content.empty()) {
         out.write(content.data(), static_cast<std::streamsize>(content.size()));
     }
-    return out.good();
+    bool ok = out.good();
+    if (ok) {
+        printf("embed: write output file %s\n", path.c_str());
+    }
+    return ok;
+}
+
+static std::string path_basename(const std::string & name) {
+    const size_t p = name.rfind('/');
+    return p == std::string::npos ? name : name.substr(p + 1);
+}
+static bool str_starts_with(const std::string & s, const char * prefix) {
+    const size_t n = strlen(prefix);
+    return s.size() >= n && s.compare(0, n, prefix) == 0;
+}
+static bool str_ends_with(const std::string & s, const char * suffix) {
+    const size_t n = strlen(suffix);
+    return s.size() >= n && s.compare(s.size() - n, n, suffix) == 0;
 }
 
 static std::string fmt(const char * pattern, ...) {
@@ -126,15 +145,19 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const std::string out_cpp = argv[1];
-    const std::string out_h   = argv[2];
+    const std::string out_cpp   = argv[1];
+    const std::string out_h     = argv[2];
+    const std::string asset_dir = (argc >= 4) ? argv[3] : std::string();
+
+    const bool        use_gzip = !asset_dir.empty() && std::filesystem::exists(asset_dir + "/_gzip");
+    const std::string in_dir   = use_gzip ? (asset_dir + "/_gzip") : asset_dir;
 
     std::vector<asset_entry> assets;
-    if (argc == 4) {
-        const std::filesystem::path dir = argv[3];
+    if (!in_dir.empty()) {
+        const std::filesystem::path dir = in_dir;
 
         std::error_code ec;
-        std::filesystem::directory_iterator it(dir, ec);
+        std::filesystem::recursive_directory_iterator it(dir, ec);
         if (ec) {
             fprintf(stderr, "embed: cannot iterate %s: %s\n", argv[3], ec.message().c_str());
             return 1;
@@ -143,7 +166,9 @@ int main(int argc, char ** argv) {
             if (!entry.is_regular_file()) {
                 continue;
             }
-            assets.push_back({ entry.path().filename().generic_string(), entry.path() });
+            // name is the relative path from dir, with forward slashes
+            const std::string name = entry.path().lexically_relative(dir).generic_string();
+            assets.push_back({ name, entry.path() });
         }
 
         // directory iteration order is unspecified; sort for reproducible output
@@ -154,18 +179,51 @@ int main(int argc, char ** argv) {
     const int n_assets = static_cast<int>(assets.size());
 
     if (n_assets > 0) {
-        bool has_index = false, has_bundle_js = false, has_bundle_css = false, has_version = false;
+        using match_fn = std::function<bool(const std::string &)>;
+        auto exact = [](const char * name) -> match_fn {
+            return [name](const std::string & base) { return base == name; };
+        };
+
+        struct required_check { const char * label; match_fn match; bool found; };
+        required_check checks[] = {
+            { "index.html",           exact("index.html"),           false },
+            { "loading.html",         exact("loading.html"),         false },
+            { "manifest.webmanifest", exact("manifest.webmanifest"), false },
+            { "sw.js",                exact("sw.js"),                false },
+            { "build.json",           exact("build.json"),           false },
+            { "version.json",         exact("version.json"),         false },
+            { "bundle[hash].js",      [](const std::string & b) {
+                return str_starts_with(b, "bundle")  && str_ends_with(b, ".js");
+            }, false },
+            { "bundle[hash].css",     [](const std::string & b) {
+                return str_starts_with(b, "bundle")  && str_ends_with(b, ".css");
+            }, false },
+            { "workbox[hash].js",     [](const std::string & b) {
+                return str_starts_with(b, "workbox") && str_ends_with(b, ".js");
+            }, false },
+        };
+
         for (const auto & a : assets) {
-            if (a.name == "index.html")   has_index      = true;
-            if (a.name == "bundle.js")    has_bundle_js  = true;
-            if (a.name == "bundle.css")   has_bundle_css = true;
-            if (a.name == "version.json") has_version    = true;
-        }
-        if (!has_index || !has_bundle_js || !has_bundle_css || !has_version) {
-            fprintf(stderr, "embed: missing required assets (need index.html, bundle.js, bundle.css, version.json); got:\n");
-            for (const auto & a : assets) {
-                fprintf(stderr, "  %s\n", a.name.c_str());
+            const std::string base = path_basename(a.name);
+            for (auto & c : checks) {
+                if (!c.found) { c.found = c.match(base); }
             }
+        }
+
+        std::vector<const char *> missing;
+        for (const auto & c : checks) {
+            if (!c.found) { missing.push_back(c.label); }
+        }
+        if (!missing.empty()) {
+            fprintf(stderr, "\ncurrent asset files:\n");
+            for (const auto & a : assets) {
+                fprintf(stderr, "    %s\n", a.name.c_str());
+            }
+            fprintf(stderr, "missing required asset(s):\n");
+            for (const char * m : missing) {
+                fprintf(stderr, "    %s\n", m);
+            }
+            fprintf(stderr, "hint: try cleaning your build directory: %s\n", in_dir.c_str());
             return 1;
         }
     }
@@ -183,7 +241,8 @@ int main(int argc, char ** argv) {
         "    std::string           etag;\n"
         "    std::string           type;\n"
         "};\n\n"
-        "const llama_ui_asset * llama_ui_find_asset(const std::string & name);\n";
+        "const llama_ui_asset * llama_ui_find_asset(const std::string & name);\n"
+        "bool llama_ui_use_gzip();\n";
     h += fmt("const std::array<llama_ui_asset, %d> & llama_ui_get_assets();\n", n_assets);
 
     std::string cpp;
@@ -195,6 +254,10 @@ int main(int argc, char ** argv) {
             if (!read_file(assets[i].path, bytes)) {
                 return 1;
             }
+            if (bytes.empty()) {
+                fprintf(stderr, "embed: empty file: %s\n", assets[i].path.generic_string().c_str());
+                return 1;
+            }
             cpp += fmt("static const unsigned char asset_%d_data[] = {", i);
             append_bytes_hex(cpp, bytes);
             const auto hash = fnv_hash(bytes.data(), bytes.size());
@@ -235,6 +298,7 @@ int main(int argc, char ** argv) {
             "    return empty;\n"
             "}\n";
     }
+    cpp += fmt("bool llama_ui_use_gzip() { return %s; }\n", use_gzip ? "true" : "false");
 
     bool ok = true;
     ok = write_if_different(out_h,   h)   && ok;

tools/ui/package.json

@@ -4,7 +4,7 @@
 	"version": "1.0.0",
 	"type": "module",
 	"scripts": {
-		"build": "npm run build-pwa-assets && vite build && node scripts/post-build.js",
+		"build": "npm run build-pwa-assets && vite build",
 		"build-pwa-assets": "npx @vite-pwa/assets-generator --root . --config pwa-assets.config.ts && npx @vite-pwa/assets-generator --root . --config pwa-assets-dark.config.ts && node scripts/make-icons-circular.js",
 		"dev": "bash scripts/dev.sh",
 		"preview": "vite preview",

tools/ui/scripts/post-build.js

@@ -1,40 +0,0 @@
-#!/usr/bin/env node
-// Post-build: copy hashed/nested assets to predictable flat names.
-// No file content is modified — the C++ server handles routing hashed URLs
-// to the correct stored asset at runtime.
-//
-// Copies:
-//   _app/immutable/bundle.HASH.js         -> bundle.js
-//   _app/immutable/assets/bundle.HASH.css -> bundle.css
-//   workbox-HEXHASH.js                    -> workbox.js
-//   _app/version.json                     -> version.json
-
-import fs from 'fs';
-import path from 'path';
-
-const outDir = process.env.LLAMA_UI_OUT_DIR ?? './dist';
-
-function findOne(dir, pattern) {
-	const files = fs.readdirSync(dir).filter((f) => pattern.test(f));
-	if (files.length === 0) throw new Error(`post-build: no file matching ${pattern} in ${dir}`);
-	return path.join(dir, files[0]);
-}
-
-function copyFlat(src, destName) {
-	const dest = path.join(outDir, destName);
-	fs.copyFileSync(src, dest);
-	console.log(`post-build: ${path.relative(outDir, src)} -> ${destName}`);
-}
-
-const bundleJs = findOne(path.join(outDir, '_app/immutable'), /^bundle\.[^.]+\.js$/);
-const bundleCss = findOne(path.join(outDir, '_app/immutable/assets'), /^bundle\.[^.]+\.css$/);
-const workbox = findOne(outDir, /^workbox-[0-9a-f]+\.js$/);
-
-copyFlat(bundleJs, 'bundle.js');
-copyFlat(bundleCss, 'bundle.css');
-copyFlat(workbox, 'workbox.js');
-
-const versionSrc = path.join(outDir, '_app/version.json');
-if (fs.existsSync(versionSrc)) {
-	copyFlat(versionSrc, 'version.json');
-}