cmake : add variable to skip installing tests (#19370)

When packaging downstream, there's usually little point in installing
test. The default behaviour remains the same.

.github/workflows/build.yml

@@ -295,6 +295,7 @@ jobs:
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Build (no OpenMP)
@@ -307,6 +308,7 @@ jobs:
             -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF
+
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Test

.github/workflows/server-webui.yml

@@ -8,10 +8,6 @@ on:
         description: 'Commit SHA1 to build'
         required: false
         type: string
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
   push:
     branches:
       - master
@@ -101,119 +97,3 @@ jobs:
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:e2e
         working-directory: tools/server/webui
-
-  server-build:
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
-
-      - name: Setup Node.js for WebUI
-        uses: actions/setup-node@v6
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Install WebUI dependencies
-        run: npm ci
-        working-directory: tools/server/webui
-
-      - name: Build WebUI
-        run: npm run build
-        working-directory: tools/server/webui
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
-        env:
-          GITHUB_ACTIONS: "true"
-        run: |
-          cd tools/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd tools/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          SLOW_TESTS=1 ./tests.sh

.github/workflows/server.yml

@@ -81,18 +81,14 @@ jobs:
             -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
             -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
             -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
-          cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
       - name: Python setup
         id: setup_python
         uses: actions/setup-python@v6
         with:
           python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip-install: -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests
@@ -102,6 +98,14 @@ jobs:
           export ${{ matrix.extra_args }}
           pytest -v -x -m "not slow"
 
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd tools/server/tests
+          export ${{ matrix.extra_args }}
+          SLOW_TESTS=1 pytest -v -x
+
   server-windows:
     runs-on: windows-2022
 
@@ -124,11 +128,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip-install: -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests

CMakeLists.txt

@@ -109,6 +109,7 @@ option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
+option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)
 
 # 3rd party libs
 option(LLAMA_HTTPLIB    "llama: httplib for downloading functionality" ON)

convert_hf_to_gguf.py

@@ -920,7 +920,7 @@ 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_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"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -4102,39 +4102,27 @@ class Qwen2MoeModel(TextModel):
         # process the experts separately
         name = name.replace("language_model.", "") # InternVL
 
-        # handle aggregated expert tensors
-        # GGUF stores dimensions reversed from PyTorch, so:
-        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
-        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
-        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
+        # handle pre-packed expert tensors (e.g. Qwen3.5 MoE, Qwen3Next)
+        # HF stores these using nn.Linear convention: [n_expert, out_features, in_features]
+        # This matches the individual expert stacking path below (which stacks
+        # per-expert [out, in] weights into [n_expert, out, in]), so no permute is needed.
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
-            # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
-            # Need PyTorch: (128, 2048, 768) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
-            permuted = data_torch.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(permuted, mapped, bid)
+            # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
+            yield from super().modify_tensors(data_torch, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
-                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            split_dim = data_torch.shape[-1] // 2
-            gate = data_torch[..., :split_dim].contiguous()
-            up = data_torch[..., split_dim:].contiguous()
-            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
-            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
-            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
-            base_name = name.removesuffix(".weight")
-            base = base_name.rsplit('.', 1)[0]
-            mapped_gate = f"{base}.gate_proj.weight"
-            mapped_up = f"{base}.up_proj.weight"
-            perm_gate = gate.permute(0, 2, 1).contiguous()
-            perm_up = up.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(perm_gate, mapped_gate, bid)
-            yield from super().modify_tensors(perm_up, mapped_up, bid)
+            # HF: [n_expert, 2*n_ff, n_embd] → split on dim=1
+            n_ff = data_torch.shape[1] // 2
+            gate = data_torch[:, :n_ff, :].contiguous()
+            up = data_torch[:, n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] → GGML: {n_embd, n_ff, n_expert} ✓
+            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
+            mapped_gate = f"{base_name}.gate_proj.weight"
+            mapped_up = f"{base_name}.up_proj.weight"
+            yield from super().modify_tensors(gate, mapped_gate, bid)
+            yield from super().modify_tensors(up, mapped_up, bid)
             return
 
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
@@ -4344,6 +4332,40 @@ class Qwen3NextModel(Qwen2MoeModel):
             yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3_5ForCausalLM", "Qwen3_5TextForCausalLM")
+class Qwen3_5Model(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3_5
+
+    # Stores whichever of in_proj_a/in_proj_b is seen first, keyed by layer
+    _pending_ba: dict[int | None, tuple[str, Tensor]] = {}
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle split in_proj_b + in_proj_a → concatenated SSM_BETA_ALPHA
+        # safetensors sorts alphabetically so in_proj_a arrives before in_proj_b
+        if "in_proj_a.weight" in name or "in_proj_b.weight" in name:
+            which = "a" if "in_proj_a" in name else "b"
+            if bid not in self._pending_ba:
+                self._pending_ba[bid] = (which, data_torch)
+                return
+            prev_which, prev_tensor = self._pending_ba.pop(bid)
+            assert prev_which != which, f"duplicate in_proj_{which} for layer {bid}"
+            b_tensor = prev_tensor if prev_which == "b" else data_torch
+            a_tensor = prev_tensor if prev_which == "a" else data_torch
+            ba_combined = torch.cat([b_tensor, a_tensor], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.SSM_BETA_ALPHA, bid, ".weight"), ba_combined)
+            return
+        else:
+            # Qwen3Next uses .qkvz tensor, so we use the super to get the other functionalities
+            # (norm correction, A_log to A etc.) for free
+            # Qwen2Moe already does the gate_up conversion properly, just use that
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3_5MoeForCausalLM", "Qwen3_5MoeTextForCausalLM")
+class Qwen3_5MoeModel(Qwen3_5Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3_5_MOE
+
+
 @ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.RND1
@@ -7912,6 +7934,135 @@ class MimoV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("Step3p5ForCausalLM")
+class Step35Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.STEP35
+
+    def set_gguf_parameters(self):
+        rope_theta = self.hparams.get("rope_theta")
+        if isinstance(rope_theta, list):
+            self.hparams["rope_theta"] = float(rope_theta[0])
+            self.hparams["local_rope_theta"] = float(rope_theta[1])
+            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
+            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
+
+        super().set_gguf_parameters()
+
+        layer_types = self.hparams.get("layer_types") or []
+        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
+        attn_other = self.hparams.get("attention_other_setting") or {}
+
+        n_head_base = self.hparams["num_attention_heads"]
+        n_kv_base = self.hparams["num_attention_groups"]
+
+        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
+        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
+
+        layer_types = layer_types[: self.block_count]
+        partial_rotary_factors = partial_rotary_factors[: self.block_count]
+        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
+        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
+        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
+        swa_pat = [lt == "sliding_attention" for lt in layer_types]
+
+        self.gguf_writer.add_head_count(head_arr)
+        self.gguf_writer.add_head_count_kv(kv_arr)
+
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern(swa_pat)
+
+        self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+        # MoE params
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
+
+        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
+        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
+
+        # leading dense blocks
+        leading_dense = 0
+        moe_layers_enum = self.hparams.get("moe_layers_enum")
+        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
+            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
+            if moe_layers:
+                leading_dense = max(0, moe_layers[0])
+        self.gguf_writer.add_leading_dense_block_count(leading_dense)
+        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
+
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
+
+        # Optional per-layer SwiGLU clamps.
+        if (limits := self.hparams.get("swiglu_limits")) is not None:
+            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
+        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
+            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # remove mtp layers
+        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
+            il = int(m.group(1))
+            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
+            if il >= n_main:
+                return
+        if name.endswith("norm.weight"):
+            data_torch += 1.0
+        # Map router bias (expert selection bias) to a GGUF bias tensor
+        if name.endswith(".moe.router_bias"):
+            name += ".bias"
+
+        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
+            data_torch = data_torch.squeeze().contiguous()
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
+        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        rope_type = rope_params.get("rope_type") or ""
+        if rope_type.lower() != "llama3":
+            return
+
+        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
+        rope_theta = self.hparams.get("rope_theta", 10000.0)
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[0]
+        base = float(rope_theta)
+        if (dim := self.hparams.get("head_dim")) is None:
+            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        dim = int(dim)
+
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+        factor = float(rope_params.get("factor", 8.0))
+        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
+        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        rope_factors: list[float] = []
+        for freq in freqs:
+            wavelen = 2 * math.pi / float(freq)
+            if wavelen < high_freq_wavelen:
+                rope_factors.append(1.0)
+            elif wavelen > low_freq_wavelen:
+                rope_factors.append(factor)
+            else:
+                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
+
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
 @ModelBase.register("PanguEmbeddedForCausalLM")
 class PanguEmbeddedModel(TextModel):
     model_arch = gguf.MODEL_ARCH.PANGU_EMBED

docs/ops.md

@@ -22,7 +22,7 @@ Legend:
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |

docs/ops/SYCL.csv

@@ -77,8 +77,8 @@
 "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
 "SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
@@ -161,8 +161,8 @@
 "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
+"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
 "SYCL0","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"

ggml/src/ggml-cuda/rope.cu

@@ -43,10 +43,15 @@ static __device__ void rope_yarn(
 template <bool forward, bool has_ff, typename T, typename D>
 static __global__ void rope_norm(const T *            x,
                                  D *                  dst,
-                                 const int            ne0,
-                                 const int            ne1,
+                                 const int            ne00,
+                                 const int            ne01,
+                                 const int            ne02,
+                                 const int            s01,
+                                 const int            s02,
+                                 const int            s03,
                                  const int            s1,
                                  const int            s2,
+                                 const int            s3,
                                  const int            n_dims,
                                  const int32_t *      pos,
                                  const float          freq_scale,
@@ -59,23 +64,23 @@ static __global__ void rope_norm(const T *            x,
                                  const int            set_rows_stride) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
-
-    int       idst = row_dst * ne0 + i0;
-    const int ix   = channel_x*s2 + row_x*s1 + i0;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
+    int       idst = i0 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 + i1 * s01 + i2 * s02 + i3 * s03;
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
     if (set_rows_stride != 0) {
-        idst = row_x * ne0 + i0;
-        idst += row_indices[channel_x] * set_rows_stride;
+        idst = i1 * s1 + i0;
+        idst += row_indices[i2] * set_rows_stride;
     }
 
     const auto & store_coaelsced = [&](float x0, float x1) {
@@ -92,7 +97,7 @@ static __global__ void rope_norm(const T *            x,
         return;
     }
 
-    const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
@@ -110,10 +115,15 @@ static __global__ void rope_norm(const T *            x,
 template <bool forward, bool has_ff, typename T, typename D>
 static __global__ void rope_neox(const T *            x,
                                  D *                  dst,
-                                 const int            ne0,
-                                 const int            ne1,
+                                 const int            ne00,
+                                 const int            ne01,
+                                 const int            ne02,
+                                 const int            s01,
+                                 const int            s02,
+                                 const int            s03,
                                  const int            s1,
                                  const int            s2,
+                                 const int            s3,
                                  const int            n_dims,
                                  const int32_t *      pos,
                                  const float          freq_scale,
@@ -126,23 +136,24 @@ static __global__ void rope_neox(const T *            x,
                                  const int            set_rows_stride) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
-    int       idst = row_dst * ne0 + i0 / 2;
-    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+    int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
     if (set_rows_stride != 0) {
-        idst = row_x * ne0 + i0 / 2;
-        idst += row_indices[channel_x] * set_rows_stride;
+        idst = i1 * s1 + i0 / 2;
+        idst += row_indices[i2] * set_rows_stride;
     }
 
     if (i0 >= n_dims) {
@@ -152,7 +163,7 @@ static __global__ void rope_neox(const T *            x,
         return;
     }
 
-    const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
@@ -168,24 +179,42 @@ static __global__ void rope_neox(const T *            x,
     dst[idst + n_dims / 2] = ggml_cuda_cast<D>(x0 * sin_theta + x1 * cos_theta);
 }
 
-template<bool forward, bool has_ff, typename T>
-static __global__ void rope_multi(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2,
-        const int n_dims, const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors, const mrope_sections sections, const bool is_imrope) {
-    const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+template <bool forward, bool has_ff, typename T>
+static __global__ void rope_multi(const T *            x,
+                                  T *                  dst,
+                                  const int            ne00,
+                                  const int            ne01,
+                                  const int            ne02,
+                                  const int            s01,
+                                  const int            s02,
+                                  const int            s03,
+                                  const int            s1,
+                                  const int            s2,
+                                  const int            s3,
+                                  const int            n_dims,
+                                  const int32_t *      pos,
+                                  const float          freq_scale,
+                                  const float          ext_factor,
+                                  const float          attn_factor,
+                                  const rope_corr_dims corr_dims,
+                                  const float          theta_scale,
+                                  const float *        freq_factors,
+                                  const mrope_sections sections,
+                                  const bool           is_imrope) {
+    const int i0 = 2 * (blockDim.y * blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
-    const int idst = row_dst*ne0 + i0/2;
-    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+    int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
     if (i0 >= n_dims) {
         dst[idst + i0/2 + 0] = x[ix + i0/2 + 0];
@@ -200,27 +229,24 @@ static __global__ void rope_multi(
 
     float theta_base = 0.0;
     if (is_imrope) {
-        if (sector % 3 == 1 && sector < 3 * sections.v[1]) { // h
-            theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
-        } else if (sector % 3 == 2 && sector < 3 * sections.v[2]) { // w
-            theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
-        } else if (sector % 3 == 0 && sector < 3 * sections.v[0]) { // t
-            theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
+        if (sector % 3 == 1 && sector < 3 * sections.v[1]) {         // h
+            theta_base = pos[i2 + ne02 * 1] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector % 3 == 2 && sector < 3 * sections.v[2]) {  // w
+            theta_base = pos[i2 + ne02 * 2] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector % 3 == 0 && sector < 3 * sections.v[0]) {  // t
+            theta_base = pos[i2] * powf(theta_scale, i0 / 2.0f);
         } else {
-            theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
+            theta_base = pos[i2 + ne02 * 3] * powf(theta_scale, i0 / 2.0f);
         }
     } else {
         if (sector < sections.v[0]) {
-            theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
-        }
-        else if (sector >= sections.v[0] && sector < sec_w) {
-            theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
-        }
-        else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
-            theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
-        }
-        else if (sector >= sec_w + sections.v[2]) {
-            theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
+            theta_base = pos[i2] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector >= sections.v[0] && sector < sec_w) {
+            theta_base = pos[i2 + ne02 * 1] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
+            theta_base = pos[i2 + ne02 * 2] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector >= sec_w + sections.v[2]) {
+            theta_base = pos[i2 + ne02 * 3] * powf(theta_scale, i0 / 2.0f);
         }
     }
 
@@ -238,37 +264,53 @@ static __global__ void rope_multi(
     dst[idst + n_dims/2] = x0*sin_theta + x1*cos_theta;
 }
 
-template<bool forward, bool has_ff, typename T>
-static __global__ void rope_vision(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims,
-        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
-        const float theta_scale, const float * freq_factors, const mrope_sections sections) {
+template <bool forward, bool has_ff, typename T>
+static __global__ void rope_vision(const T *            x,
+                                   T *                  dst,
+                                   const int            ne00,
+                                   const int            ne01,
+                                   const int            ne02,
+                                   const int            s01,
+                                   const int            s02,
+                                   const int            s03,
+                                   const int            s1,
+                                   const int            s2,
+                                   const int            s3,
+                                   const int            n_dims,
+                                   const int32_t *      pos,
+                                   const float          freq_scale,
+                                   const float          ext_factor,
+                                   const float          attn_factor,
+                                   const rope_corr_dims corr_dims,
+                                   const float          theta_scale,
+                                   const float *        freq_factors,
+                                   const mrope_sections sections) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
-    const int idst = row_dst*ne0 + i0/2;
-    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+    int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
     const int sect_dims = sections.v[0] + sections.v[1];
-    const int sec_w = sections.v[1] + sections.v[0];
-    const int sector = (i0 / 2) % sect_dims;
+    const int sec_w     = sections.v[1] + sections.v[0];
+    const int sector    = (i0 / 2) % sect_dims;
 
     float theta_base = 0.0;
     if (sector < sections.v[0]) {
         const int p = sector;
-        theta_base = pos[channel_x]*powf(theta_scale, p);
-    }
-    else if (sector >= sections.v[0] && sector < sec_w) {
+        theta_base  = pos[i2] * powf(theta_scale, p);
+    } else if (sector >= sections.v[0] && sector < sec_w) {
         const int p = sector - sections.v[0];
-        theta_base = pos[channel_x + ne2]*powf(theta_scale, p);
+        theta_base  = pos[i2 + ne02] * powf(theta_scale, p);
     }
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
@@ -288,10 +330,15 @@ static __global__ void rope_vision(
 template <bool forward, typename T, typename D>
 static void rope_norm_cuda(const T *            x,
                            D *                  dst,
-                           const int            ne0,
-                           const int            ne1,
+                           const int            ne00,
+                           const int            ne01,
+                           const int            ne02,
+                           const int            s01,
+                           const int            s02,
+                           const int            s03,
                            const int            s1,
                            const int            s2,
+                           const int            s3,
                            const int            n_dims,
                            const int            nr,
                            const int32_t *      pos,
@@ -304,31 +351,36 @@ static void rope_norm_cuda(const T *            x,
                            const int64_t *      row_indices,
                            const int            set_rows_stride,
                            cudaStream_t         stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
         rope_norm<forward, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     } else {
         rope_norm<forward, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     }
 }
 
 template <bool forward, typename T, typename D>
 static void rope_neox_cuda(const T *            x,
                            D *                  dst,
-                           const int            ne0,
-                           const int            ne1,
+                           const int            ne00,
+                           const int            ne01,
+                           const int            ne02,
+                           const int            s01,
+                           const int            s02,
+                           const int            s03,
                            const int            s1,
                            const int            s2,
+                           const int            s3,
                            const int            n_dims,
                            const int            nr,
                            const int32_t *      pos,
@@ -341,55 +393,92 @@ static void rope_neox_cuda(const T *            x,
                            const int64_t *      row_indices,
                            const int            set_rows_stride,
                            cudaStream_t         stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
         rope_neox<forward, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     } else {
         rope_neox<forward, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     }
 }
 
-template<bool forward, typename T>
-static void rope_multi_cuda(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, const bool is_imrope, cudaStream_t stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+template <bool forward, typename T>
+static void rope_multi_cuda(const T *            x,
+                            T *                  dst,
+                            const int            ne00,
+                            const int            ne01,
+                            const int            ne02,
+                            const int            s01,
+                            const int            s02,
+                            const int            s03,
+                            const int            s1,
+                            const int            s2,
+                            const int            s3,
+                            const int            n_dims,
+                            const int            nr,
+                            const int32_t *      pos,
+                            const float          freq_scale,
+                            const float          freq_base,
+                            const float          ext_factor,
+                            const float          attn_factor,
+                            const rope_corr_dims corr_dims,
+                            const float *        freq_factors,
+                            const mrope_sections sections,
+                            const bool           is_imrope,
+                            cudaStream_t         stream) {
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
         rope_multi<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
     } else {
         rope_multi<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
     }
 }
 
-template<bool forward, typename T>
-static void rope_vision_cuda(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, cudaStream_t stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+template <bool forward, typename T>
+static void rope_vision_cuda(const T *            x,
+                             T *                  dst,
+                             const int            ne00,
+                             const int            ne01,
+                             const int            ne02,
+                             const int            s01,
+                             const int            s02,
+                             const int            s03,
+                             const int            s1,
+                             const int            s2,
+                             const int            s3,
+                             const int            n_dims,
+                             const int            nr,
+                             const int32_t *      pos,
+                             const float          freq_scale,
+                             const float          freq_base,
+                             const float          ext_factor,
+                             const float          attn_factor,
+                             const rope_corr_dims corr_dims,
+                             const float *        freq_factors,
+                             const mrope_sections sections,
+                             cudaStream_t         stream) {
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
     // break down (head_dim, heads, seq) into (CUDA_ROPE_BLOCK_SIZE, x, heads * seq)
     // where x ~= ceil(head_dim / CUDA_ROPE_BLOCK_SIZE);
@@ -398,11 +487,11 @@ static void rope_vision_cuda(
 
     if (freq_factors == nullptr) {
         rope_vision<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections);
     } else {
         rope_vision<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections);
     }
 }
@@ -445,6 +534,11 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
 
     const size_t s01 = src0->nb[1] / ggml_type_size(src0->type);
     const size_t s02 = src0->nb[2] / ggml_type_size(src0->type);
+    const size_t s03 = src0->nb[3] / ggml_type_size(src0->type);
+
+    const size_t s1 = dst->nb[1] / ggml_type_size(dst->type);
+    const size_t s2 = dst->nb[2] / ggml_type_size(dst->type);
+    const size_t s3 = dst->nb[3] / ggml_type_size(dst->type);
 
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
@@ -495,57 +589,63 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
     // compute
     if (is_neox) {
         if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
-            rope_neox_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                  nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                  freq_factors, row_indices, set_rows_stride, stream);
+            rope_neox_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                  s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                  ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                  set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
-            rope_neox_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                 nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                 freq_factors, row_indices, set_rows_stride, stream);
+            rope_neox_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                 s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                 ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                 set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
-            rope_neox_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
-                                                pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                freq_factors, row_indices, set_rows_stride, stream);
+            rope_neox_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                set_rows_stride, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else if (is_mrope && !is_vision) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_multi_cuda<forward>(
-                (const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, is_imrope, stream);
+            rope_multi_cuda<forward>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                     s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                     corr_dims, freq_factors, sections, is_imrope, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_multi_cuda<forward>(
-                (const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, is_imrope, stream);
+            rope_multi_cuda<forward>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                     s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                     corr_dims, freq_factors, sections, is_imrope, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else if (is_vision) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_vision_cuda<forward>(
-                (const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+            rope_vision_cuda<forward>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                      s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                      corr_dims, freq_factors, sections, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_vision_cuda<forward>(
-                (const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+            rope_vision_cuda<forward>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                      s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                      corr_dims, freq_factors, sections, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else {
         if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
-            rope_norm_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                  nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                  freq_factors, row_indices, set_rows_stride, stream);
+            rope_norm_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                  s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                  ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                  set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
-            rope_norm_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                 nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                 freq_factors, row_indices, set_rows_stride, stream);
+            rope_norm_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                 s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                 ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                 set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
-            rope_norm_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
-                                                pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                freq_factors, row_indices, set_rows_stride, stream);
+            rope_norm_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                set_rows_stride, stream);
         } else {
             GGML_ABORT("fatal error");
         }

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

@@ -394,7 +394,7 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con
         [encoder endEncoding];
 
         ggml_metal_event_t ev_cpy = ggml_metal_get_ev_cpy(ctx_src);
-        ggml_metal_event_record(ctx_src, ev_cpy);
+        ggml_metal_event_encode_signal(ev_cpy, cmd_buf);
 
         [cmd_buf commit];
 

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

@@ -1392,34 +1392,78 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_flash_attn_ext_v
     GGML_UNUSED(op);
 }
 
-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(
-        ggml_metal_library_t lib,
-        ggml_op op,
-        int32_t n_fuse,
-        bool row) {
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(ggml_metal_library_t lib, const ggml_tensor * op, int32_t n_fuse) {
     char base[256];
     char name[256];
 
-    const char * op_str = "undefined";
-    switch (op) {
-        case GGML_OP_ADD:   op_str = "add";   break;
-        case GGML_OP_SUB:   op_str = "sub";   break;
-        case GGML_OP_MUL:   op_str = "mul";   break;
-        case GGML_OP_DIV:   op_str = "div";   break;
+    int op_num = -1;
+
+    switch (op->op) {
+        case GGML_OP_ADD: op_num = 0; break;
+        case GGML_OP_SUB: op_num = 1; break;
+        case GGML_OP_MUL: op_num = 2; break;
+        case GGML_OP_DIV: op_num = 3; break;
         default: GGML_ABORT("fatal error");
     };
 
-    if (row) {
-        snprintf(base, 256, "kernel_%s_row_c4_fuse_%d", op_str, n_fuse);
-    } else {
-        snprintf(base, 256, "kernel_%s_fuse_%d", op_str, n_fuse);
-    }
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t1_str = ggml_type_name(op->src[1]->type);
+    const char * t_str  = ggml_type_name(op->type);
 
-    snprintf(name, 256, "%s", base);
+    const bool is_c4 = (op->src[0]->ne[0] % 4 == 0) && (op->src[1]->ne[0] % 4 == 0);
+
+    const bool is_rb = ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && (ggml_nrows(op->src[1]) == 1) && ggml_nelements(op) < 65536;
+
+    snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s%s", t0_str, t1_str, t_str, is_c4 ? "_4" : "");
+    snprintf(name, 256, "%s_op=%d_nf=%d_rb=%d", base, op_num, n_fuse, is_rb);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0);
+        ggml_metal_cv_set_int16(cv, n_fuse, FC_BIN + 1);
+        ggml_metal_cv_set_bool (cv, is_rb,  FC_BIN + 2);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
+    }
+
+    res.c4  = is_c4;
+    res.cnt = is_rb;
+
+    return res;
+}
+
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one(ggml_metal_library_t lib, ggml_op op) {
+    char base[256];
+    char name[256];
+
+    int op_num = -1;
+
+    switch (op) {
+        case GGML_OP_ADD: op_num = 0; break;
+        case GGML_OP_SUB: op_num = 1; break;
+        case GGML_OP_MUL: op_num = 2; break;
+        case GGML_OP_DIV: op_num = 3; break;
+        default: GGML_ABORT("fatal error");
+    };
+
+    snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s", "f32", "f32", "f32");
+    snprintf(name, 256, "%s_op=%d_nf=%d", base, op_num, 1);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0);
+        ggml_metal_cv_set_int16(cv, 1,      FC_BIN + 1);
+        ggml_metal_cv_set_bool (cv, false,  FC_BIN + 2);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
     return res;

ggml/src/ggml-metal/ggml-metal-device.h

@@ -53,6 +53,9 @@ struct ggml_metal_pipeline_with_params {
     int nr1;
 
     size_t smem;
+
+    bool c4;
+    bool cnt;
 };
 
 int ggml_metal_pipeline_max_theads_per_threadgroup(struct ggml_metal_pipeline_with_params pipeline);
@@ -134,7 +137,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge     (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k             (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge       (ggml_metal_library_t lib, const struct ggml_tensor * op);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse );
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one           (ggml_metal_library_t lib, enum ggml_op op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm           (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm        (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_norm              (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);

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

@@ -346,10 +346,12 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_meta
 
     struct ggml_metal_pipeline_with_params res = {
         /*.pipeline =*/ nil,
+        /*.nsg      =*/ 0,
         /*.nr0      =*/ 0,
         /*.nr1      =*/ 0,
-        /*.nsg      =*/ 0,
         /*.smem     =*/ 0,
+        /*.c4       =*/ false,
+        /*.cnt      =*/ false,
     };
 
     res.pipeline = ggml_metal_pipelines_get(lib->pipelines, name);
@@ -362,10 +364,12 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_meta
 struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_metal_library_t lib, const char * base, const char * name, ggml_metal_cv_t cv) {
     struct ggml_metal_pipeline_with_params res = {
         /*.pipeline =*/ nil,
+        /*.nsg      =*/ 0,
         /*.nr0      =*/ 0,
         /*.nr1      =*/ 0,
-        /*.nsg      =*/ 0,
         /*.smem     =*/ 0,
+        /*.c4       =*/ false,
+        /*.cnt      =*/ false,
     };
 
     [lib->lock lock];
@@ -1054,7 +1058,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_MUL:
         case GGML_OP_DIV:
         case GGML_OP_ADD_ID:
-            return op->src[0]->type == GGML_TYPE_F32;
+            return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ACC:
         case GGML_OP_REPEAT:
         case GGML_OP_SCALE:

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

@@ -80,6 +80,7 @@
 #define FC_SSM_CONV                    900
 #define FC_SOLVE_TRI                   1000
 #define FC_COUNT_EQUAL                 1100
+#define FC_BIN                         1200
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8

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

@@ -707,7 +707,7 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) {
         /*.o1   =*/ { 0 },
     };
 
-    auto pipeline = ggml_metal_library_get_pipeline_bin(lib, GGML_OP_ADD, 1, false);
+    auto pipeline = ggml_metal_library_get_pipeline_bin_one(lib, GGML_OP_ADD);
 
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
@@ -2895,8 +2895,6 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
     GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
     GGML_ASSERT(ggml_is_contiguous_rows(op->src[1]));
 
-    bool bcast_row = false;
-
     ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
     ggml_metal_buffer_id bid_src1 = ggml_metal_get_buffer_id(op->src[1]);
     ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
@@ -2990,18 +2988,7 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
 
     struct ggml_metal_pipeline_with_params pipeline;
 
-    if (ggml_nelements(op->src[1]) == ne10 && ggml_is_contiguous(op->src[1]) && ne00 % 4 == 0 && ne10 % 4 == 0) {
-        GGML_ASSERT(ggml_is_contiguous(op->src[0]));
-
-        // src1 is a row
-        GGML_ASSERT(ne11 == 1);
-
-        pipeline = ggml_metal_library_get_pipeline_bin(lib, op->op, n_fuse, true);
-
-        bcast_row = true;
-    } else {
-        pipeline = ggml_metal_library_get_pipeline_bin(lib, op->op, n_fuse, false);
-    }
+    pipeline = ggml_metal_library_get_pipeline_bin(lib, op, n_fuse);
 
     if (n_fuse > 1) {
         bid_dst = ggml_metal_get_buffer_id(ctx->node(idx + n_fuse - 1));
@@ -3015,20 +3002,28 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
         }
     }
 
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne10 = ne10/4;
+        args.ne0  = ne0/4;
+    }
+
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
     ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
     ggml_metal_encoder_set_buffer  (enc, bid_src1, 2);
     ggml_metal_encoder_set_buffer  (enc, bid_dst,  3);
 
-    if (bcast_row) {
-        const int64_t n = ggml_nelements(op)/4;
+    if (pipeline.cnt) {
+        const int n = pipeline.c4 ? ggml_nelements(op)/4 : ggml_nelements(op);
 
         ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
     } else {
-        int nth = 32;
+        const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
 
-        while (16*nth < ne0 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+        int nth = 1;
+
+        while (2*nth < args.ne0 && nth < nth_max) {
             nth *= 2;
         }
 

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

@@ -895,11 +895,13 @@ enum ggml_sort_order {
     GGML_SORT_ORDER_DESC,
 };
 
-// general-purpose kernel for addition, subtraction, multiplication and division of two tensors
-// pros: works for non-contiguous tensors, supports broadcast across all dims
-// cons: not very efficient
-template <int F>
-kernel void kernel_add_fuse_impl(
+// OP: 0 - add, 1 - sub, 2 - mul, 3 - div
+constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
+constant short FC_bin_f  [[function_constant(FC_BIN + 1)]];
+constant bool  FC_bin_rb [[function_constant(FC_BIN + 2)]];
+
+template <typename T0, typename T1, typename T>
+kernel void kernel_bin_fuse_impl(
         constant ggml_metal_kargs_bin & args,
         device const char * src0,
         device const char * src1,
@@ -907,138 +909,152 @@ kernel void kernel_add_fuse_impl(
         uint3   tgpig[[threadgroup_position_in_grid]],
         ushort3 tpitg[[thread_position_in_threadgroup]],
         ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
+#define FC_OP FC_bin_op
+#define FC_F  FC_bin_f
+#define FC_RB FC_bin_rb
 
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
+    if (FC_RB) {
+        // row broadcast
+        const uint i0 = tgpig.x;
+        const uint i1 = i0%args.ne10;
 
-    device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
-    device       float * dst_ptr  = (device       float *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs);
+        device const T0 * src0_row = (device const T0 *) (src0);
+        device       T  * dst_row  = (device       T  *) (dst);
 
-    device const float * src1_ptr[F];
-    for (short j = 0; j < F; ++j) {
-        src1_ptr[j] = (device const float *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
-    }
+        if (FC_F == 1) {
+            device const T1 * src1_row = (device const T1 *) (src1 + args.o1[0]);
 
-    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-        const int i10 = i0%args.ne10;
+            if (FC_OP == 0) {
+                dst_row[i0] = src0_row[i0] + src1_row[i1];
+            }
 
-        float res = src0_ptr[i0];
+            if (FC_OP == 1) {
+                dst_row[i0] = src0_row[i0] - src1_row[i1];
+            }
 
-#pragma unroll
-        for (short j = 0; j < F; ++j) {
-            res += src1_ptr[j][i10];
-        }
+            if (FC_OP == 2) {
+                dst_row[i0] = src0_row[i0] * src1_row[i1];
+            }
 
-        dst_ptr[i0] = res;
-    }
-}
+            if (FC_OP == 3) {
+                dst_row[i0] = src0_row[i0] / src1_row[i1];
+            }
+        } else {
+            T0 res = src0_row[i0];
 
-typedef decltype(kernel_add_fuse_impl<2>) kernel_add_fuse_t;
+            if (FC_OP == 0) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res += ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-template [[host_name("kernel_add_fuse_1")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<1>;
-template [[host_name("kernel_add_fuse_2")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<2>;
-template [[host_name("kernel_add_fuse_3")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<3>;
-template [[host_name("kernel_add_fuse_4")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<4>;
-template [[host_name("kernel_add_fuse_5")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<5>;
-template [[host_name("kernel_add_fuse_6")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<6>;
-template [[host_name("kernel_add_fuse_7")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<7>;
-template [[host_name("kernel_add_fuse_8")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<8>;
+            if (FC_OP == 1) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res -= ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-kernel void kernel_sub_fuse_1(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
+            if (FC_OP == 2) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res *= ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
+            if (FC_OP == 3) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res /= ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-    device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
-    device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
-    device       char * dst_ptr  = dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs;
-
-    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-        const int i10 = i0%args.ne10;
-        *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) - *((device float *)(src1_ptr + i10*args.nb10));
-    }
-}
-
-kernel void kernel_mul_fuse_1(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
-
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
-
-    device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
-    device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
-    device       char * dst_ptr  = dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs;
-
-    if (args.ne10 == 1) {
-        const float x = *((device float *)(src1_ptr));
-        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * x;
+            dst_row[i0] = res;
         }
     } else {
-        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-            const int i10 = i0%args.ne10;
-            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * *((device float *)(src1_ptr + i10*args.nb10));
+        const int i03 = tgpig.z;
+        const int i02 = tgpig.y;
+        const int i01 = tgpig.x;
+
+        if (i01 >= args.ne01) {
+            return;
+        }
+
+        const int i13 = i03%args.ne13;
+        const int i12 = i02%args.ne12;
+        const int i11 = i01%args.ne11;
+
+        device const T0 * src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
+        device       T  * dst_ptr  = (device       T  *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs);
+
+        if (FC_F == 1) {
+            device const T1 * src1_ptr = (device const T1 *) (src1 + args.o1[0] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
+
+            for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+                const int i10 = i0%args.ne10;
+
+                if (FC_OP == 0) {
+                    dst_ptr[i0] = src0_ptr[i0] + src1_ptr[i10];
+                }
+
+                if (FC_OP == 1) {
+                    dst_ptr[i0] = src0_ptr[i0] - src1_ptr[i10];
+                }
+
+                if (FC_OP == 2) {
+                    dst_ptr[i0] = src0_ptr[i0] * src1_ptr[i10];
+                }
+
+                if (FC_OP == 3) {
+                    dst_ptr[i0] = src0_ptr[i0] / src1_ptr[i10];
+                }
+            }
+        } else {
+            device const T1 * src1_ptr[8];
+            FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                src1_ptr[j] = (device const T1 *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
+            }
+
+            for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+                const int i10 = i0%args.ne10;
+
+                T res = src0_ptr[i0];
+
+                if (FC_OP == 0) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res += src1_ptr[j][i10];
+                    }
+                }
+
+                if (FC_OP == 1) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res -= src1_ptr[j][i10];
+                    }
+                }
+
+                if (FC_OP == 2) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res *= src1_ptr[j][i10];
+                    }
+                }
+
+                if (FC_OP == 3) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res /= src1_ptr[j][i10];
+                    }
+                }
+
+                dst_ptr[i0] = res;
+            }
         }
     }
+
+#undef FC_OP
+#undef FC_F
+#undef FC_RB
 }
 
-kernel void kernel_div_fuse_1(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
+typedef decltype(kernel_bin_fuse_impl<float, float, float>) kernel_bin_fuse_t;
 
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
-
-    device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
-    device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
-    device       char * dst_ptr  = dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs;
-
-    if (args.ne10 == 1) {
-        const float x = 1.0f / *((device float *)(src1_ptr));
-        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * x;
-        }
-    } else {
-        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-            const int i10 = i0%args.ne10;
-            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) / *((device float *)(src1_ptr + i10*args.nb10));
-        }
-    }
-}
+template [[host_name("kernel_bin_fuse_f32_f32_f32")]]   kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float,  float,  float>;
+template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float4, float4, float4>;
 
 kernel void kernel_add_id(
         constant ggml_metal_kargs_add_id & args,
@@ -1057,7 +1073,7 @@ kernel void kernel_add_id(
     const size_t nb1 = args.ne0 * sizeof(float);
     const size_t nb2 = args.ne1 * nb1;
 
-    device       float * dst_row  = (device       float *)((device char *)dst + i1*nb1 + i2*nb2);
+    device       float * dst_row  = (device       float *)((device char *)dst  +  i1*nb1       + i2*nb2);
     device const float * src0_row = (device const float *)((device char *)src0 +  i1*args.nb01 + i2*args.nb02);
     device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
 
@@ -1098,141 +1114,6 @@ template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat
 template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
 template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
 
-// assumption: src1 is a row
-// broadcast src1 into src0
-template <short F>
-kernel void kernel_add_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res += ((device const float4 *) (src1 + args.o1[j]))[i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_add_row_c4_fuse_impl<1>) kernel_add_row_c4_fuse_t;
-
-template [[host_name("kernel_add_row_c4_fuse_1")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<1>;
-template [[host_name("kernel_add_row_c4_fuse_2")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<2>;
-template [[host_name("kernel_add_row_c4_fuse_3")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<3>;
-template [[host_name("kernel_add_row_c4_fuse_4")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<4>;
-template [[host_name("kernel_add_row_c4_fuse_5")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<5>;
-template [[host_name("kernel_add_row_c4_fuse_6")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<6>;
-template [[host_name("kernel_add_row_c4_fuse_7")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<7>;
-template [[host_name("kernel_add_row_c4_fuse_8")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<8>;
-
-template <short F>
-kernel void kernel_sub_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    device const float4 * src1_row[F];
-    for (short j = 0; j < F; ++j) {
-        src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
-    }
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res -= src1_row[j][i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_sub_row_c4_fuse_impl<1>) kernel_sub_row_c4_fuse_t;
-
-template [[host_name("kernel_sub_row_c4_fuse_1")]] kernel kernel_sub_row_c4_fuse_t kernel_sub_row_c4_fuse_impl<1>;
-
-template <short F>
-kernel void kernel_mul_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    device const float4 * src1_row[F];
-    for (short j = 0; j < F; ++j) {
-        src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
-    }
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res *= src1_row[j][i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_mul_row_c4_fuse_impl<1>) kernel_mul_row_c4_fuse_t;
-
-template [[host_name("kernel_mul_row_c4_fuse_1")]] kernel kernel_mul_row_c4_fuse_t kernel_mul_row_c4_fuse_impl<1>;
-
-template <short F>
-kernel void kernel_div_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    device const float4 * src1_row[F];
-    for (short j = 0; j < F; ++j) {
-        src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
-    }
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res /= src1_row[j][i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_div_row_c4_fuse_impl<1>) kernel_div_row_c4_fuse_t;
-
-template [[host_name("kernel_div_row_c4_fuse_1")]] kernel kernel_div_row_c4_fuse_t kernel_div_row_c4_fuse_impl<1>;
-
 kernel void kernel_scale_f32(
         constant ggml_metal_kargs_scale & args,
         device const float * src0,

ggml/src/ggml-sycl/element_wise.cpp

@@ -836,16 +836,9 @@ static inline void ggml_sycl_op_floor(ggml_backend_sycl_context & ctx, ggml_tens
 }
 
 static inline void ggml_sycl_op_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
-        [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
-            const int num_blocks = ceil_div(k_elements, 256);
-            stream->parallel_for(
-                sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
-                                  sycl::range<1>(256)),
-                [=](sycl::nd_item<1> item_ct1) {
-                    unary_op_ceil_kernel(src, dst_ptr, k_elements, item_ct1);
-                });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary(ctx, dst, [](auto x) {
+        return op_ceil(x);
+    });
 }
 
 static inline void ggml_sycl_op_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {

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

@@ -4591,9 +4591,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_ELU:
+                case GGML_UNARY_OP_CEIL:
                     return true;
                 case GGML_UNARY_OP_FLOOR:
-                case GGML_UNARY_OP_CEIL:
                 case GGML_UNARY_OP_ROUND:
                 case GGML_UNARY_OP_TRUNC:
 #if defined (GGML_SYCL_F16)

ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

@@ -465,4 +465,73 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
     return result;
 }
 
+/** Binary **/
+
+struct ggml_webgpu_binary_pipeline_key {
+    int  type;
+    int  op;
+    bool inplace;
+    bool overlap;
+
+    bool operator==(const ggml_webgpu_binary_pipeline_key & other) const {
+        return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap;
+    }
+};
+
+struct ggml_webgpu_binary_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.type);
+        ggml_webgpu_hash_combine(seed, key.op);
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        ggml_webgpu_hash_combine(seed, key.overlap);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_binary_shader_lib_context {
+    ggml_webgpu_binary_pipeline_key key;
+    uint32_t                        max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
+    pre_wgsl::Preprocessor &                      preprocessor,
+    const char *                                  shader_src,
+    const ggml_webgpu_binary_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              op_name = ggml_op_name((ggml_op) context.key.op);
+    std::string              variant = op_name;
+
+    defines.push_back(std::string("OP_") + op_name);
+
+    switch (context.key.type) {
+        case GGML_TYPE_F32:
+            defines.push_back("TYPE_F32");
+            variant += "_f32";
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("TYPE_F16");
+            variant += "_f16";
+            break;
+        default:
+            GGML_ABORT("Unsupported type for binary shader");
+    }
+
+    if (context.key.inplace) {
+        defines.push_back("INPLACE");
+        variant += "_inplace";
+    } else if (context.key.overlap) {
+        defines.push_back("OVERLAP");
+        variant += "_overlap";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+    ggml_webgpu_processed_shader result;
+    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
+    result.variant                                   = variant;
+    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
+    decisions->wg_size                               = context.max_wg_size;
+    result.decisions                                 = decisions;
+    return result;
+}
 #endif  // GGML_WEBGPU_SHADER_LIB_HPP

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

@@ -348,13 +348,12 @@ struct webgpu_context_struct {
 
     std::unordered_map<ggml_webgpu_set_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_set_rows_pipeline_key_hash>
                                                   set_rows_pipelines;
-    std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines;                 // src_type, vectorized
+    std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines;  // src_type, vectorized
 
-    std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines;                      // src_type, dst_type
-    std::map<int, std::map<int, webgpu_pipeline>> add_pipelines;                      // type, inplace
-    std::map<int, std::map<int, webgpu_pipeline>> sub_pipelines;                      // type, inplace
-    std::map<int, std::map<int, webgpu_pipeline>> mul_pipelines;                      // type, inplace
-    std::map<int, std::map<int, webgpu_pipeline>> div_pipelines;                      // type, inplace
+    std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines;       // src_type, dst_type
+
+    std::unordered_map<ggml_webgpu_binary_pipeline_key, webgpu_pipeline, ggml_webgpu_binary_pipeline_key_hash>
+        binary_pipelines;
 
     std::map<int, webgpu_pipeline>                               rms_norm_pipelines;  // inplace
     std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> rope_pipelines;      // type, ff, inplace
@@ -823,6 +822,28 @@ static bool ggml_webgpu_tensor_equal(ggml_tensor * a, ggml_tensor * b) {
            (ggml_webgpu_tensor_offset(a) == ggml_webgpu_tensor_offset(b));
 }
 
+// Used to determine if two tensors share the same buffer and their byte ranges overlap,
+static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) {
+    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
+           ggml_webgpu_tensor_offset(a) < (ggml_webgpu_tensor_offset(b) + ggml_nbytes(b)) &&
+           ggml_webgpu_tensor_offset(b) < (ggml_webgpu_tensor_offset(a) + ggml_nbytes(a));
+}
+
+struct binary_overlap_flags {
+    bool inplace;  // src0 == dst
+    bool overlap;  // src1 == dst
+};
+
+static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0,
+                                                              ggml_tensor * src1,
+                                                              ggml_tensor * dst) {
+    binary_overlap_flags flags = {};
+    flags.inplace              = ggml_webgpu_tensor_equal(src0, dst);
+    flags.overlap              = ggml_webgpu_tensor_overlap(src1, dst);
+
+    return flags;
+}
+
 static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1375,14 +1396,42 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
-static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
-                                            ggml_tensor *     src0,
-                                            ggml_tensor *     src1,
-                                            ggml_tensor *     dst,
-                                            webgpu_pipeline & pipeline,
-                                            bool              inplace) {
+static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
+                                            ggml_tensor *    src0,
+                                            ggml_tensor *    src1,
+                                            ggml_tensor *    dst) {
+    binary_overlap_flags flags = ggml_webgpu_detect_binary_overlap(src0, src1, dst);
+
+    ggml_webgpu_binary_pipeline_key pipeline_key = {
+        .type    = dst->type,
+        .op      = dst->op,
+        .inplace = flags.inplace,
+        .overlap = flags.overlap,
+    };
+    ggml_webgpu_binary_shader_lib_context shader_lib_ctx = {
+        .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->binary_pipelines.find(pipeline_key);
+    if (it != ctx->binary_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_binary_shader(ctx->p, wgsl_binary, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->binary_pipelines.emplace(pipeline_key, pipeline);
+    }
+
+    ggml_webgpu_generic_shader_decisions decisions =
+        *static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
+
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
     std::vector<uint32_t> params = {
-        (uint32_t) ggml_nelements(dst),
+        ne,
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
@@ -1399,24 +1448,30 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
         (uint32_t) src1->ne[3],
     };
 
-    std::vector<wgpu::BindGroupEntry> entries = {
-        { .binding = 0,
-         .buffer  = ggml_webgpu_tensor_buf(src0),
-         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
-         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
-        { .binding = 1,
-         .buffer  = ggml_webgpu_tensor_buf(src1),
-         .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
-         .size    = ggml_webgpu_tensor_binding_size(ctx, src1) }
-    };
-    if (!inplace) {
+    std::vector<wgpu::BindGroupEntry> entries;
+
+    entries.push_back({
+        .binding = 0,
+        .buffer  = ggml_webgpu_tensor_buf(src0),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src0),
+    });
+
+    entries.push_back({
+        .binding = 1,
+        .buffer  = ggml_webgpu_tensor_buf(src1),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src1),
+    });
+
+    if (!flags.inplace && !flags.overlap) {
         entries.push_back({ .binding = 2,
                             .buffer  = ggml_webgpu_tensor_buf(dst),
                             .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
                             .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
     }
 
-    uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
+    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -2038,25 +2093,10 @@ static std::optional<webgpu_command> ggml_webgpu_encode_node(webgpu_context ctx,
             return std::nullopt;
 #endif
         case GGML_OP_ADD:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_SUB:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_MUL:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_DIV:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipelines[node->type][inplace], inplace);
-            }
+            return ggml_webgpu_binary_op(ctx, src0, src1, node);
         case GGML_OP_RMS_NORM:
             return ggml_webgpu_rms_norm(ctx, src0, node);
         case GGML_OP_ROPE:
@@ -2665,58 +2705,6 @@ static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
         ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants);
 }
 
-static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32, "add_f32", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16, "add_f16", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32, "sub_f32", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16, "sub_f16", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32, "mul_f32", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16, "mul_f16", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32, "div_f32", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16, "div_f16", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants);
-}
-
 static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) {
     std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE);
 
@@ -3018,10 +3006,6 @@ static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) {
     ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
     ggml_webgpu_init_get_rows_pipeline(webgpu_ctx);
     ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
-    ggml_webgpu_init_add_pipeline(webgpu_ctx);
-    ggml_webgpu_init_sub_pipeline(webgpu_ctx);
-    ggml_webgpu_init_mul_pipeline(webgpu_ctx);
-    ggml_webgpu_init_div_pipeline(webgpu_ctx);
     ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx);
     ggml_webgpu_init_rope_pipeline(webgpu_ctx);
     ggml_webgpu_init_glu_pipeline(webgpu_ctx);

ggml/src/ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl

@@ -1,188 +0,0 @@
-#define(VARIANTS)
-
-[
-  {
-    "SHADER_NAME": "add_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "+"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "+"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "+"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "+"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "*"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "*"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "*"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "*"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "-"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "-"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "-"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "-"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "/"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "/"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "/"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "/"
-    },
-    "DECLS": ["INPLACE"]
-  }
-]
-
-#end(VARIANTS)
-
-#define(DECLS)
-
-#decl(NOT_INPLACE)
-
-fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
-    dst[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
-}
-
-@group(0) @binding(2)
-var<storage, read_write> dst: array<{{TYPE}}>;
-
-@group(0) @binding(3)
-var<uniform> params: Params;
-
-#enddecl(NOT_INPLACE)
-
-#decl(INPLACE)
-
-fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
-    src0[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
-}
-
-@group(0) @binding(2)
-var<uniform> params: Params;
-
-#enddecl(INPLACE)
-
-#end(DECLS)
-
-
-#define(SHADER)
-
-enable f16;
-
-#include "binary_head.tmpl"
-
-@group(0) @binding(0)
-var<storage, read_write> src0: array<{{TYPE}}>;
-
-@group(0) @binding(1)
-var<storage, read_write> src1: array<{{TYPE}}>;
-
-DECLS
-
-override wg_size: u32;
-@compute @workgroup_size(wg_size)
-fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
-    if (gid.x < params.ne) {
-        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
-    }
-}
-
-#end(SHADER)

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

@@ -0,0 +1,107 @@
+enable f16;
+
+struct Params {
+    ne: u32,
+
+    // offsets in elements
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+
+    stride_src1_0: u32,
+    stride_src1_1: u32,
+    stride_src1_2: u32,
+    stride_src1_3: u32,
+
+    a_ne0: u32,
+    a_ne1: u32,
+    a_ne2: u32,
+
+    b_ne0: u32,
+    b_ne1: u32,
+    b_ne2: u32,
+    b_ne3: u32,
+};
+
+fn src1_index(_i: u32) -> u32 {
+    var i = _i;
+    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    let a_i2 = i / (params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne1 * params.a_ne0);
+    let a_i1 = i / params.a_ne0;
+    let a_i0 = i % params.a_ne0;
+
+    // handle repetition of b
+    // index loops back to the beginning and repeats after elements are exhausted = modulo
+    let b_i0 = a_i0 % params.b_ne0;
+    let b_i1 = a_i1 % params.b_ne1;
+    let b_i2 = a_i2 % params.b_ne2;
+    let b_i3 = a_i3 % params.b_ne3;
+
+    // compute index for position in b's flat array
+    return b_i0 * params.stride_src1_0 +
+           b_i1 * params.stride_src1_1 +
+           b_i2 * params.stride_src1_2 +
+           b_i3 * params.stride_src1_3;
+}
+
+#ifdef TYPE_F32
+#define DataType f32
+#endif
+#ifdef TYPE_F16
+#define DataType f16
+#endif
+
+@group(0) @binding(0)
+var<storage, read_write> src0: array<DataType>;
+
+@group(0) @binding(1)
+var<storage, read_write> src1 : array<DataType>;
+
+#ifdef INPLACE
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#elif defined(OVERLAP)
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#else
+@group(0) @binding(2)
+var<storage, read_write> dst: array<DataType>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+#endif
+
+fn op(a: DataType, b: DataType) -> DataType {
+#ifdef OP_ADD
+    return a + b;
+#elif defined(OP_SUB)
+    return a - b;
+#elif defined(OP_MUL)
+    return a * b;
+#elif defined(OP_DIV)
+    return a / b;
+#endif
+}
+
+fn update(dst_i: u32, src0_i: u32, src1_i: u32){
+    let result = op(src0[src0_i], src1[src1_i]);
+
+#ifdef INPLACE
+    src0[dst_i] = result;
+#elif defined(OVERLAP)
+    src1[dst_i] = result;
+#else
+    dst[dst_i] = result;
+#endif
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x < params.ne) {
+        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
+    }
+}

ggml/src/ggml-webgpu/wgsl-shaders/binary_head.tmpl

@@ -1,45 +0,0 @@
-struct Params {
-    ne: u32,
-
-    // offsets in elements
-    offset_src0: u32,
-    offset_src1: u32,
-    offset_dst: u32,
-
-    stride_src1_0: u32,
-    stride_src1_1: u32,
-    stride_src1_2: u32,
-    stride_src1_3: u32,
-
-    a_ne0: u32,
-    a_ne1: u32,
-    a_ne2: u32,
-
-    b_ne0: u32,
-    b_ne1: u32,
-    b_ne2: u32,
-    b_ne3: u32,
-};
-
-fn src1_index(_i: u32) -> u32 {
-    var i = _i;
-    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
-    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
-    let a_i2 = i / (params.a_ne1 * params.a_ne0);
-    i = i % (params.a_ne1 * params.a_ne0);
-    let a_i1 = i / params.a_ne0;
-    let a_i0 = i % params.a_ne0;
-
-    // handle repetition of b
-    // index loops back to the beginning and repeats after elements are exhausted = modulo
-    let b_i0 = a_i0 % params.b_ne0;
-    let b_i1 = a_i1 % params.b_ne1;
-    let b_i2 = a_i2 % params.b_ne2;
-    let b_i3 = a_i3 % params.b_ne3;
-
-    // compute index for position in b's flat array
-    return b_i0 * params.stride_src1_0 +
-           b_i1 * params.stride_src1_1 +
-           b_i2 * params.stride_src1_2 +
-           b_i3 * params.stride_src1_3;
-}

gguf-py/gguf/constants.py

@@ -146,6 +146,8 @@ class Keys:
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
         EMBD_LENGTH_PER_LAYER_INP         = "{arch}.embedding_length_per_layer_input"
+        SWIGLU_CLAMP_EXP                  = "{arch}.swiglu_clamp_exp"
+        SWIGLU_CLAMP_SHEXP                = "{arch}.swiglu_clamp_shexp"
         DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
 
@@ -179,20 +181,20 @@ class Keys:
         TEMPERATURE_SCALE            = "{arch}.attention.temperature_scale"
 
     class Rope:
-        DIMENSION_COUNT          = "{arch}.rope.dimension_count"
-        DIMENSION_SECTIONS       = "{arch}.rope.dimension_sections"
-        FREQ_BASE                = "{arch}.rope.freq_base"
-        FREQ_BASE_SWA            = "{arch}.rope.freq_base_swa"
-        SCALING_TYPE             = "{arch}.rope.scaling.type"
-        SCALING_FACTOR           = "{arch}.rope.scaling.factor"
-        SCALING_ATTN_FACTOR      = "{arch}.rope.scaling.attn_factor"
-        SCALING_ORIG_CTX_LEN     = "{arch}.rope.scaling.original_context_length"
-        SCALING_FINETUNED        = "{arch}.rope.scaling.finetuned"
-        SCALING_YARN_LOG_MUL     = "{arch}.rope.scaling.yarn_log_multiplier"
-        SCALING_YARN_EXT_FACTOR  = "{arch}.rope.scaling.yarn_ext_factor"
-        SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor"
-        SCALING_YARN_BETA_FAST   = "{arch}.rope.scaling.yarn_beta_fast"
-        SCALING_YARN_BETA_SLOW   = "{arch}.rope.scaling.yarn_beta_slow"
+        DIMENSION_COUNT           = "{arch}.rope.dimension_count"
+        DIMENSION_SECTIONS        = "{arch}.rope.dimension_sections"
+        FREQ_BASE                 = "{arch}.rope.freq_base"
+        FREQ_BASE_SWA             = "{arch}.rope.freq_base_swa"
+        SCALING_TYPE              = "{arch}.rope.scaling.type"
+        SCALING_FACTOR            = "{arch}.rope.scaling.factor"
+        SCALING_ATTN_FACTOR       = "{arch}.rope.scaling.attn_factor"
+        SCALING_ORIG_CTX_LEN      = "{arch}.rope.scaling.original_context_length"
+        SCALING_FINETUNED         = "{arch}.rope.scaling.finetuned"
+        SCALING_YARN_LOG_MUL      = "{arch}.rope.scaling.yarn_log_multiplier"
+        SCALING_YARN_EXT_FACTOR   = "{arch}.rope.scaling.yarn_ext_factor"
+        SCALING_YARN_ATTN_FACTOR  = "{arch}.rope.scaling.yarn_attn_factor"
+        SCALING_YARN_BETA_FAST    = "{arch}.rope.scaling.yarn_beta_fast"
+        SCALING_YARN_BETA_SLOW    = "{arch}.rope.scaling.yarn_beta_slow"
 
     class Split:
         LLM_KV_SPLIT_NO            = "split.no"
@@ -380,6 +382,8 @@ class MODEL_ARCH(IntEnum):
     QWEN3            = auto()
     QWEN3MOE         = auto()
     QWEN3NEXT        = auto()
+    QWEN3_5          = auto()
+    QWEN3_5_MOE      = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
     PHI2             = auto()
@@ -462,6 +466,7 @@ class MODEL_ARCH(IntEnum):
     PANGU_EMBED      = auto()
     MISTRAL3         = auto()
     MIMO2            = auto()
+    STEP35           = auto()
     LLAMA_EMBED      = auto()
     MAINCODER        = auto()
     KIMI_LINEAR      = auto()
@@ -809,6 +814,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN3:            "qwen3",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
+    MODEL_ARCH.QWEN3_5:          "qwen3_5",
+    MODEL_ARCH.QWEN3_5_MOE:      "qwen3_5moe",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
     MODEL_ARCH.PHI2:             "phi2",
@@ -892,6 +899,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
     MODEL_ARCH.MISTRAL3:         "mistral3",
     MODEL_ARCH.MIMO2:            "mimo2",
+    MODEL_ARCH.STEP35:           "step35",
     MODEL_ARCH.LLAMA_EMBED:      "llama-embed",
     MODEL_ARCH.MAINCODER:        "maincoder",
     MODEL_ARCH.KIMI_LINEAR:      "kimi-linear",
@@ -1780,6 +1788,61 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.SSM_BETA_ALPHA,
         MODEL_TENSOR.SSM_OUT
     ],
+    MODEL_ARCH.QWEN3_5: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT,
+    ],
+    MODEL_ARCH.QWEN3_5_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT,
+    ],
     MODEL_ARCH.QWEN3VL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3364,6 +3427,32 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_EXP_PROBS_B,
     ],
+    MODEL_ARCH.STEP35: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        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_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
     MODEL_ARCH.LLAMA_EMBED: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3753,12 +3842,12 @@ KEY_ATTENTION_LAYERNORM_EPS     = Keys.Attention.LAYERNORM_EPS
 KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
 
 # RoPE
-KEY_ROPE_DIMENSION_COUNT      = Keys.Rope.DIMENSION_COUNT
-KEY_ROPE_FREQ_BASE            = Keys.Rope.FREQ_BASE
-KEY_ROPE_SCALING_TYPE         = Keys.Rope.SCALING_TYPE
-KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
-KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
-KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED
+KEY_ROPE_DIMENSION_COUNT           = Keys.Rope.DIMENSION_COUNT
+KEY_ROPE_FREQ_BASE                 = Keys.Rope.FREQ_BASE
+KEY_ROPE_SCALING_TYPE              = Keys.Rope.SCALING_TYPE
+KEY_ROPE_SCALING_FACTOR            = Keys.Rope.SCALING_FACTOR
+KEY_ROPE_SCALING_ORIG_CTX_LEN      = Keys.Rope.SCALING_ORIG_CTX_LEN
+KEY_ROPE_SCALING_FINETUNED         = Keys.Rope.SCALING_FINETUNED
 
 # SSM
 KEY_SSM_CONV_KERNEL    = Keys.SSM.CONV_KERNEL

gguf-py/gguf/gguf_writer.py

@@ -824,6 +824,12 @@ class GGUFWriter:
     def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
         self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
 
+    def add_swiglu_clamp_exp(self, values: Sequence[float]) -> None:
+        self.add_array(Keys.LLM.SWIGLU_CLAMP_EXP.format(arch=self.arch), values)
+
+    def add_swiglu_clamp_shexp(self, values: Sequence[float]) -> None:
+        self.add_array(Keys.LLM.SWIGLU_CLAMP_SHEXP.format(arch=self.arch), values)
+
     def add_expert_group_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -228,6 +228,7 @@ class TensorNameMap:
             "transformer_encoder.{bid}.qkv",                                       # neobert
             "layers.{bid}.attn.Wqkv",                                              # modern-bert
             "model.layers.{bid}.self_attn.language_expert_query_key_value",        # cogvlm
+            "model.layers.{bid}.linear_attn.in_proj_qkv",                          # qwen3.5
         ),
 
         # Attention query
@@ -358,7 +359,9 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_GATE: (
-            "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.self_attn.gate_proj",   # afmoe
+            "model.layers.{bid}.self_attn.g_proj",      # step3.5 head-wise attention gate
+            "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
         ),
 
         # Feed-forward norm
@@ -423,6 +426,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.router.gate",               # afmoe
             "layers.{bid}.gate",                                # mistral-large
             "backbone.layers.{bid}.mixer.gate",                 # nemotron-h-moe
+            "model.layers.{bid}.moe.gate",                      # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -439,6 +443,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.mixer.gate.e_score_correction",          # nemotron-h-moe
             "model.layers.{bid}.mlp.e_score_correction",                    # exaone-moe
             "model.layers.{bid}.block_sparse_moe.gate.e_score_correction",  # kimi
+            "model.layers.{bid}.moe.router_bias",                           # step3.5 expert selection bias
         ),
 
         # Feed-forward up
@@ -493,6 +498,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.experts.up_proj",      # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w1",              # nomic-bert-moe
             "model.layers.{bid}.block_sparse_moe.experts.up", # smallthinker
+            "model.layers.{bid}.moe.up_proj",                       # step3.5
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
@@ -504,6 +510,7 @@ class TensorNameMap:
             "layers.{bid}.shared_experts.w3",                        # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.up_proj",    # nemotron-h-moe
             "model.layers.{bid}.block_sparse_moe.shared_experts.up_proj", # kimi
+            "model.layers.{bid}.share_expert.up_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_UP_CHEXP: (
@@ -543,6 +550,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.experts.w1",           # phimoe (merged)
             "model.layers.{bid}.feed_forward.experts.gate_proj",        # llama4
             "model.layers.{bid}.block_sparse_moe.experts.gate",         # smallthinker
+            "model.layers.{bid}.moe.gate_proj",                         # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
@@ -552,6 +560,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.gate_proj",             # hunyuan
             "layers.{bid}.shared_experts.w1",                          # mistral-large
             "model.layers.{bid}.block_sparse_moe.shared_experts.gate_proj", # kimi
+            "model.layers.{bid}.share_expert.gate_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_CHEXP: (
@@ -606,6 +615,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.experts.down_proj",    # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w2",              # nomic-bert-moe
             "model.layers.{bid}.block_sparse_moe.experts.down",     # smallthinker
+            "model.layers.{bid}.moe.down_proj",                     # step3.5
         ),
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
@@ -617,6 +627,7 @@ class TensorNameMap:
             "layers.{bid}.shared_experts.w2",                          # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.down_proj",    # nemotron-h-moe
             "model.layers.{bid}.block_sparse_moe.shared_experts.down_proj", # kimi
+            "model.layers.{bid}.share_expert.down_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_DOWN_CHEXP: (

gguf-py/pyproject.toml

@@ -23,7 +23,7 @@ numpy = ">=1.17"
 tqdm = ">=4.27"
 pyyaml = ">=5.1"
 requests = ">=2.25"
-sentencepiece = { version = ">=0.1.98,<=0.2.0", optional = true }
+sentencepiece = { version = ">=0.1.98,<0.3.0", optional = true }
 PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true }
 
 [tool.poetry.dev-dependencies]

pyproject.toml

@@ -17,7 +17,7 @@ classifiers = [
 [tool.poetry.dependencies]
 python = ">=3.9"
 numpy = "^1.25.0"
-sentencepiece = ">=0.1.98,<=0.2.0"
+sentencepiece = ">=0.1.98,<0.3.0"
 transformers = ">=4.35.2,<5.0.0"
 protobuf = ">=4.21.0,<5.0.0"
 gguf = { path = "./gguf-py" }

requirements/requirements-convert_legacy_llama.txt

@@ -1,5 +1,5 @@
 numpy~=1.26.4
-sentencepiece~=0.2.0
+sentencepiece>=0.1.98,<0.3.0
 
 transformers>=4.57.1,<5.0.0
 

src/CMakeLists.txt

@@ -57,6 +57,7 @@ add_library(llama
             models/deci.cpp
             models/deepseek.cpp
             models/deepseek2.cpp
+            models/delta.cpp
             models/dots1.cpp
             models/dream.cpp
             models/ernie4-5-moe.cpp
@@ -122,6 +123,8 @@ add_library(llama
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
             models/qwen3next.cpp
+            models/qwen3-5.cpp
+            models/qwen3-5moe.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp
@@ -135,6 +138,7 @@ add_library(llama
             models/stablelm.cpp
             models/starcoder.cpp
             models/starcoder2.cpp
+            models/step35-iswa.cpp
             models/t5-dec.cpp
             models/t5-enc.cpp
             models/wavtokenizer-dec.cpp

src/llama-arch.cpp

@@ -35,6 +35,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN3,            "qwen3"            },
     { LLM_ARCH_QWEN3MOE,         "qwen3moe"         },
     { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
+    { LLM_ARCH_QWEN3_5,          "qwen3_5"          },
+    { LLM_ARCH_QWEN3_5_MOE,      "qwen3_5moe"       },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
     { LLM_ARCH_PHI2,             "phi2"             },
@@ -117,7 +119,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_RND1,             "rnd1"             },
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
-    { LLM_ARCH_MIMO2,            "mimo2"           },
+    { LLM_ARCH_MIMO2,            "mimo2"            },
+    { LLM_ARCH_STEP35,           "step35"           },
     { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
     { LLM_ARCH_MAINCODER,        "maincoder"        },
     { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },
@@ -162,6 +165,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
     { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
     { LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  "%s.expert_chunk_feed_forward_length"  },
+    { LLM_KV_SWIGLU_CLAMP_EXP,                  "%s.swiglu_clamp_exp"                  },
+    { LLM_KV_SWIGLU_CLAMP_SHEXP,                "%s.swiglu_clamp_shexp"                },
     { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
     { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
     { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
@@ -220,21 +225,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
 
-    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
-    { LLM_KV_ROPE_DIMENSION_SECTIONS,       "%s.rope.dimension_sections"              },
-    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
-    { LLM_KV_ROPE_FREQ_BASE_SWA,            "%s.rope.freq_base_swa"                   },
-    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
-    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
-    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
-    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
-    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
-    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
-    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
-    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,  "%s.rope.scaling.yarn_ext_factor"         },
-    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor"        },
-    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   "%s.rope.scaling.yarn_beta_fast"          },
-    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   "%s.rope.scaling.yarn_beta_slow"          },
+    { LLM_KV_ROPE_DIMENSION_COUNT,           "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_DIMENSION_SECTIONS,        "%s.rope.dimension_sections"              },
+    { LLM_KV_ROPE_FREQ_BASE,                 "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_FREQ_BASE_SWA,             "%s.rope.freq_base_swa"                   },
+    { LLM_KV_ROPE_SCALE_LINEAR,              "%s.rope.scale_linear"                    },
+    { LLM_KV_ROPE_SCALING_TYPE,              "%s.rope.scaling.type"                    },
+    { LLM_KV_ROPE_SCALING_FACTOR,            "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,       "%s.rope.scaling.attn_factor"             },
+    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,      "%s.rope.scaling.original_context_length" },
+    { LLM_KV_ROPE_SCALING_FINETUNED,         "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,      "%s.rope.scaling.yarn_log_multiplier"     },
+    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,   "%s.rope.scaling.yarn_ext_factor"         },
+    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR,  "%s.rope.scaling.yarn_attn_factor"        },
+    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,    "%s.rope.scaling.yarn_beta_fast"          },
+    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,    "%s.rope.scaling.yarn_beta_slow"          },
 
     { LLM_KV_SPLIT_NO,            "split.no"            },
     { LLM_KV_SPLIT_COUNT,         "split.count"         },
@@ -982,6 +987,63 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
+        case LLM_ARCH_QWEN3_5:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_QWEN3_5_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_HUNYUAN_DENSE:
@@ -2279,6 +2341,35 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_FFN_UP_EXPS,
                 LLM_TENSOR_FFN_EXP_PROBS_B,
             };
+        case LLM_ARCH_STEP35:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
         case LLM_ARCH_GPTJ:
         case LLM_ARCH_UNKNOWN:
             return {
@@ -2642,6 +2733,8 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_KIMI_LINEAR:
             return true;
         default:

src/llama-arch.h

@@ -39,6 +39,8 @@ enum llm_arch {
     LLM_ARCH_QWEN3,
     LLM_ARCH_QWEN3MOE,
     LLM_ARCH_QWEN3NEXT,
+    LLM_ARCH_QWEN3_5,
+    LLM_ARCH_QWEN3_5_MOE,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
     LLM_ARCH_PHI2,
@@ -122,6 +124,7 @@ enum llm_arch {
     LLM_ARCH_PANGU_EMBED,
     LLM_ARCH_MISTRAL3,
     LLM_ARCH_MIMO2,
+    LLM_ARCH_STEP35,
     LLM_ARCH_LLAMA_EMBED,
     LLM_ARCH_MAINCODER,
     LLM_ARCH_KIMI_LINEAR,
@@ -166,6 +169,8 @@ enum llm_kv {
     LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,
+    LLM_KV_SWIGLU_CLAMP_EXP,
+    LLM_KV_SWIGLU_CLAMP_SHEXP,
     LLM_KV_USE_PARALLEL_RESIDUAL,
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,

src/llama-context.cpp

@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
         return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());

src/llama-graph.cpp

@@ -13,6 +13,8 @@
 #include <cassert>
 #include <cmath>
 #include <cstring>
+#include <numeric>
+#include <sstream>
 #include <unordered_set>
 
 void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
@@ -1014,6 +1016,26 @@ ggml_tensor * llm_graph_context::build_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate && type_gate == LLM_FFN_PAR) {
+                // Step35: HF clamps gate (after SiLU) and up before multiplication
+                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                    const float limit = hparams.swiglu_clamp_shexp[il];
+                    constexpr float eps = 1e-6f;
+                    if (limit > eps) {
+                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                        cb(gate_act, "ffn_silu", il);
+                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                        cb(gate_act, "ffn_silu_clamped", il);
+
+                        tmp = ggml_clamp(ctx0, tmp, -limit, limit);
+                        cb(tmp, "ffn_up_clamped", il);
+
+                        cur = ggml_mul(ctx0, gate_act, tmp);
+                        cb(cur, "ffn_swiglu_limited", il);
+                        type_gate = LLM_FFN_SEQ;
+                        break;
+                    }
+                }
+
                 cur = ggml_swiglu_split(ctx0, cur, tmp);
                 cb(cur, "ffn_swiglu", il);
                 type_gate = LLM_FFN_SEQ;
@@ -1316,6 +1338,25 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate_exps) {
+                // Step35: per-layer clamp for routed experts
+                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                    const float limit = hparams.swiglu_clamp_exp[il];
+                    constexpr float eps = 1e-6f;
+                    if (limit > eps) {
+                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                        cb(gate_act, "ffn_moe_silu", il);
+                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                        cb(gate_act, "ffn_moe_silu_clamped", il);
+
+                        up = ggml_clamp(ctx0, up, -limit, limit);
+                        cb(up, "ffn_moe_up_clamped", il);
+
+                        cur = ggml_mul(ctx0, gate_act, up);
+                        cb(cur, "ffn_moe_swiglu_limited", il);
+                        break;
+                    }
+                }
+
                 cur = ggml_swiglu_split(ctx0, cur, up);
                 cb(cur, "ffn_moe_swiglu", il);
             } else {

src/llama-hparams.h

@@ -206,6 +206,11 @@ struct llama_hparams {
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
     enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
+
+    // Step35: optional per-layer clamps for (Swi)GLU
+    std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_exp; // clamping for expert FFN
+    std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_shexp; // shared expert
+
     // this value n_pattern means that every nth layer is dense (i.e. non-SWA)
     // dense_first means whether the pattern is start with a dense layer
     // note that if n_pattern == 0, all layers are SWA

src/llama-kv-cache-iswa.cpp

@@ -218,7 +218,9 @@ llama_memory_context_ptr llama_kv_cache_iswa::init_update(llama_context * lctx,
 }
 
 bool llama_kv_cache_iswa::get_can_shift() const {
-    return kv_base->get_size() == kv_swa->get_size();
+    return kv_base->get_can_shift() &&
+           kv_swa->get_can_shift() &&
+           kv_base->get_size() == kv_swa->get_size();
 }
 
 void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {

src/llama-kv-cache.cpp

@@ -974,6 +974,10 @@ void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch &
 }
 
 bool llama_kv_cache::get_can_shift() const {
+    // Step35 uses per-layer RoPE dims; K-shift assumes a single global n_rot.
+    if (model.arch == LLM_ARCH_STEP35) {
+        return false;
+    }
     return true;
 }
 

src/llama-model.cpp

@@ -130,6 +130,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
         case LLM_TYPE_102B_A12B:     return "102B.A12B";
         case LLM_TYPE_106B_A12B:     return "106B.A12B";
+        case LLM_TYPE_196B_A11B:     return "196B.A11B";
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
         case LLM_TYPE_235B_A22B:     return "235B.A22B";
         case LLM_TYPE_300B_A47B:     return "300B.A47B";
@@ -560,6 +561,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
     std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
     std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
+    std::fill(hparams.swiglu_clamp_exp.begin(),   hparams.swiglu_clamp_exp.end(),   0.0f);
+    std::fill(hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.end(), 0.0f);
 
     ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
     ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
@@ -2409,6 +2412,25 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+                // Load linear attention (gated delta net) parameters
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+                // Mark recurrent layers (linear attention layers)
+                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
+                }
+            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -2482,6 +2504,35 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_STEP35:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+                // MoE + SWA parameters
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+
+                // Step35 uses sigmoid gating by default (if not set in GGUF)
+                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+                }
+
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
+                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa);
+                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer, false);
+                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
+
+                switch (hparams.n_layer) {
+                    case 45: type = LLM_TYPE_196B_A11B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -7062,6 +7113,129 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
 
+                        // Shared experts
+                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
+                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
+                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
+                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN3_5:
+                {
+                    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 == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+                    }
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    const int64_t ba_dim = n_v_heads * 2;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Full attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        // Dense FFN for all layers
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN3_5_MOE:
+                {
+                    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 == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+                    }
+
+                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    const int64_t ba_dim = n_v_heads * 2;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Full attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        // MoE FFN
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+
                         // Shared experts
                         layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
                         layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
@@ -7107,6 +7281,72 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
                     }
                 } break;
+            case LLM_ARCH_STEP35:
+                {
+                    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}, 0);
+
+                    // STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
+                    // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
+                    uint32_t n_rot_max = 0;
+                    for (int i = 0; i < n_layer; ++i) {
+                        n_rot_max = std::max(n_rot_max, hparams.n_rot);
+                    }
+                    if (n_rot_max == 0) {
+                        n_rot_max = n_rot;
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        const uint32_t n_head_l      = hparams.n_head(i);
+                        const uint32_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+                        const uint32_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+
+                        // optional rope factors (llama3) / longrope tensors
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        } else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head_l}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
+
+                        // head-wise attention gate (Step35 self_attn.g_proj)
+                        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        // dense MLP (leading dense blocks)
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+
+                        // MoE routed experts + selection bias (router_bias)
+                        const int64_t n_ff_exp = hparams.n_ff_exp;
+                        layer.ffn_gate_inp      = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_gate_exps     = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_exps       = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_exp_probs_b   = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+                        // shared expert MLP
+                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
             case LLM_ARCH_MAINCODER:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -7447,6 +7687,8 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
         arch == LLM_ARCH_QWEN3NEXT ||
+        arch == LLM_ARCH_QWEN3_5 ||
+        arch == LLM_ARCH_QWEN3_5_MOE ||
         arch == LLM_ARCH_NEMOTRON_H ||
         arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
@@ -8245,6 +8487,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_qwen3next>(*this, params);
             } break;
+        case LLM_ARCH_QWEN3_5:
+            {
+                llm = std::make_unique<llm_build_qwen3_5>(*this, params);
+            } break;
+        case LLM_ARCH_QWEN3_5_MOE:
+            {
+                llm = std::make_unique<llm_build_qwen3_5_moe>(*this, params);
+            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 llm = std::make_unique<llm_build_mistral3>(*this, params);
@@ -8257,6 +8507,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_kimi_linear>(*this, params);
             } break;
+        case LLM_ARCH_STEP35:
+            {
+                llm = std::make_unique<llm_build_step35_iswa>(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -8501,7 +8755,10 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_PANGU_EMBED:
         case LLM_ARCH_AFMOE:
         case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_MIMO2:
+        case LLM_ARCH_STEP35:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:

src/llama-model.h

@@ -123,6 +123,7 @@ enum llm_type {
     LLM_TYPE_100B_A6B,
     LLM_TYPE_102B_A12B, // Solar-Open
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
+    LLM_TYPE_196B_A11B, // Step3.5-Flash
     LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_235B_A22B,
     LLM_TYPE_300B_A47B, // Ernie MoE big

src/models/delta.cpp

@@ -0,0 +1,618 @@
+#include "models.h"
+#include "ggml.h"
+#include <cmath>
+#include <utility>
+#include <cassert>
+
+llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
+
+/**
+ * Unified Delta Net implementation supporting both GDA and KDA modes.
+ *
+ * GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
+ *   - Per-head gating, broadcasts over K dimension
+ *
+ * KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
+ *   - Per-key gating
+ *
+ * The mode is auto-detected based on g's dimensionality.
+ *
+ * Tensor dimension convention:
+ *   GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
+ *   PyTorch: dim 0 is outermost, dim -1 is innermost
+ *   So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
+ */
+
+// Helper to get a slice along dimension 2 (n_chunks dimension)
+static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
+    return ggml_view_4d(ctx, t,
+        t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3],
+        chunk * t->nb[2]);
+}
+
+/**
+ * Unified chunked Delta Net implementation.
+ *
+ * Input tensor format matches qwen3next conventions:
+ * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
+ * @param g         Gate tensor:
+ *                    GDA: [H_v, n_tokens, n_seqs]
+ *                    KDA: [S_k, H_v, n_tokens, n_seqs]
+ * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
+ * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
+ * @param causal_mask   Lower triangular mask [chunk_size, chunk_size]
+ * @param identity      Identity matrix [chunk_size, chunk_size]
+ * @param diag_mask     Diagonal mask [chunk_size, chunk_size]
+ * @param il            Layer index (for debugging callbacks)
+ * @param chunk_size    Chunk size for chunked processing
+ * @param eps_norm      Epsilon for L2 normalization
+ *
+ * @return Pair of (output_tokens, new_state)
+ */
+std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_chunking(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state_reshaped,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm) {
+
+    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    // Detect KDA vs GDA based on g's shape
+    // GDA: g has shape [H_v, n_tokens, n_seqs]
+    // KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
+    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
+
+    // Validate tensor shapes
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(H_k == H_v);
+
+    if (is_kda) {
+        // KDA: g shape [S_k, H_v, n_tokens, n_seqs]
+        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
+    } else {
+        // GDA: g shape [H_v, n_tokens, n_seqs]
+        GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    }
+
+    // L2 normalize q and k
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf((float)S_v);
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    // Permute tensors to working format [S, n_tokens, H, n_seqs]
+    // Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    if (is_kda) {
+        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    } else {
+        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+    }
+    beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state_reshaped, "state_in", il);
+
+    // Padding for chunk processing
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    // Reshape to chunks
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+    beta   = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    // Reshape g for chunks
+    ggml_tensor * g_cumsum;
+    ggml_tensor * g_cumsum_t;
+    if (is_kda) {
+        // KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
+        g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
+        // Cumsum along chunk_size dimension (ne[1])
+        // GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
+        g = ggml_cont(ctx0, ggml_transpose(ctx0, g));  // [chunk_size, S_k, n_chunks, H_k * n_seqs]
+        g_cumsum_t = ggml_cumsum(ctx0, g);
+        g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t));  // [S_k, chunk_size, n_chunks, H_k * n_seqs]
+    } else {
+        // GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
+        g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+        g_cumsum = ggml_cumsum(ctx0, g);
+        g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
+    }
+
+    cb(g_cumsum, "g_cumsum", il);
+
+    // Build attention matrix A for the WY representation solve
+    // For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
+    // For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
+    // KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
+
+    ggml_tensor * k_decay;
+    ggml_tensor * decay_mask = nullptr;
+    ggml_tensor * g_exp_pos = nullptr;
+
+    if (is_kda) {
+        // KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
+        // A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
+        // By putting S_k in dim 0, mul_mat implicitly sums over it
+
+        const int64_t CHB = n_chunks * H_k * n_seqs;
+
+        // g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
+        // Reshape to [chunk_size, S_k, CHB] then build decay mask
+        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
+        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
+        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
+
+        // Build decay mask: [chunk_size, chunk_size, S_k, CHB]
+        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
+        decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
+
+        cb(decay_mask, "decay_mask_kda", il);
+
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+        decay_mask = ggml_exp(ctx0, decay_mask);
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
+        decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
+
+        // Reshape k and k_beta for broadcasting with decay_mask
+        // k_i: indexed at position i (dim 2 of decay_mask)
+        // k_beta_j: indexed at position j (dim 1 of decay_mask)
+        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
+        ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
+
+        // decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
+        ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
+
+        // mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
+        k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
+        k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
+
+        // g_exp_pos is still needed for later (kbeta_gexp, etc.)
+        g_exp_pos = ggml_exp(ctx0, g_cumsum);
+    } else {
+        // GDA: Use decay mask approach (g broadcasts over K dimension)
+        // g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
+        ggml_tensor * gcs_i = g_cumsum;
+        ggml_tensor * gcs_j = g_cumsum_t;
+        g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
+        ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+        decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+
+        cb(decay_mask, "decay_mask", il);
+
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+        decay_mask = ggml_exp(ctx0, decay_mask);
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+        ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+        k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    }
+
+    ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+
+    cb(attn, "attn_pre_solve", il);
+
+    // Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+    ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn = ggml_add(ctx0, attn, identity);
+
+    cb(attn, "attn_solved", il);
+
+    // Compute u = A @ v and w = A @ (g.exp() * k)
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
+    cb(kbeta_gexp, "kbeta_gexp", il);
+
+    ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
+        ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il);
+
+    // Attention scores q @ k^T with decay
+    // For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
+    // For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
+    ggml_tensor * attn_kq;
+    if (is_kda) {
+        // KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
+        const int64_t CHB = n_chunks * H_k * n_seqs;
+
+        // Rebuild decay mask (same structure as k_decay)
+        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
+        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
+        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
+        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
+        ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
+
+        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
+        decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
+        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
+
+        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
+        decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
+
+        // q_j: indexed at position j, k_i: indexed at position i
+        ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
+        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
+
+        // decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
+        ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
+
+        // mul_mat sums over S_k
+        attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
+        attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
+    } else {
+        // GDA: Use decay mask
+        attn_kq = ggml_mul_mat(ctx0, k, q);
+        attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+        attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    }
+    cb(attn_kq, "attn_kq", il);
+
+    // Compute g_last and g_diff for state updates
+    ggml_tensor * g_last;
+    ggml_tensor * g_diff_exp;
+    ggml_tensor * g_last_exp;
+
+    if (is_kda) {
+        // KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
+        // Get last element along chunk_size dimension (ne[1])
+        g_last = ggml_view_4d(ctx0, g_cumsum,
+            g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
+            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+            (g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
+        g_last = ggml_cont(ctx0, g_last);
+        g_last_exp = ggml_exp(ctx0, g_last);
+
+        // g_diff = g_last - g_cumsum
+        ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
+            g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
+        ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
+        g_diff_exp = ggml_exp(ctx0, g_diff);
+    } else {
+        // GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
+        g_last = ggml_view_4d(ctx0, g_cumsum,
+            1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+            (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+        g_last = ggml_cont(ctx0, g_last);
+        g_last_exp = ggml_exp(ctx0, g_last);
+
+        ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+        g_diff_exp = ggml_exp(ctx0, g_diff);
+    }
+
+    cb(g_last, "g_last", il);
+    cb(g_last_exp, "g_last_exp", il);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
+    cb(key_gdiff, "key_gdiff", il);
+
+    // Process chunks
+    ggml_tensor * new_state = state_reshaped;
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
+
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
+            S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = k_cumdecay @ state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il);
+
+        // v_new = v - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q * g.exp()) @ state
+        ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // output = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il);
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // State update: state = state * g_last_exp + key_gdiff^T @ v_new
+        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
+
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+
+        if (is_kda) {
+            // KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
+            // State: [S_v, S_v, H_v, n_seqs]
+            // Need to reshape g_last_exp to broadcast correctly over V dimension only
+            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
+                1, gexp_last_chunk->ne[0], H_v, n_seqs);  // [1, S_k, H_v, n_seqs]
+            // Transpose to [S_k, 1, H_v, n_seqs] then broadcast
+            gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
+        } else {
+            // GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
+            // Broadcasts over both K and V dimensions
+            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
+                gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
+        }
+
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, gexp_last_chunk),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // Truncate padding and permute back
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+        S_v, n_tokens, H_v, n_seqs,
+        ggml_row_size(core_attn_out->type, S_v),
+        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    cb(output_tokens, "output_tokens", il);
+
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+
+/**
+ * Unified autoregressive Delta Net implementation (single token processing).
+ *
+ * This implementation uses matrix multiplication instead of elementwise operations + summation,
+ * which is more efficient and mathematically equivalent. See inline comments for equivalences.
+ *
+ * Input tensor format matches qwen3next conventions:
+ * @param q         Query tensor [S_k, H_k, 1, n_seqs]
+ * @param k         Key tensor [S_k, H_k, 1, n_seqs]
+ * @param v         Value tensor [S_v, H_v, 1, n_seqs]
+ * @param g         Gate tensor:
+ *                    GDA: [H_v, 1, n_seqs]
+ *                    KDA: [S_k, H_v, 1, n_seqs]
+ * @param beta      Beta tensor [H_v, 1, 1, n_seqs]
+ * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
+ * @param il        Layer index (for debugging callbacks)
+ * @param eps_norm  Epsilon for L2 normalization
+ *
+ * @return Pair of (output_tokens, new_state)
+ */
+std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_autoregressive(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il,
+        float         eps_norm) {
+
+    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // Autoregressive mode is for single token
+
+    // Detect KDA vs GDA based on g's shape
+    // GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
+    // KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
+    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
+
+    // Validate shapes
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(H_k == H_v);
+
+    if (is_kda) {
+        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
+    } else {
+        GGML_ASSERT(g->ne[0] == H_v);
+    }
+
+    // L2 normalize q and k
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf((float)S_v);
+    q = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    // Reshape g and beta for broadcasting
+    ggml_tensor * g_t;
+    ggml_tensor * beta_t;
+
+    if (is_kda) {
+        // KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
+        // For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
+        g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
+    } else {
+        // GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
+        // For state multiplication, broadcasts over both K and V
+        g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    }
+
+    beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // State decay: state = state * exp(g)
+    if (is_kda) {
+        // KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
+        // Need to broadcast g_t over V dimension (ne[0] of state)
+        // Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
+        ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
+        state = ggml_mul(ctx0, state, g_broadcast);
+    } else {
+        // GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
+        state = ggml_mul(ctx0, state, g_t);
+    }
+
+    // Equivalence to previous version:
+    // Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
+    // Current:  k_state = state_t @ k_t using matrix multiplication
+    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
+    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
+    ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
+    ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
+
+    // v_diff = v - k_state (equivalent to v - kv_mem in previous version)
+    ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
+
+    // Equivalence to previous version:
+    // Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
+    // Current:  state += v_diff^T @ k_beta^T using matrix multiplication
+    // These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
+    state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
+
+    // Equivalence to previous version:
+    // Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
+    // Current:  core_attn_out = state_t @ q using matrix multiplication
+    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
+    q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
+    state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
+    ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
+
+/**
+ * Main entry point that dispatches to chunked or autoregressive based on n_tokens.
+ *
+ * Input tensor format matches qwen3next conventions:
+ * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
+ * @param g         Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
+ * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
+ * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
+ */
+std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm) {
+
+    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
+    const int64_t n_tokens = q->ne[2];
+
+    if (n_tokens == 1) {
+        return build_delta_net_unified_autoregressive(
+            ctx0, q, k, v, g, beta, state, il, eps_norm);
+    }
+    return build_delta_net_unified_chunking(
+        ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
+        il, chunk_size, eps_norm);
+}

src/models/kimi-linear.cpp

@@ -1,5 +1,4 @@
 #include "models.h"
-#include "ggml.h"
 
 #define CHUNK_SIZE 64
 

src/models/models.h

@@ -17,6 +17,53 @@ struct llm_graph_context_mamba : public llm_graph_context {
 
 };
 
+struct llm_graph_context_delta : public llm_graph_context_mamba {
+    llm_graph_context_delta(const llm_graph_params & params);
+
+    virtual ~llm_graph_context_delta() = default;
+
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_chunking(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm);
+
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_autoregressive(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il,
+        float         eps_norm);
+
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm);
+};
+
 // Base class for RWKV-related models
 struct llm_build_rwkv6_base : public llm_graph_context {
     const llama_model & model;
@@ -476,7 +523,7 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
     llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
-struct llm_build_qwen3next : public llm_graph_context_mamba {
+struct llm_build_qwen3next : public llm_graph_context_delta {
     llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
 private:
     ggml_tensor * build_layer_attn(
@@ -534,6 +581,59 @@ private:
     const llama_model & model;
 };
 
+struct llm_build_qwen3_5 : public llm_graph_context_delta {
+    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params);
+
+protected:
+    // Tag type for subclass constructors that need to call build_graph() themselves
+    // (to ensure virtual dispatch works correctly)
+    struct defer_graph_build_t {};
+
+    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t);
+
+    void build_graph();
+
+    virtual ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il);
+
+    const llama_model & model;
+
+private:
+    ggml_tensor * build_layer_attn(
+    llm_graph_input_attn_kv * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                        int   il);
+
+    ggml_tensor * build_layer_attn_linear(
+         llm_graph_input_rs * inp,
+                ggml_tensor * cur,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    ggml_tensor * build_norm_gated(
+                ggml_tensor * input,
+                ggml_tensor * weights,
+                ggml_tensor * gate,
+                        int   layer);
+
+    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+};
+
+struct llm_build_qwen3_5_moe : public llm_build_qwen3_5 {
+    llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params);
+
+protected:
+    ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il) override;
+};
+
 struct llm_build_qwen : public llm_graph_context {
     llm_build_qwen(const llama_model & model, const llm_graph_params & params);
 };
@@ -583,6 +683,10 @@ struct llm_build_starcoder : public llm_graph_context {
     llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_step35_iswa : public llm_graph_context {
+    llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_t5_dec : public llm_graph_context {
     llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
 };

src/models/qwen3-5.cpp

@@ -0,0 +1,421 @@
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_delta(params), model(model) {
+    build_graph();
+}
+
+// virtual call in constructor fix
+llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
+    llm_graph_context_delta(params), model(model) {
+}
+
+void llm_build_qwen3_5::build_graph() {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "model.embed_tokens", -1);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * causal_mask =
+        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+    ggml_build_forward_expand(gf, causal_mask);
+    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        if (hparams.is_recurrent(il)) {
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+        } else {
+            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "attn_residual", il);
+
+        ggml_tensor * ffn_residual = cur;
+
+        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+        cb(attn_post_norm, "attn_post_norm", il);
+
+        cur = build_layer_ffn(attn_post_norm, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_residual);
+        cb(cur, "post_ffn", il);
+
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_qwen3_5::build_norm_gated(
+        ggml_tensor * input,
+        ggml_tensor * weights,
+        ggml_tensor * gate,
+        int           layer) {
+    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+    return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen3_5::build_layer_attn(
+        llm_graph_input_attn_kv * inp,
+        ggml_tensor *             cur,
+        ggml_tensor *             inp_pos,
+        int                       il) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
+    cb(Qcur_full, "Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
+    cb(Qcur, "Qcur_reshaped", il);
+
+    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "Qcur_normed", il);
+
+    ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+    cb(Kcur, "Kcur", il);
+
+    ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+    cb(Vcur, "Vcur", il);
+
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "Kcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+        ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "gate_reshaped", il);
+
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+    Qcur = ggml_rope_ext(
+            ctx0, Qcur, inp_pos, nullptr,
+            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, nullptr,
+            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);
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    cur = build_attn(inp,
+                nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_pregate", il);
+
+    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+    cb(gate_sigmoid, "gate_sigmoid", il);
+
+    cur = ggml_mul(ctx0, cur, gate_sigmoid);
+    cb(cur, "attn_gated", il);
+
+    cur = build_lora_mm(model.layers[il].wo, cur);
+    cb(cur, "attn_output", il);
+
+    return cur;
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3_5::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    if (model.layers[il].wqkv) {
+        ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+        qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+        cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+        ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+        cb(z, "z", il);
+
+        return { qkv_mixed, z };
+
+    }
+    // legacy path for combined in_proj_qkvz
+    ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
+    cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
+
+    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
+    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+
+    int64_t split_sizes_qkvz[4] = {
+        head_k_dim,
+        head_k_dim,
+        head_v_dim * num_v_heads / num_k_heads,
+        head_v_dim * num_v_heads / num_k_heads
+    };
+
+    ggml_tensor * query =
+        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
+                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
+    cb(query, "q", il);
+
+    ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
+                                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                                    split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
+    cb(key, "k", il);
+
+    ggml_tensor * value =
+        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
+                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                    (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
+    cb(value, "v", il);
+
+    ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
+                                mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                                (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
+    z = ggml_cont(ctx0, z);
+    cb(z, "z", il);
+
+    ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
+    cb(query_flat, "query_flat", il);
+
+    ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
+    cb(key_flat, "key_flat", il);
+
+    ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(value_flat, "value_flat", il);
+
+    ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
+    qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
+    cb(qkv_mixed, "qkv_mixed", il);
+
+    return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        cur,
+        ggml_tensor *        causal_mask,
+        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
+        int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
+
+    ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
+    cb(mixed_ba, "linear_attn_mixed_ba", il);
+
+    int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
+    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+
+    int64_t split_sizes_ba[2] = {
+        num_v_heads / num_k_heads,
+        num_v_heads / num_k_heads
+    };
+
+    ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
+                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
+    cb(b, "b", il);
+
+    ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
+                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
+                                   split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
+    cb(a, "a", il);
+
+    ggml_tensor * beta  = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
+
+    ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+    cb(alpha_softplus, "a_softplus", il);
+    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);
+    cb(gate, "gate", il);
+
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
+    const int64_t conv_kernel_size = conv_kernel->ne[0];
+    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    ggml_tensor * last_conv_states =
+        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+    cb(last_conv_states, "last_conv_states", il);
+
+    ggml_tensor * state_update_target =
+        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+    cb(state_update_target, "state_update_target", il);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    cb(conv_states_all, "conv_states_updated", il);
+
+    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+    cb(conv_output_proper, "conv_output_raw", il);
+
+    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+    cb(conv_output_silu, "conv_output_silu", il);
+
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+    ggml_tensor * q_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+    cb(q_conv, "q_conv", il);
+    ggml_tensor * k_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(k_conv, "k_conv", il);
+    ggml_tensor * v_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(v_conv, "v_conv", il);
+
+    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
+    cb(state, "state_predelta", il);
+
+    if (num_k_heads != num_v_heads) {
+        GGML_ASSERT(num_v_heads % num_k_heads == 0);
+        int64_t repeat_factor = num_v_heads / num_k_heads;
+
+        ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
+        ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
+
+        ggml_tensor * q_repeated =
+            ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
+        ggml_tensor * k_repeated =
+            ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
+
+        q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
+        k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
+    }
+
+    cb(q_conv, "q_conv_predelta", il);
+    cb(k_conv, "k_conv_predelta", il);
+    cb(v_conv, "v_conv_predelta", il);
+
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
+            gate, beta, state, causal_mask, identity, diag_mask,
+            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
+
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
+
+    ggml_build_forward_expand(gf,
+                              ggml_cpy(ctx0, new_state,
+                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(final_output, "final_output", il);
+
+    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+    cb(cur, "linear_attn_out", il);
+
+    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Qwen3.5 Dense always uses dense FFN
+    cur = build_ffn(cur,
+        model.layers[il].ffn_up, NULL, NULL,
+        model.layers[il].ffn_gate, NULL, NULL,
+        model.layers[il].ffn_down, NULL, NULL,
+        NULL,
+        LLM_FFN_SILU, LLM_FFN_PAR, il);
+    cb(cur, "ffn_out", il);
+    return cur;
+}

src/models/qwen3-5moe.cpp

@@ -0,0 +1,52 @@
+#include "models.h"
+
+llm_build_qwen3_5_moe::llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params) :
+    llm_build_qwen3_5(model, params, defer_graph_build_t{}) {
+    build_graph();
+}
+
+ggml_tensor * llm_build_qwen3_5_moe::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Check if this is an MoE layer
+    if (model.layers[il].ffn_gate_inp != nullptr) {
+        // MoE branch
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used, LLM_FFN_SILU,
+                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        // Add shared experts if present
+        if (model.layers[il].ffn_up_shexp != nullptr) {
+            ggml_tensor * ffn_shexp =
+                build_ffn(cur,
+                    model.layers[il].ffn_up_shexp, NULL, NULL,
+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            // Apply shared expert gating (sigmoid)
+            ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
+            cb(shared_gate, "shared_expert_gate", il);
+
+            shared_gate = ggml_sigmoid(ctx0, shared_gate);
+            cb(shared_gate, "shared_expert_gate_sigmoid", il);
+
+            ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
+            cb(ffn_shexp, "ffn_shexp_gated", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = moe_out;
+        }
+    } else {
+        // Dense FFN branch (fallback)
+        cur = llm_build_qwen3_5::build_layer_ffn(cur, il);
+    }
+    return cur;
+}

src/models/qwen3next.cpp

@@ -1,10 +1,9 @@
-#include "ggml.h"
 #include "models.h"
 
 #define CHUNK_SIZE 64
 
 llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_mamba(params), model(model) {
+    llm_graph_context_delta(params), model(model) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -86,362 +85,6 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
-// utility to get one slice from the third dimension
-// input dim:  [x, y, c, b]
-// output dim: [x, y, 1, b]
-static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
-    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
-        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
-}
-
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il) {
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
-
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-
-    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
-
-    const float eps_norm = hparams.f_norm_rms_eps;
-
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf(S_v);
-
-    q = ggml_scale(ctx0, q, scale);
-
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-
-    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
-    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
-
-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state, "state_in", il);
-
-    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
-    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
-
-    // Do padding
-    const int64_t chunk_size = CHUNK_SIZE;
-
-    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
-    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
-    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
-    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
-    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
-    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
-    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
-
-    cb(q, "q_pad", il);
-    cb(k, "k_pad", il);
-    cb(v, "v_pad", il);
-    cb(beta, "beta_pad", il);
-    cb(g, "g_pad", il);
-
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
-    cb(v_beta, "v_beta", il);
-    cb(k_beta, "k_beta", il);
-
-    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-
-    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
-
-    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
-    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
-    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
-
-    ggml_tensor * gcs_j_broadcast =
-        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-
-    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-    decay_mask = ggml_exp(ctx0, decay_mask);
-    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-
-    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-
-    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn                     = ggml_add(ctx0, attn, identity);
-    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
-    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
-    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * k_cumdecay =
-        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
-    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
-    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
-    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-
-    // vectorized calculation of key_gdiff
-    // improved from the chunked version:
-    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
-    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
-    //   key_gdiff = key * g_diff.unsqueeze(-1)
-    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-
-    // get last element in g_cumsum along chunk_size dimension (ne0)
-    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
-    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
-                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
-    g_last = ggml_cont(ctx0, g_last);
-    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
-    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
-    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
-                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
-    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
-    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
-
-
-    // state to be updated per chunk
-    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
-    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
-
-    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
-    ggml_tensor * core_attn_out = nullptr;
-
-    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
-        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
-
-        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
-        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
-
-        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
-        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
-
-        // shape: (chunk_size, 1, H_v * n_seqs)
-        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
-
-        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-        // replaced by precomputed attn_kq
-        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
-        cb(attn_chunk, "attn_chunk", il);
-
-        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
-
-        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
-        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
-        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
-
-        // v_new = v_i - v_prime
-        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
-        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-        cb(v_new, "v_new_chunk", il);
-
-        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
-        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
-        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-        cb(attn_inter, "attn_inter_chunk", il);
-
-        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
-        cb(v_attn, "v_attn_chunk", il);
-
-        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
-        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
-
-        core_attn_out = core_attn_out == nullptr
-            ? core_attn_out_chunk
-            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
-        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
-        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
-
-        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
-        new_state = ggml_add(ctx0,
-            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
-            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
-    }
-
-    // truncate padded tokens
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
-            S_v, n_tokens, H_v, n_seqs,
-            ggml_row_size(core_attn_out->type, S_v),
-            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
-            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-    cb(output_tokens, "output_tokens", il);
-
-    // permute back to (S_v, H_v, n_tokens, n_seqs)
-    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    return {output_tokens, new_state};
-}
-
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il) {
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
-
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-
-    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
-
-    const float eps_norm = hparams.f_norm_rms_eps;
-
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf(S_v);
-
-    q    = ggml_scale(ctx0, q, scale);
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
-
-    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
-    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
-    // Apply exponential to g_t
-    g_t = ggml_exp(ctx0, g_t);
-
-    // Apply the gated delta rule for the single timestep
-    // last_recurrent_state = last_recurrent_state * g_t
-    state = ggml_mul(ctx0, state, g_t);
-
-    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
-    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
-    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
-    // we need to sum over dim=-2, so we transpose, sum, then transpose again
-    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
-
-    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
-    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
-    // delta = (v_t - kv_mem) * beta_t
-    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
-    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
-
-    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
-    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
-    state                   = ggml_add(ctx0, state, k_t_delta);
-
-    // Compute the attention output
-    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
-    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
-    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
-    // again, since it's over dim = -2, transpose, sum, transpose back
-    ggml_tensor * core_attn_out =
-        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
-
-    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
-    cb(core_attn_out, "output_tokens", il);
-    cb(state, "new_state", il);
-
-    return {core_attn_out, state};
-}
-
 ggml_tensor * llm_build_qwen3next::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
@@ -752,7 +395,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
     cb(state, "state_predelta", il);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
@@ -781,13 +424,10 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
-    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
-    if (n_seq_tokens == 1) {
-        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
-    } else {
-        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
-    }
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
+            gate, beta, state, causal_mask, identity, diag_mask,
+            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
+
     ggml_tensor * output    = attn_out.first;
     ggml_tensor * new_state = attn_out.second;
     cb(output, "attn_output", il);

src/models/step35-iswa.cpp

@@ -0,0 +1,168 @@
+#include "models.h"
+
+llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    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();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        const uint32_t n_head_l    = hparams.n_head(il);
+        const uint32_t n_head_kv_l = hparams.n_head_kv(il);
+
+        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+        cur = inpL;
+
+        // dump pre-attn RMSNorm input to pinpoint layer boundary issues
+        cb(cur, "attn_norm_in", il);
+
+        // self-attention
+        {
+            cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
+
+            // Q/K per-head RMSNorm (Step35 q_norm / k_norm)
+            if (model.layers[il].attn_q_norm) {
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+            }
+            if (model.layers[il].attn_k_norm) {
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+            }
+
+            // RoPE (partial rotary factors per layer)
+            const bool is_swa = hparams.is_swa(il);
+            ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
+            const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2);
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, rope_factors,
+                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+            );
+            Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, rope_factors,
+                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+            );
+            cb(Qcur, "Qcur_pos", il);
+            cb(Kcur, "Kcur_pos", il);
+
+            const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
+            ggml_tensor * attn_out = build_attn(inp_attn,
+                    nullptr, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(attn_out, "attn_out", il);
+            // head-wise attention gate: sigmoid(g_proj(x)) in torch
+            if (model.layers[il].wqkv_gate) {
+                ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]
+                cb(gate, "attn_gate", il);
+
+                gate = ggml_sigmoid(ctx0, gate);
+                cb(gate, "attn_gate_sigmoid", il);
+
+                // reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]
+                ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
+                ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate,       1,          n_head_l, n_tokens);
+                cb(gate_3d, "attn_gate_3d", il);
+
+                attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
+                cb(attn_3d, "attn_gated_3d", il);
+
+                attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
+                cb(attn_out, "attn_gated", il);
+            }
+
+            // output projection
+            cur = build_lora_mm(model.layers[il].wo, attn_out);
+            cb(cur, "attn_proj", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            // dense MLP
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   nullptr,
+                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
+                    nullptr,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE routed experts
+            const bool  norm_w  = hparams.expert_weights_norm;
+            const float w_scale = hparams.expert_weights_scale;
+            const bool  scale_w = w_scale != 0.0f;
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU,
+                    norm_w, scale_w, w_scale,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // shared expert MLP (always added on MoE layers in Step35)
+            ggml_tensor * sh_out = build_ffn(cur,
+                    model.layers[il].ffn_up_shexp,   nullptr, nullptr,
+                    model.layers[il].ffn_gate_shexp, nullptr, nullptr,
+                    model.layers[il].ffn_down_shexp, nullptr, nullptr,
+                    nullptr,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(sh_out, "ffn_shared_out", il);
+
+            cur = ggml_add(ctx0, moe_out, sh_out);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

tests/CMakeLists.txt

@@ -11,7 +11,9 @@ function(llama_build source)
 
     add_executable(${TEST_TARGET} ${TEST_SOURCES})
     target_link_libraries(${TEST_TARGET} PRIVATE common)
-    install(TARGETS ${TEST_TARGET} RUNTIME)
+    if (LLAMA_TESTS_INSTALL)
+        install(TARGETS ${TEST_TARGET} RUNTIME)
+    endif()
 endfunction()
 
 function(llama_test target)
@@ -100,7 +102,9 @@ function(llama_build_and_test source)
     endif()
 
     add_executable(${TEST_TARGET} ${TEST_SOURCES})
-    install(TARGETS ${TEST_TARGET} RUNTIME)
+    if (LLAMA_TESTS_INSTALL)
+        install(TARGETS ${TEST_TARGET} RUNTIME)
+    endif()
     target_link_libraries(${TEST_TARGET} PRIVATE common)
 
     add_test(

tools/quantize/quantize.cpp

@@ -119,27 +119,48 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 [[noreturn]]
 static void usage(const char * executable) {
     printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable);
-    printf("       [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file] [--prune-layers] [--keep-split] [--override-kv]\n");
+    printf("       [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file]\n");
+    printf("       [--prune-layers] [--keep-split] [--override-kv]\n");
     printf("       model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
-    printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
-    printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
-    printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
-    printf("  --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
-    printf("  --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
-    printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
-    printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
-    printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
-    printf("  --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
-    printf("      Advanced option to selectively quantize tensors. May be specified multiple times.\n");
-    printf("  --tensor-type-file tensor_type.txt: list of tensors to quantize to specific ggml_type. example: --tensor-type-file tensor_type_list.txt\n");
-    printf("      Advanced option to selectively quantize a long list of tensors. Format to be tensor_name=ggml_type, separated by spaces/newline.\n");
-    printf("  --prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model\n");
-    printf("      Advanced option to remove all tensors from the given layers\n");
-    printf("  --keep-split: will generate quantized model in the same shards as input\n");
+    printf("  --allow-requantize\n");
+    printf("                                      allow requantizing tensors that have already been quantized\n");
+    printf("                                      WARNING: this can severely reduce quality compared to quantizing\n");
+    printf("                                               from 16bit or 32bit!\n");
+    printf("  --leave-output-tensor\n");
+    printf("                                      leave output.weight un(re)quantized\n");
+    printf("                                      increases model size but may also increase quality, especially when requantizing\n");
+    printf("  --pure\n");
+    printf("                                      disable k-quant mixtures and quantize all tensors to the same type\n");
+    printf("  --imatrix file_name\n");
+    printf("                                      use data in file_name as importance matrix for quant optimizations\n");
+    printf("  --include-weights tensor_name\n");
+    printf("                                      use importance matrix for this/these tensor(s)\n");
+    printf("  --exclude-weights tensor_name\n");
+    printf("                                      do not use importance matrix for this/these tensor(s)\n");
+    printf("  --output-tensor-type ggml_type\n");
+    printf("                                      use this ggml_type for the output.weight tensor\n");
+    printf("  --token-embedding-type ggml_type\n");
+    printf("                                      use this ggml_type for the token embeddings tensor\n");
+    printf("  --tensor-type tensor_name=ggml_type\n");
+    printf("                                      quantize this tensor to this ggml_type\n");
+    printf("                                      this is an advanced option to selectively quantize tensors. may be specified multiple times.\n");
+    printf("                                      example: --tensor-type attn_q=q8_0\n");
+    printf("  --tensor-type-file tensor_types.txt\n");
+    printf("                                      list of tensors to quantize to a specific ggml_type\n");
+    printf("                                      this is an advanced option to selectively quantize a long list of tensors.\n");
+    printf("                                      the file should use the same format as above, separated by spaces or newlines.\n");
+    printf("  --prune-layers L0,L1,L2...\n");
+    printf("                                      comma-separated list of layer numbers to prune from the model\n");
+    printf("                                      WARNING: this is an advanced option, use with care.\n");
+    printf("  --keep-split\n");
+    printf("                                      generate quantized model in the same shards as input\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
-    printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
-    printf("Note: --include-weights and --exclude-weights cannot be used together\n");
-    printf("\nAllowed quantization types:\n");
+    printf("                                      override model metadata by key in the quantized model. may be specified multiple times.\n");
+    printf("                                      WARNING: this is an advanced option, use with care.\n\n");
+    printf("note: --include-weights and --exclude-weights cannot be used together\n\n");
+    printf("-----------------------------------------------------------------------------\n");
+    printf(" allowed quantization types\n");
+    printf("-----------------------------------------------------------------------------\n\n");
     for (const auto & it : QUANT_OPTIONS) {
         if (it.name != "COPY") {
             printf("  %2d  or  ", it.ftype);

tools/rpc/rpc-server.cpp

@@ -1,12 +1,7 @@
-#if defined(_MSC_VER)
-#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
-#endif
-
 #include "ggml-rpc.h"
 #ifdef _WIN32
 #  define NOMINMAX
 #  define DIRECTORY_SEPARATOR '\\'
-#  include <locale>
 #  include <windows.h>
 #  include <fcntl.h>
 #  include <io.h>
@@ -15,23 +10,43 @@
 #  include <unistd.h>
 #  include <sys/stat.h>
 #endif
-#include <codecvt>
 #include <string>
 #include <stdio.h>
 #include <vector>
-#include <filesystem>
 #include <algorithm>
 #include <thread>
 #include <regex>
 
-namespace fs = std::filesystem;
+#if defined(__linux__)
+#include <sys/types.h>
+#include <pwd.h>
+#endif
+
+// NOTE: this is copied from common.cpp to avoid linking with libcommon
+#ifdef _WIN32
+static std::wstring utf8_to_wstring(const std::string & str) {
+    if (str.empty()) {
+        return std::wstring();
+    }
+
+    int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);
+
+    if (size <= 0) {
+        return std::wstring();
+    }
+
+    std::wstring wstr(size, 0);
+    MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);
+
+    return wstr;
+}
+#endif
 
 // NOTE: this is copied from common.cpp to avoid linking with libcommon
 // returns true if successful, false otherwise
 static bool fs_create_directory_with_parents(const std::string & path) {
 #ifdef _WIN32
-    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
-    std::wstring wpath = converter.from_bytes(path);
+    std::wstring wpath = utf8_to_wstring(path);
 
     // if the path already exists, check whether it's a directory
     const DWORD attributes = GetFileAttributesW(wpath.c_str());
@@ -44,9 +59,16 @@ static bool fs_create_directory_with_parents(const std::string & path) {
     // process path from front to back, procedurally creating directories
     while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
         const std::wstring subpath = wpath.substr(0, pos_slash);
-        const wchar_t * test = subpath.c_str();
 
-        const bool success = CreateDirectoryW(test, NULL);
+        pos_slash += 1;
+
+        // skip the drive letter, in some systems it can return an access denied error
+        if (subpath.length() == 2 && subpath[1] == ':') {
+            continue;
+        }
+
+        const bool success = CreateDirectoryW(subpath.c_str(), NULL);
+
         if (!success) {
             const DWORD error = GetLastError();
 
@@ -60,8 +82,6 @@ static bool fs_create_directory_with_parents(const std::string & path) {
                 return false;
             }
         }
-
-        pos_slash += 1;
     }
 
     return true;
@@ -115,13 +135,27 @@ static std::string fs_get_cache_directory() {
 #if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
-        } else {
+        } else if (std::getenv("HOME")) {
             cache_directory = std::getenv("HOME") + std::string("/.cache/");
+        } else {
+#if defined(__linux__)
+            /* no $HOME is defined, fallback to getpwuid */
+            struct passwd *pw = getpwuid(getuid());
+            if ((!pw) || (!pw->pw_dir)) {
+                throw std::runtime_error("Failed to find $HOME directory");
+            }
+
+            cache_directory = std::string(pw->pw_dir) + std::string("/.cache/");
+#else /* defined(__linux__) */
+            throw std::runtime_error("Failed to find $HOME directory");
+#endif /* defined(__linux__) */
         }
 #elif defined(__APPLE__)
         cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
 #elif defined(_WIN32)
         cache_directory = std::getenv("LOCALAPPDATA");
+#elif defined(__EMSCRIPTEN__)
+        GGML_ABORT("not implemented on this platform");
 #else
 #  error Unknown architecture
 #endif

tools/server/server-context.cpp

@@ -2507,7 +2507,8 @@ private:
                         slot.n_prompt_tokens_processed++;
 
                         // process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
-                        if (do_checkpoint && slot.task->n_tokens() - slot.prompt.n_tokens() == 64) {
+                        const int n_last = std::min(n_batch, 512);
+                        if (do_checkpoint && slot.task->n_tokens() == slot.prompt.n_tokens() + n_last) {
                             break;
                         }
                     }