model: add dots3-note (#27060)

* text: conversion

* init impl

* address review comments

* fix rope

* move to a new llama_kv_cache_dsa_iswa

conversion/__init__.py

@@ -64,6 +64,9 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "DistilBertForSequenceClassification": "bert",
     "DistilBertModel": "bert",
     "Dots1ForCausalLM": "dots1",
+    "Dots3NoteForCausalLM": "dots3",
+    "Dots3NoteForConditionalGeneration": "dots3",
+    "Dots3NoteTextForCausalLM": "dots3",
     "DotsOCRForCausalLM": "qwen",
     "DreamModel": "dream",
     "Ernie4_5ForCausalLM": "ernie",

conversion/dots3.py

@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+import math
+import re
+
+from typing import TYPE_CHECKING, Callable, Iterable
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .deepseek import DeepseekV2Model
+
+
+@ModelBase.register("Dots3NoteForCausalLM", "Dots3NoteForConditionalGeneration", "Dots3NoteTextForCausalLM")
+class Dots3NoteModel(DeepseekV2Model):
+    model_arch = gguf.MODEL_ARCH.DOTS3NOTE
+    skip_mtp = False
+    supports_mtp_export = True
+
+    # trunk layer count, stashed before indexing for filter_tensors (mirrors DeepseekV32Model)
+    _n_main_layers: int | None = None
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        hparams = self.hparams
+
+        # config file doesn't specify MTP block, detect it from model weight
+        self.n_nextn = 1 if "model.mtp.embed_tokens.weight" in self.model_tensors else 0
+        if self.n_nextn:
+            self.block_count += self.n_nextn
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        self.layer_types = hparams["layer_types"]
+        if len(self.layer_types) < hparams["num_hidden_layers"]:
+            raise ValueError("layer_types is shorter than num_hidden_layers")
+
+        if hparams.get("use_dsa", True) is not True:
+            raise ValueError("dots3-note conversion requires use_dsa=true")
+        if hparams.get("normalization", "RMSNorm") != "RMSNorm" or hparams.get("final_norm", "RMSNorm") != "RMSNorm":
+            raise ValueError("dots3-note conversion only supports RMSNorm")
+        if hparams.get("k_rope_only_layernorm", True) is not True:
+            raise ValueError("dots3-note conversion requires k_rope_only_layernorm=true")
+        if hparams.get("topk_method", "noaux_tc") != "noaux_tc" or hparams.get("scoring_func") != "sigmoid":
+            raise ValueError("dots3-note conversion only supports noaux_tc/sigmoid expert gating")
+        if hparams.get("n_group", 1) != 1 or hparams.get("topk_group", 1) != 1:
+            raise ValueError("dots3-note conversion does not support grouped expert routing")
+        if hparams.get("use_dynamic_rsf", False) or hparams.get("moe_gating_fp32", False):
+            raise ValueError("dots3-note conversion does not support use_dynamic_rsf/moe_gating_fp32")
+        for key in ("attention_gate_type", "swa_attention_gate_type"):
+            if hparams.get(key, "headwise") != "headwise":
+                raise ValueError(f"dots3-note conversion only supports headwise attention gate, got {key}={hparams.get(key)!r}")
+        if hparams["swa_qk_nope_head_dim"] + hparams["swa_qk_rope_head_dim"] != hparams.get("swa_head_dim", 256):
+            raise ValueError("swa_head_dim must equal swa_qk_nope_head_dim + swa_qk_rope_head_dim")
+        if hparams["swa_qk_rope_head_dim"] != hparams["qk_rope_head_dim"]:
+            # both layer kinds share a single rope_dimension_count
+            raise ValueError("swa_qk_rope_head_dim must match qk_rope_head_dim")
+
+        self.apply_lora_rescale = hparams.get("apply_mla_qkv_lora_rescale", False)
+
+    def _is_swa_layer(self, bid: int) -> bool:
+        if bid >= self.hparams["num_hidden_layers"]:
+            # note: the NextN/MTP block uses the sliding-attention MLA
+            return True
+        return self.layer_types[bid] == "sliding_attention"
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endofassistant|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+        if name.startswith(("vision_encoder.", "audio_encoder.")):
+            return None
+
+        assert cls._n_main_layers is not None
+        is_mtp = name.startswith("model.mtp.") or \
+            ((m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers)
+
+        # --no-mtp: drop the NextN/MTP block; --mtp: keep only that block plus the shared embeddings/norm/lm_head
+        if is_mtp and cls.no_mtp:
+            return None
+        if cls.mtp_only and not is_mtp and name not in (
+            "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+        ):
+            return None
+
+        return name, gen
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        # head_count is a per-layer array because the two layer kinds have different head counts
+        n_layer = hparams["num_hidden_layers"]
+        hparams["num_attention_heads"] = [
+            hparams["swa_num_attention_heads"] if self._is_swa_layer(il) else hparams["num_attention_heads"]
+            for il in range(self.block_count)
+        ]
+
+        # prevent the base class from emitting key/value_length from the unused head_dim
+        hparams.pop("head_dim", None)
+
+        super().set_gguf_parameters()
+
+        # MLA geometry of the sliding-window layers (rope.freq_base_swa is emitted by the base class)
+        swa_kv_lora_rank = hparams["swa_kv_lora_rank"]
+        self.gguf_writer.add_sliding_window(hparams["sliding_window_size"])
+        self.gguf_writer.add_sliding_window_pattern([self._is_swa_layer(il) for il in range(n_layer)])
+        self.gguf_writer.add_kv_lora_rank_swa(swa_kv_lora_rank)
+        self.gguf_writer.add_key_length_swa(swa_kv_lora_rank + hparams["swa_qk_rope_head_dim"])
+        self.gguf_writer.add_value_length_swa(swa_kv_lora_rank)
+        self.gguf_writer.add_key_length_mla_swa(hparams["swa_qk_nope_head_dim"] + hparams["swa_qk_rope_head_dim"])
+        self.gguf_writer.add_value_length_mla_swa(hparams["swa_v_head_dim"])
+        if hparams["swa_q_lora_rank"] != hparams["q_lora_rank"]:
+            raise ValueError("dots3-note conversion assumes a shared q_lora_rank for both layer kinds")
+
+        if self.n_nextn:
+            self.gguf_writer.add_nextn_predict_layers(self.n_nextn)
+
+        # DSA indexer (full-attention layers only)
+        self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
+        self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
+        self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
+        self.gguf_writer.add_indexer_types([not self._is_swa_layer(il) for il in range(n_layer)])
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # move the MTP token embedding into the NextN block so the standard nextn mapping picks it up
+        if name == "model.mtp.embed_tokens.weight":
+            name = f"model.layers.{self.hparams['num_hidden_layers']}.embed_tokens.weight"
+            bid = self.hparams["num_hidden_layers"]
+
+        # fold the activation rescale sqrt(n_embd/lora_rank) into the preceding RMSNorm weight
+        # this also covers the indexer wq_b, which reads the same rescaled q_lora activation
+        if self.apply_lora_rescale and bid is not None:
+            if name.endswith("q_a_layernorm.weight"):
+                data_torch = data_torch * math.sqrt(self.hparams["hidden_size"] / self.hparams["q_lora_rank"])
+            elif name.endswith("kv_a_layernorm.weight"):
+                rank = self.hparams["swa_kv_lora_rank"] if self._is_swa_layer(bid) else self.hparams["kv_lora_rank"]
+                data_torch = data_torch * math.sqrt(self.hparams["hidden_size"] / rank)
+
+        # MLA absorption: split kv_b_proj into k_b (transposed) and v_b, per-layer-kind geometry
+        if name.endswith("kv_b_proj.weight"):
+            assert bid is not None
+            if self._is_swa_layer(bid):
+                n_head = self.hparams["swa_num_attention_heads"]
+                qk_nope_head_dim = self.hparams["swa_qk_nope_head_dim"]
+                v_head_dim = self.hparams["swa_v_head_dim"]
+            else:
+                n_head = self.hparams["num_attention_heads"]
+                qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+                v_head_dim = self.hparams["v_head_dim"]
+            if isinstance(n_head, list):  # set_gguf_parameters turns this into a per-layer array
+                n_head = n_head[bid]
+
+            assert data_torch.shape[0] == n_head * (qk_nope_head_dim + v_head_dim)
+
+            kv_b = data_torch.view(n_head, qk_nope_head_dim + v_head_dim, data_torch.shape[-1])
+            k_b, v_b = kv_b.split([qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+
+            yield from ModelBase.modify_tensors(self, k_b, name.replace("kv_b_proj", "k_b_proj"), bid)
+            yield from ModelBase.modify_tensors(self, v_b, name.replace("kv_b_proj", "v_b_proj"), bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)

gguf-py/gguf/constants.py

@@ -205,6 +205,9 @@ class Keys:
         VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
         KEY_LENGTH_SWA               = "{arch}.attention.key_length_swa"
         VALUE_LENGTH_SWA             = "{arch}.attention.value_length_swa"
+        KEY_LENGTH_MLA_SWA           = "{arch}.attention.key_length_mla_swa"
+        VALUE_LENGTH_MLA_SWA         = "{arch}.attention.value_length_mla_swa"
+        KV_LORA_RANK_SWA             = "{arch}.attention.kv_lora_rank_swa"
         SHARED_KV_LAYERS             = "{arch}.attention.shared_kv_layers"
         SLIDING_WINDOW_PATTERN       = "{arch}.attention.sliding_window_pattern"
         TEMPERATURE_SCALE            = "{arch}.attention.temperature_scale"
@@ -558,6 +561,7 @@ class MODEL_ARCH(IntEnum):
     BAILINGMOE2      = auto()
     BAILINGMOE3      = auto()
     DOTS1            = auto()
+    DOTS3NOTE        = auto()
     ARCEE            = auto()
     AFMOE            = auto()
     LAGUNA           = auto()
@@ -1275,6 +1279,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.BAILINGMOE2:      "bailingmoe2",
     MODEL_ARCH.BAILINGMOE3:      "bailingmoe3",
     MODEL_ARCH.DOTS1:            "dots1",
+    MODEL_ARCH.DOTS3NOTE:        "dots3note",
     MODEL_ARCH.ARCEE:            "arcee",
     MODEL_ARCH.AFMOE:            "afmoe",
     MODEL_ARCH.LAGUNA:           "laguna",
@@ -4334,6 +4339,44 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
     ],
+    MODEL_ARCH.DOTS3NOTE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        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_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.INDEXER_K_NORM,
+        MODEL_TENSOR.INDEXER_PROJ,
+        MODEL_TENSOR.INDEXER_ATTN_K,
+        MODEL_TENSOR.INDEXER_ATTN_Q_B,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
     MODEL_ARCH.ARCEE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/gguf_writer.py

@@ -785,6 +785,15 @@ class GGUFWriter:
     def add_key_length_swa(self, length: int) -> None:
         self.add_uint32(Keys.Attention.KEY_LENGTH_SWA.format(arch=self.arch), length)
 
+    def add_key_length_mla_swa(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KEY_LENGTH_MLA_SWA.format(arch=self.arch), length)
+
+    def add_value_length_mla_swa(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA_SWA.format(arch=self.arch), length)
+
+    def add_kv_lora_rank_swa(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KV_LORA_RANK_SWA.format(arch=self.arch), length)
+
     def add_value_length_swa(self, length: int) -> None:
         self.add_uint32(Keys.Attention.VALUE_LENGTH_SWA.format(arch=self.arch), length)
 

gguf-py/gguf/tensor_mapping.py

@@ -723,6 +723,7 @@ class TensorNameMap:
             "model.layers.layers.{bid}.mixer.k",                              # plamo2
             "model.layers.layers.{bid}.mixer.k_norm",                         # plamo3
             "layers.{bid}.self_attn.k_norm",                                  # qwen3-embedding
+            "model.layers.{bid}.self_attn.k_rope_only_layernorm",             # dots3note
             "model.layers.{bid}.attention.key_layernorm",                     # apertus
         ),
 

src/CMakeLists.txt

@@ -25,6 +25,7 @@ add_library(llama
             llama-kv-cache.cpp
             llama-kv-cache-iswa.cpp
             llama-kv-cache-dsa.cpp
+            llama-kv-cache-dsa-iswa.cpp
             llama-kv-cache-msa.cpp
             llama-kv-cache-dsv4.cpp
             llama-memory.cpp

src/llama-arch.cpp

@@ -110,6 +110,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_BAILINGMOE2,      "bailingmoe2"      },
     { LLM_ARCH_BAILINGMOE3,      "bailingmoe3"      },
     { LLM_ARCH_DOTS1,            "dots1"            },
+    { LLM_ARCH_DOTS3NOTE,        "dots3note"        },
     { LLM_ARCH_ARCEE,            "arcee"            },
     { LLM_ARCH_AFMOE,            "afmoe"            },
     { LLM_ARCH_LAGUNA,           "laguna"           },
@@ -273,6 +274,9 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
     { LLM_KV_ATTENTION_KEY_LENGTH_SWA,               "%s.attention.key_length_swa"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_SWA,             "%s.attention.value_length_swa"             },
+    { LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,           "%s.attention.key_length_mla_swa"           },
+    { LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA,         "%s.attention.value_length_mla_swa"         },
+    { LLM_KV_ATTENTION_KV_LORA_RANK_SWA,             "%s.attention.kv_lora_rank_swa"             },
     { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,           "%s.attention.indexer.head_count"           },
     { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,           "%s.attention.indexer.key_length"           },
     { LLM_KV_ATTENTION_INDEXER_TOP_K,                "%s.attention.indexer.top_k"                },
@@ -1056,6 +1060,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK32:
         case LLM_ARCH_DEEPSEEK4:
+        case LLM_ARCH_DOTS3NOTE:
         case LLM_ARCH_GLM_DSA:
         case LLM_ARCH_BITNET:
         case LLM_ARCH_T5:

src/llama-arch.h

@@ -115,6 +115,7 @@ enum llm_arch {
     LLM_ARCH_BAILINGMOE2,
     LLM_ARCH_BAILINGMOE3,
     LLM_ARCH_DOTS1,
+    LLM_ARCH_DOTS3NOTE,
     LLM_ARCH_ARCEE,
     LLM_ARCH_AFMOE,
     LLM_ARCH_LAGUNA,
@@ -278,6 +279,9 @@ enum llm_kv {
     LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
     LLM_KV_ATTENTION_KEY_LENGTH_SWA,
     LLM_KV_ATTENTION_VALUE_LENGTH_SWA,
+    LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,
+    LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA,
+    LLM_KV_ATTENTION_KV_LORA_RANK_SWA,
     LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
     LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
     LLM_KV_ATTENTION_INDEXER_TOP_K,

src/llama-graph.cpp

@@ -9,6 +9,7 @@
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
 #include "llama-kv-cache-dsa.h"
+#include "llama-kv-cache-dsa-iswa.h"
 #include "llama-kv-cache-msa.h"
 #include "llama-kv-cache-dsv4.h"
 #include "llama-memory-hybrid.h"
@@ -507,10 +508,12 @@ void llm_graph_input_attn_k::set_input(const llama_ubatch * ubatch) {
 }
 
 bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) {
-    const auto * mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
+    mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
 
-    this->mctx = mctx;
+    return can_reuse_impl(params);
+}
 
+bool llm_graph_input_attn_k::can_reuse_impl(const llm_graph_params & params) {
     bool res = true;
 
     res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
@@ -567,10 +570,12 @@ void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) {
 }
 
 bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
-    const auto * mctx = static_cast<const llama_kv_cache_dsa_context *>(params.mctx);
+    mctx = static_cast<const llama_kv_cache_dsa_context *>(params.mctx);
 
-    this->mctx = mctx;
+    return can_reuse_impl(params);
+}
 
+bool llm_graph_input_attn_k_dsa::can_reuse_impl(const llm_graph_params & params) {
     bool res = true;
 
     res &= self_k_idxs_mla->ne[0] == params.ubatch.n_tokens;
@@ -582,6 +587,25 @@ bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
     return res;
 }
 
+void llm_graph_input_attn_k_dsa_iswa::set_input(const llama_ubatch * ubatch) {
+    inp_dsa->set_input(ubatch);
+    inp_swa->set_input(ubatch);
+}
+
+bool llm_graph_input_attn_k_dsa_iswa::can_reuse(const llm_graph_params & params) {
+    mctx = static_cast<const llama_kv_cache_dsa_iswa_context *>(params.mctx);
+
+    inp_dsa->mctx = mctx->get_dsa();
+    inp_swa->mctx = mctx->get_swa();
+
+    bool res = true;
+
+    res &= inp_dsa->can_reuse_impl(params);
+    res &= inp_swa->can_reuse_impl(params);
+
+    return res;
+}
+
 void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
     // base tensors may not be allocated if there are no non-SWA attention layers
     if (self_k_idxs && self_k_idxs->buffer) {
@@ -3210,8 +3234,12 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
-llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
+static std::unique_ptr<llm_graph_input_attn_k_dsa> build_attn_inp_k_dsa_impl(
-    const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_context *>(mctx);
+           ggml_context * ctx0,
+     const llama_ubatch & ubatch,
+    const llama_hparams & hparams,
+    const llama_cparams & cparams,
+    const llama_kv_cache_dsa_context * mctx_cur) {
 
     auto inp = std::make_unique<llm_graph_input_attn_k_dsa>(hparams, cparams, mctx_cur);
 
@@ -3235,9 +3263,35 @@ llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
         inp->self_k_rot_lid = mctx_cur->get_lid()->build_input_k_rot(ctx0);
     }
 
+    return inp;
+}
+
+llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_context *>(mctx);
+
+    auto inp = build_attn_inp_k_dsa_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
+
     return (llm_graph_input_attn_k_dsa *) res->add_input(std::move(inp));
 }
 
+llm_graph_input_attn_k_dsa_iswa * llm_graph_context::build_attn_inp_k_dsa_iswa() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_iswa_context *>(mctx);
+
+    auto inp_dsa = build_attn_inp_k_dsa_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_dsa());
+
+    // build_attn_inp_k_impl rejects SWA caches, so construct the input directly
+    auto inp_swa = std::make_unique<llm_graph_input_attn_k>(hparams, cparams, mctx_cur->get_swa());
+
+    inp_swa->self_k_idxs = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
+
+    inp_swa->self_kq_mask = build_attn_inp_kq_mask(ctx0, mctx_cur->get_swa(), ubatch, cparams);
+    inp_swa->self_kq_mask_cnv = inp_swa->self_kq_mask;
+
+    auto inp = std::make_unique<llm_graph_input_attn_k_dsa_iswa>(std::move(inp_dsa), std::move(inp_swa), mctx_cur);
+
+    return (llm_graph_input_attn_k_dsa_iswa *) res->add_input(std::move(inp));
+}
+
 llm_graph_input_attn_kv_msa * llm_graph_context::build_attn_inp_kv_msa(bool msa_enabled) const {
     const auto * mctx_cur = static_cast<const llama_kv_cache_msa_context *>(mctx);
 

src/llama-graph.h

@@ -23,6 +23,7 @@ struct llama_memory_context_i;
 
 class llama_kv_cache_context;
 class llama_kv_cache_dsa_context;
+class llama_kv_cache_dsa_iswa_context;
 class llama_kv_cache_msa_context;
 class llama_kv_cache_dsv4_raw_context;
 class llama_kv_cache_dsv4_context;
@@ -374,6 +375,9 @@ public:
 
     bool can_reuse(const llm_graph_params & params) override;
 
+    // like can_reuse, but does not re-bind mctx
+    bool can_reuse_impl(const llm_graph_params & params);
+
     ggml_tensor * get_k_idxs() const { return self_k_idxs; }
 
     ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
@@ -405,6 +409,9 @@ public:
 
     bool can_reuse(const llm_graph_params & params) override;
 
+    // like can_reuse, but does not re-bind mctx
+    bool can_reuse_impl(const llm_graph_params & params);
+
     ggml_tensor * get_k_idxs_mla() const { return self_k_idxs_mla; }
     ggml_tensor * get_k_idxs_lid() const { return self_k_idxs_lid; }
 
@@ -427,6 +434,32 @@ public:
     const llama_kv_cache_dsa_context * mctx;
 };
 
+// DSA input (full-attention layers + indexer) with K-only input for the SWA layers
+class llm_graph_input_attn_k_dsa_iswa : public llm_graph_input_i {
+public:
+    llm_graph_input_attn_k_dsa_iswa(
+            std::unique_ptr<llm_graph_input_attn_k_dsa> inp_dsa,
+            std::unique_ptr<llm_graph_input_attn_k>     inp_swa,
+            const llama_kv_cache_dsa_iswa_context *     mctx) :
+        inp_dsa(std::move(inp_dsa)),
+        inp_swa(std::move(inp_swa)),
+        mctx(mctx) {
+    }
+    ~llm_graph_input_attn_k_dsa_iswa() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    bool can_reuse(const llm_graph_params & params) override;
+
+    llm_graph_input_attn_k_dsa * get_dsa() const { return inp_dsa.get(); }
+    llm_graph_input_attn_k     * get_swa() const { return inp_swa.get(); }
+
+    std::unique_ptr<llm_graph_input_attn_k_dsa> inp_dsa;
+    std::unique_ptr<llm_graph_input_attn_k>     inp_swa;
+
+    const llama_kv_cache_dsa_iswa_context * mctx;
+};
+
 // standard K/V attention input against the base cache, plus destination indices for the indexer key cache
 class llm_graph_input_attn_kv_msa : public llm_graph_input_attn_kv {
 public:
@@ -1191,6 +1224,8 @@ struct llm_graph_context {
 
     llm_graph_input_attn_k_dsa * build_attn_inp_k_dsa() const;
 
+    llm_graph_input_attn_k_dsa_iswa * build_attn_inp_k_dsa_iswa() const;
+
     llm_graph_input_attn_kv_msa * build_attn_inp_kv_msa(bool msa_enabled) const;
 
     ggml_tensor * build_attn(

src/llama-hparams.h

@@ -101,6 +101,11 @@ struct llama_hparams {
     uint32_t n_group_used       = 0;
     uint32_t n_group_experts    = 0;
 
+    // MLA + SWA (i.e. dots3note)
+    uint32_t n_lora_kv_swa           = 0;
+    uint32_t n_embd_head_k_mla_swa   = 0;
+    uint32_t n_embd_head_v_mla_swa   = 0;
+
     float    expert_group_scale   = 0.05f;
     float    expert_weights_scale = 0.0f;
     bool     expert_weights_norm  = false;

src/llama-kv-cache-dsa-iswa.cpp

@@ -0,0 +1,341 @@
+#include "llama-kv-cache-dsa-iswa.h"
+
+#include "llama-impl.h"
+#include "llama-batch.h"
+#include "llama-model.h"
+
+#include <algorithm>
+#include <cassert>
+
+//
+// llama_kv_cache_dsa_iswa
+//
+
+llama_kv_cache_dsa_iswa::llama_kv_cache_dsa_iswa(
+        const llama_model & model,
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                     bool   offload,
+                     bool   swa_full,
+                     bool   unified,
+                 uint32_t   kv_size,
+                 uint32_t   n_seq_max,
+                 uint32_t   n_ubatch,
+                 uint32_t   n_pad,
+    const layer_filter_cb & filter_mla,
+    const layer_filter_cb & filter_lid,
+    const  layer_reuse_cb & reuse) : unified(unified) {
+
+    const auto & hparams = model.hparams;
+
+    // chain filters
+    const layer_filter_cb filter_dsa = [&](int32_t il) {
+        if (filter_mla && !filter_mla(il)) {
+            return false;
+        }
+
+        return !hparams.is_swa(il);
+    };
+
+    const layer_filter_cb filter_swa = [&](int32_t il) {
+        if (filter_mla && !filter_mla(il)) {
+            return false;
+        }
+
+        return hparams.is_swa(il);
+    };
+
+    const uint32_t size_dsa = kv_size;
+
+    // note: the SWA cache is always padded to 256 for performance
+    //       https://github.com/ggml-org/llama.cpp/issues/17037
+    uint32_t size_swa = GGML_PAD(std::min(size_dsa, hparams.n_swa*(unified ? n_seq_max : 1) + n_ubatch), 256);
+
+    // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
+    if (swa_full) {
+        LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n",
+                __func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
+
+        size_swa = size_dsa;
+    }
+
+    LLAMA_LOG_INFO("%s: creating DSA KV cache, size = %u cells\n", __func__, size_dsa);
+
+    kv_dsa = std::make_unique<llama_kv_cache_dsa>(
+            model, type_k, type_v,
+            v_trans, offload, unified, size_dsa, n_seq_max, n_pad,
+            0, LLAMA_SWA_TYPE_NONE, filter_dsa, filter_lid, reuse);
+
+    LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
+
+    kv_swa = std::make_unique<llama_kv_cache>(
+            model, hparams, type_k, type_v,
+            v_trans, offload, unified, size_swa, n_seq_max, n_pad,
+            hparams.n_swa, hparams.swa_type, nullptr, filter_swa, reuse, nullptr);
+}
+
+void llama_kv_cache_dsa_iswa::clear(bool data) {
+    kv_dsa->clear(data);
+    kv_swa->clear(data);
+}
+
+bool llama_kv_cache_dsa_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    bool res = true;
+
+    res = res & kv_dsa->seq_rm(seq_id, p0, p1);
+    res = res & kv_swa->seq_rm(seq_id, p0, p1);
+
+    return res;
+}
+
+void llama_kv_cache_dsa_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    kv_dsa->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+    kv_swa->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+}
+
+void llama_kv_cache_dsa_iswa::seq_keep(llama_seq_id seq_id) {
+    kv_dsa->seq_keep(seq_id);
+    kv_swa->seq_keep(seq_id);
+}
+
+void llama_kv_cache_dsa_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    kv_dsa->seq_add(seq_id, p0, p1, shift);
+    kv_swa->seq_add(seq_id, p0, p1, shift);
+}
+
+void llama_kv_cache_dsa_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+    kv_dsa->seq_div(seq_id, p0, p1, d);
+    kv_swa->seq_div(seq_id, p0, p1, d);
+}
+
+llama_pos llama_kv_cache_dsa_iswa::seq_pos_min(llama_seq_id seq_id) const {
+    // the DSA cache is a superset of the SWA cache, so we can just check the SWA cache
+    return kv_swa->seq_pos_min(seq_id);
+}
+
+llama_pos llama_kv_cache_dsa_iswa::seq_pos_max(llama_seq_id seq_id) const {
+    return kv_swa->seq_pos_max(seq_id);
+}
+
+std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_dsa_iswa::memory_breakdown() const {
+    std::map<ggml_backend_buffer_type_t, size_t> mb = kv_dsa->memory_breakdown();
+    for (const auto & buft_size : kv_swa->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    return mb;
+}
+
+llama_memory_context_ptr llama_kv_cache_dsa_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
+    GGML_UNUSED(embd_all);
+
+    // first try simple split
+    do {
+        if (!unified) {
+            // requires equal splits, so we skip the simple split
+            break;
+        }
+
+        balloc.split_reset();
+
+        std::vector<llama_ubatch> ubatches;
+        while (true) {
+            auto ubatch = balloc.split_simple(n_ubatch);
+
+            if (ubatch.n_tokens == 0) {
+                break;
+            }
+
+            ubatches.push_back(std::move(ubatch)); // NOLINT
+        }
+
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
+        auto sinfos_mla = kv_dsa->get_mla()->prepare(ubatches);
+        if (sinfos_mla.empty()) {
+            break;
+        }
+
+        auto sinfos_lid = kv_dsa->get_lid()->prepare(ubatches);
+        if (sinfos_lid.empty()) {
+            break;
+        }
+
+        auto sinfos_swa = kv_swa->prepare(ubatches);
+        if (sinfos_swa.empty()) {
+            break;
+        }
+
+        assert(sinfos_mla.size() == sinfos_swa.size());
+
+        return std::make_unique<llama_kv_cache_dsa_iswa_context>(
+                this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(sinfos_swa), std::move(ubatches));
+    } while (false);
+
+    // if it fails, try equal split
+    do {
+        balloc.split_reset();
+
+        std::vector<llama_ubatch> ubatches;
+        while (true) {
+            auto ubatch = balloc.split_equal(n_ubatch, !unified, 0);
+
+            if (ubatch.n_tokens == 0) {
+                break;
+            }
+
+            ubatches.push_back(std::move(ubatch)); // NOLINT
+        }
+
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
+        auto sinfos_mla = kv_dsa->get_mla()->prepare(ubatches);
+        if (sinfos_mla.empty()) {
+            break;
+        }
+
+        auto sinfos_lid = kv_dsa->get_lid()->prepare(ubatches);
+        if (sinfos_lid.empty()) {
+            break;
+        }
+
+        auto sinfos_swa = kv_swa->prepare(ubatches);
+        if (sinfos_swa.empty()) {
+            break;
+        }
+
+        assert(sinfos_mla.size() == sinfos_swa.size());
+
+        return std::make_unique<llama_kv_cache_dsa_iswa_context>(
+                this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(sinfos_swa), std::move(ubatches));
+    } while (false);
+
+    return std::make_unique<llama_kv_cache_dsa_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+}
+
+llama_memory_context_ptr llama_kv_cache_dsa_iswa::init_full() {
+    return std::make_unique<llama_kv_cache_dsa_iswa_context>(this);
+}
+
+llama_memory_context_ptr llama_kv_cache_dsa_iswa::init_update(llama_context * lctx, bool optimize) {
+    return std::make_unique<llama_kv_cache_dsa_iswa_context>(this, lctx, optimize);
+}
+
+bool llama_kv_cache_dsa_iswa::get_can_shift() const {
+    return kv_dsa->get_can_shift() &&
+           kv_swa->get_can_shift() &&
+           kv_dsa->get_mla()->get_size() == kv_swa->get_size();
+}
+
+void llama_kv_cache_dsa_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
+        kv_dsa->state_write(io, seq_id, flags);
+    }
+
+    kv_swa->state_write(io, seq_id, flags);
+}
+
+void llama_kv_cache_dsa_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
+        kv_dsa->state_read(io, seq_id, flags);
+    }
+
+    kv_swa->state_read(io, seq_id, flags);
+}
+
+llama_kv_cache_dsa * llama_kv_cache_dsa_iswa::get_dsa() const {
+    return kv_dsa.get();
+}
+
+llama_kv_cache * llama_kv_cache_dsa_iswa::get_swa() const {
+    return kv_swa.get();
+}
+
+//
+// llama_kv_cache_dsa_iswa_context
+//
+
+llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(llama_memory_status status) : status(status) {}
+
+llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(
+        llama_kv_cache_dsa_iswa * kv) :
+    ctx_dsa(kv->get_dsa()->init_full()),
+    ctx_swa(kv->get_swa()->init_full()),
+    status(llama_memory_status_combine(ctx_dsa->get_status(), ctx_swa->get_status())) {
+}
+
+llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(
+        llama_kv_cache_dsa_iswa * kv,
+        llama_context * lctx,
+        bool optimize) :
+    ctx_dsa(kv->get_dsa()->init_update(lctx, optimize)),
+    ctx_swa(kv->get_swa()->init_update(lctx, optimize)),
+    status(llama_memory_status_combine(ctx_dsa->get_status(), ctx_swa->get_status())) {
+}
+
+llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(
+        llama_kv_cache_dsa_iswa * kv,
+        slot_info_vec_t sinfos_mla,
+        slot_info_vec_t sinfos_lid,
+        slot_info_vec_t sinfos_swa,
+        std::vector<llama_ubatch> ubatches) :
+    ubatches(std::move(ubatches)),
+    // note: here we copy the ubatches. not sure if this is ideal
+    ctx_dsa(new llama_kv_cache_dsa_context(kv->get_dsa(), std::move(sinfos_mla), std::move(sinfos_lid), this->ubatches)),
+    ctx_swa(new llama_kv_cache_context(kv->get_swa(), std::move(sinfos_swa), this->ubatches)),
+    status(llama_memory_status_combine(ctx_dsa->get_status(), ctx_swa->get_status())) {
+}
+
+llama_kv_cache_dsa_iswa_context:: ~llama_kv_cache_dsa_iswa_context() = default;
+
+bool llama_kv_cache_dsa_iswa_context::next() {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    ctx_dsa->next();
+    ctx_swa->next();
+
+    if (++i_next >= ubatches.size()) {
+        return false;
+    }
+
+    return true;
+}
+
+bool llama_kv_cache_dsa_iswa_context::apply() {
+    assert(!llama_memory_status_is_fail(status));
+
+    bool res = true;
+
+    res = res & ctx_dsa->apply();
+    res = res & ctx_swa->apply();
+
+    return res;
+}
+
+llama_memory_status llama_kv_cache_dsa_iswa_context::get_status() const {
+    return status;
+}
+
+const llama_ubatch & llama_kv_cache_dsa_iswa_context::get_ubatch() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ubatches[i_next];
+}
+
+const llama_kv_cache_dsa_context * llama_kv_cache_dsa_iswa_context::get_dsa() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return static_cast<const llama_kv_cache_dsa_context *>(ctx_dsa.get());
+}
+
+const llama_kv_cache_context * llama_kv_cache_dsa_iswa_context::get_swa() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return static_cast<const llama_kv_cache_context *>(ctx_swa.get());
+}

src/llama-kv-cache-dsa-iswa.h

@@ -0,0 +1,134 @@
+#pragma once
+
+#include "llama-kv-cache-dsa.h"
+
+#include <vector>
+
+//
+// llama_kv_cache_dsa_iswa
+//
+
+// utilizes two child memories: llama_kv_cache_dsa for the full-attention (DSA) layers and llama_kv_cache for the SWA layers
+
+class llama_kv_cache_dsa_iswa : public llama_memory_i {
+public:
+    llama_kv_cache_dsa_iswa(
+            const llama_model & model,
+                    ggml_type   type_k,
+                    ggml_type   type_v,
+                         bool   v_trans,
+                         bool   offload,
+                         bool   swa_full,
+                         bool   unified,
+                     uint32_t   kv_size,
+                     uint32_t   n_seq_max,
+                     uint32_t   n_ubatch,
+                     uint32_t   n_pad,
+        const layer_filter_cb & filter_mla,
+        const layer_filter_cb & filter_lid,
+        const  layer_reuse_cb & reuse);
+
+    ~llama_kv_cache_dsa_iswa() = default;
+
+    //
+    // llama_memory_i
+    //
+
+    llama_memory_context_ptr init_batch(
+            llama_batch_allocr & balloc,
+            uint32_t n_ubatch,
+            bool embd_all) override;
+
+    llama_memory_context_ptr init_full() override;
+
+    llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
+
+    bool get_can_shift() const override;
+
+    void clear(bool data) override;
+
+    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
+    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
+    void seq_keep(llama_seq_id seq_id)                                                          override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
+    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
+
+    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+    llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+    std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
+
+    // state write/load
+
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
+
+    //
+    // llama_kv_cache_dsa_iswa specific API
+    //
+
+    llama_kv_cache_dsa * get_dsa() const;
+    llama_kv_cache     * get_swa() const;
+
+private:
+    const bool unified;
+
+    std::unique_ptr<llama_kv_cache_dsa> kv_dsa;
+    std::unique_ptr<llama_kv_cache>     kv_swa;
+};
+
+class llama_kv_cache_dsa_iswa_context : public llama_memory_context_i {
+public:
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
+
+    // used for errors
+    llama_kv_cache_dsa_iswa_context(llama_memory_status status);
+
+    // used to create a full-cache context
+    llama_kv_cache_dsa_iswa_context(
+            llama_kv_cache_dsa_iswa * kv);
+
+    // used to create an update context
+    llama_kv_cache_dsa_iswa_context(
+            llama_kv_cache_dsa_iswa * kv,
+            llama_context * lctx,
+            bool optimize);
+
+    // used to create a batch processing context from a batch
+    llama_kv_cache_dsa_iswa_context(
+            llama_kv_cache_dsa_iswa * kv,
+            slot_info_vec_t sinfos_mla,
+            slot_info_vec_t sinfos_lid,
+            slot_info_vec_t sinfos_swa,
+            std::vector<llama_ubatch> ubatches);
+
+    virtual ~llama_kv_cache_dsa_iswa_context();
+
+    //
+    // llama_memory_context_i
+    //
+
+    bool next()  override;
+    bool apply() override;
+
+    llama_memory_status  get_status() const override;
+    const llama_ubatch & get_ubatch() const override;
+
+    //
+    // llama_kv_cache_dsa_iswa_context specific API
+    //
+
+    const llama_kv_cache_dsa_context * get_dsa() const;
+    const llama_kv_cache_context     * get_swa() const;
+
+private:
+    // the index of the next ubatch to process
+    size_t i_next = 0;
+
+    std::vector<llama_ubatch> ubatches;
+
+    const llama_memory_context_ptr ctx_dsa;
+    const llama_memory_context_ptr ctx_swa;
+
+    const llama_memory_status status;
+};

src/llama-kv-cache.cpp

@@ -323,7 +323,8 @@ llama_kv_cache::llama_kv_cache(
             hparams.n_embd_head_k() % 64 == 0;
 
         // always create Hadamard rotation tensors for DeepSeek lightning indexers
-        if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 || model.arch == LLM_ARCH_GLM_DSA) &&
+        if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 ||
+                model.arch == LLM_ARCH_GLM_DSA || model.arch == LLM_ARCH_DOTS3NOTE) &&
                 hparams.n_embd_head_k_full == hparams.indexer_head_size) {
             attn_rot_k = true;
         }

src/llama-model-saver.cpp

@@ -31,6 +31,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
         case LLM_ARCH_MELLUM:
         case LLM_ARCH_LAGUNA:
         case LLM_ARCH_GRANITE_SWA:
+        case LLM_ARCH_DOTS3NOTE: // TODO: need to handle SWA pattern and MLA+SWA config
             return false;
         default:
             return true;

src/llama-model.cpp

@@ -11,6 +11,7 @@
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
 #include "llama-kv-cache-dsa.h"
+#include "llama-kv-cache-dsa-iswa.h"
 #include "llama-kv-cache-msa.h"
 #include "llama-kv-cache-dsv4.h"
 #include "llama-memory-hybrid.h"
@@ -194,6 +195,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_deepseek2ocr(params);
         case LLM_ARCH_DEEPSEEK32:
             return new llama_model_deepseek32(params);
+        case LLM_ARCH_DOTS3NOTE:
+            return new llama_model_dots3note(params);
         case LLM_ARCH_DEEPSEEK4:
             return new llama_model_deepseek4(params);
         case LLM_ARCH_GLM_DSA:
@@ -851,6 +854,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
         case LLM_TYPE_428B_A23B:     return "428B.A23B";
         case LLM_TYPE_235B_A22B:     return "235B.A22B";
+        case LLM_TYPE_288B_A19B:     return "288B.A19B";
         case LLM_TYPE_300B_A47B:     return "300B.A47B";
         case LLM_TYPE_310B_A15B:     return "310B.A15B";
         case LLM_TYPE_355B_A32B:     return "355B.A32B";
@@ -1924,7 +1928,9 @@ void llama_model::print_info() const {
             LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
         }
 
-        if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
+        if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR ||
+                arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA ||
+                arch == LLM_ARCH_DOTS3NOTE || arch == LLM_ARCH_MISTRAL4) {
             LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
             LLAMA_LOG_INFO("%s: n_lora_q              = %d\n",     __func__, hparams.n_lora_q);
             LLAMA_LOG_INFO("%s: n_lora_kv             = %d\n",     __func__, hparams.n_lora_kv);
@@ -2193,6 +2199,57 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             nullptr);
                 }
             } break;
+        case LLM_ARCH_DOTS3NOTE:
+            {
+                GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
+
+                if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) {
+                    // MTP draft context: plain attention KV cache holding only the nextn layer
+                    llama_kv_cache::layer_filter_cb filter =
+                        [&](uint32_t il) { return il >= hparams.n_layer(); };
+
+                    res = new llama_kv_cache(
+                            *this,
+                            hparams,
+                            params.type_k,
+                            params.type_v,
+                            !cparams.flash_attn,
+                            cparams.offload_kqv,
+                            cparams.kv_unified,
+                            cparams.n_ctx_seq,
+                            cparams.n_seq_max,
+                            1,
+                            hparams.n_swa,
+                            hparams.swa_type,
+                            nullptr,
+                            filter,
+                            nullptr,
+                            nullptr);
+                } else {
+                    // main context: DSA cache for the trunk full-attention layers plus a window-sized SWA cache
+                    llama_kv_cache::layer_filter_cb filter_mla = nullptr;
+                    if (hparams.n_layer_nextn > 0) {
+                        filter_mla = [&](uint32_t il) { return il < hparams.n_layer(); };
+                    }
+                    llama_kv_cache::layer_filter_cb filter_lid = [&](uint32_t il) { return il < hparams.n_layer() && hparams.is_indexer_full(il); };
+
+                    res = new llama_kv_cache_dsa_iswa(
+                            *this,
+                            params.type_k,
+                            params.type_v,
+                            !cparams.flash_attn,
+                            cparams.offload_kqv,
+                            params.swa_full,
+                            cparams.kv_unified,
+                            cparams.n_ctx_seq,
+                            cparams.n_seq_max,
+                            cparams.n_ubatch,
+                            1,
+                            filter_mla,
+                            filter_lid,
+                            nullptr);
+                }
+            } break;
         case LLM_ARCH_DEEPSEEK4:
             {
                 GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
@@ -2661,6 +2718,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_LLAMA_EMBED:
         case LLM_ARCH_MAINCODER:
         case LLM_ARCH_GLM_DSA:
+        case LLM_ARCH_DOTS3NOTE:
         case LLM_ARCH_NANBEIGE:
         case LLM_ARCH_POCKETTTS:
             return LLAMA_ROPE_TYPE_NORM;

src/llama-model.h

@@ -140,6 +140,7 @@ enum llm_type {
     LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_428B_A23B, // Minimax M3
     LLM_TYPE_235B_A22B,
+    LLM_TYPE_288B_A19B, // dots3-note
     LLM_TYPE_300B_A47B, // Ernie MoE big
     LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
     LLM_TYPE_355B_A32B, // GLM-4.5

src/models/dots3note.cpp

@@ -0,0 +1,480 @@
+#include "models.h"
+
+#include "llama-kv-cache.h"
+#include "llama-kv-cache-dsa.h"
+
+// note: code adapted from deepseek32.cpp (DSA indexer + absorbed MLA) and step35.cpp (head-wise output gate)
+
+void llama_model_dots3note::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    hparams.f_norm_eps = 1e-6;  // eps for the indexer k_norm layer norm
+
+    // TODO: use MTP layer
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
+    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
+
+    // MoE parameters
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,  hparams.n_layer_dense_lead);
+    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);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,         hparams.expert_gating_func);
+
+    // MLA parameters of the full-attention layers
+    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,      hparams.n_lora_q);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   hparams.n_embd_head_k_mla_impl);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
+
+    // MLA parameters of the sliding-window layers
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK_SWA,     hparams.n_lora_kv_swa);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,   hparams.n_embd_head_k_mla_swa);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, hparams.n_embd_head_v_mla_swa);
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    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_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
+
+    // DSA parameters
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k);
+    ml.get_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl);
+
+    switch (hparams.n_layer()) {
+        case 46: type = LLM_TYPE_288B_A19B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_dots3note::load_arch_tensors(llama_model_loader & ml) {
+    LLAMA_LOAD_LOCALS;
+    GGML_UNUSED(ml);
+
+    if (!hparams.is_mla()) {
+        throw std::runtime_error("DOTS3NOTE architecture requires MLA");
+    }
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+
+    const int64_t q_lora_rank     = hparams.n_lora_q;
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    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) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer_all; ++i) {
+        auto & layer = layers[i];
+
+        const bool is_mtp = i >= n_layer;
+        // the NextN/MTP block uses the sliding-attention geometry
+        const bool is_swa = is_mtp || hparams.is_swa(i);
+
+        // MTP tensors are preserved in the GGUF but there is no MTP graph yet
+        const int flags = is_mtp ? TENSOR_SKIP | TENSOR_NOT_REQUIRED : 0;
+
+        const int64_t n_head_l = hparams.n_head(i);
+
+        const int64_t kv_lora_rank       = is_swa ? hparams.n_lora_kv_swa         : hparams.n_lora_kv;
+        const int64_t n_embd_head_k_mla  = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla();
+        const int64_t n_embd_head_v_mla  = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla();
+        const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
+
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, flags);
+        layer.attn_q_a_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM,  "weight", i), {q_lora_rank}, flags);
+        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
+        // norm applied on the shared rope key before rope
+        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_qk_rope}, flags);
+
+        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
+        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head_l * n_embd_head_k_mla}, flags);
+
+        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
+
+        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head_l}, flags);
+        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head_l}, flags);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head_l * n_embd_head_v_mla, n_embd}, flags);
+
+        // head-wise sigmoid output gate
+        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, flags);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
+
+        // DSA indexer
+        if (!is_mtp && hparams.is_indexer_full(i)) {
+            layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags);
+            layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags);
+            layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags);
+            layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags);
+            layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
+        }
+
+        if (is_mtp || i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);
+        } else {
+            if (n_expert == 0 || n_expert_used == 0) {
+                throw std::runtime_error("n_expert and n_expert_used must be > 0");
+            }
+
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, flags);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, flags);
+
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, flags);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
+
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, flags);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
+        }
+
+        if (is_mtp) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), { 2 * n_embd, n_embd }, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), { n_embd }, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), { n_embd }, flags);
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), { n_embd, n_vocab }, flags);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_dots3note::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_dots3note::graph::graph(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    GGML_ASSERT(hparams.is_mla());
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+
+    const int64_t n_indexer_head      = hparams.indexer_n_head;
+    const int64_t n_embd_indexer_head = hparams.indexer_head_size;
+    const uint32_t n_indexer_top_k = hparams.indexer_top_k;
+
+    // the indexer head layout is [rope | nope]
+    GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    llm_graph_input_attn_k_dsa_iswa * inp_attn = build_attn_inp_k_dsa_iswa();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        const bool is_swa = hparams.is_swa(il);
+
+        const int64_t n_head_l = hparams.n_head(il);
+
+        const int64_t kv_lora_rank        = is_swa ? hparams.n_lora_kv_swa         : hparams.n_lora_kv;
+        const int64_t n_embd_head_k_mla   = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla();
+        const int64_t n_embd_head_v_mla   = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla();
+        const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
+
+        const float kq_scale    = 1.0f/sqrtf(float(n_embd_head_k_mla));
+        const float freq_base_l = model.get_rope_freq_base(cparams, il);
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self_attention
+        {
+            ggml_tensor * attn_inp = cur;
+
+            ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+            cb(qr, "qr", il);
+
+            qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
+            cb(qr, "qr", il);
+
+            ggml_tensor * top_k = nullptr;
+
+            // lightning indexer (full-attention layers only)
+            if (!is_swa) {
+                ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
+                cb(indexer_q, "indexer_q", il);
+
+                // {n_embd_indexer_head, n_indexer_head, n_tokens}
+                indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens);
+                indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot,
+                                     LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
+                                     ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(indexer_q, "indexer_q", il);
+
+                ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
+                cb(indexer_k, "indexer_k", il);
+
+                indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
+                cb(indexer_k, "indexer_k", il);
+
+                // {n_embd_indexer_head, 1, n_tokens}
+                indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens);
+                indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot,
+                                     LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
+                                     ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(indexer_k, "indexer_k", il);
+
+                // perform Hadamard transform on indexer q and k
+                indexer_q = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_q);
+                cb(indexer_q, "indexer_q", il);
+                indexer_k = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_k);
+                cb(indexer_k, "indexer_k", il);
+
+                // store indexer keys to KV cache
+                const auto * mctx_lid = inp_attn->get_dsa()->mctx->get_lid();
+                const auto & k_idxs_lid = inp_attn->get_dsa()->get_k_idxs_lid();
+                ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));
+
+                ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur);
+                cb(indexer_weights, "indexer_weights", il);
+
+                indexer_k = mctx_lid->get_k(ctx0, il);
+
+                // split the batch into streams if needed
+                const auto n_stream = indexer_k->ne[3];
+                indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
+                indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
+
+                // pre-scale weights to avoid scaling operations on huge indexer_score tensor
+                indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));
+                cb(indexer_weights, "indexer_weights", il);
+
+                ggml_tensor * indexer_score = nullptr;
+                if (cparams.fused_lid) {
+                    indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn->get_dsa()->get_kq_mask_lid());
+                    cb(indexer_score, "indexer_score", il);
+                    res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});
+                } else {
+                    indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
+                    cb(indexer_q, "indexer_q", il);
+                    indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
+                    cb(indexer_k, "indexer_k", il);
+
+                    ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
+                    cb(indexer_kq, "indexer_kq", il);
+
+                    // ReLU requires contiguous tensors
+                    indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
+                    cb(indexer_kq, "indexer_kq", il);
+
+                    indexer_score = ggml_relu(ctx0, indexer_kq);
+                    cb(indexer_score, "indexer_score", il);
+
+                    indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
+                    cb(indexer_score, "indexer_score", il);
+
+                    // sum by q n_indexer_head dimension
+                    indexer_score = ggml_sum_rows(ctx0, indexer_score);
+                    cb(indexer_score, "indexer_score", il);
+
+                    // permute result to match KQ mask
+                    indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
+                    cb(indexer_score, "indexer_score", il);
+
+                    ggml_tensor * indexer_kq_mask = inp_attn->get_dsa()->get_kq_mask_lid();
+                    indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
+                    cb(indexer_score, "indexer_score", il);
+                }
+
+                // get indices of top k indexer scores
+                uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;
+                top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
+                cb(top_k, "top_k", il);
+            }
+
+            ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr);
+            cb(q, "q", il);
+
+            // split into {n_embd_head_qk_nope, n_head_l, n_tokens}
+            ggml_tensor * q_nope =
+                ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla),
+                             ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, 0);
+            cb(q_nope, "q_nope", il);
+
+            // and {n_embd_head_qk_rope, n_head_l, n_tokens}
+            ggml_tensor * q_pe = ggml_view_3d(
+                ctx0, q, n_embd_head_qk_rope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla),
+                ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, ggml_row_size(q->type, n_embd_head_qk_nope));
+            cb(q_pe, "q_pe", il);
+
+            ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+            cb(kv_cmpr_pe, "kv_cmpr_pe", il);
+
+            // split into {kv_lora_rank, n_tokens}
+            ggml_tensor * kv_cmpr =
+                ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+                             ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+            cb(kv_cmpr, "kv_cmpr", il);
+
+            // and {n_embd_head_qk_rope, 1, n_tokens}
+            ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+            cb(k_pe, "k_pe", il);
+
+            // norm on the shared rope key, applied before rope
+            k_pe = build_norm(k_pe, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+            cb(k_pe, "k_pe", il);
+
+            q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(q_pe, "q_pe", il);
+
+            k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(k_pe, "k_pe", il);
+
+            kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+            cb(kv_cmpr, "kv_cmpr", il);
+
+            // MLA attention with the absorption optimization
+            {
+                // {n_embd_head_qk_nope, n_tokens, n_head_l}
+                q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+                cb(q_nope, "q_nope_perm", il);
+
+                // {n_embd_head_qk_nope, kv_lora_rank, n_head_l} x {n_embd_head_qk_nope, n_tokens, n_head_l}
+                ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
+                cb(q_nope_absorbed, "q_nope_absorbed", il);
+
+                // {kv_lora_rank, n_head_l, n_tokens}
+                q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+                cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
+
+                // {n_embd_head_qk_rope + kv_lora_rank, n_head_l, n_tokens}
+                ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
+                cb(Qcur, "Qcur", il);
+
+                kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+                cb(kv_cmpr, "kv_cmpr_reshape", il);
+
+                // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
+                ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
+                cb(Kcur, "Kcur", il);
+
+                // {kv_lora_rank, 1, n_tokens}
+                ggml_tensor * Vcur = kv_cmpr;
+                cb(Vcur, "Vcur", il);
+
+                // apply the head-wise output gate before o_proj, so wo stays out of build_attn
+                if (is_swa) {
+                    cur = build_attn(inp_attn->get_swa(),
+                            nullptr, nullptr, nullptr,
+                            Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
+                } else {
+                    cur = build_attn(inp_attn->get_dsa(),
+                            nullptr, nullptr, nullptr,
+                            Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
+                }
+                cb(cur, "attn_out", il);
+
+                ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
+                cb(gate, "attn_gate", il);
+
+                gate = ggml_sigmoid(ctx0, gate);
+                cb(gate, "attn_gate_sigmoid", il);
+
+                // broadcast the per-head gate over the head dimension
+                ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, cur, n_embd_head_v_mla, n_head_l, n_tokens);
+                ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate,                1, n_head_l, n_tokens);
+                attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
+                cb(attn_3d, "attn_gated", il);
+
+                cur = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v_mla * n_head_l, n_tokens);
+
+                cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
+                cb(cur, "attn_output", 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, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
+                model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
+                model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            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, hparams.expert_weights_norm,
+                hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il,
+                nullptr,
+                model.layers[il].ffn_gate_up_exps,
+                model.layers[il].ffn_up_exps_s,
+                model.layers[il].ffn_gate_exps_s,
+                model.layers[il].ffn_down_exps_s);
+            cb(moe_out, "ffn_moe_out", il);
+
+            ggml_tensor * ffn_shexp =
+                build_ffn(cur,
+                    model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
+                    model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
+                    model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            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, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/models.h

@@ -1156,6 +1156,18 @@ struct llama_model_deepseek32 : public llama_model_base {
 };
 
 
+struct llama_model_dots3note : public llama_model_base {
+    llama_model_dots3note(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
 struct llama_model_deepseek4 : public llama_model_base {
     llama_model_deepseek4(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

tests/test-llama-archs.cpp

@@ -104,6 +104,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     } else if (arch == LLM_ARCH_DEEPSEEK2
             || arch == LLM_ARCH_DEEPSEEK32
             || arch == LLM_ARCH_GLM_DSA
+            || arch == LLM_ARCH_DOTS3NOTE
             || arch == LLM_ARCH_KIMI_LINEAR
             || arch == LLM_ARCH_BAILINGMOE3
             || arch == LLM_ARCH_KIMI_K3
@@ -166,6 +167,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     if (arch == LLM_ARCH_DEEPSEEK2
             || arch == LLM_ARCH_DEEPSEEK32
             || arch == LLM_ARCH_GLM_DSA
+            || arch == LLM_ARCH_DOTS3NOTE
             || arch == LLM_ARCH_KIMI_LINEAR
             || arch == LLM_ARCH_BAILINGMOE3
             || arch == LLM_ARCH_KIMI_K3
@@ -175,6 +177,22 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
         ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   uint32_t(192));
         ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, uint32_t(128));
+        if (arch == LLM_ARCH_DOTS3NOTE) {
+            // SWA layers reuse the same MLA geometry as the full layers in this fixture
+            ms.add_kv(LLM_KV_ATTENTION_KV_LORA_RANK_SWA,     uint32_t(512));
+            ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA,       uint32_t(576));
+            ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,     uint32_t(512));
+            ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,   uint32_t(192));
+            ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, uint32_t(128));
+            ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,             10000.0f);
+            // indexer on the full-attention layers (inverse of the swa pattern)
+            std::vector<uint32_t> indexer_types;
+            indexer_types.reserve(n_layer);
+            for (uint32_t il = 0; il < n_layer; il++) {
+                indexer_types.push_back(il % 2 ? 0 : 1);
+            }
+            ms.add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, indexer_types);
+        }
     } else if (arch == LLM_ARCH_MINIMAX_M3) {
         // partial rotary: n_rot must not exceed the indexer key length (64)
         ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
@@ -197,7 +215,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,              10000.0f);
         // SWA pattern: every 5th layer is full attention (matches E2B layer_types)
         ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5));
-    } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_GRANITE_SWA) {
+    } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35 ||
+            arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE) {
         std::vector<uint32_t> pattern;
         pattern.reserve(n_layer);
         for (uint32_t il = 0; il < n_layer; il++) {
@@ -365,6 +384,7 @@ static bool moe_mandatory(const llm_arch arch) {
         case LLM_ARCH_DEEPSEEK:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK32:
+        case LLM_ARCH_DOTS3NOTE:
         case LLM_ARCH_GLM4_MOE:
         case LLM_ARCH_GLM_DSA:
         case LLM_ARCH_EXAONE_MOE: