From 045b1ace5533a5554c84d85f84fdb25aec957c3e Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 00:21:17 +0900
Subject: [PATCH 01/28] llama: attempt to add modern-bert

---
 convert_hf_to_gguf.py                  |  25 ++++
 convert_hf_to_gguf_update.py           |   1 +
 gguf-py/gguf/constants.py              |  19 +++
 gguf-py/gguf/gguf_writer.py            |   3 +
 gguf-py/gguf/tensor_mapping.py         |  14 ++
 models/ggml-vocab-modern-bert.gguf     | Bin 0 -> 1114684 bytes
 models/ggml-vocab-modern-bert.gguf.inp | 112 +++++++++++++++
 models/ggml-vocab-modern-bert.gguf.out |  46 ++++++
 src/llama-arch.cpp                     |  20 +++
 src/llama-arch.h                       |   3 +
 src/llama-context.cpp                  |   1 +
 src/llama-graph.cpp                    | 113 ++++++++++++++-
 src/llama-graph.h                      |  40 +++++-
 src/llama-model-saver.cpp              |   1 +
 src/llama-model.cpp                    | 192 ++++++++++++++++++++++++-
 src/llama-model.h                      |   3 +
 16 files changed, 590 insertions(+), 3 deletions(-)
 create mode 100644 models/ggml-vocab-modern-bert.gguf
 create mode 100644 models/ggml-vocab-modern-bert.gguf.inp
 create mode 100644 models/ggml-vocab-modern-bert.gguf.out

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index ec3b5697d8f6f..c6d80a9220cd3 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -809,6 +809,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
+        if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152":
+            # ref: https://huggingface.co/answerdotai/ModernBERT-base
+            res = "modern-bert"
 
         if res is None:
             logger.warning("\n")
@@ -3932,6 +3935,28 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return super().modify_tensors(data_torch, name, bid)
 
+@ModelBase.register("ModernBert", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
+class ModernBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.MODERN_BERT
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
+        self.gguf_writer.add_rope_freq_base(self.hparams["global_rope_theta"])
+        self.gguf_writer.add_rope_freq_base_swa(self.hparams["local_rope_theta"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        super().set_gguf_parameters()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # These layers act as MLM head, so we don't need them
+        if name.startswith("decoder."):
+            return []
+
+        if name.startswith("model."):
+            name = name[6:]
+
+        return super().modify_tensors(data_torch, name, bid)
 
 @ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
 class RobertaModel(BertModel):
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 2f733f0973686..dc8a956b8f160 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -128,6 +128,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
     {"name": "pixtral",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
     {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
+    {"name": "modern-bert",      "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 3ee2b2064e1b4..0e1730d728976 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -147,6 +147,7 @@ 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"
@@ -289,6 +290,7 @@ class MODEL_ARCH(IntEnum):
     STARCODER        = auto()
     REFACT           = auto()
     BERT             = auto()
+    MODERN_BERT      = auto()
     NOMIC_BERT       = auto()
     NOMIC_BERT_MOE   = auto()
     JINA_BERT_V2     = auto()
@@ -477,6 +479,7 @@ class MODEL_TENSOR(IntEnum):
     ENC_FFN_UP           = auto()
     ENC_OUTPUT_NORM      = auto()
     CLS                  = auto() # classifier
+    CLS_NORM             = auto() # classifier normalization
     CLS_OUT              = auto() # classifier output projection
     CONV1D               = auto()
     CONVNEXT_DW          = auto()
@@ -569,6 +572,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.STARCODER:        "starcoder",
     MODEL_ARCH.REFACT:           "refact",
     MODEL_ARCH.BERT:             "bert",
+    MODEL_ARCH.MODERN_BERT:      "modern-bert",
     MODEL_ARCH.NOMIC_BERT:       "nomic-bert",
     MODEL_ARCH.NOMIC_BERT_MOE:   "nomic-bert-moe",
     MODEL_ARCH.JINA_BERT_V2:     "jina-bert-v2",
@@ -757,6 +761,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.ENC_FFN_UP:                "enc.blk.{bid}.ffn_up",
     MODEL_TENSOR.ENC_OUTPUT_NORM:           "enc.output_norm",
     MODEL_TENSOR.CLS:                       "cls",
+    MODEL_TENSOR.CLS_NORM:                  "cls.norm",
     MODEL_TENSOR.CLS_OUT:                   "cls.output",
     MODEL_TENSOR.CONV1D:                    "conv1d",
     MODEL_TENSOR.CONVNEXT_DW:               "convnext.{bid}.dw",
@@ -1047,6 +1052,20 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.CLS,
         MODEL_TENSOR.CLS_OUT,
     ],
+    MODEL_ARCH.MODERN_BERT: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ENC_OUTPUT_NORM,
+        MODEL_TENSOR.CLS,
+        MODEL_TENSOR.CLS_NORM,
+        MODEL_TENSOR.CLS_OUT,
+    ],
     MODEL_ARCH.NOMIC_BERT: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.TOKEN_EMBD_NORM,
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index de6e45ae827b9..b8aadc4e216a4 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -812,6 +812,9 @@ def add_rope_dimension_sections(self, dims: Sequence[int]) -> None:
 
     def add_rope_freq_base(self, value: float) -> None:
         self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
+    
+    def add_rope_freq_base_swa(self, value: float) -> None:
+        self.add_float32(Keys.Rope.FREQ_BASE_SWA.format(arch=self.arch), value)
 
     def add_rope_scaling_type(self, value: RopeScalingType) -> None:
         self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 93dd1d8028f3d..dbdcb69c77d29 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -16,6 +16,7 @@ class TensorNameMap:
             "model.embed_tokens",                        # llama-hf nemotron olmoe olmo2 rwkv6qwen2 glm4-0414
             "tok_embeddings",                            # llama-pth
             "embeddings.word_embeddings",                # bert nomic-bert
+            "embeddings.tok_embeddings",                 # modern-bert
             "language_model.embedding.word_embeddings",  # persimmon
             "wte",                                       # gpt2
             "transformer.embd.wte",                      # phi2
@@ -42,6 +43,7 @@ class TensorNameMap:
         MODEL_TENSOR.TOKEN_EMBD_NORM: (
             "word_embeddings_layernorm",  # bloom
             "embeddings.LayerNorm",       # bert
+            "embeddings.norm",            # modern-bert
             "emb_ln",                     # nomic-bert
             "transformer.norm",           # openelm
             "rwkv.blocks.0.pre_ln",       # rwkv
@@ -134,6 +136,7 @@ class TensorNameMap:
             "rwkv.blocks.{bid}.ln1",                                # rwkv6
             "model.layers.{bid}.ln1",                               # rwkv7
             "model.layers.{bid}.input_layernorm",                   # llama4
+            "layers.{bid}.attn_norm",                               # modern-bert
         ),
 
         # Attention norm 2
@@ -161,6 +164,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.qkv_proj",                               # phi3
             "encoder.layers.{bid}.self_attention.query_key_value",                 # chatglm
             "transformer.layers.{bid}.attn.qkv_proj",                              # openelm
+            "layers.{bid}.attn.Wqkv",                                              # modern-bert
         ),
 
         # Attention query
@@ -236,6 +240,7 @@ class TensorNameMap:
             "transformer.layers.{bid}.attn.out_proj",                       # openelm
             "transformer.h.{bid}.attn.attention.out_proj",                  # exaone
             "model.layers.{bid}.self_attn.o_proj",                          # llama4
+            "layers.{bid}.attn.Wo",                                         # modern-bert
         ),
 
         # Attention output norm
@@ -245,6 +250,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.norm1",                      # nomic-bert
             "transformer.decoder_layer.{bid}.rms_norm_1",      # Grok
             "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
+            "layers.{bid}.mlp_norm"                            # modern-bert
         ),
 
         MODEL_TENSOR.ATTN_POST_NORM: (
@@ -338,6 +344,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.dense_h_to_4h",                 # chatglm
             "transformer.h.{bid}.mlp.c_fc_1",                         # exaone
             "model.layers.{bid}.feed_forward.up_proj",                # llama4
+            "layers.{bid}.mlp.Wi"                                     # modern-bert
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -420,6 +427,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.dense_4h_to_h",                 # chatglm
             "model.layers.h.{bid}.mlp.c_proj",                        # exaone
             "model.layers.{bid}.feed_forward.down_proj",              # llama4
+            "layers.{bid}.mlp.Wo"                                     # modern-bert
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
@@ -830,12 +838,18 @@ class TensorNameMap:
         # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
+            "final_norm",               # modern-bert
         ),
 
         MODEL_TENSOR.CLS: (
             "classifier",       # jina
             "classifier.dense", # roberta
             "pre_classifier",   # distillbert
+            "head.dense",       # modern-bert
+        ),
+
+        MODEL_TENSOR.CLS_NORM: (
+            "head.norm",        # modern-bert
         ),
 
         MODEL_TENSOR.CLS_OUT: (
diff --git a/models/ggml-vocab-modern-bert.gguf b/models/ggml-vocab-modern-bert.gguf
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/models/ggml-vocab-modern-bert.gguf.inp b/models/ggml-vocab-modern-bert.gguf.inp
new file mode 100644
index 0000000000000..86b934e4020fb
--- /dev/null
+++ b/models/ggml-vocab-modern-bert.gguf.inp
@@ -0,0 +1,112 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Äpfel
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-modern-bert.gguf.out b/models/ggml-vocab-modern-bert.gguf.out
new file mode 100644
index 0000000000000..ca62669ad0945
--- /dev/null
+++ b/models/ggml-vocab-modern-bert.gguf.out
@@ -0,0 +1,46 @@
+ 728 577 24142 2607
+ 37515 18569 293
+
+ 209
+ 50276
+ 50275
+ 186
+ 187
+ 535
+ 2756
+ 186 187
+ 12092 1533
+ 24387 1533
+ 12092 3645
+ 24387 3645
+ 24387 3645 2
+ 12092 13 1533 2
+ 24387 13 1533 2
+ 436 310 22692 101 236 15 14161
+ 88 27244 818 16853 16392 20505 4989 11917
+ 32520 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389
+ 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 18081 212 18081 238 18081 216 39936 212 18081 220
+ 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 313 7483 802 80 8020 326 556 697 1211 10669 10
+ 12092
+ 24387
+ 50276 12092
+ 50275 12092
+ 50274 12092
+ 50274 12092 187 50274 12092
+ 313
+ 187 426
+ 8 8685
+ 12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241
+ 18963 4672
+ 20
+ 1610
+ 20084
+ 26409
+ 1610 20084
+ 26409 1610
+ 26409 20084
+ 26409 26409
+ 26409 1610 20084
+ 36 6829 244 66 17721 35177 85
+ 1262 2196
+ 586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index c0590e105c889..98c712d1c5b54 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -18,6 +18,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_STARCODER,        "starcoder"        },
     { LLM_ARCH_REFACT,           "refact"           },
     { LLM_ARCH_BERT,             "bert"             },
+    { LLM_ARCH_MODERN_BERT,      "modern-bert"      },
     { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
     { LLM_ARCH_NOMIC_BERT_MOE,   "nomic-bert-moe"   },
     { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
@@ -148,6 +149,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { 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"                  },
@@ -462,6 +464,23 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_CLS_OUT,         "cls.output" },
         },
     },
+    {
+        LLM_ARCH_MODERN_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
+            { LLM_TENSOR_CLS,             "cls" },
+            { LLM_TENSOR_CLS_NORM,        "cls.norm" },
+            { LLM_TENSOR_CLS_OUT,         "cls.output" },
+        },
+    },
     {
         LLM_ARCH_NOMIC_BERT,
         {
@@ -1572,6 +1591,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CLS_NORM,                   {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_OUTPUT_NORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_DEC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_ENC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 930cb4eca33ab..cffc8bd36d59f 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -22,6 +22,7 @@ enum llm_arch {
     LLM_ARCH_STARCODER,
     LLM_ARCH_REFACT,
     LLM_ARCH_BERT,
+    LLM_ARCH_MODERN_BERT,
     LLM_ARCH_NOMIC_BERT,
     LLM_ARCH_NOMIC_BERT_MOE,
     LLM_ARCH_JINA_BERT_V2,
@@ -152,6 +153,7 @@ enum llm_kv {
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_DIMENSION_SECTIONS,
     LLM_KV_ROPE_FREQ_BASE,
+    LLM_KV_ROPE_FREQ_BASE_SWA,
     LLM_KV_ROPE_SCALE_LINEAR,
     LLM_KV_ROPE_SCALING_TYPE,
     LLM_KV_ROPE_SCALING_FACTOR,
@@ -347,6 +349,7 @@ enum llm_tensor {
     LLM_TENSOR_ENC_FFN_UP,
     LLM_TENSOR_ENC_OUTPUT_NORM,
     LLM_TENSOR_CLS,
+    LLM_TENSOR_CLS_NORM,
     LLM_TENSOR_CLS_OUT,
     LLM_TENSOR_CONV1D,
     LLM_TENSOR_CONVNEXT_DW,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 4ab5743879400..f34adb0ed068b 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2006,6 +2006,7 @@ void llama_context::opt_init(struct llama_model * model, struct llama_opt_params
     llama_set_param(model->output_norm_enc, param_filter, param_filter_ud);
     llama_set_param(model->cls,             param_filter, param_filter_ud);
     llama_set_param(model->cls_b,           param_filter, param_filter_ud);
+    llama_set_param(model->cls_norm,        param_filter, param_filter_ud);
     llama_set_param(model->cls_out,         param_filter, param_filter_ud);
     llama_set_param(model->cls_out_b,       param_filter, param_filter_ud);
 
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 727e119e334f6..a9e6697cd1453 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -363,6 +363,111 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     }
 }
 
+void llm_graph_input_attn_no_cache_iswa::set_input(const llama_ubatch * ubatch) {
+    // Standard attention mask
+    if (kq_mask) {
+        if (cparams.causal_attn) {
+            const int64_t n_kv         = ubatch->n_tokens;
+            const int64_t n_tokens     = ubatch->n_tokens;
+            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
+            const int64_t n_seqs       = ubatch->n_seqs;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
+            float * data = (float *) kq_mask->data;
+
+            for (int h = 0; h < 1; ++h) {
+                for (int s1 = 0; s1 < n_seqs; ++s1) {
+                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
+
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        const int32_t tj = s1*n_seq_tokens + j;
+
+                        for (int s0 = 0; s0 < n_seqs; ++s0) {
+                            for (int i = 0; i < n_seq_tokens; ++i) {
+                                const int32_t ti = s0 * n_seq_tokens + i;
+                                float         f  = -INFINITY;
+
+                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
+                                    if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
+                                        if (hparams.use_alibi) {
+                                            f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
+                                        } else {
+                                            f = 0.0f;
+                                        }
+                                        break;
+                                    }
+                                }
+
+                                data[h * (n_kv * n_tokens) + tj * n_kv + ti] = f;
+                            }
+                        }
+                    }
+                }
+
+                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                    for (int j = 0; j < n_kv; ++j) {
+                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    // SWA attention mask
+    if (kq_mask_swa) {
+        if (cparams.causal_attn) {
+            const int64_t n_kv         = ubatch->n_tokens;
+            const int64_t n_tokens     = ubatch->n_tokens;
+            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
+            const int64_t n_seqs       = ubatch->n_seqs;
+            const int64_t window_size  = hparams.n_swa;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask_swa->buffer));
+            float * data = (float *) kq_mask_swa->data;
+
+            for (int h = 0; h < 1; ++h) {
+                for (int s1 = 0; s1 < n_seqs; ++s1) {
+                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
+
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        const int32_t tj = s1*n_seq_tokens + j;
+
+                        for (int s0 = 0; s0 < n_seqs; ++s0) {
+                            for (int i = 0; i < n_seq_tokens; ++i) {
+                                const int32_t ti = s0 * n_seq_tokens + i;
+                                float         f  = -INFINITY;
+
+                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
+                                    if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
+                                        const bool in_window = (ubatch->pos[tj] - ubatch->pos[ti]) <= window_size;
+                                        
+                                        if (in_window) {
+                                            if (hparams.use_alibi) {
+                                                f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
+                                            } else {
+                                                f = 0.0f;
+                                            }
+                                        }
+                                        break;
+                                    }
+                                }
+
+                                data[h * (n_kv * n_tokens) + tj * n_kv + ti] = f;
+                            }
+                        }
+                    }
+                }
+
+                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                    for (int j = 0; j < n_kv; ++j) {
+                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+}
+
 void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
     if (self_kq_mask) {
         kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
@@ -1523,7 +1628,8 @@ void llm_graph_context::build_pooling(
         ggml_tensor * cls,
         ggml_tensor * cls_b,
         ggml_tensor * cls_out,
-        ggml_tensor * cls_out_b) const {
+        ggml_tensor * cls_out_b,
+        ggml_tensor * cls_norm) const {
     if (!cparams.embeddings) {
         return;
     }
@@ -1571,6 +1677,11 @@ void llm_graph_context::build_pooling(
                     cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
                     cur = ggml_tanh(ctx0, cur);
 
+                    if (cls_norm) {
+                        // normalization head
+                        cur = build_norm(cur, cls_norm, nullptr, LLM_NORM, 0);
+                    }
+
                     // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
                     // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
                     if (cls_out) {
diff --git a/src/llama-graph.h b/src/llama-graph.h
index d1c5dd1bf036f..7cf63d5e6d0ab 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -243,6 +243,28 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
     const llama_cparams & cparams;
 };
 
+class llm_graph_input_attn_no_cache_iswa : public llm_graph_input_i {
+public:
+    llm_graph_input_attn_no_cache_iswa(const llama_hparams & hparams, const llama_cparams & cparams) :
+        hparams(hparams),
+        cparams(cparams) {
+    }
+    ~llm_graph_input_attn_no_cache_iswa() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * get_kq_mask()     const { return kq_mask_cnv; }
+    ggml_tensor * get_kq_mask_swa() const { return kq_mask_swa_cnv; }
+
+    ggml_tensor * kq_mask         = nullptr; // F32 [n_tokens, n_batch]
+    ggml_tensor * kq_mask_cnv     = nullptr; //     [n_tokens, n_batch]
+    ggml_tensor * kq_mask_swa     = nullptr; // F32 [n_tokens, n_batch]
+    ggml_tensor * kq_mask_swa_cnv = nullptr; //     [n_tokens, n_batch]
+
+    const llama_hparams & hparams;
+    const llama_cparams & cparams;
+};
+
 class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
 public:
     llm_graph_input_attn_kv_unified(
@@ -543,6 +565,8 @@ struct llm_graph_context {
 
     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
 
+    llm_graph_input_attn_no_cache_iswa * build_attn_inp_no_cache_iswa() const;
+
     ggml_tensor * build_attn(
             llm_graph_input_attn_no_cache * inp,
             ggml_cgraph * gf,
@@ -556,6 +580,19 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
+    ggml_tensor * build_attn(
+            llm_graph_input_attn_no_cache_iswa * inp,
+            ggml_cgraph * gf,
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
+            ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
+            ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
+            ggml_tensor * kq_b,
+            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
+                  float   kq_scale,
+                    int   il) const;
+
     llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const;
 
     ggml_tensor * build_attn(
@@ -634,7 +671,8 @@ struct llm_graph_context {
             ggml_tensor * cls,
             ggml_tensor * cls_b,
             ggml_tensor * cls_out,
-            ggml_tensor * cls_out_b) const;
+            ggml_tensor * cls_out_b,
+            ggml_tensor * cls_norm) const;
 };
 
 // TODO: better name
diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp
index a70b9892347cb..69de2eb961b4e 100644
--- a/src/llama-model-saver.cpp
+++ b/src/llama-model-saver.cpp
@@ -265,6 +265,7 @@ void llama_model_saver::add_tensors_from_model() {
     add_tensor(model.output_norm_enc);
     add_tensor(model.cls);
     add_tensor(model.cls_b);
+    add_tensor(model.cls_norm);
     add_tensor(model.cls_out);
     add_tensor(model.cls_out_b);
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 50264a69aac0e..82a341640ef92 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -35,12 +35,14 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_80M:           return "80M";
         case LLM_TYPE_109M:          return "109M";
         case LLM_TYPE_137M:          return "137M";
+        case LLM_TYPE_149M:          return "149M";
         case LLM_TYPE_160M:          return "160M";
         case LLM_TYPE_190M:          return "190M";
         case LLM_TYPE_220M:          return "220M";
         case LLM_TYPE_250M:          return "250M";
         case LLM_TYPE_270M:          return "270M";
         case LLM_TYPE_335M:          return "335M";
+        case LLM_TYPE_395M:          return "395M";
         case LLM_TYPE_410M:          return "410M";
         case LLM_TYPE_450M:          return "450M";
         case LLM_TYPE_475M:          return "475M";
@@ -508,6 +510,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
 
     // by default assume that the sliding-window layers use the same scaling type as the non-sliding-window layers
     hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
     hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
 
     ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
@@ -704,6 +707,28 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_MODERN_BERT:
+            {
+                hparams.rope_freq_base_train = 160000.0f;
+                hparams.rope_freq_base_train_swa = 10000.0f;
+                hparams.n_swa = 128;
+
+                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+                hparams.set_swa_pattern(3);
+
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+                ml.get_arr_n(LLM_KV_CLASSIFIER_OUTPUT_LABELS, hparams.n_cls_out, false);
+
+                switch (hparams.n_layer) {
+                    case 22:
+                        type = LLM_TYPE_149M; break; // modern-bert-base
+                    case 28:
+                        type = LLM_TYPE_395M; break; // modern-bert-large
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_JINA_BERT_V2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
@@ -2176,6 +2201,32 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
                     }
                 } break;
+            case LLM_ARCH_MODERN_BERT:
+                {
+                    tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
+
+                    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}, TENSOR_NOT_REQUIRED);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, 2*n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd}, 0);
+                    }
+
+                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+                    cls        = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+                    cls_norm   = create_tensor(tn(LLM_TENSOR_CLS_NORM, "weight"), {n_embd}, TENSOR_NOT_REQUIRED);
+
+                    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+                } break;
             case LLM_ARCH_JINA_BERT_V2:
                 {
                     tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
@@ -6054,6 +6105,140 @@ struct llm_build_bert : public llm_graph_context {
     }
 };
 
+struct llm_build_modern_bert : public llm_graph_context {
+    llm_build_modern_bert(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+        ggml_tensor * inp_pos = build_inp_pos();  // Initialize inp_pos with build_inp_pos()
+
+        // construct input embeddings (token, type, position)
+        inpL = build_inp_embd(model.tok_embd);
+        cb(inpL, "inp_embd", -1);
+
+        // embed layer norm
+        inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+        cb(inpL, "inp_norm", -1);
+
+        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+
+        // iterate layers
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * cur = inpL;
+
+            ggml_tensor * Qcur;
+            ggml_tensor * Kcur;
+            ggml_tensor * Vcur;
+
+            float rope_theta = il % 3 == 0 ? hparams.rope_freq_base_train : hparams.rope_freq_base_train_swa;
+
+            // attention layer norm
+            if (model.layers[il].attn_norm) {
+                cur = build_norm(inpL,
+                        model.layers[il].attn_norm, NULL,
+                        LLM_NORM, il);
+                cb(cur, "attn_norm", il);
+            }
+
+            // self-attention
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            // RoPE
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, rope_theta, 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, rope_theta, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn, gf,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            cb(cur, "kqv_out", il);
+
+            if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // re-add the layer input
+            cur = ggml_add(ctx0, cur, inpL);
+
+            // attention layer norm
+            cur = build_norm(cur, model.layers[il].attn_out_norm, nullptr, LLM_NORM, il);
+
+            ggml_tensor * ffn_inp = cur;
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            ggml_tensor * ffn_up = build_lora_mm(model.layers[il].ffn_up, cur);
+            cb(ffn_up, "ffn_up", il);
+            
+            int64_t split_point = ffn_up->ne[0] / 2;
+            ggml_tensor * output_ffn_up = ggml_cont(ctx0, ggml_view_2d(
+                                            ctx0, ffn_up, split_point,
+                                            ffn_up->ne[1], ffn_up->nb[1], 0
+                                        ));
+            ggml_tensor * output_ffn_gate = ggml_cont(ctx0, ggml_view_2d(
+                                            ctx0, ffn_up, split_point,
+                                            ffn_up->ne[1], ffn_up->nb[1], 
+                                            split_point * ggml_element_size(ffn_up)
+                                        ));
+
+            // Apply activation function
+            output_ffn_up = ggml_gelu(ctx0, output_ffn_up);
+            
+            // Element-wise multiplication
+            ggml_tensor * gated = ggml_mul(ctx0, output_ffn_up, output_ffn_gate);
+            cb(gated, "ffn_gated", il);
+            
+            // Final projection
+            cur = build_lora_mm(model.layers[il].ffn_down, gated);
+
+            // attentions bypass the intermediate layer
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm_enc, NULL,
+                LLM_NORM, -1);
+
+        cb(cur, "result_embd", -1);
+        res->t_embd = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 struct llm_build_bloom : public llm_graph_context {
     llm_build_bloom(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
         const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -13303,6 +13488,10 @@ llm_graph_result_ptr llama_model::build_graph(
             {
                 llm = std::make_unique<llm_build_bert>(*this, params, gf);
             } break;
+        case LLM_ARCH_MODERN_BERT:
+            {
+                llm = std::make_unique<llm_build_modern_bert>(*this, params, gf);
+            } break;
         case LLM_ARCH_BLOOM:
             {
                 llm = std::make_unique<llm_build_bloom>(*this, params, gf);
@@ -13530,7 +13719,7 @@ llm_graph_result_ptr llama_model::build_graph(
     }
 
     // add on pooling layer
-    llm->build_pooling(gf, cls, cls_b, cls_out, cls_out_b);
+    llm->build_pooling(gf, cls, cls_b, cls_out, cls_out_b, cls_norm);
 
     return std::move(llm->res);
 }
@@ -13671,6 +13860,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_BERT:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
+        case LLM_ARCH_MODERN_BERT:
         case LLM_ARCH_STABLELM:
         case LLM_ARCH_BITNET:
         case LLM_ARCH_QWEN:
diff --git a/src/llama-model.h b/src/llama-model.h
index cbea2cb331b62..c0b6492198610 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -28,12 +28,14 @@ enum llm_type {
     LLM_TYPE_80M,
     LLM_TYPE_109M,
     LLM_TYPE_137M,
+    LLM_TYPE_149M,
     LLM_TYPE_160M,
     LLM_TYPE_190M,
     LLM_TYPE_220M,
     LLM_TYPE_250M,
     LLM_TYPE_270M,
     LLM_TYPE_335M,
+    LLM_TYPE_395M,
     LLM_TYPE_410M,
     LLM_TYPE_450M,
     LLM_TYPE_475M,
@@ -340,6 +342,7 @@ struct llama_model {
     struct ggml_tensor * output          = nullptr;
     struct ggml_tensor * output_b        = nullptr;
     struct ggml_tensor * output_norm_enc = nullptr;
+    struct ggml_tensor * cls_norm        = nullptr;
 
     // classifier
     struct ggml_tensor * cls       = nullptr;

From eab776e9e1bf9f2115a6dd9df777036b2d721366 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 00:35:33 +0900
Subject: [PATCH 02/28] re-format and delete unused implementations

---
 convert_hf_to_gguf.py       |   2 +
 gguf-py/gguf/gguf_writer.py |   2 +-
 src/llama-graph.cpp         | 105 ------------------------------------
 src/llama-graph.h           |  37 -------------
 src/llama-model.cpp         |   8 +--
 5 files changed, 7 insertions(+), 147 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index c6d80a9220cd3..7b7d72db29bcf 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3935,6 +3935,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return super().modify_tensors(data_torch, name, bid)
 
+
 @ModelBase.register("ModernBert", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
 class ModernBertModel(BertModel):
     model_arch = gguf.MODEL_ARCH.MODERN_BERT
@@ -3958,6 +3959,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return super().modify_tensors(data_torch, name, bid)
 
+
 @ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
 class RobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index b8aadc4e216a4..962c58ed7ebd7 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -812,7 +812,7 @@ def add_rope_dimension_sections(self, dims: Sequence[int]) -> None:
 
     def add_rope_freq_base(self, value: float) -> None:
         self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
-    
+
     def add_rope_freq_base_swa(self, value: float) -> None:
         self.add_float32(Keys.Rope.FREQ_BASE_SWA.format(arch=self.arch), value)
 
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 7315cab615a91..b06d9b44b7835 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -363,111 +363,6 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     }
 }
 
-void llm_graph_input_attn_no_cache_iswa::set_input(const llama_ubatch * ubatch) {
-    // Standard attention mask
-    if (kq_mask) {
-        if (cparams.causal_attn) {
-            const int64_t n_kv         = ubatch->n_tokens;
-            const int64_t n_tokens     = ubatch->n_tokens;
-            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
-            const int64_t n_seqs       = ubatch->n_seqs;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
-            float * data = (float *) kq_mask->data;
-
-            for (int h = 0; h < 1; ++h) {
-                for (int s1 = 0; s1 < n_seqs; ++s1) {
-                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const int32_t tj = s1*n_seq_tokens + j;
-
-                        for (int s0 = 0; s0 < n_seqs; ++s0) {
-                            for (int i = 0; i < n_seq_tokens; ++i) {
-                                const int32_t ti = s0 * n_seq_tokens + i;
-                                float         f  = -INFINITY;
-
-                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
-                                    if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
-                                        if (hparams.use_alibi) {
-                                            f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
-                                        } else {
-                                            f = 0.0f;
-                                        }
-                                        break;
-                                    }
-                                }
-
-                                data[h * (n_kv * n_tokens) + tj * n_kv + ti] = f;
-                            }
-                        }
-                    }
-                }
-
-                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                    for (int j = 0; j < n_kv; ++j) {
-                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
-
-    // SWA attention mask
-    if (kq_mask_swa) {
-        if (cparams.causal_attn) {
-            const int64_t n_kv         = ubatch->n_tokens;
-            const int64_t n_tokens     = ubatch->n_tokens;
-            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
-            const int64_t n_seqs       = ubatch->n_seqs;
-            const int64_t window_size  = hparams.n_swa;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask_swa->buffer));
-            float * data = (float *) kq_mask_swa->data;
-
-            for (int h = 0; h < 1; ++h) {
-                for (int s1 = 0; s1 < n_seqs; ++s1) {
-                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const int32_t tj = s1*n_seq_tokens + j;
-
-                        for (int s0 = 0; s0 < n_seqs; ++s0) {
-                            for (int i = 0; i < n_seq_tokens; ++i) {
-                                const int32_t ti = s0 * n_seq_tokens + i;
-                                float         f  = -INFINITY;
-
-                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
-                                    if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
-                                        const bool in_window = (ubatch->pos[tj] - ubatch->pos[ti]) <= window_size;
-                                        
-                                        if (in_window) {
-                                            if (hparams.use_alibi) {
-                                                f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
-                                            } else {
-                                                f = 0.0f;
-                                            }
-                                        }
-                                        break;
-                                    }
-                                }
-
-                                data[h * (n_kv * n_tokens) + tj * n_kv + ti] = f;
-                            }
-                        }
-                    }
-                }
-
-                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                    for (int j = 0; j < n_kv; ++j) {
-                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
-}
-
 void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
     if (self_kq_mask) {
         kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 7cf63d5e6d0ab..a1f4fc567c800 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -243,28 +243,6 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
     const llama_cparams & cparams;
 };
 
-class llm_graph_input_attn_no_cache_iswa : public llm_graph_input_i {
-public:
-    llm_graph_input_attn_no_cache_iswa(const llama_hparams & hparams, const llama_cparams & cparams) :
-        hparams(hparams),
-        cparams(cparams) {
-    }
-    ~llm_graph_input_attn_no_cache_iswa() = default;
-
-    void set_input(const llama_ubatch * ubatch) override;
-
-    ggml_tensor * get_kq_mask()     const { return kq_mask_cnv; }
-    ggml_tensor * get_kq_mask_swa() const { return kq_mask_swa_cnv; }
-
-    ggml_tensor * kq_mask         = nullptr; // F32 [n_tokens, n_batch]
-    ggml_tensor * kq_mask_cnv     = nullptr; //     [n_tokens, n_batch]
-    ggml_tensor * kq_mask_swa     = nullptr; // F32 [n_tokens, n_batch]
-    ggml_tensor * kq_mask_swa_cnv = nullptr; //     [n_tokens, n_batch]
-
-    const llama_hparams & hparams;
-    const llama_cparams & cparams;
-};
-
 class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
 public:
     llm_graph_input_attn_kv_unified(
@@ -565,8 +543,6 @@ struct llm_graph_context {
 
     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
 
-    llm_graph_input_attn_no_cache_iswa * build_attn_inp_no_cache_iswa() const;
-
     ggml_tensor * build_attn(
             llm_graph_input_attn_no_cache * inp,
             ggml_cgraph * gf,
@@ -580,19 +556,6 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
-    ggml_tensor * build_attn(
-            llm_graph_input_attn_no_cache_iswa * inp,
-            ggml_cgraph * gf,
-            ggml_tensor * wo,
-            ggml_tensor * wo_b,
-            ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
-            ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
-            ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
-            ggml_tensor * kq_b,
-            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
-                  float   kq_scale,
-                    int   il) const;
-
     llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const;
 
     ggml_tensor * build_attn(
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index a43be4145cbf4..dd25d298a8832 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6203,7 +6203,7 @@ struct llm_build_modern_bert : public llm_graph_context {
             // feed-forward network
             ggml_tensor * ffn_up = build_lora_mm(model.layers[il].ffn_up, cur);
             cb(ffn_up, "ffn_up", il);
-            
+
             int64_t split_point = ffn_up->ne[0] / 2;
             ggml_tensor * output_ffn_up = ggml_cont(ctx0, ggml_view_2d(
                                             ctx0, ffn_up, split_point,
@@ -6211,17 +6211,17 @@ struct llm_build_modern_bert : public llm_graph_context {
                                         ));
             ggml_tensor * output_ffn_gate = ggml_cont(ctx0, ggml_view_2d(
                                             ctx0, ffn_up, split_point,
-                                            ffn_up->ne[1], ffn_up->nb[1], 
+                                            ffn_up->ne[1], ffn_up->nb[1],
                                             split_point * ggml_element_size(ffn_up)
                                         ));
 
             // Apply activation function
             output_ffn_up = ggml_gelu(ctx0, output_ffn_up);
-            
+
             // Element-wise multiplication
             ggml_tensor * gated = ggml_mul(ctx0, output_ffn_up, output_ffn_gate);
             cb(gated, "ffn_gated", il);
-            
+
             // Final projection
             cur = build_lora_mm(model.layers[il].ffn_down, gated);
 

From 7143840bd0f1e08d5279035c582017c404f34d8b Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 01:13:15 +0900
Subject: [PATCH 03/28] overload set_swa_pattern for modern bert

---
 src/llama-hparams.cpp |  6 ++++++
 src/llama-hparams.h   | 12 ++++++++++++
 src/llama-model.cpp   |  2 +-
 3 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 1499eb08a5dd9..c411e483d74a7 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -8,6 +8,12 @@ void llama_hparams::set_swa_pattern(uint32_t n_pattern) {
     }
 }
 
+void llama_hparams::set_swa_pattern(uint32_t n_pattern, uint32_t remainder) {
+    for (uint32_t il = 0; il < n_layer; ++il) {
+        swa_layers[il] = n_pattern == 0 || (il % n_pattern != remainder);
+    }
+}
+
 bool llama_hparams::is_swa_any() const {
     for (uint32_t il = 0; il < n_layer; ++il) {
         if (swa_layers[il]) {
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index b2bcb8b01a18b..3285c26707df7 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -162,6 +162,18 @@ struct llama_hparams {
     //   etc ...
     void set_swa_pattern(uint32_t n_pattern);
 
+    // Overload that allows specifying which position in the pattern is dense
+    // The remainder parameter specifies which position in the pattern is dense
+    // example: n_pattern = 3, remainder = 2
+    //   il == 0: swa     (0 % 3 = 0, which is not equal to 2)
+    //   il == 1: swa     (1 % 3 = 1, which is not equal to 2) 
+    //   il == 2: dense   (2 % 3 = 2, which equals 2)
+    //   il == 3: swa     (3 % 3 = 0, which is not equal to 2)
+    //   il == 4: swa     (4 % 3 = 1, which is not equal to 2)
+    //   il == 5: dense   (5 % 3 = 2, which equals 2)
+    //   etc ...
+    void set_swa_pattern(uint32_t n_pattern, uint32_t remainder);
+
     // return true if one of the layers is SWA
     bool is_swa_any() const;
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index dd25d298a8832..00117d0814579 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -714,7 +714,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 hparams.n_swa = 128;
 
                 hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                hparams.set_swa_pattern(3);
+                hparams.set_swa_pattern(3, 0);
 
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);

From 6aa133591e94160e19b566632c4708b2316504a4 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 01:20:50 +0900
Subject: [PATCH 04/28] modern-bert doesn't have bias

---
 src/llama-model.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 00117d0814579..47d0b443f307b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6127,7 +6127,7 @@ struct llm_build_modern_bert : public llm_graph_context {
         cb(inpL, "inp_embd", -1);
 
         // embed layer norm
-        inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+        inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
         cb(inpL, "inp_norm", -1);
 
         auto * inp_attn = build_attn_inp_kv_unified_iswa();
@@ -6180,7 +6180,7 @@ struct llm_build_modern_bert : public llm_graph_context {
             cb(Vcur, "Vcur", il);
 
             cur = build_attn(inp_attn, gf,
-                    model.layers[il].wo, model.layers[il].bo,
+                    model.layers[il].wo, nullptr,
                     Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             cb(cur, "kqv_out", il);
 

From 9e1179a6d3a18b03e91fdfa86572108e2c0b7135 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 07:55:04 +0900
Subject: [PATCH 05/28] delete unnecessary files

---
 convert_hf_to_gguf.py                  |   7 +-
 convert_hf_to_gguf_update.py           |   1 -
 models/ggml-vocab-modern-bert.gguf     | Bin 1114684 -> 0 bytes
 models/ggml-vocab-modern-bert.gguf.inp | 112 -------------------------
 models/ggml-vocab-modern-bert.gguf.out |  46 ----------
 src/llama-hparams.h                    |   2 +-
 6 files changed, 3 insertions(+), 165 deletions(-)
 delete mode 100644 models/ggml-vocab-modern-bert.gguf
 delete mode 100644 models/ggml-vocab-modern-bert.gguf.inp
 delete mode 100644 models/ggml-vocab-modern-bert.gguf.out

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 7b7d72db29bcf..8a81d142fd3e4 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -809,9 +809,6 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
-        if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152":
-            # ref: https://huggingface.co/answerdotai/ModernBERT-base
-            res = "modern-bert"
 
         if res is None:
             logger.warning("\n")
@@ -3941,14 +3938,14 @@ class ModernBertModel(BertModel):
     model_arch = gguf.MODEL_ARCH.MODERN_BERT
 
     def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
         self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
         self.gguf_writer.add_rope_freq_base(self.hparams["global_rope_theta"])
         self.gguf_writer.add_rope_freq_base_swa(self.hparams["local_rope_theta"])
         self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
         self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
 
-        super().set_gguf_parameters()
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # These layers act as MLM head, so we don't need them
         if name.startswith("decoder."):
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index dc8a956b8f160..2f733f0973686 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -128,7 +128,6 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
     {"name": "pixtral",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
     {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
-    {"name": "modern-bert",      "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
diff --git a/models/ggml-vocab-modern-bert.gguf b/models/ggml-vocab-modern-bert.gguf
deleted file mode 100644
index 0b612dfa861b98da7cb128ac8368fe601e4d61a6..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/models/ggml-vocab-modern-bert.gguf.inp b/models/ggml-vocab-modern-bert.gguf.inp
deleted file mode 100644
index 86b934e4020fb..0000000000000
--- a/models/ggml-vocab-modern-bert.gguf.inp
+++ /dev/null
@@ -1,112 +0,0 @@
-ied 4 ½ months
-__ggml_vocab_test__
-Äpfel
-__ggml_vocab_test__
-
-__ggml_vocab_test__
- 
-__ggml_vocab_test__
-  
-__ggml_vocab_test__
-   
-__ggml_vocab_test__
-	
-__ggml_vocab_test__
-
-
-__ggml_vocab_test__
-
-
-
-__ggml_vocab_test__
-
-
-
-
-__ggml_vocab_test__
-	
-
-__ggml_vocab_test__
-Hello world
-__ggml_vocab_test__
- Hello world
-__ggml_vocab_test__
-Hello World
-__ggml_vocab_test__
- Hello World
-__ggml_vocab_test__
- Hello World!
-__ggml_vocab_test__
-Hello, world!
-__ggml_vocab_test__
- Hello, world!
-__ggml_vocab_test__
- this is 🦙.cpp
-__ggml_vocab_test__
-w048 7tuijk dsdfhu
-__ggml_vocab_test__
-нещо на Български
-__ggml_vocab_test__
-កាន់តែពិសេសអាចខលចេញ
-__ggml_vocab_test__
-🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
-__ggml_vocab_test__
-Hello
-__ggml_vocab_test__
- Hello
-__ggml_vocab_test__
-  Hello
-__ggml_vocab_test__
-   Hello
-__ggml_vocab_test__
-    Hello
-__ggml_vocab_test__
-    Hello
-    Hello
-__ggml_vocab_test__
- (
-__ggml_vocab_test__
-
- =
-__ggml_vocab_test__
-' era
-__ggml_vocab_test__
-Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
-__ggml_vocab_test__
-!!!!!!
-__ggml_vocab_test__
-3
-__ggml_vocab_test__
-33
-__ggml_vocab_test__
-333
-__ggml_vocab_test__
-3333
-__ggml_vocab_test__
-33333
-__ggml_vocab_test__
-333333
-__ggml_vocab_test__
-3333333
-__ggml_vocab_test__
-33333333
-__ggml_vocab_test__
-333333333
-__ggml_vocab_test__
-Cửa Việt
-__ggml_vocab_test__
- discards
-__ggml_vocab_test__
-
- 
-
- 
-
-
- 	 		 	
-  
-   
-    
-     
-🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
-__ggml_vocab_test__
diff --git a/models/ggml-vocab-modern-bert.gguf.out b/models/ggml-vocab-modern-bert.gguf.out
deleted file mode 100644
index ca62669ad0945..0000000000000
--- a/models/ggml-vocab-modern-bert.gguf.out
+++ /dev/null
@@ -1,46 +0,0 @@
- 728 577 24142 2607
- 37515 18569 293
-
- 209
- 50276
- 50275
- 186
- 187
- 535
- 2756
- 186 187
- 12092 1533
- 24387 1533
- 12092 3645
- 24387 3645
- 24387 3645 2
- 12092 13 1533 2
- 24387 13 1533 2
- 436 310 22692 101 236 15 14161
- 88 27244 818 16853 16392 20505 4989 11917
- 32520 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389
- 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 18081 212 18081 238 18081 216 39936 212 18081 220
- 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 313 7483 802 80 8020 326 556 697 1211 10669 10
- 12092
- 24387
- 50276 12092
- 50275 12092
- 50274 12092
- 50274 12092 187 50274 12092
- 313
- 187 426
- 8 8685
- 12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241
- 18963 4672
- 20
- 1610
- 20084
- 26409
- 1610 20084
- 26409 1610
- 26409 20084
- 26409 26409
- 26409 1610 20084
- 36 6829 244 66 17721 35177 85
- 1262 2196
- 586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 3285c26707df7..184fdad24d3b0 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -166,7 +166,7 @@ struct llama_hparams {
     // The remainder parameter specifies which position in the pattern is dense
     // example: n_pattern = 3, remainder = 2
     //   il == 0: swa     (0 % 3 = 0, which is not equal to 2)
-    //   il == 1: swa     (1 % 3 = 1, which is not equal to 2) 
+    //   il == 1: swa     (1 % 3 = 1, which is not equal to 2)
     //   il == 2: dense   (2 % 3 = 2, which equals 2)
     //   il == 3: swa     (3 % 3 = 0, which is not equal to 2)
     //   il == 4: swa     (4 % 3 = 1, which is not equal to 2)

From fa23480981d4988bc7e2f64bad7e5f9c13cc88dc Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 22:32:28 +0900
Subject: [PATCH 06/28] add build_attn_inp_no_cache_iswa with symmetric swa

---
 src/llama-graph.cpp | 71 ++++++++++++++++++++++++++++++++++++++++++++-
 src/llama-graph.h   | 12 +++++++-
 src/llama-model.cpp |  2 +-
 3 files changed, 82 insertions(+), 3 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index b06d9b44b7835..40cac0a634912 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -278,7 +278,61 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
 
 void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     if (kq_mask) {
-        if (cparams.causal_attn) {
+        // Check if we're using sliding window attention
+        if (n_swa > 0) {
+            const int64_t n_tokens     = ubatch->n_tokens;
+            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
+            const int64_t n_seqs       = ubatch->n_seqs;
+            const int64_t n_stride     = ubatch->n_tokens;
+            const int64_t half_n_swa   = n_swa / 2;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
+            float * data = (float *) kq_mask->data;
+
+            // Implement symmetric sliding window attention
+            // token i attends to tokens [i - n_swa/2, i + n_swa/2]
+            for (int h = 0; h < 1; ++h) {
+                for (int s1 = 0; s1 < n_seqs; ++s1) {
+                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
+
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        const int32_t tj = s1*n_seq_tokens + j;
+                        const int64_t pos_j = ubatch->pos[tj];
+
+                        for (int s0 = 0; s0 < n_seqs; ++s0) {
+                            for (int i = 0; i < n_seq_tokens; ++i) {
+                                const int32_t ti = s0*n_seq_tokens + i;
+                                float f = -INFINITY;
+
+                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
+                                    if (ubatch->seq_id[s0][s] == seq_id) {
+                                        const int64_t pos_i = ubatch->pos[ti];
+                                        const int64_t pos_diff = pos_j - pos_i;
+
+                                        // Apply sliding window constraint
+                                        // [i - n_swa/2, i + n_swa/2]
+                                        if (pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
+                                            if (hparams.use_alibi) {
+                                                f = -std::abs(pos_diff);
+                                            } else {
+                                                f = 0.0f;
+                                            }
+                                        }
+                                        break;
+                                    }
+                                }
+
+                                data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
+                            }
+                        }
+
+                        for (int i = n_tokens; i < n_stride; ++i) {
+                            data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
+                        }
+                    }
+                }
+            }
+        } else if (cparams.causal_attn) {
             const int64_t n_kv         = ubatch->n_tokens;
             const int64_t n_tokens     = ubatch->n_tokens;
             const int64_t n_seq_tokens = ubatch->n_seq_tokens;
@@ -1188,6 +1242,21 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
     return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp));
 }
 
+llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache_iswa() const {
+    // Default sliding window size - can be made configurable via cparams
+    const int n_swa = 128;
+    
+    auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams, n_swa);
+
+    // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch
+    inp->kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+    ggml_set_input(inp->kq_mask);
+
+    inp->kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask, GGML_TYPE_F16) : inp->kq_mask;
+
+    return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp));
+}
+
 ggml_tensor * llm_graph_context::build_attn(
         llm_graph_input_attn_no_cache * inp,
         ggml_cgraph * gf,
diff --git a/src/llama-graph.h b/src/llama-graph.h
index a1f4fc567c800..4b74721e65e03 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -228,8 +228,16 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
 public:
     llm_graph_input_attn_no_cache(const llama_hparams & hparams, const llama_cparams & cparams) :
         hparams(hparams),
-        cparams(cparams) {
+        cparams(cparams),
+        n_swa(0) {
+    }
+
+    llm_graph_input_attn_no_cache(const llama_hparams & hparams, const llama_cparams & cparams, int n_swa) :
+        hparams(hparams),
+        cparams(cparams),
+        n_swa(n_swa) {
     }
+
     ~llm_graph_input_attn_no_cache() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
@@ -241,6 +249,7 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
 
     const llama_hparams & hparams;
     const llama_cparams & cparams;
+    const int n_swa;  // Sliding window attention size (0 = disabled)
 };
 
 class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
@@ -542,6 +551,7 @@ struct llm_graph_context {
                    float   kq_scale) const;
 
     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
+    llm_graph_input_attn_no_cache * build_attn_inp_no_cache_iswa() const;
 
     ggml_tensor * build_attn(
             llm_graph_input_attn_no_cache * inp,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 47d0b443f307b..f6c10b1d3f827 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6130,7 +6130,7 @@ struct llm_build_modern_bert : public llm_graph_context {
         inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
         cb(inpL, "inp_norm", -1);
 
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_no_cache_iswa();
 
         // iterate layers
         for (int il = 0; il < n_layer; ++il) {

From a72cb3be33e6edce7ef783f8c6b3f09a2e7de0fe Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 22:33:42 +0900
Subject: [PATCH 07/28] add modern-bert to llama_model::create_memory

---
 src/llama-model.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index f6c10b1d3f827..df1d6949b7e7d 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -13383,6 +13383,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
         case LLM_ARCH_WAVTOKENIZER_DEC:
+        case LLM_ARCH_MODERN_BERT:
             {
                 res = nullptr;
             } break;

From adea1c9b99fc0696c1e132dc11e2181b1e89ba86 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 22:41:02 +0900
Subject: [PATCH 08/28] fix lint

---
 src/llama-graph.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 40cac0a634912..e3d5f05b3b907 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1245,7 +1245,7 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
 llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache_iswa() const {
     // Default sliding window size - can be made configurable via cparams
     const int n_swa = 128;
-    
+
     auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams, n_swa);
 
     // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch

From cfebb6e73744787a43d2cbcbb446d608a0c2df03 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 5 Jun 2025 22:48:59 +0900
Subject: [PATCH 09/28] access n_swa via hparams

---
 src/llama-graph.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index e3d5f05b3b907..3585fee3830bb 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1243,8 +1243,9 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
 }
 
 llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache_iswa() const {
-    // Default sliding window size - can be made configurable via cparams
-    const int n_swa = 128;
+    // Use the sliding window size from hyperparameters
+    // If hparams.n_swa is 0, use a default value (128)
+    const int n_swa = hparams.n_swa > 0 ? hparams.n_swa : 128;
 
     auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams, n_swa);
 

From 31e87e465d0f0155df61bb6208c85bf0a9a3eb9f Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Fri, 6 Jun 2025 11:37:19 +0900
Subject: [PATCH 10/28] revert changes in convert script

---
 convert_hf_to_gguf.py        | 3 +++
 convert_hf_to_gguf_update.py | 1 +
 2 files changed, 4 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 8a81d142fd3e4..e5851df39fdf7 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -809,6 +809,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
+        if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152":
+            # ref: https://huggingface.co/answerdotai/ModernBERT-base
+            res = "modern-bert"
 
         if res is None:
             logger.warning("\n")
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 2f733f0973686..53294fa5f82bc 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -128,6 +128,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
     {"name": "pixtral",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
     {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
+    {"name": "modern-bert",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

From 1004327f658b5293108ea83c2f0cb5a52f83bbda Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Fri, 6 Jun 2025 11:38:53 +0900
Subject: [PATCH 11/28] add set_vocab to modernbert convert class

---
 convert_hf_to_gguf.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index e5851df39fdf7..0d4c0543e193d 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3940,6 +3940,11 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 class ModernBertModel(BertModel):
     model_arch = gguf.MODEL_ARCH.MODERN_BERT
 
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(True)
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         self._try_set_pooling_type()

From 03693fa2a8355bf360aabe809699630a8b3d6336 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Fri, 6 Jun 2025 16:51:37 +0900
Subject: [PATCH 12/28] parmas-related fix

---
 convert_hf_to_gguf.py | 1 -
 src/llama-model.cpp   | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 3fd8f684c8afc..c7d1364aad593 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3946,7 +3946,6 @@ def set_vocab(self):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self._try_set_pooling_type()
         self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
         self.gguf_writer.add_rope_freq_base(self.hparams["global_rope_theta"])
         self.gguf_writer.add_rope_freq_base_swa(self.hparams["local_rope_theta"])
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index bf543a3040a68..6cfc3004d96a5 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -724,7 +724,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                 ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
-                ml.get_arr_n(LLM_KV_CLASSIFIER_OUTPUT_LABELS, hparams.n_cls_out, false);
 
                 switch (hparams.n_layer) {
                     case 22:
@@ -2237,6 +2236,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     cls_norm   = create_tensor(tn(LLM_TENSOR_CLS_NORM, "weight"), {n_embd}, TENSOR_NOT_REQUIRED);
 
                     cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+                    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
                 } break;
             case LLM_ARCH_JINA_BERT_V2:
                 {

From 2f5a72f1ea7f3d74ca64aee6ebd0b5235fc209c5 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Fri, 6 Jun 2025 17:15:53 +0900
Subject: [PATCH 13/28] handle mask token in modern-bert bpe

---
 src/llama-vocab.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index ba2e1864ec005..a0021ae1ca933 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -2103,6 +2103,12 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             } else {
                 _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
             }
+        } else if (_contains_any(general_arch, {"modern-bert"})) {
+            if (token_to_id.count("[MASK]") == 0) {
+                LLAMA_LOG_WARN("%s: Mask token is missing in vocab, please reconvert model!\n", __func__);
+            } else {
+                _set_token_attr("[MASK]", LLAMA_TOKEN_ATTR_LSTRIP, true);
+            }
         } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
             for (auto id : cache_special_tokens) {
                 _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);

From 68f399e70a64aeec337e01c79c687cfb13672223 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Fri, 6 Jun 2025 17:20:55 +0900
Subject: [PATCH 14/28] add modern-bert to pre_type check

---
 src/llama-vocab.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index a0021ae1ca933..ae1d8cb5b2010 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1554,7 +1554,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "jina-v2-es" ||
                     tokenizer_pre == "jina-v2-de" ||
                     tokenizer_pre == "jina-v2-code" ||
-                    tokenizer_pre == "roberta-bpe") {
+                    tokenizer_pre == "roberta-bpe" ||
+                    tokenizer_pre == "modern-bert") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
             } else if (
                     tokenizer_pre == "refact") {

From ad2a19af7cf3d2cf85723c971dd6615c1f4ac69d Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Fri, 6 Jun 2025 17:30:11 +0900
Subject: [PATCH 15/28] change log warning when no mask token of modern-bert

---
 src/llama-vocab.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index ae1d8cb5b2010..dee849312e1e3 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -2106,7 +2106,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             }
         } else if (_contains_any(general_arch, {"modern-bert"})) {
             if (token_to_id.count("[MASK]") == 0) {
-                LLAMA_LOG_WARN("%s: Mask token is missing in vocab, please reconvert model!\n", __func__);
+                LLAMA_LOG_WARN("%s: Mask token not found in vocab!\n", __func__);
             } else {
                 _set_token_attr("[MASK]", LLAMA_TOKEN_ATTR_LSTRIP, true);
             }

From c6b84e2f31817780ec8f0683f72568dd9168795a Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Sun, 8 Jun 2025 11:49:49 +0900
Subject: [PATCH 16/28] fix modern-bert swa logic

---
 src/llama-graph.cpp            | 79 ++++------------------------------
 src/llama-graph.h              | 11 +----
 src/llama-hparams.h            |  1 +
 src/llama-kv-cache-unified.cpp |  6 +++
 src/llama-model.cpp            |  4 +-
 5 files changed, 18 insertions(+), 83 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 3585fee3830bb..dd5711c817fee 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -279,60 +279,7 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
 void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     if (kq_mask) {
         // Check if we're using sliding window attention
-        if (n_swa > 0) {
-            const int64_t n_tokens     = ubatch->n_tokens;
-            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
-            const int64_t n_seqs       = ubatch->n_seqs;
-            const int64_t n_stride     = ubatch->n_tokens;
-            const int64_t half_n_swa   = n_swa / 2;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
-            float * data = (float *) kq_mask->data;
-
-            // Implement symmetric sliding window attention
-            // token i attends to tokens [i - n_swa/2, i + n_swa/2]
-            for (int h = 0; h < 1; ++h) {
-                for (int s1 = 0; s1 < n_seqs; ++s1) {
-                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const int32_t tj = s1*n_seq_tokens + j;
-                        const int64_t pos_j = ubatch->pos[tj];
-
-                        for (int s0 = 0; s0 < n_seqs; ++s0) {
-                            for (int i = 0; i < n_seq_tokens; ++i) {
-                                const int32_t ti = s0*n_seq_tokens + i;
-                                float f = -INFINITY;
-
-                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
-                                    if (ubatch->seq_id[s0][s] == seq_id) {
-                                        const int64_t pos_i = ubatch->pos[ti];
-                                        const int64_t pos_diff = pos_j - pos_i;
-
-                                        // Apply sliding window constraint
-                                        // [i - n_swa/2, i + n_swa/2]
-                                        if (pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
-                                            if (hparams.use_alibi) {
-                                                f = -std::abs(pos_diff);
-                                            } else {
-                                                f = 0.0f;
-                                            }
-                                        }
-                                        break;
-                                    }
-                                }
-
-                                data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
-                            }
-                        }
-
-                        for (int i = n_tokens; i < n_stride; ++i) {
-                            data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
-                        }
-                    }
-                }
-            }
-        } else if (cparams.causal_attn) {
+        if (cparams.causal_attn) {
             const int64_t n_kv         = ubatch->n_tokens;
             const int64_t n_tokens     = ubatch->n_tokens;
             const int64_t n_seq_tokens = ubatch->n_seq_tokens;
@@ -375,6 +322,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
             const int64_t n_seq_tokens = ubatch->n_seq_tokens;
             const int64_t n_seqs       = ubatch->n_seqs;
             const int64_t n_stride     = ubatch->n_tokens;
+            const int64_t half_n_swa   = hparams.n_swa / 2;
 
             GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
 
@@ -386,6 +334,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 
                     for (int j = 0; j < n_seq_tokens; ++j) {
                         const int32_t tj = s1*n_seq_tokens + j;
+                        const int64_t pos_j = ubatch->pos[tj];
 
                         for (int s0 = 0; s0 < n_seqs; ++s0) {
                             for (int i = 0; i < n_seq_tokens; ++i) {
@@ -394,7 +343,11 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 
                                 for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
                                     if (ubatch->seq_id[s0][s] == seq_id) {
-                                        if (hparams.use_alibi) {
+                                        const int64_t pos_i = ubatch->pos[ti];
+                                        const int64_t pos_diff = pos_j - pos_i;
+
+                                        if (hparams.use_alibi &&
+                                                (pos_diff >= -half_n_swa && pos_diff <= half_n_swa)) {
                                             f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
                                         } else {
                                             f = 0.0f;
@@ -1242,22 +1195,6 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
     return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp));
 }
 
-llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache_iswa() const {
-    // Use the sliding window size from hyperparameters
-    // If hparams.n_swa is 0, use a default value (128)
-    const int n_swa = hparams.n_swa > 0 ? hparams.n_swa : 128;
-
-    auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams, n_swa);
-
-    // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch
-    inp->kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-    ggml_set_input(inp->kq_mask);
-
-    inp->kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask, GGML_TYPE_F16) : inp->kq_mask;
-
-    return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp));
-}
-
 ggml_tensor * llm_graph_context::build_attn(
         llm_graph_input_attn_no_cache * inp,
         ggml_cgraph * gf,
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 4c8bc7f4f4c30..87328db566212 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -228,14 +228,7 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
 public:
     llm_graph_input_attn_no_cache(const llama_hparams & hparams, const llama_cparams & cparams) :
         hparams(hparams),
-        cparams(cparams),
-        n_swa(0) {
-    }
-
-    llm_graph_input_attn_no_cache(const llama_hparams & hparams, const llama_cparams & cparams, int n_swa) :
-        hparams(hparams),
-        cparams(cparams),
-        n_swa(n_swa) {
+        cparams(cparams) {
     }
 
     ~llm_graph_input_attn_no_cache() = default;
@@ -249,7 +242,6 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
 
     const llama_hparams & hparams;
     const llama_cparams & cparams;
-    const int n_swa;  // Sliding window attention size (0 = disabled)
 };
 
 class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
@@ -551,7 +543,6 @@ struct llm_graph_context {
                    float   kq_scale) const;
 
     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
-    llm_graph_input_attn_no_cache * build_attn_inp_no_cache_iswa() const;
 
     ggml_tensor * build_attn(
             llm_graph_input_attn_no_cache * inp,
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 184fdad24d3b0..6ae6724fe712d 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -18,6 +18,7 @@ enum llama_swa_type {
     LLAMA_SWA_TYPE_NONE     = 0,
     LLAMA_SWA_TYPE_STANDARD = 1,
     LLAMA_SWA_TYPE_CHUNKED  = 2,
+    LLAMA_SWA_TYPE_SYMMETRIC  = 3,
 };
 
 struct llama_hparams_posnet {
diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index 5354f808cd959..8d6237901e7da 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -1265,6 +1265,12 @@ bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
                     return true;
                 }
             } break;
+        case LLAMA_SWA_TYPE_SYMMETRIC:
+            {
+                if ( p1 - p0 <= (int32_t) n_swa / 2 || p0 - p1 >= (int32_t) n_swa / 2) {
+                    return true;
+                }
+            } break;
     }
 
     return false;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 6cfc3004d96a5..c0570b2c9b33c 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -718,7 +718,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 hparams.rope_freq_base_train_swa = 10000.0f;
                 hparams.n_swa = 128;
 
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+                hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
                 hparams.set_swa_pattern(3, 0);
 
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
@@ -6144,7 +6144,7 @@ struct llm_build_modern_bert : public llm_graph_context {
         inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
         cb(inpL, "inp_norm", -1);
 
-        auto * inp_attn = build_attn_inp_no_cache_iswa();
+        auto * inp_attn = build_attn_inp_no_cache();
 
         // iterate layers
         for (int il = 0; il < n_layer; ++il) {

From 6751e6970ade8d37c9d0216dba1a883b5975f900 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Sun, 8 Jun 2025 11:50:01 +0900
Subject: [PATCH 17/28] fix modern-bert class register

---
 convert_hf_to_gguf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index c7d1364aad593..e142596dfad50 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3935,7 +3935,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("ModernBert", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
+@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
 class ModernBertModel(BertModel):
     model_arch = gguf.MODEL_ARCH.MODERN_BERT
 

From 4d6b8042b6d7532ba9d4c66ceebbefca4f3461d1 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 9 Jun 2025 13:32:11 +0900
Subject: [PATCH 18/28] handle when no cls_b and cls_out_b

---
 src/llama-graph.cpp | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index dd5711c817fee..0a69d4e2a23fd 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1572,10 +1572,13 @@ void llm_graph_context::build_pooling(
                 ggml_tensor * inp_cls = build_inp_cls();
                 inp = ggml_get_rows(ctx0, inp, inp_cls);
 
-                if (cls != nullptr && cls_b != nullptr) {
+                if (cls != nullptr) {
                     // classification head
                     // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
-                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
+                    cur = ggml_mul_mat(ctx0, cls, inp);
+                    if (cls_b != nullptr) {
+                        cur = ggml_add(ctx0, cur, cls_b);
+                    }
                     cur = ggml_tanh(ctx0, cur);
 
                     if (cls_norm) {
@@ -1586,16 +1589,22 @@ void llm_graph_context::build_pooling(
                     // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
                     // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
                     if (cls_out) {
-                        GGML_ASSERT(cls_out_b != nullptr);
-                        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
+                        cur = ggml_mul_mat(ctx0, cls_out, cur);
+                        if (cls_out_b != nullptr) {
+                            cur = ggml_add(ctx0, cur, cls_out_b);
+                        }
                     }
                 } else if (cls_out) {
                     // Single layer classification head (direct projection)
                     // https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/bert/modeling_bert.py#L1476
-                    GGML_ASSERT(cls_out_b != nullptr);
-                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, inp), cls_out_b);
+                    cur = ggml_mul_mat(ctx0, cls_out, inp);
+                    if (cls_out_b != nullptr) {
+                        cur = ggml_add(ctx0, cur, cls_out_b);
+                    }
                 } else {
-                    GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
+                    // Some models may not have either classification heads
+                    // In this case, just use the CLS/pooled embedding directly
+                    cur = inp;
                 }
             } break;
         default:

From 5821d6c291cc7f87794e2291e18a2eced10f08e8 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 9 Jun 2025 23:17:37 +0900
Subject: [PATCH 19/28] fix unnecessary operations

---
 src/llama-graph.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 0a69d4e2a23fd..3b51cba514fac 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1572,25 +1572,25 @@ void llm_graph_context::build_pooling(
                 ggml_tensor * inp_cls = build_inp_cls();
                 inp = ggml_get_rows(ctx0, inp, inp_cls);
 
-                if (cls != nullptr) {
+                if (cls) {
                     // classification head
                     // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
                     cur = ggml_mul_mat(ctx0, cls, inp);
-                    if (cls_b != nullptr) {
+                    if (cls_b) {
                         cur = ggml_add(ctx0, cur, cls_b);
                     }
                     cur = ggml_tanh(ctx0, cur);
 
                     if (cls_norm) {
                         // normalization head
-                        cur = build_norm(cur, cls_norm, nullptr, LLM_NORM, 0);
+                        cur = build_norm(cur, cls_norm, NULL, LLM_NORM, -1);
                     }
 
                     // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
                     // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
                     if (cls_out) {
                         cur = ggml_mul_mat(ctx0, cls_out, cur);
-                        if (cls_out_b != nullptr) {
+                        if (cls_out_b) {
                             cur = ggml_add(ctx0, cur, cls_out_b);
                         }
                     }
@@ -1598,7 +1598,7 @@ void llm_graph_context::build_pooling(
                     // Single layer classification head (direct projection)
                     // https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/bert/modeling_bert.py#L1476
                     cur = ggml_mul_mat(ctx0, cls_out, inp);
-                    if (cls_out_b != nullptr) {
+                    if (cls_out_b) {
                         cur = ggml_add(ctx0, cur, cls_out_b);
                     }
                 } else {

From 820cee12356e342ba395ab5114081b52f7a3e4d5 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 9 Jun 2025 23:19:53 +0900
Subject: [PATCH 20/28] revert incorrect change

---
 src/llama-graph.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 3b51cba514fac..1fbd66cc00bb4 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1602,9 +1602,7 @@ void llm_graph_context::build_pooling(
                         cur = ggml_add(ctx0, cur, cls_out_b);
                     }
                 } else {
-                    // Some models may not have either classification heads
-                    // In this case, just use the CLS/pooled embedding directly
-                    cur = inp;
+                    GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
                 }
             } break;
         default:

From 16b73d4a777ea2bf4853344fc1bd47a6cb6b88c9 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 9 Jun 2025 23:32:46 +0900
Subject: [PATCH 21/28] use build_ffn with LLM_FFN_GEGLU

---
 src/llama-model.cpp | 30 ++++++------------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index dc21501204591..cb8e4667067a1 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6214,30 +6214,12 @@ struct llm_build_modern_bert : public llm_graph_context {
             ggml_tensor * ffn_inp = cur;
             cb(ffn_inp, "ffn_inp", il);
 
-            // feed-forward network
-            ggml_tensor * ffn_up = build_lora_mm(model.layers[il].ffn_up, cur);
-            cb(ffn_up, "ffn_up", il);
-
-            int64_t split_point = ffn_up->ne[0] / 2;
-            ggml_tensor * output_ffn_up = ggml_cont(ctx0, ggml_view_2d(
-                                            ctx0, ffn_up, split_point,
-                                            ffn_up->ne[1], ffn_up->nb[1], 0
-                                        ));
-            ggml_tensor * output_ffn_gate = ggml_cont(ctx0, ggml_view_2d(
-                                            ctx0, ffn_up, split_point,
-                                            ffn_up->ne[1], ffn_up->nb[1],
-                                            split_point * ggml_element_size(ffn_up)
-                                        ));
-
-            // Apply activation function
-            output_ffn_up = ggml_gelu(ctx0, output_ffn_up);
-
-            // Element-wise multiplication
-            ggml_tensor * gated = ggml_mul(ctx0, output_ffn_up, output_ffn_gate);
-            cb(gated, "ffn_gated", il);
-
-            // Final projection
-            cur = build_lora_mm(model.layers[il].ffn_down, gated);
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,
+                    NULL, NULL, NULL, NULL, NULL,
+                    model.layers[il].ffn_down,
+                    NULL, NULL, NULL,
+                    LLM_FFN_GEGLU, LLM_FFN_SEQ, il);
 
             // attentions bypass the intermediate layer
             cur = ggml_add(ctx0, cur, ffn_inp);

From 333eeed8dee265dc6baa0013e90d02a6365f5c13 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Tue, 10 Jun 2025 08:07:36 +0900
Subject: [PATCH 22/28] fix for use of models without n_swa

---
 src/llama-graph.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 3bc0d6e716fa1..0baa4e9b59245 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -347,7 +347,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                                         const int64_t pos_diff = pos_j - pos_i;
 
                                         if (hparams.use_alibi &&
-                                                (pos_diff >= -half_n_swa && pos_diff <= half_n_swa)) {
+                                                (hparams.n_swa == 0 || (pos_diff >= -half_n_swa && pos_diff <= half_n_swa))) {
                                             f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
                                         } else {
                                             f = 0.0f;

From 454d7b797e3ba566ecdd98721ab83a881882bf1a Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Thu, 12 Jun 2025 13:32:02 +0900
Subject: [PATCH 23/28] fix review comment

---
 src/llama-model.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index cb8e4667067a1..a2588028dc6a4 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -714,13 +714,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
             } break;
         case LLM_ARCH_MODERN_BERT:
             {
-                hparams.rope_freq_base_train = 160000.0f;
-                hparams.rope_freq_base_train_swa = 10000.0f;
-                hparams.n_swa = 128;
-
                 hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
                 hparams.set_swa_pattern(3, 0);
 
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,   hparams.n_swa);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                 ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);

From 0925ad0f2d758cf336ea6202ae400096078ff3e6 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 16 Jun 2025 14:32:15 +0900
Subject: [PATCH 24/28] try to add kq_mask_swa to build_attn_inp_no_cache

---
 src/llama-graph.cpp | 76 +++++++++++++++++++++++++++++++++++++++------
 src/llama-graph.h   |  7 +++--
 2 files changed, 72 insertions(+), 11 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 83ec6162fe841..66ce186c0d1ec 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -266,7 +266,6 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
 
 void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     if (kq_mask) {
-        // Check if we're using sliding window attention
         if (cparams.causal_attn) {
             const int64_t n_kv         = ubatch->n_tokens;
             const int64_t n_tokens     = ubatch->n_tokens;
@@ -311,7 +310,6 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
             const int64_t n_seq_tokens = ubatch->n_seq_tokens;
             const int64_t n_seqs       = ubatch->n_seqs;
             const int64_t n_stride     = ubatch->n_tokens;
-            const int64_t half_n_swa   = hparams.n_swa / 2;
 
             GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
 
@@ -323,7 +321,6 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 
                     for (int j = 0; j < n_seq_tokens; ++j) {
                         const int32_t tj = s1*n_seq_tokens + j;
-                        const int64_t pos_j = ubatch->pos[tj];
 
                         for (int s0 = 0; s0 < n_seqs; ++s0) {
                             for (int i = 0; i < n_seq_tokens; ++i) {
@@ -333,11 +330,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                                 // TODO: fix indexing [UBATCH_IDX]
                                 for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
                                     if (ubatch->seq_id[s0][s] == seq_id) {
-                                        const int64_t pos_i = ubatch->pos[ti];
-                                        const int64_t pos_diff = pos_j - pos_i;
-
-                                        if (hparams.use_alibi &&
-                                                (hparams.n_swa == 0 || (pos_diff >= -half_n_swa && pos_diff <= half_n_swa))) {
+                                        if (hparams.use_alibi) {
                                             f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
                                         } else {
                                             f = 0.0f;
@@ -358,6 +351,61 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
             }
         }
     }
+
+    // Handle symmetric SWA mask separately if it exists
+    if (kq_mask_swa) {
+        const int64_t n_tokens     = ubatch->n_tokens;
+        const int64_t n_seq_tokens = ubatch->n_seq_tokens;
+        const int64_t n_seqs       = ubatch->n_seqs;
+        const int64_t n_stride     = ubatch->n_tokens;
+        const int64_t half_n_swa   = hparams.n_swa / 2;
+
+        GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask_swa->buffer));
+
+        float * data = (float *) kq_mask_swa->data;
+
+        for (int h = 0; h < 1; ++h) {
+            for (int s1 = 0; s1 < n_seqs; ++s1) {
+                const llama_seq_id seq_id = ubatch->seq_id[s1][0];
+
+                for (int j = 0; j < n_seq_tokens; ++j) {
+                    const int32_t tj = s1*n_seq_tokens + j;
+                    const int64_t pos_j = ubatch->pos[tj];
+
+                    for (int s0 = 0; s0 < n_seqs; ++s0) {
+                        for (int i = 0; i < n_seq_tokens; ++i) {
+                            const int32_t ti = s0*n_seq_tokens + i;
+                            float f = -INFINITY;
+
+                            // TODO: fix indexing [UBATCH_IDX]
+                            for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
+                                if (ubatch->seq_id[s0][s] == seq_id) {
+                                    const int64_t pos_i = ubatch->pos[ti];
+                                    const int64_t pos_diff = pos_j - pos_i;
+
+                                    // Apply symmetric sliding window attention logic
+                                    if (pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
+                                        if (hparams.use_alibi) {
+                                            f = -std::abs(pos_i - pos_j);
+                                        } else {
+                                            f = 0.0f;
+                                        }
+                                    }
+                                    break;
+                                }
+                            }
+
+                            data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
+                        }
+                    }
+
+                    for (int i = n_tokens; i < n_stride; ++i) {
+                        data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
 }
 
 void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
@@ -1181,6 +1229,15 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
 
     inp->kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask, GGML_TYPE_F16) : inp->kq_mask;
 
+    // Create SWA mask for symmetric sliding window attention if SWA is enabled
+    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE && hparams.n_swa > 0) {
+        inp->kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        //cb(inp_kq_mask_swa, "KQ_mask_swa", -1);
+        ggml_set_input(inp->kq_mask_swa);
+
+        inp->kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask_swa, GGML_TYPE_F16) : inp->kq_mask_swa;
+    }
+
     return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp));
 }
 
@@ -1204,7 +1261,8 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_build_forward_expand(gf, k_cur);
     ggml_build_forward_expand(gf, v_cur);
 
-    const auto & kq_mask = inp->get_kq_mask();
+    // Select appropriate mask based on SWA type
+    const auto & kq_mask = hparams.is_swa(il) && inp->kq_mask_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
 
     ggml_tensor * q = q_cur;
     ggml_tensor * k = k_cur;
diff --git a/src/llama-graph.h b/src/llama-graph.h
index d45686b9c14e5..e4ff8d1512072 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -225,9 +225,12 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
     void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * get_kq_mask() const { return kq_mask_cnv; }
+    ggml_tensor * get_kq_mask_swa() const { return kq_mask_swa_cnv; }
 
-    ggml_tensor * kq_mask     = nullptr; // F32 [n_tokens, n_batch]
-    ggml_tensor * kq_mask_cnv = nullptr; //     [n_tokens, n_batch]
+    ggml_tensor * kq_mask         = nullptr; // F32 [n_tokens, n_batch]
+    ggml_tensor * kq_mask_cnv     = nullptr; //     [n_tokens, n_batch]
+    ggml_tensor * kq_mask_swa     = nullptr; // F32 [n_tokens, n_batch] - for SWA
+    ggml_tensor * kq_mask_swa_cnv = nullptr; //     [n_tokens, n_batch] - for SWA
 
     const llama_hparams & hparams;
     const llama_cparams & cparams;

From 72b56c44458094aa6b942e8580cbba9511245fcc Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 16 Jun 2025 14:43:32 +0900
Subject: [PATCH 25/28] fix is_masked_swa

---
 src/llama-kv-cache-unified.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index 4e73a7b1d4906..43b6ce917f4ee 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -1330,7 +1330,11 @@ bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
             } break;
         case LLAMA_SWA_TYPE_SYMMETRIC:
             {
-                if ( p1 - p0 <= (int32_t) n_swa / 2 || p0 - p1 >= (int32_t) n_swa / 2) {
+                const int32_t half_n_swa = (int32_t) n_swa / 2;
+                const int32_t pos_diff = p1 - p0;
+                
+                // Mask if outside the symmetric window
+                if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
                     return true;
                 }
             } break;

From d8a1c8ac340dbbe14e3330506495ceab9a6b8cac Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 16 Jun 2025 14:44:20 +0900
Subject: [PATCH 26/28] improve
 llm_graph_input_attn_no_cache::set_input:if(kq_mask_swa) to handle
 causal_attn

---
 src/llama-graph.cpp | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 66ce186c0d1ec..b78e33fa3ad69 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -383,8 +383,16 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                                     const int64_t pos_i = ubatch->pos[ti];
                                     const int64_t pos_diff = pos_j - pos_i;
 
+                                    // Check both causal attention and symmetric sliding window
+                                    bool masked = false;
+                                    
+                                    // Apply causal attention if enabled (only allow attention to past tokens)
+                                    if (cparams.causal_attn && pos_i > pos_j) {
+                                        masked = true;
+                                    }
+                                    
                                     // Apply symmetric sliding window attention logic
-                                    if (pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
+                                    if (!masked && pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
                                         if (hparams.use_alibi) {
                                             f = -std::abs(pos_i - pos_j);
                                         } else {

From 33875868fd2de82390916551120e5941df711f8e Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 16 Jun 2025 14:47:39 +0900
Subject: [PATCH 27/28] Fix lint

---
 src/llama-graph.cpp            | 4 ++--
 src/llama-kv-cache-unified.cpp | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index b78e33fa3ad69..7e5615bf442e3 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -385,12 +385,12 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 
                                     // Check both causal attention and symmetric sliding window
                                     bool masked = false;
-                                    
+
                                     // Apply causal attention if enabled (only allow attention to past tokens)
                                     if (cparams.causal_attn && pos_i > pos_j) {
                                         masked = true;
                                     }
-                                    
+
                                     // Apply symmetric sliding window attention logic
                                     if (!masked && pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
                                         if (hparams.use_alibi) {
diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index 43b6ce917f4ee..a0a2f78a5791d 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -1332,7 +1332,7 @@ bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
             {
                 const int32_t half_n_swa = (int32_t) n_swa / 2;
                 const int32_t pos_diff = p1 - p0;
-                
+
                 // Mask if outside the symmetric window
                 if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
                     return true;

From 51eda9299851762f21f2c89beb21bd761bc7a7b1 Mon Sep 17 00:00:00 2001
From: dinhhuy <huy.dinh@brains-tech.co.jp>
Date: Mon, 16 Jun 2025 19:40:34 +0900
Subject: [PATCH 28/28] refactor llm_graph_input_attn_no_cache::set_input

---
 src/llama-graph.cpp | 192 +++++++++++++++++++-------------------------
 1 file changed, 84 insertions(+), 108 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 7e5615bf442e3..2cce90ef427f3 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -265,104 +265,57 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
-    if (kq_mask) {
-        if (cparams.causal_attn) {
-            const int64_t n_kv         = ubatch->n_tokens;
-            const int64_t n_tokens     = ubatch->n_tokens;
-            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
-            const int64_t n_seqs       = ubatch->n_seqs;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
-            float * data = (float *) kq_mask->data;
-
-            for (int h = 0; h < 1; ++h) {
-                for (int s1 = 0; s1 < n_seqs; ++s1) {
-                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const int32_t tj = s1*n_seq_tokens + j;
-
-                        for (int s0 = 0; s0 < n_seqs; ++s0) {
-                            for (int i = 0; i < n_seq_tokens; ++i) {
-                                const int32_t ti = s0*n_seq_tokens + i;
-                                float f = -INFINITY;
-
-                                // TODO: fix indexing [UBATCH_IDX]
-                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
-                                    if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
-                                        if (hparams.use_alibi) {
-                                            f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
-                                        } else {
-                                            f = 0.0f;
-                                        }
-                                        break;
-                                    }
-                                }
+    // Helper function for SWA masking logic - mirrors llama_kv_cache_unified::is_masked_swa
+    auto is_masked_swa = [this](llama_pos p0, llama_pos p1) -> bool {
+        assert(p0 >= 0 && p1 >= 0);
 
-                                data[h*(n_kv*n_tokens) + tj*n_kv + ti] = f;
-                            }
-                        }
+        switch (hparams.swa_type) {
+            case LLAMA_SWA_TYPE_NONE:
+                {
+                } break;
+            case LLAMA_SWA_TYPE_STANDARD:
+                {
+                    if (p1 - p0 >= (int32_t) hparams.n_swa) {
+                        return true;
                     }
-                }
-            }
-        } else {
-            const int64_t n_tokens     = ubatch->n_tokens;
-            const int64_t n_seq_tokens = ubatch->n_seq_tokens;
-            const int64_t n_seqs       = ubatch->n_seqs;
-            const int64_t n_stride     = ubatch->n_tokens;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
-
-            float * data = (float *) kq_mask->data;
-
-            for (int h = 0; h < 1; ++h) {
-                for (int s1 = 0; s1 < n_seqs; ++s1) {
-                    const llama_seq_id seq_id = ubatch->seq_id[s1][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const int32_t tj = s1*n_seq_tokens + j;
-
-                        for (int s0 = 0; s0 < n_seqs; ++s0) {
-                            for (int i = 0; i < n_seq_tokens; ++i) {
-                                const int32_t ti = s0*n_seq_tokens + i;
-                                float f = -INFINITY;
-
-                                // TODO: fix indexing [UBATCH_IDX]
-                                for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
-                                    if (ubatch->seq_id[s0][s] == seq_id) {
-                                        if (hparams.use_alibi) {
-                                            f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
-                                        } else {
-                                            f = 0.0f;
-                                        }
-                                        break;
-                                    }
-                                }
+                } break;
+            case LLAMA_SWA_TYPE_CHUNKED:
+                {
+                    const llama_pos pos_chunk_start = (p1 / hparams.n_swa) * hparams.n_swa;
 
-                                data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
-                            }
-                        }
+                    if (p0 < pos_chunk_start) {
+                        return true;
+                    }
+                } break;
+            case LLAMA_SWA_TYPE_SYMMETRIC:
+                {
+                    const int32_t half_n_swa = (int32_t) hparams.n_swa / 2;
+                    const int32_t pos_diff = p1 - p0;
 
-                        for (int i = n_tokens; i < n_stride; ++i) {
-                            data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
-                        }
+                    // Mask if outside the symmetric window
+                    if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
+                        return true;
                     }
-                }
-            }
+                } break;
+        }
+
+        return false;
+    };
+
+    // Helper function for setting attention mask
+    auto set_mask = [this, ubatch, &is_masked_swa](ggml_tensor * mask, bool apply_swa) {
+        if (!mask) {
+            return;
         }
-    }
 
-    // Handle symmetric SWA mask separately if it exists
-    if (kq_mask_swa) {
         const int64_t n_tokens     = ubatch->n_tokens;
         const int64_t n_seq_tokens = ubatch->n_seq_tokens;
         const int64_t n_seqs       = ubatch->n_seqs;
-        const int64_t n_stride     = ubatch->n_tokens;
-        const int64_t half_n_swa   = hparams.n_swa / 2;
+        const int64_t n_kv         = ubatch->n_tokens;
+        const int64_t n_stride     = cparams.causal_attn ? n_kv : n_tokens;
 
-        GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask_swa->buffer));
-
-        float * data = (float *) kq_mask_swa->data;
+        GGML_ASSERT(ggml_backend_buffer_is_host(mask->buffer));
+        float * data = (float *) mask->data;
 
         for (int h = 0; h < 1; ++h) {
             for (int s1 = 0; s1 < n_seqs; ++s1) {
@@ -370,50 +323,73 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 
                 for (int j = 0; j < n_seq_tokens; ++j) {
                     const int32_t tj = s1*n_seq_tokens + j;
-                    const int64_t pos_j = ubatch->pos[tj];
+                    const llama_pos pos_j = ubatch->pos[tj];
 
                     for (int s0 = 0; s0 < n_seqs; ++s0) {
                         for (int i = 0; i < n_seq_tokens; ++i) {
                             const int32_t ti = s0*n_seq_tokens + i;
+                            const llama_pos pos_i = ubatch->pos[ti];
                             float f = -INFINITY;
 
-                            // TODO: fix indexing [UBATCH_IDX]
+                            // Check sequence match
+                            bool sequence_match = false;
                             for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
                                 if (ubatch->seq_id[s0][s] == seq_id) {
-                                    const int64_t pos_i = ubatch->pos[ti];
-                                    const int64_t pos_diff = pos_j - pos_i;
+                                    sequence_match = true;
+                                    break;
+                                }
+                            }
 
-                                    // Check both causal attention and symmetric sliding window
-                                    bool masked = false;
+                            if (sequence_match) {
+                                bool masked = false;
 
-                                    // Apply causal attention if enabled (only allow attention to past tokens)
-                                    if (cparams.causal_attn && pos_i > pos_j) {
-                                        masked = true;
-                                    }
+                                // Apply causal attention if enabled
+                                if (cparams.causal_attn && pos_i > pos_j) {
+                                    masked = true;
+                                }
+
+                                // Apply SWA masking if needed
+                                if (!masked && apply_swa) {
+                                    masked = masked || is_masked_swa(pos_i, pos_j);
+                                }
 
-                                    // Apply symmetric sliding window attention logic
-                                    if (!masked && pos_diff >= -half_n_swa && pos_diff <= half_n_swa) {
-                                        if (hparams.use_alibi) {
-                                            f = -std::abs(pos_i - pos_j);
-                                        } else {
-                                            f = 0.0f;
-                                        }
+                                if (!masked) {
+                                    if (hparams.use_alibi) {
+                                        f = -std::abs(pos_i - pos_j);
+                                    } else {
+                                        f = 0.0f;
                                     }
-                                    break;
                                 }
                             }
 
-                            data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
+                            const int idx = h*(n_tokens*n_tokens) + tj*n_stride + ti;
+                            data[idx] = f;
                         }
                     }
 
+                    // Pad the rest of the row with -INFINITY
                     for (int i = n_tokens; i < n_stride; ++i) {
-                        data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
+                        const int idx = h*(n_tokens*n_tokens) + tj*n_stride + i;
+                        data[idx] = -INFINITY;
                     }
                 }
             }
         }
-    }
+
+        // Pad any remaining entries with -INFINITY
+        for (int tj = n_tokens; tj < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++tj) {
+            for (int ti = 0; ti < n_stride; ++ti) {
+                const int idx = 0*(n_tokens*n_tokens) + tj*n_stride + ti;
+                data[idx] = -INFINITY;
+            }
+        }
+    };
+
+    // Set regular attention mask
+    set_mask(kq_mask, false);
+
+    // Set SWA attention mask if available
+    set_mask(kq_mask_swa, true);
 }
 
 void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {