Model-Saver: Write the SWA pattern, 15 more architectures roundtrip (#29042)

* llama: read the SWA pattern as a period or a per-layer array

Add llama_model_base::load_swa_pattern(), which reads
sliding_window_pattern either as one flag per layer or as a period
expanded by set_swa_pattern(), and use it in every loader that reads
the key as a period.

These loaders silently ignored an array and applied their default
period, although the converters of olmo2, gemma3n and exaone4 write
arrays. The published GGUFs match the defaults, so their outputs do
not change. The loaders that already accepted both forms lose their
duplicated scalar-then-array block, and use their declared default
period when the key is absent.

* model-saver: write the SWA pattern and the MLA SWA geometry

Write sliding_window_pattern as one flag per layer, nextn layers
included, for every model using SWA. The array is never collapsed to
a scalar, since the loaders read a scalar as a period.

Also write the MLA key/value lengths and KV LoRA rank of the SWA
layers, required by dots3note.

This enables the saver for plamo3, gemma3, cohere2, cohere2moe,
olmo2, exaone-moe, afmoe, mimo2, spark2_5, muse-glimmer, mellum,
laguna, granite_swa, dots3note and maple, all passing the bit-exact
roundtrip of test-llama-archs.

src/llama-model-saver.cpp

@@ -15,26 +15,11 @@
 
 bool llama_model_saver_supports_arch(llm_arch arch) {
     switch (arch) {
-        case LLM_ARCH_PLAMO3:
-        case LLM_ARCH_GEMMA3:
         case LLM_ARCH_GEMMA3N:
-        case LLM_ARCH_COHERE2:
-        case LLM_ARCH_COHERE2MOE:
-        case LLM_ARCH_OLMO2:
         case LLM_ARCH_BITNET:
         case LLM_ARCH_T5:
-        case LLM_ARCH_EXAONE_MOE:
-        case LLM_ARCH_AFMOE:
         case LLM_ARCH_APERTUS:
-        case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
-        case LLM_ARCH_SPARK2_5:
-        case LLM_ARCH_MUSE_GLIMMER:
-        case LLM_ARCH_MELLUM:
-        case LLM_ARCH_LAGUNA:
-        case LLM_ARCH_GRANITE_SWA:
-        case LLM_ARCH_DOTS3NOTE: // TODO: need to handle SWA pattern and MLA+SWA config
-        case LLM_ARCH_MAPLE:
             return false;
         default:
             return true;
@@ -290,7 +275,11 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,  hparams.n_rel_attn_bkts);
     add_kv(LLM_KV_ATTENTION_ROPE_PATTERN,            hparams.rope_pattern, true);
     add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW,          hparams.n_swa);
-    // add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,  ???);
+    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+        // never collapsed to a scalar: the loaders read a scalar as a period
+        add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, std::vector<uint32_t>(
+                hparams.is_swa_impl.begin(), hparams.is_swa_impl.begin() + hparams.n_layer_all));
+    }
     add_kv(LLM_KV_ATTENTION_SCALE,                   hparams.f_attention_scale);
     add_kv(LLM_KV_ATTENTION_OUTPUT_SCALE,            hparams.f_attn_out_scale);
     add_kv(LLM_KV_ATTENTION_VALUE_SCALE,             hparams.f_attn_value_scale);
@@ -300,6 +289,9 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,        hparams.n_embd_head_v_mla_impl);
     add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA,          hparams.n_embd_head_k_swa);
     add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,        hparams.n_embd_head_v_swa);
+    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,      hparams.n_embd_head_k_mla_swa);
+    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA,    hparams.n_embd_head_v_mla_swa);
+    add_kv(LLM_KV_ATTENTION_KV_LORA_RANK_SWA,        hparams.n_lora_kv_swa);
     add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,      hparams.indexer_n_head);
     add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,      hparams.indexer_head_size);
     add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K,           hparams.indexer_top_k);

src/llama-model.cpp

@@ -3305,6 +3305,15 @@ void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
     }
 }
 
+void llama_model_base::load_swa_pattern(llama_model_loader & ml, uint32_t n_pattern, bool dense_first) {
+    if (ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, false)) {
+        return;
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, n_pattern, false);
+    hparams.set_swa_pattern(n_pattern, dense_first);
+}
+
 const int32_t * llama_model_target_layer_ids(const struct llama_model * model) {
     const auto & v = model->target_layer_ids;
     return v.empty() ? nullptr : v.data();

src/llama-model.h

@@ -814,6 +814,9 @@ struct llama_model_base : public llama_model {
                 int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
                 int flags);
 
+    // helper: read the SWA pattern as one flag per layer, or as a period expanded by set_swa_pattern
+    void load_swa_pattern(llama_model_loader & ml, uint32_t n_pattern, bool dense_first = false);
+
     void load_stats  (llama_model_loader & ml) override;
     void load_hparams(llama_model_loader & ml) override;
     void load_vocab  (llama_model_loader & ml) override;

src/models/afmoe.cpp

@@ -14,9 +14,7 @@ void llama_model_afmoe::load_arch_hparams(llama_model_loader & ml) {
     // Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)
     if (hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-        uint32_t swa_period = 4;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period);
+        load_swa_pattern(ml, 4);
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/cohere2.cpp

@@ -2,9 +2,7 @@
 
 void llama_model_cohere2::load_arch_hparams(llama_model_loader & ml) {
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    uint32_t swa_period = 4;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-    hparams.set_swa_pattern(swa_period);
+    load_swa_pattern(ml, 4);
 
     hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
     hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/cohere2moe.cpp

@@ -25,12 +25,7 @@ void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
     }
 
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    uint32_t swa_period = 4;
-    if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
-        hparams.set_swa_pattern(swa_period, true);
-    } else {
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
-    }
+    load_swa_pattern(ml, 4, true);
 
     hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
     hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/exaone-moe.cpp

@@ -3,9 +3,7 @@
 void llama_model_exaone_moe::load_arch_hparams(llama_model_loader & ml) {
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
     hparams.n_swa = 128;
-    uint32_t swa_period = 4;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-    hparams.set_swa_pattern(swa_period);
+    load_swa_pattern(ml, 4);
     hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
     hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
 

src/models/exaone4.cpp

@@ -4,9 +4,7 @@ void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {
     if (hparams.n_layer() == 64) {    // 32B
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
         hparams.n_swa = 4096;
-        uint32_t swa_period = 4;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period);
+        load_swa_pattern(ml, 4);
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/gemma-embedding.cpp

@@ -2,9 +2,7 @@
 
 void llama_model_gemma_embedding::load_arch_hparams(llama_model_loader & ml) {
     hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
-    uint32_t swa_period = 6;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-    hparams.set_swa_pattern(swa_period);
+    load_swa_pattern(ml, 6);
 
     hparams.causal_attn = false; // embeddings do not use causal attention
 

src/models/gemma2.cpp

@@ -3,9 +3,7 @@
 void llama_model_gemma2::load_arch_hparams(llama_model_loader & ml) {
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
     hparams.n_swa = 4096; // default value of gemma 2
-    uint32_t swa_period = 2;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-    hparams.set_swa_pattern(swa_period);
+    load_swa_pattern(ml, 2);
     hparams.attn_soft_cap = true;
     hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
     hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/gemma3.cpp

@@ -4,9 +4,7 @@ void llama_model_gemma3::load_arch_hparams(llama_model_loader & ml) {
     const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
     if (found_swa && hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-        uint32_t swa_period = 6;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period);
+        load_swa_pattern(ml, 6);
 
         ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
     } else {

src/models/gemma3n.cpp

@@ -1,10 +1,8 @@
 #include "models.h"
 
 void llama_model_gemma3n::load_arch_hparams(llama_model_loader & ml) {
-    uint32_t swa_period = 5;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    hparams.set_swa_pattern(swa_period);
+    load_swa_pattern(ml, 5);
 
     hparams.n_layer_kv_from_start = 20;
     hparams.f_attention_scale     = 1.0f;

src/models/laguna.cpp

@@ -36,9 +36,7 @@ void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
     if (hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
 
-        uint32_t swa_period = 4;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period, /*dense_first=*/true);  // XS.2: FULL at il%4==0
+        load_swa_pattern(ml, 4, /*dense_first=*/true);  // XS.2: FULL at il%4==0
 
         // Per-layer-type RoPE: full layers use YaRN θ=500000 over 64 dims;
         // SWA layers use default RoPE θ=10000 over 128 dims. Base load_hparams

src/models/llama4.cpp

@@ -16,9 +16,7 @@ void llama_model_llama4::load_arch_hparams(llama_model_loader & ml) {
         hparams.f_attn_temp_scale       = 0.1f;
         hparams.f_attn_temp_offset      = 1.0f;
 
-        uint32_t swa_period = 4; // pattern: 3 chunked - 1 full
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period);
+        load_swa_pattern(ml, 4); // pattern: 3 chunked - 1 full
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/mellum.cpp

@@ -8,13 +8,7 @@ void llama_model_mellum::load_arch_hparams(llama_model_loader & ml) {
     if (hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
 
-        uint32_t swa_period = 4;
-        const auto res = ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        if (res) {
-            hparams.set_swa_pattern(swa_period);
-        } else {
-            ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
-        }
+        load_swa_pattern(ml, 4);
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/modern-bert.cpp

@@ -5,9 +5,7 @@ void llama_model_modern_bert::load_arch_hparams(llama_model_loader & ml) {
     if (found_swa && hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
         ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-        uint32_t swa_period = 3;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period, true);
+        load_swa_pattern(ml, 3, true);
     } else {
         hparams.swa_type = LLAMA_SWA_TYPE_NONE;
     }

src/models/muse-glimmer.cpp

@@ -10,12 +10,7 @@ void llama_model_muse_glimmer::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
 
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    uint32_t swa_period = 4;
-    if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
-        hparams.set_swa_pattern(swa_period);
-    } else {
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
-    }
+    load_swa_pattern(ml, 4);
 
     switch (hparams.n_layer()) {
         case 52: type = LLM_TYPE_30B; break;

src/models/olmo2.cpp

@@ -6,9 +6,7 @@ void llama_model_olmo2::load_arch_hparams(llama_model_loader & ml) {
     const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
     if (found_swa && hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-        uint32_t swa_period = 4;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period);
+        load_swa_pattern(ml, 4);
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = 1.0; // See olmo2.cpp

src/models/openai-moe.cpp

@@ -6,9 +6,7 @@ void llama_model_openai_moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
 
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    uint32_t swa_period = 2;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-    hparams.set_swa_pattern(swa_period);
+    load_swa_pattern(ml, 2);
 
     hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
     hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

src/models/plamo3.cpp

@@ -6,9 +6,7 @@ void llama_model_plamo3::load_arch_hparams(llama_model_loader & ml) {
     if (found_swa && hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
         ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-        uint32_t swa_period = 8;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period);
+        load_swa_pattern(ml, 8);
     } else {
         hparams.swa_type = LLAMA_SWA_TYPE_NONE;
     }

src/models/smallthinker.cpp

@@ -6,9 +6,7 @@ void llama_model_smallthinker::load_arch_hparams(llama_model_loader & ml) {
     if (found_swa && hparams.n_swa > 0) {
         hparams.swa_type    = LLAMA_SWA_TYPE_STANDARD;
         hparams.n_swa       = 4096;
-        uint32_t swa_period = 4;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-        hparams.set_swa_pattern(swa_period, true);
+        load_swa_pattern(ml, 4, true);
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;