tests : add support for qwen3 SSM archs (#24031)

* tests : add support for qwen3 SSM archs

* arch : add LLM_KV_ATTENTION_RECURRENT_LAYERS

* cont : naming + TODOs

src/llama-arch.cpp

@@ -247,6 +247,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,           "%s.attention.indexer.key_length"           },
     { LLM_KV_ATTENTION_INDEXER_TOP_K,                "%s.attention.indexer.top_k"                },
     { LLM_KV_ATTENTION_SHARED_KV_LAYERS,             "%s.attention.shared_kv_layers"             },
+    { LLM_KV_ATTENTION_RECURRENT_LAYERS,             "%s.attention.recurrent_layers"             },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,           "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_DIMENSION_COUNT_SWA,       "%s.rope.dimension_count_swa"             },

src/llama-arch.h

@@ -251,6 +251,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
     LLM_KV_ATTENTION_INDEXER_TOP_K,
     LLM_KV_ATTENTION_SHARED_KV_LAYERS,
+    LLM_KV_ATTENTION_RECURRENT_LAYERS,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_DIMENSION_COUNT_SWA,

src/llama-hparams.cpp

@@ -8,18 +8,31 @@
 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
     if (dense_first) {
         for (uint32_t il = 0; il < n_layer; ++il) {
-            swa_layers[il] = n_pattern == 0 || (il % n_pattern != 0);
+            is_swa_impl[il] = n_pattern == 0 || (il % n_pattern != 0);
         }
     } else {
         for (uint32_t il = 0; il < n_layer; ++il) {
-            swa_layers[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
+            is_swa_impl[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
         }
     }
 }
 
+// TODO: implement
+//void llama_hparams::set_recr_pattern(uint32_t n_pattern, bool dense_first) {
+//    if (dense_first) {
+//        for (uint32_t il = 0; il < n_layer; ++il) {
+//            is_recr_impl[il] = n_pattern == 0 || (il % n_pattern != 0);
+//        }
+//    } else {
+//        for (uint32_t il = 0; il < n_layer; ++il) {
+//            is_recr_impl[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
+//        }
+//    }
+//}
+
 bool llama_hparams::is_swa_any() const {
     for (uint32_t il = 0; il < n_layer; ++il) {
-        if (swa_layers[il]) {
+        if (is_swa_impl[il]) {
             return true;
         }
     }
@@ -193,9 +206,9 @@ uint32_t llama_hparams::n_embd_s() const {
     return ssm_d_state * ssm_d_inner;
 }
 
-bool llama_hparams::is_recurrent(uint32_t il) const {
+bool llama_hparams::is_recr(uint32_t il) const {
     if (il < n_layer) {
-        return recurrent_layer_arr[il];
+        return is_recr_impl[il];
     }
 
     GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer);
@@ -207,7 +220,7 @@ uint32_t llama_hparams::n_pos_per_embd() const {
 
 bool llama_hparams::is_swa(uint32_t il) const {
     if (il < n_layer) {
-        return swa_layers[il];
+        return is_swa_impl[il];
     }
 
     GGML_ABORT("fatal error");

src/llama-hparams.h

@@ -37,6 +37,9 @@ struct llama_hparams_convnext {
 };
 
 struct llama_hparams {
+    // note: use the `_impl` suffix to avoid name conflict between members and getters
+    //       for example: n_embd_out() vs n_embd_out_impl
+
     bool vocab_only;
     bool no_alloc;
     bool rope_finetuned;
@@ -137,11 +140,15 @@ struct llama_hparams {
     llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
     // the size of the sliding window (0 - no SWA)
     uint32_t n_swa = 0;
-    // if swa_layers[il] == 1, then layer il is SWA
+
-    // if swa_layers[il] == 0, then layer il is dense (i.e. non-SWA)
+    // if is_swa_impl[il] == 1, then layer il is SWA
+    // if is_swa_impl[il] == 0, then layer il is dense (i.e. non-SWA)
     // by default, all layers are dense
     // note: using uint32_t type for compatibility reason
-    std::array<uint32_t, LLAMA_MAX_LAYERS> swa_layers;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> is_swa_impl;
+
+    // for hybrid state space models
+    std::array<uint32_t, LLAMA_MAX_LAYERS> is_recr_impl;
 
     // for State Space Models
     uint32_t ssm_d_conv  = 0;
@@ -153,9 +160,6 @@ struct llama_hparams {
     // for Kimi Linear KDA
     uint32_t n_embd_head_kda = 0;
 
-    // for hybrid state space models
-    std::array<bool, LLAMA_MAX_LAYERS> recurrent_layer_arr;
-
     bool ssm_dt_b_c_rms = false;
 
     float f_clamp_kqv      = 0.0f;
@@ -266,6 +270,14 @@ struct llama_hparams {
     // return true if one of the layers is SWA
     bool is_swa_any() const;
 
+    bool is_swa(uint32_t il) const;
+
+    // TODO: implement
+    //void set_recr_pattern(uint32_t n_pattern, bool dense_first = false);
+
+    // whether or not the given layer is recurrent (for hybrid models)
+    bool is_recr(uint32_t il) const;
+
     uint32_t n_head(uint32_t il = 0) const;
 
     uint32_t n_head_kv(uint32_t il = 0) const;
@@ -307,13 +319,8 @@ struct llama_hparams {
     // dimension of the recurrent state embeddings
     uint32_t n_embd_s() const;
 
-    // whether or not the given layer is recurrent (for hybrid models)
-    bool is_recurrent(uint32_t il) const;
-
     uint32_t n_pos_per_embd() const;
 
-    bool is_swa(uint32_t il) const;
-
     // note: currently only support if either all or none of the layers are MLA
     bool is_mla() const;
 

src/llama-memory-hybrid-iswa.cpp

@@ -44,7 +44,7 @@ llama_memory_hybrid_iswa::llama_memory_hybrid_iswa(
         n_ubatch,
         n_pad,
         filter_attn == nullptr ?
-            [&](int32_t il) { return !hparams.is_recurrent(il); }
+            [&](int32_t il) { return !hparams.is_recr(il); }
             : filter_attn,
         nullptr
     )),
@@ -57,7 +57,7 @@ llama_memory_hybrid_iswa::llama_memory_hybrid_iswa(
         n_seq_max,
         n_rs_seq,
         filter_recr == nullptr ?
-            [&](int32_t il) { return hparams.is_recurrent(il); }
+            [&](int32_t il) { return hparams.is_recr(il); }
             : filter_recr
     )) {}
 

src/llama-memory-hybrid.cpp

@@ -45,7 +45,7 @@ llama_memory_hybrid::llama_memory_hybrid(
         n_swa,
         swa_type,
         filter_attn == nullptr ?
-            [&](int32_t il) { return !hparams.is_recurrent(il); }
+            [&](int32_t il) { return !hparams.is_recr(il); }
             : filter_attn,
         nullptr
     )),
@@ -58,7 +58,7 @@ llama_memory_hybrid::llama_memory_hybrid(
         n_seq_max,
         n_rs_seq,
         filter_recr == nullptr ?
-            [&](int32_t il) { return hparams.is_recurrent(il); }
+            [&](int32_t il) { return hparams.is_recr(il); }
             : filter_recr
     )) {}
 

src/llama-model-loader.cpp

@@ -146,7 +146,7 @@ namespace GGUFMeta {
             const enum gguf_type arr_type = gguf_get_arr_type(ctx, k);
             return ArrayInfo {
                 arr_type,
-                size_t(gguf_get_arr_n(ctx, k)),
+                gguf_get_arr_n(ctx, k),
                 arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx, k),
             };
         }
@@ -445,7 +445,7 @@ namespace GGUFMeta {
         }
 
         if (n > N_MAX) {
-            throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
+            throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", n, (uint32_t) N_MAX, key.c_str()));
         }
 
         if (gguf_get_kv_type(metadata, kid) == GGUF_TYPE_ARRAY) {

src/llama-model-saver.cpp

@@ -14,9 +14,6 @@
 
 bool llama_model_saver_supports_arch(llm_arch arch) {
     switch (arch) {
-        case LLM_ARCH_QWEN3NEXT:
-        case LLM_ARCH_QWEN35:
-        case LLM_ARCH_QWEN35MOE:
         case LLM_ARCH_PLAMO3:
         case LLM_ARCH_GEMMA3:
         case LLM_ARCH_GEMMA3N:
@@ -107,6 +104,8 @@ void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, c
         gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT8, value.data(), n_values);
     } else if (std::is_same<typename Container::value_type, uint32_t>::value) {
         gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT32, value.data(), n_values);
+    } else if (std::is_same<typename Container::value_type, bool>::value) {
+        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_BOOL, value.data(), n_values);
     } else if (std::is_same<typename Container::value_type, int32_t>::value) {
         gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT32, value.data(), n_values);
     } else if (std::is_same<typename Container::value_type, float>::value) {
@@ -245,7 +244,7 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_EMBEDDING_SCALE,                   hparams.f_embedding_scale);
     add_kv(LLM_KV_TOKEN_SHIFT_COUNT,                 hparams.token_shift_count);
     add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         hparams.n_moe_layer_step);
-    // add_kv(LLM_KV_FULL_ATTENTION_INTERVAL,           ???);
+    // add_kv(LLM_KV_FULL_ATTENTION_INTERVAL,           ???); // saved as LLM_KV_ATTENTION_RECURRENT_LAYERS instead
 
     add_kv(LLM_KV_ATTENTION_HEAD_COUNT,              hparams.n_head_arr, true);
     add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV,           hparams.n_head_kv_arr, true);
@@ -279,6 +278,7 @@ void llama_model_saver::add_kv_from_model() {
     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);
+    add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS,        hparams.is_recr_impl, true);
 
     const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
 

src/llama-model.cpp

@@ -373,10 +373,10 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
         //     count only the same type of previous layers to avoid this
         auto get_il_eff = [&](const size_t il){
             size_t ret = 0;
-            const bool il_is_recurrent = hparams.is_recurrent(il);
+            const bool il_is_recr = hparams.is_recr(il);
             const bool il_is_swa  = hparams.is_swa(il);
             for (size_t il_prev = 0; il_prev < il; il_prev++) {
-                ret += hparams.is_recurrent(il_prev) == il_is_recurrent && hparams.is_swa(il_prev) == il_is_swa;
+                ret += hparams.is_recr(il_prev) == il_is_recr && hparams.is_swa(il_prev) == il_is_swa;
             }
             return ret;
         };
@@ -553,7 +553,7 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
     };
 
     auto get_split_granularity = [&](int64_t blck_size, uint32_t il, const std::vector<std::pair<int64_t, uint32_t>> & segments) -> std::vector<int64_t> {
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // linear attention
             const int64_t head_dim  = hparams.ssm_d_state;
             const int64_t granularity_qkv = std::lcm(blck_size, head_dim);
@@ -1076,18 +1076,16 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
     std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
     std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
     std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
-    std::fill(
-        hparams.recurrent_layer_arr.begin(),
-        hparams.recurrent_layer_arr.end(),
-        llm_arch_is_recurrent(ml.get_arch()));
 
     std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0);
-    std::fill(hparams.swa_layers.begin(), hparams.swa_layers.end(), 0);
+    std::fill(hparams.is_swa_impl.begin(),   hparams.is_swa_impl.end(), 0);
+    std::fill(hparams.is_recr_impl.begin(),  hparams.is_recr_impl.end(),  llm_arch_is_recurrent(ml.get_arch()) ? 1 : 0);
 
     std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f);
     std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
     std::fill(hparams.xielu_beta.begin(),    hparams.xielu_beta.end(), 0.0f);
     std::fill(hparams.xielu_eps.begin(),     hparams.xielu_eps.end(), 0.0f);
+
     std::fill(hparams.swiglu_clamp_exp.begin(),   hparams.swiglu_clamp_exp.end(),   0.0f);
     std::fill(hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.end(), 0.0f);
 
@@ -2040,18 +2038,18 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         filter_recr = [&](int32_t) { return true; };
                     } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
                         filter_attn = [&](int32_t il) {
-                            return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
+                            return !hparams.is_recr(il) && hparams.n_ff(il) == 0;
                         };
                         filter_recr = [&](int32_t il) {
-                            return hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
+                            return hparams.is_recr(il) && hparams.n_ff(il) == 0;
                         };
                     } else if (arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) {
                         const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
                         filter_attn = [&, n_main](int32_t il) {
-                            return (uint32_t)il < n_main && !hparams.is_recurrent(il);
+                            return (uint32_t)il < n_main && !hparams.is_recr(il);
                         };
                         filter_recr = [&, n_main](int32_t il) {
-                            return (uint32_t)il < n_main && hparams.is_recurrent(il);
+                            return (uint32_t)il < n_main && hparams.is_recr(il);
                         };
                     }
 

src/models/falcon-h1.cpp

@@ -11,7 +11,7 @@ void llama_model_falcon_h1::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
-    std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), true);
+    std::fill(hparams.is_recr_impl.begin(), hparams.is_recr_impl.end(), true);
 
     switch (hparams.n_layer) {
         case 36:

src/models/gemma4.cpp

@@ -2,7 +2,7 @@
 
 void llama_model_gemma4::load_arch_hparams(llama_model_loader & ml) {
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer);
 
     uint32_t n_kv_shared_layers = 0;
     ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);

src/models/granite-hybrid.cpp

@@ -20,7 +20,7 @@ void llama_model_granite_hybrid::load_arch_hparams(llama_model_loader & ml) {
 
     // A layer is recurrent IFF the n_head_kv value is set to 0
     for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
+        hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;
     }
 
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -71,7 +71,7 @@ void llama_model_granite_hybrid::load_arch_tensors(llama_model_loader &) {
         // norm
         layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
 
-        if (hparams.is_recurrent(i)) {
+        if (hparams.is_recr(i)) {
             // ssm layers
             layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
 
@@ -158,7 +158,7 @@ llama_model_granite_hybrid::graph::graph(const llama_model & model, const llm_gr
         cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
         cb(cur, "attn_norm", il);
 
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // ssm layer //
             cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
         } else {

src/models/jamba.cpp

@@ -9,7 +9,7 @@ void llama_model_jamba::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
     for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
+        hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;
     }
 
     switch (hparams.n_layer) {

src/models/kimi-linear.cpp

@@ -15,7 +15,7 @@ void llama_model_kimi_linear::load_arch_hparams(llama_model_loader & ml) {
     // Mark KDA layers as recurrent using n_head_kv pattern (like Jamba)
     // Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention)
     for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;  // KDA layers are recurrent
+        hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;  // KDA layers are recurrent
     }
 
     // MoE parameters - Kimi uses moe_intermediate_size = 1024
@@ -53,7 +53,7 @@ void llama_model_kimi_linear::load_arch_tensors(llama_model_loader &) {
         const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda;
         const int64_t ssm_d_conv = hparams.ssm_d_conv;
 
-        if (hparams.is_recurrent(i)) {
+        if (hparams.is_recr(i)) {
             // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1)
             // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner]
             layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
@@ -285,7 +285,7 @@ llama_model_kimi_linear::graph::graph(const llama_model & model, const llm_graph
 
         ggml_build_forward_expand(gf, cur);
 
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // === KDA Layer (Kimi Delta Attention) with Recurrent State ===
             // Reference: vLLM kda.py
             const auto * mctx_cur = inp_rs->mctx;

src/models/lfm2.cpp

@@ -6,7 +6,7 @@ void llama_model_lfm2::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SHORTCONV_L_CACHE,           hparams.n_shortconv_l_cache);
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
     for (uint32_t il = 0; il < hparams.n_layer; ++il) {
-        hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
+        hparams.is_recr_impl[il] = hparams.n_head_kv(il) == 0;
     }
     hparams.n_layer_dense_lead = hparams.n_layer;
     switch (hparams.n_ff()) {
@@ -19,7 +19,7 @@ void llama_model_lfm2::load_arch_hparams(llama_model_loader & ml) {
     if (const auto is_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); is_swa && hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
         for (uint32_t il = 0; il < hparams.n_layer; ++il) {
-            hparams.swa_layers[il] = !hparams.recurrent_layer_arr[il];
+            hparams.is_swa_impl[il] = !hparams.is_recr_impl[il];
         }
     }
 }
@@ -59,7 +59,7 @@ void llama_model_lfm2::load_arch_tensors(llama_model_loader &) {
         // for operator_norm
         layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
 
-        if (!hparams.is_recurrent(i)) {
+        if (!hparams.is_recr(i)) {
             layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
             layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
             GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);
@@ -235,7 +235,7 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
         cur             = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
         cb(cur, "model.layers.{}.operator_norm", il);
 
-        cur = hparams.is_recurrent(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
+        cur = hparams.is_recr(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
                                     build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il);
 
         if (il == n_layer - 1 && inp_out_ids) {

src/models/lfm2moe.cpp

@@ -10,7 +10,7 @@ void llama_model_lfm2moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func);
 
     for (uint32_t il = 0; il < hparams.n_layer; ++il) {
-        hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
+        hparams.is_recr_impl[il] = hparams.n_head_kv(il) == 0;
     }
 
     switch (hparams.n_layer) {
@@ -55,7 +55,7 @@ void llama_model_lfm2moe::load_arch_tensors(llama_model_loader &) {
         // for operator_norm
         layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
 
-        if (!hparams.is_recurrent(i)) {
+        if (!hparams.is_recr(i)) {
             layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
             layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
             GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);

src/models/llama4.cpp

@@ -15,6 +15,7 @@ void llama_model_llama4::load_arch_hparams(llama_model_loader & ml) {
         hparams.n_attn_temp_floor_scale = 8192;
         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);

src/models/mellum.cpp

@@ -13,7 +13,7 @@ void llama_model_mellum::load_arch_hparams(llama_model_loader & ml) {
         if (res) {
             hparams.set_swa_pattern(swa_period);
         } else {
-            ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+            ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer);
         }
 
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;

src/models/mimo2.cpp

@@ -8,7 +8,8 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,   hparams.n_swa);
     ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,         hparams.rope_freq_base_train_swa, false);
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer);
 
     float value_scale = 0.0f;
     if (ml.get_key(LLM_KV_ATTENTION_VALUE_SCALE, value_scale, false) && value_scale != 1.0f) {

src/models/nemotron-h.cpp

@@ -10,7 +10,7 @@ void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
     // A layer is recurrent IFF the n_head_kv value is set to 0 and
     // the n_ff value is set to 0
     for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        hparams.recurrent_layer_arr[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
+        hparams.is_recr_impl[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
     }
 
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -62,7 +62,7 @@ void llama_model_nemotron_h::load_arch_tensors(llama_model_loader &) {
         // all blocks use the attn norm
         layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
 
-        if (hparams.is_recurrent(i)) {
+        if (hparams.is_recr(i)) {
             // ssm layers
             layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
 
@@ -143,7 +143,7 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
         cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
         cb(cur, "attn_norm", il);
 
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // ssm layer //
             cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
         } else if (hparams.n_ff(il) == 0) {

src/models/plamo2.cpp

@@ -12,7 +12,7 @@ void llama_model_plamo2::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
     for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
+        hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;
     }
 
     switch (hparams.n_layer) {
@@ -54,7 +54,7 @@ void llama_model_plamo2::load_arch_tensors(llama_model_loader &) {
 
     for (int i = 0; i < n_layer; ++i) {
         auto & layer = layers[i];
-        bool is_mamba_layer = hparams.is_recurrent(i);
+        bool is_mamba_layer = hparams.is_recr(i);
 
         layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
 
@@ -128,7 +128,7 @@ llama_model_plamo2::graph::graph(const llama_model & model, const llm_graph_para
         cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
 
         // check if this layer is Mamba or Attention
-        const bool is_mamba_layer = hparams.is_recurrent(il);
+        const bool is_mamba_layer = hparams.is_recr(il);
 
         if (is_mamba_layer) {
             // PLaMo-2 Mamba layer

src/models/qwen35.cpp

@@ -18,12 +18,13 @@ void llama_model_qwen35::load_arch_hparams(llama_model_loader & ml) {
 
     // Mark recurrent layers (linear attention layers). MTP layers are dense
     // attention-only and must be flagged non-recurrent.
-    {
+    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer, false)) {
         const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
+
         uint32_t full_attn_interval = 4;
         ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
         for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-            hparams.recurrent_layer_arr[i] = (i < n_main) && ((i + 1) % full_attn_interval != 0);
+            hparams.is_recr_impl[i] = (i < n_main) && ((i + 1) % full_attn_interval != 0);
         }
     }
 
@@ -69,7 +70,7 @@ void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) {
         layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, flags);
         layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags);
 
-        if (!hparams.is_recurrent(il)) {
+        if (!hparams.is_recr(il)) {
             // Attention layers
             create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags);
             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, flags);
@@ -168,7 +169,7 @@ llama_model_qwen35::graph::graph(const llama_model & model, const llm_graph_para
         ggml_build_forward_expand(gf, cur);
 
         // Determine layer type and build appropriate attention mechanism
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // Linear attention layer (gated delta net)
             cur = build_layer_attn_linear(inp->get_recr(), cur, il);
         } else {

src/models/qwen35moe.cpp

@@ -21,12 +21,13 @@ void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {
 
     // Mark recurrent layers (linear attention layers). MTP layers are dense
     // attention-only and must be flagged non-recurrent.
-    {
+    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer, false)) {
         const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
+
         uint32_t full_attn_interval = 4;
         ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
         for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-            hparams.recurrent_layer_arr[i] = (i < n_main) && ((i + 1) % full_attn_interval != 0);
+            hparams.is_recr_impl[i] = (i < n_main) && ((i + 1) % full_attn_interval != 0);
         }
     }
 
@@ -75,7 +76,7 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
         layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, flags);
         layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags);
 
-        if (!hparams.is_recurrent(il)) {
+        if (!hparams.is_recr(il)) {
             // Attention layers
             create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags);
             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, flags);
@@ -191,7 +192,7 @@ llama_model_qwen35moe::graph::graph(const llama_model & model, const llm_graph_p
         ggml_build_forward_expand(gf, cur);
 
         // Determine layer type and build appropriate attention mechanism
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // Linear attention layer (gated delta net)
             cur = build_layer_attn_linear(inp->get_recr(), cur, il);
         } else {

src/models/qwen3next.cpp

@@ -14,11 +14,11 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
     // Mark recurrent layers (linear attention layers)
-    {
+    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer, false)) {
         uint32_t full_attn_interval = 4;
         ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
         for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-            hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+            hparams.is_recr_impl[i] = ((i + 1) % full_attn_interval != 0);
         }
     }
 
@@ -68,7 +68,7 @@ void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) {
         layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
         layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
 
-        if (!hparams.is_recurrent(i)) {
+        if (!hparams.is_recr(i)) {
             // Attention layers
             create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
@@ -129,7 +129,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
         ggml_build_forward_expand(gf, cur);
 
         // Determine layer type and build appropriate attention mechanism
-        if (hparams.is_recurrent(il)) {
+        if (hparams.is_recr(il)) {
             // Linear attention layer (gated delta net)
             cur = build_layer_attn_linear(inp->get_recr(), cur, il);
         } else {

src/models/step35.cpp

@@ -22,7 +22,9 @@ void llama_model_step35::load_arch_hparams(llama_model_loader & ml) {
 
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
     ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa, false);
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer);
+
     ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer, false);
     ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);