Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.devops/openvino.Dockerfile
#	.github/actions/windows-setup-cuda/action.yml
#	.github/workflows/build-cache.yml
#	.github/workflows/build-cpu.yml
#	.github/workflows/build-cuda-windows.yml
#	.github/workflows/build-openvino.yml
#	.github/workflows/build-self-hosted.yml
#	.github/workflows/build-vulkan.yml
#	.github/workflows/docker.yml
#	.github/workflows/make-release.yml
#	.github/workflows/release.yml
#	AUTHORS
#	CMakeLists.txt
#	README.md
#	build-xcframework.sh
#	ci/run.sh
#	common/CMakeLists.txt
#	docs/backend/OPENVINO.md
#	examples/gguf-hash/CMakeLists.txt
#	examples/gguf-hash/gguf-hash.cpp
#	ggml/CMakeLists.txt
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-et/ggml-et.cpp
#	ggml/src/ggml-hexagon/ggml-hexagon.cpp
#	ggml/src/ggml-hexagon/htp/flash-attn-ops.c
#	ggml/src/ggml-hexagon/htp/flash-attn-ops.h
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
#	ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
#	ggml/src/ggml-opencl/kernels/moe_sort_by_expert.cl
#	ggml/src/ggml-openvino/ggml-openvino.cpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
#	scripts/sync-ggml.last
#	tests/CMakeLists.txt
#	tests/test-backend-ops.cpp
#	tests/test-llama-archs.cpp
#	tools/mtmd/CMakeLists.txt
#	tools/mtmd/mtmd-helper.cpp
#	tools/perplexity/perplexity.cpp
#	tools/server/README.md
#	tools/ui/src/lib/hooks/use-tools-panel.svelte.ts
#	vendor/hash/CMakeLists.txt

src/llama-arch.cpp

@@ -102,6 +102,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_GRANITE_MOE,      "granitemoe"       },
     { LLM_ARCH_GRANITE_HYBRID,   "granitehybrid"    },
     { LLM_ARCH_GRANITE_SWITCH,   "graniteswitch"    },
+    { LLM_ARCH_GRANITE_SWA,      "granite_swa"      },
     { LLM_ARCH_CHAMELEON,        "chameleon"        },
     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
     { LLM_ARCH_PLM,              "plm"              },
@@ -261,6 +262,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,       "%s.attention.relative_buckets_count"       },
     { LLM_KV_ATTENTION_SLIDING_WINDOW,               "%s.attention.sliding_window"               },
     { LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,       "%s.attention.sliding_window_pattern"       },
+    { LLM_KV_ATTENTION_ROPE_PATTERN,                 "%s.attention.rope_pattern"                 },
+
     { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
     { LLM_KV_ATTENTION_OUTPUT_SCALE,                 "%s.attention.output_scale"                 },
     { LLM_KV_ATTENTION_VALUE_SCALE,                  "%s.attention.value_scale"                  },

src/llama-arch.h

@@ -107,6 +107,7 @@ enum llm_arch {
     LLM_ARCH_GRANITE_MOE,
     LLM_ARCH_GRANITE_HYBRID,
     LLM_ARCH_GRANITE_SWITCH,
+    LLM_ARCH_GRANITE_SWA,
     LLM_ARCH_CHAMELEON,
     LLM_ARCH_WAVTOKENIZER_DEC,
     LLM_ARCH_PLM,
@@ -267,6 +268,8 @@ enum llm_kv {
     LLM_KV_ATTENTION_SLIDING_WINDOW,
     LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,
     LLM_KV_ATTENTION_SCALE,
+    LLM_KV_ATTENTION_ROPE_PATTERN,
+
     LLM_KV_ATTENTION_OUTPUT_SCALE,
     LLM_KV_ATTENTION_VALUE_SCALE,
     LLM_KV_ATTENTION_TEMPERATURE_LENGTH,

src/llama-hparams.cpp

@@ -291,7 +291,11 @@ bool llama_hparams::has_rope(uint32_t il) const {
         return false;
     }
 
-    return true;
+    if (il < n_layer_all) {
+        return rope_pattern[il] != 0;
+    }
+
+    GGML_ABORT("%s: il (%u) out of bounds (n_layer_all: %u)\n", __func__, il, n_layer_all);
 }
 
 uint32_t llama_hparams::n_layer() const {

src/llama-hparams.h

@@ -144,6 +144,10 @@ struct llama_hparams {
 
     std::array<int, 4> rope_sections;
 
+    // Per-layer RoPE enable flags (1 = use RoPE, 0 = NoPE)
+    // by default, all layers use RoPE (controlled by rope_finetuned)
+    std::array<uint32_t, LLAMA_MAX_LAYERS> rope_pattern;
+
     // Sliding Window Attention (SWA)
     llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
     // the size of the sliding window (0 - no SWA)

src/llama-model-loader.cpp

@@ -1396,6 +1396,11 @@ void llama_model_loader::get_mapping_range(size_t * first, size_t * last, void *
     }
 }
 
+void llama_model_loader::unmap_weight(const llama_tensor_weight & w) const {
+    if (!use_mmap) { return; }
+    mappings.at(w.idx)->unmap_fragment(w.offs, w.offs + ggml_nbytes(w.tensor));
+}
+
 void llama_model_loader::load_data_for(struct ggml_tensor * cur) const {
     const auto & w = require_weight(ggml_get_name(cur));
 

src/llama-model-loader.h

@@ -194,6 +194,9 @@ struct llama_model_loader {
 
     void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const;
 
+    // release a weight's mmap pages
+    void unmap_weight(const llama_tensor_weight & w) const;
+
     // for backwards compatibility, does not support ggml-backend
     void load_data_for(struct ggml_tensor * cur) const;
 

src/llama-model-saver.cpp

@@ -30,6 +30,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
         case LLM_ARCH_MUSE_GLIMMER:
         case LLM_ARCH_MELLUM:
         case LLM_ARCH_LAGUNA:
+        case LLM_ARCH_GRANITE_SWA:
             return false;
         default:
             return true;
@@ -272,6 +273,7 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
     add_kv(LLM_KV_ATTENTION_GATE_LORA_RANK,          hparams.n_lora_gate);
     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,  ???);
     add_kv(LLM_KV_ATTENTION_SCALE,                   hparams.f_attention_scale);

src/llama-model.cpp

@@ -92,6 +92,7 @@
 #include "models/gptneox.cpp"
 #include "models/granite-hybrid.cpp"
 #include "models/granite-moe.cpp"
+#include "models/granite-swa.cpp"
 #include "models/granite-switch.cpp"
 #include "models/granite.cpp"
 #include "models/grok.cpp"
@@ -395,6 +396,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_minicpm(params);
         case LLM_ARCH_GRANITE_HYBRID:
             return new llama_model_granite_hybrid(params);
+        case LLM_ARCH_GRANITE_SWA:
+            return new llama_model_granite_swa(params);
         case LLM_ARCH_CHAMELEON:
             return new llama_model_chameleon(params);
         case LLM_ARCH_WAVTOKENIZER_DEC:
@@ -1306,6 +1309,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
     std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
 
     std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0);
+    std::fill(hparams.rope_pattern.begin(),  hparams.rope_pattern.end(), 1);
     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.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 0);
@@ -2788,6 +2792,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_GRANITE_MOE:
         case LLM_ARCH_GRANITE_HYBRID:
         case LLM_ARCH_GRANITE_SWITCH:
+        case LLM_ARCH_GRANITE_SWA:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_BAILINGMOE:
         case LLM_ARCH_BAILINGMOE3:

src/llama-quant.cpp

@@ -1272,7 +1272,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             total_size_org += tensor_size;
             total_size_new += new_size;
 
-            // update the gguf meta data as we go
+            // update the gguf metadata as we go
             gguf_set_tensor_type(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_type);
             GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), metadata[i].name.c_str())) == new_size);
             gguf_set_tensor_data(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_data);
@@ -1280,6 +1280,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             // write tensor data + padding
             fout.write((const char *) new_data, new_size);
             zeros(fout, GGML_PAD(new_size, align) - new_size);
+
+            // unmap the tensor to free memory
+            if (ml.use_mmap) { ml.unmap_weight(weight); }
+
         } // no --dry-run
     } // main loop
 

src/models/deepseek32.cpp

@@ -10,8 +10,6 @@ void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
 
     // MoE parameters
-    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
-    ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
     ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
     ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);

src/models/glm-dsa.cpp

@@ -32,8 +32,6 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
 
     // MoE parameters
-    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
-    ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
     ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
     ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);

src/models/glm4-moe.cpp

@@ -6,8 +6,6 @@ void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
 
     // MoE parameters
-    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
-    ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
     ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
     ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);

src/models/granite-hybrid.cpp

@@ -16,7 +16,8 @@ void llama_model_granite_hybrid::load_arch_hparams(llama_model_loader & ml) {
     // Granite uses rope_finetuned as a switch for rope, so default to true
     bool rope_finetuned = true;
     ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-    hparams.rope_finetuned = rope_finetuned;
+    hparams.rope_finetuned = rope_finetuned; // needed for round trip save
+    std::fill(hparams.rope_pattern.begin(), hparams.rope_pattern.end(), rope_finetuned);
 
     // A layer is recurrent IFF the n_head_kv value is set to 0
     for (uint32_t i = 0; i < hparams.n_layer(); ++i) {
@@ -147,7 +148,7 @@ llama_model_granite_hybrid::graph::graph(const llama_model & model, const llm_gr
 
     // Positional embeddings populated if rope enabled
     ggml_tensor * inp_pos = nullptr;
-    if (hparams.rope_finetuned) {
+    if (hparams.has_rope(0)) {
         inp_pos = build_inp_pos();
     }
 
@@ -206,8 +207,7 @@ ggml_tensor * llama_model_granite_hybrid::graph::build_attention_layer(ggml_tens
                                                               const int                 il) {
     auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);
 
-    const bool use_rope = hparams.rope_finetuned;
-    if (use_rope) {
+    if (hparams.has_rope(il)) {
         ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
         Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                              ext_factor, attn_factor, beta_fast, beta_slow);

src/models/granite-moe.cpp

@@ -7,11 +7,6 @@ void llama_model_granite_moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);
     ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);
 
-    // Granite uses rope_finetuned as a switch for rope, so default to true
-    bool rope_finetuned = true;
-    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-    hparams.rope_finetuned = rope_finetuned;
-
     switch (hparams.n_layer()) {
         case 32: type = LLM_TYPE_3B; break;
         case 40: type = LLM_TYPE_3B; break;

src/models/granite-swa.cpp

@@ -0,0 +1,319 @@
+#include "models.h"
+
+#include <sstream>
+
+void llama_model_granite_swa::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
+    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, false);
+    ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);
+    ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);
+
+    // MoE expert configuration
+    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert, false);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used, false);
+
+     // iSWA configuration
+    ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+    // Granite4 Vision uses array deepstack_mapping
+    ml.get_arr(LLM_KV_DEEPSTACK_MAPPING, hparams.deepstack_mapping_arr, false);
+
+    // Count the unique deepstack input indices
+    std::unordered_set<uint32_t> unique_deepstack_idxs;
+    for (const auto val : hparams.deepstack_mapping_arr) {
+        if (val >= 0) {
+            unique_deepstack_idxs.insert(val);
+        }
+    }
+    hparams.n_deepstack_layers = unique_deepstack_idxs.size();
+
+    // Ensure all values are valid (avoid overflow attacks)
+    for (const auto val : unique_deepstack_idxs) {
+        if (val > hparams.n_deepstack_layers) {
+            std::stringstream ss;
+            ss << "Invalid deepstack index: " << val << " > " << hparams.n_deepstack_layers;
+            throw std::runtime_error(ss.str());
+        }
+    }
+
+    // Per-layer RoPE pattern (optional)
+    ml.get_arr(LLM_KV_ATTENTION_ROPE_PATTERN, hparams.rope_pattern, false);
+
+    switch (hparams.n_layer()) {
+        case 32: type = LLM_TYPE_3B; break;
+        case 40: type = LLM_TYPE_3B; break;
+        // Add additional layer/vocab/etc checks here for other model sizes
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // For Granite MoE Shared
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
+}
+
+void llama_model_granite_swa::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, 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);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        // Per-layer attention sinks for iSWA
+        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            create_tensor_gate_up_exps(layer, i, n_embd, n_ff, n_expert, 0);
+
+            // For Granite MoE Shared - gate+up kept fused in ffn_up_shexp (see LLM_FFN_SWIGLU below)
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, 2*hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_granite_swa::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_granite_swa::graph::graph(
+    const llama_model & model,
+    const llm_graph_params & params)
+    : llm_graph_context(params) {
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - built only if rope enabled
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn = build_attn_inp_kv_iswa();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+
+        // Granite Vision 4.1 deepstack: inject the projector stream that
+        // targets decoder layer `il` before the decoder runs.
+        // NOTE: skip the first deepstack layer since that's inpL
+        const auto & deepstack_emb_idx = hparams.deepstack_mapping_arr[il];
+        if (il > 0 && deepstack_emb_idx >= 0) {
+            ggml_tensor * ds = ggml_view_2d(ctx0,
+                res->t_inp_embd, n_embd, n_tokens,
+                res->t_inp_embd->nb[1],
+                deepstack_emb_idx * n_embd * sizeof(float));
+            inpL = ggml_add(ctx0, inpL, ds);
+            cb(inpL, "deepstack_in", il);
+        }
+
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        cur = build_attention_layer(
+            cur, inp_pos, inp_attn,
+            model, n_embd_head, il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        // ffn
+        cur = build_layer_ffn(cur, inpSA, model, il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur, model.output_s);
+
+    // For Granite architectures - scale logits
+    cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llama_model_granite_swa::graph::build_attention_layer(
+          ggml_tensor                  * cur,
+          ggml_tensor                  * inp_pos,
+          llm_graph_input_attn_kv_iswa * inp_attn,
+    const llama_model                  & model,
+    const int64_t                        n_embd_head,
+    const int                            il) {
+
+    auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+            n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);
+
+    const bool use_rope = hparams.has_rope(il);
+    if (use_rope) {
+        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+        Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, rope_factors,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+        Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, rope_factors,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+    }
+
+    cb(Qcur, "Qcur", il);
+    cb(Kcur, "Kcur", il);
+    cb(Vcur, "Vcur", il);
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    // Pass layer.attn_sinks to build_attn for sink-based attention modulation
+    cur = build_attn(inp_attn,
+            model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
+            Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, kq_scale, il);
+    cb(cur, "attn_out", il);
+    return cur;
+}
+
+ggml_tensor * llama_model_granite_swa::graph::build_layer_ffn(
+          ggml_tensor * cur,
+          ggml_tensor * inpSA,
+    const llama_model & model,
+    const int           il) {
+
+    // For Granite architectures - scale residual
+    if (hparams.f_residual_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+    }
+    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+    cb(ffn_inp, "ffn_inp", il);
+
+    // feed-forward network (non-MoE)
+    if (model.layers[il].ffn_gate_inp == nullptr) {
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+    } else {
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                hparams.expert_weights_scale,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il,
+                nullptr, model.layers[il].ffn_gate_up_exps);
+        cb(moe_out, "ffn_moe_out", il);
+
+        // For Granite MoE Shared - gate+up kept fused in ffn_up_shexp
+        if (hparams.n_ff_shexp > 0) {
+            ggml_tensor * ffn_shexp = build_ffn(cur,
+                model.layers[il].ffn_up_shexp,   NULL, NULL,
+                NULL,                            NULL, NULL,
+                model.layers[il].ffn_down_shexp, NULL, NULL,
+                NULL,
+                LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = moe_out;
+        }
+    }
+
+    // For Granite architectures - scale residual
+    if (hparams.f_residual_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+    }
+    cur = ggml_add(ctx0, cur, ffn_inp);
+    cb(cur, "ffn_out", il);
+
+    cur = build_cvec(cur, il);
+    cb(cur, "l_out", il);
+
+    return cur;
+}

src/models/granite-switch.cpp

@@ -11,7 +11,8 @@ void llama_model_granite_switch::load_arch_hparams(llama_model_loader & ml) {
 
     bool rope_finetuned = true;
     ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-    hparams.rope_finetuned = rope_finetuned;
+    hparams.rope_finetuned = rope_finetuned; // needed for round trip save
+    std::fill(hparams.rope_pattern.begin(), hparams.rope_pattern.end(), rope_finetuned);
 
     switch (hparams.n_layer()) {
         case 40: type = hparams.n_embd == 4096 ? LLM_TYPE_8B : LLM_TYPE_3B; break;
@@ -254,7 +255,7 @@ llama_model_granite_switch::graph::graph(
     cb(inpL, "inp_embd", -1);
 
     ggml_tensor * inp_pos = nullptr;
-    if (hparams.rope_finetuned) {
+    if (hparams.has_rope(0)) {
         inp_pos = build_inp_pos();
     }
     auto * inp_attn = build_attn_inp_kv();
@@ -361,7 +362,7 @@ ggml_tensor * llama_model_granite_switch::graph::build_attention_layer(
     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);
 
-    if (hparams.rope_finetuned) {
+    if (hparams.has_rope(il)) {
         ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
         Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
                 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

src/models/granite.cpp

@@ -33,7 +33,8 @@ void llama_model_granite::load_arch_hparams(llama_model_loader & ml) {
     // Granite uses rope_finetuned as a switch for rope, so default to true
     bool rope_finetuned = true;
     ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-    hparams.rope_finetuned = rope_finetuned;
+    hparams.rope_finetuned = rope_finetuned; // needed for round trip save
+    std::fill(hparams.rope_pattern.begin(), hparams.rope_pattern.end(), rope_finetuned);
 
     switch (hparams.n_layer()) {
         case 32: type = LLM_TYPE_3B; break;
@@ -127,7 +128,7 @@ llama_model_granite::graph::graph(
 
     // inp_pos - built only if rope enabled
     ggml_tensor * inp_pos = nullptr;
-    if (hparams.rope_finetuned) {
+    if (hparams.has_rope(0)) {
         inp_pos = build_inp_pos();
     }
     auto * inp_attn = build_attn_inp_kv();
@@ -203,8 +204,7 @@ ggml_tensor * llama_model_granite::graph::build_attention_layer(
     auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
             n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);
 
-    const bool use_rope = hparams.rope_finetuned;
-    if (use_rope) {
+    if (hparams.has_rope(il)) {
         ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
         Qcur = ggml_rope_ext(
                 ctx0, Qcur, inp_pos, rope_factors,

src/models/models.h

@@ -1719,6 +1719,34 @@ struct llama_model_granite_hybrid : public llama_model_base {
 };
 
 
+struct llama_model_granite_swa : public llama_model_base {
+    llama_model_granite_swa(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+    private:
+        ggml_tensor * build_attention_layer(
+                  ggml_tensor                  * cur,
+                  ggml_tensor                  * inp_pos,
+                  llm_graph_input_attn_kv_iswa * inp_attn,
+            const llama_model                  & model,
+            const int64_t                        n_embd_head,
+            const int                            il);
+
+        ggml_tensor * build_layer_ffn(
+                  ggml_tensor * cur,
+                  ggml_tensor * inpSA,
+            const llama_model & model,
+            const int           il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_chameleon : public llama_model_base {
     llama_model_chameleon(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

src/unicode.cpp

@@ -1241,7 +1241,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
         { unicode_cpt_flags::LETTER,      "\x41-\x5A\x61-\x7A" }, // A-Za-z
         { unicode_cpt_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
         { unicode_cpt_flags::ACCENT_MARK, "" }, // no sub-128 codepoints
-        { unicode_cpt_flags::SYMBOL,      "\\\x24\\\x2B\x3C-\x3E\x5E\x60\\\x7C" }, // $+<=>^`|
+        { unicode_cpt_flags::SYMBOL,      "\\\x24\\\x2B\x3C-\x3E\x5E\x60\\\x7C\\\x7E" }, // $+<=>^`|~
     };
 
     // compute collapsed codepoints only if needed by at least one regex