Merge commit '213c4a0b81788e058c30479842954fb0815be61a' into concedo_experimental

# Conflicts:
#	CODEOWNERS
#	common/CMakeLists.txt
#	common/chat-peg-parser.cpp
#	common/chat.cpp
#	docs/backend/SYCL.md
#	docs/development/parsing.md
#	docs/ops.md
#	docs/ops/SYCL.csv
#	embd_res/templates/Apriel-1.6-15b-Thinker-fixed.jinja
#	embd_res/templates/Bielik-11B-v3.0-Instruct.jinja
#	embd_res/templates/GLM-4.7-Flash.jinja
#	embd_res/templates/LFM2-8B-A1B.jinja
#	embd_res/templates/StepFun3.5-Flash.jinja
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-sycl/CMakeLists.txt
#	ggml/src/ggml-sycl/backend.hpp
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/convert.cpp
#	ggml/src/ggml-sycl/convert.hpp
#	ggml/src/ggml-sycl/count-equal.cpp
#	ggml/src/ggml-sycl/dpct/helper.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/presets.hpp
#	ggml/src/ggml-sycl/softmax.cpp
#	ggml/src/ggml-sycl/vecdotq.hpp
#	models/templates/Apertus-8B-Instruct.jinja
#	models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja
#	models/templates/Qwen-QwQ-32B.jinja
#	models/templates/Qwen3-Coder.jinja
#	models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja
#	models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja
#	models/templates/deepseek-ai-DeepSeek-V3.1.jinja
#	models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
#	models/templates/moonshotai-Kimi-K2.jinja
#	models/templates/unsloth-Apriel-1.5.jinja
#	tests/CMakeLists.txt
#	tests/peg-parser/test-basic.cpp
#	tests/peg-parser/tests.h
#	tests/test-backend-ops.cpp
#	tests/test-chat-peg-parser.cpp
#	tests/test-chat-template.cpp
#	tests/test-chat.cpp
#	tests/test-json-schema-to-grammar.cpp
#	tests/test-peg-parser.cpp
#	tools/CMakeLists.txt
#	tools/cli/cli.cpp

src/llama-context.cpp

@@ -153,6 +153,9 @@ llama_context::llama_context(
     cparams.flash_attn = params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED;
     cparams.auto_fa    = params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO;
 
+    cparams.fused_gdn_ar = true;
+    cparams.fused_gdn_ch = false; // TODO: implement
+
     // with causal attention, the batch size is limited by the context size
     cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
 
@@ -431,7 +434,7 @@ void llama_context::sched_reserve() {
     if (cparams.auto_fa) {
         auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
         if (!gf) {
-            throw std::runtime_error("failed to split graph for Flash Attention check");
+            throw std::runtime_error("failed to reserve graph for Flash Attention check");
         }
 
         const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
@@ -441,8 +444,7 @@ void llama_context::sched_reserve() {
             if (n->op != GGML_OP_FLASH_ATTN_EXT) {
                 continue;
             }
-            ggml_backend_dev_t device_fa = ggml_backend_get_device(
-                    ggml_backend_sched_get_tensor_backend(sched.get(), n));
+            ggml_backend_dev_t device_fa = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
 
             // TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
             GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
@@ -457,6 +459,7 @@ void llama_context::sched_reserve() {
                 break;
             }
         }
+
         if (fa_device_mismatch) {
             cparams.flash_attn = false;
             LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
@@ -468,6 +471,39 @@ void llama_context::sched_reserve() {
         cparams.auto_fa = false;
     }
 
+    if (cparams.fused_gdn_ar) {
+        auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
+        if (!gf) {
+            throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check");
+        }
+
+        const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDNAR) + 1;
+        bool gdn_device_mismatch = false;
+        for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
+            ggml_tensor * n = ggml_graph_node(gf, i);
+            if (n->op != GGML_OP_GATED_DELTA_NET) {
+                continue;
+            }
+            ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
+
+            GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDNAR "-", prefix_len) == 0);
+            const int il = std::stoi(n->name + prefix_len);
+            ggml_backend_dev_t device_kv = model.dev_layer(il);
+            if (device_gdn != device_kv) {
+                LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor "
+                        "is assigned to device %s (usually due to missing support)\n",
+                        __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn));
+                gdn_device_mismatch = true;
+                break;
+            }
+        }
+
+        if (gdn_device_mismatch) {
+            cparams.fused_gdn_ar = false;
+            LLAMA_LOG_WARN("%s: fused Gated Delta Net not supported, set to disabled\n", __func__);
+        }
+    }
+
     // reserve worst-case graph
     int n_splits_pp = -1;
     int n_nodes_pp  = -1;