Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.github/workflows/build.yml
#	.github/workflows/release.yml
#	CONTRIBUTING.md
#	docs/backend/CANN.md
#	examples/eval-callback/eval-callback.cpp
#	examples/model-conversion/requirements.txt
#	examples/model-conversion/scripts/causal/run-org-model.py
#	ggml/src/ggml-cann/aclnn_ops.cpp
#	ggml/src/ggml-cann/common.h
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-cpu/CMakeLists.txt
#	ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
#	ggml/src/ggml-cuda/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-rpc/ggml-rpc.cpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-zdnn/ggml-zdnn.cpp
#	models/templates/README.md
#	requirements/requirements-convert_hf_to_gguf.txt
#	requirements/requirements-convert_legacy_llama.txt
#	requirements/requirements-tool_bench.txt
#	tests/.gitignore
#	tests/test-backend-ops.cpp
#	tests/test-chat-parser.cpp
#	tests/test-chat.cpp
#	tests/test-json-schema-to-grammar.cpp
#	tests/test-tokenizer-random.py

src/llama-arch.cpp

@@ -137,6 +137,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
     { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
     { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
+    { LLM_KV_DECODER_BLOCK_COUNT,               "%s.decoder_block_count"               },
     { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
     { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
     { LLM_KV_SWIN_NORM,                         "%s.swin_norm"                         },

src/llama-arch.h

@@ -141,6 +141,7 @@ enum llm_kv {
     LLM_KV_POOLING_TYPE,
     LLM_KV_LOGIT_SCALE,
     LLM_KV_DECODER_START_TOKEN_ID,
+    LLM_KV_DECODER_BLOCK_COUNT,
     LLM_KV_ATTN_LOGIT_SOFTCAPPING,
     LLM_KV_FINAL_LOGIT_SOFTCAPPING,
     LLM_KV_SWIN_NORM,

src/llama-context.cpp

@@ -1447,7 +1447,9 @@ ggml_status llama_context::graph_compute(
     if (backend_cpu != nullptr) {
         auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend_cpu));
         auto * set_threadpool_fn = (decltype(ggml_backend_cpu_set_threadpool) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_set_threadpool");
-        set_threadpool_fn(backend_cpu, tp);
+        if (set_threadpool_fn) {
+            set_threadpool_fn(backend_cpu, tp);
+        }
     }
 
     // set the number of threads for all the backends

src/llama-graph.cpp

@@ -1273,7 +1273,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
     // split the batch into streams if needed
     const auto n_stream = k->ne[3];
 
-    q = ggml_reshape_4d(ctx0, q, q->ne[0], q->ne[1], q->ne[2]/n_stream, n_stream);
+    q = ggml_view_4d(ctx0, q, q->ne[0], q->ne[1], q->ne[2]/n_stream, n_stream, q->nb[1], q->nb[2], q->nb[3]/n_stream, 0);
 
     q = ggml_permute(ctx0, q, 0, 2, 1, 3);
     k = ggml_permute(ctx0, k, 0, 2, 1, 3);

src/llama-hparams.h

@@ -159,6 +159,7 @@ struct llama_hparams {
     // needed by encoder-decoder models (e.g. T5, FLAN-T5)
     // ref: https://github.com/ggerganov/llama.cpp/pull/8141
     llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
+    uint32_t    dec_n_layer        = 0;
 
     enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;

src/llama-model.cpp

@@ -1547,6 +1547,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     hparams.dec_start_token_id = dec_start_token_id;
                 }
 
+                hparams.dec_n_layer = hparams.n_layer;
+                ml.get_key(LLM_KV_DECODER_BLOCK_COUNT, hparams.dec_n_layer, false);
+
                 switch (hparams.n_layer) {
                     case 6:  type = LLM_TYPE_60M;  break; // t5-small
                     case 8:  type = LLM_TYPE_80M;  break; // flan-t5-small
@@ -4510,6 +4513,14 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
                     }
 
+                    // n_layer:     number of encoder_layers
+                    // dec_n_layer: number of decoder_layers
+                    const int dec_n_layer = hparams.dec_n_layer;
+                    if (dec_n_layer > n_layer) {
+                        layers.resize(dec_n_layer);
+                    }
+
+                    // load encoder layers
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
 
@@ -4525,6 +4536,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
                         layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                         layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+
+                    // load decoder layers
+                    for (int i = 0; i < dec_n_layer; ++i) {
+                        auto & layer = layers[i];
 
                         layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);
                         layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
@@ -13609,7 +13625,9 @@ struct llm_build_t5_dec : public llm_graph_context {
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
-        for (int il = 0; il < n_layer; ++il) {
+        const int64_t dec_n_layer = hparams.dec_n_layer;
+
+        for (int il = 0; il < dec_n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
 
             // norm
@@ -13700,7 +13718,7 @@ struct llm_build_t5_dec : public llm_graph_context {
                 //cb(cur, "kqv_out", il);
             }
 
-            if (il == n_layer - 1 && inp_out_ids) {
+            if (il == dec_n_layer - 1 && inp_out_ids) {
                 cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                 inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
             }
@@ -13721,8 +13739,8 @@ struct llm_build_t5_dec : public llm_graph_context {
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
                         NULL,
-                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
-                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
+                        model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
+                        model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
                         il);
                 cb(cur, "ffn_out", il);
             }

src/llama.cpp

@@ -114,7 +114,9 @@ void llama_numa_init(enum ggml_numa_strategy numa) {
         GGML_ASSERT(dev && "CPU backend is not loaded");
         auto * reg = ggml_backend_dev_backend_reg(dev);
         auto * numa_init_fn = (decltype(ggml_numa_init) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_numa_init");
-        numa_init_fn(numa);
+        if (numa_init_fn) {
+            numa_init_fn(numa);
+        }
     }
 }