Update GGML to b6646 (#12245)

Notable EOLs with this change:
- MacOS v12 and v13 are no longer supported (v14+ required)
- AMD gfx900 and gfx906 are no longer supported

ml/backend/ggml/ggml/include/ggml-backend.h

@@ -132,6 +132,8 @@ extern "C" {
         GGML_BACKEND_DEVICE_TYPE_CPU,
         // GPU device using dedicated memory
         GGML_BACKEND_DEVICE_TYPE_GPU,
+        // integrated GPU device using host memory
+        GGML_BACKEND_DEVICE_TYPE_IGPU,
         // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
         GGML_BACKEND_DEVICE_TYPE_ACCEL
     };
@@ -150,12 +152,22 @@ extern "C" {
 
     // all the device properties
     struct ggml_backend_dev_props {
+        // device name
         const char * name;
+        // device description
         const char * description;
-        const char * id;
+        // device free memory in bytes
         size_t memory_free;
+        const char * id;
+        // device total memory in bytes
         size_t memory_total;
+        // device type
         enum ggml_backend_dev_type type;
+        // device id
+        //   for PCI devices, this should be the PCI bus id formatted as "domain:bus:device.function" (e.g. "0000:01:00.0")
+        //   if the id is unknown, this should be NULL
+        const char * device_id;
+        // device capabilities
         struct ggml_backend_dev_caps caps;
         int driver_major;
         int driver_minor;
@@ -314,12 +326,16 @@ extern "C" {
     GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
     GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
 
-    GGML_API size_t               ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
-    GGML_API size_t               ggml_backend_sched_get_attempted_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
+    GGML_API ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend);
+    GGML_API size_t                     ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
+    GGML_API size_t                     ggml_backend_sched_get_attempted_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
 
     GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
     GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
+    // Split graph without allocating it
+    GGML_API void                 ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+
     // Allocate and compute graph on the backend scheduler
     GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
     GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);

ml/backend/ggml/ggml/include/ggml-cpu.h

@@ -101,7 +101,6 @@ extern "C" {
     GGML_BACKEND_API int ggml_cpu_has_riscv_v    (void);
     GGML_BACKEND_API int ggml_cpu_has_vsx        (void);
     GGML_BACKEND_API int ggml_cpu_has_vxe        (void);
-    GGML_BACKEND_API int ggml_cpu_has_nnpa       (void);
     GGML_BACKEND_API int ggml_cpu_has_wasm_simd  (void);
     GGML_BACKEND_API int ggml_cpu_has_llamafile  (void);
 
@@ -135,6 +134,7 @@ extern "C" {
     GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
 
     GGML_BACKEND_API void ggml_cpu_fp32_to_fp32(const float *,       float *, int64_t);
+    GGML_BACKEND_API void ggml_cpu_fp32_to_i32 (const float *,     int32_t *, int64_t);
     GGML_BACKEND_API void ggml_cpu_fp32_to_fp16(const float *, ggml_fp16_t *, int64_t);
     GGML_BACKEND_API void ggml_cpu_fp16_to_fp32(const ggml_fp16_t *, float *, int64_t);
     GGML_BACKEND_API void ggml_cpu_fp32_to_bf16(const float *, ggml_bf16_t *, int64_t);

ml/backend/ggml/ggml/include/ggml-metal.h

@@ -39,18 +39,13 @@ extern "C" {
 // user-code should use only these functions
 //
 
+// TODO: remove in the future
 GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void);
 
 GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend);
 
-GGML_DEPRECATED(
-        GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size),
-        "obsoleted by the new device interface - https://github.com/ggml-org/llama.cpp/pull/9713");
-
 GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
 
-GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
-
 // helper to check if the device supports a specific family
 // ideally, the user code should be doing these checks
 // ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf

ml/backend/ggml/ggml/include/ggml-opt.h

@@ -74,16 +74,26 @@ extern "C" {
         GGML_OPT_BUILD_TYPE_OPT     = 30,
     };
 
+    enum ggml_opt_optimizer_type {
+        GGML_OPT_OPTIMIZER_TYPE_ADAMW,
+        GGML_OPT_OPTIMIZER_TYPE_SGD,
+
+        GGML_OPT_OPTIMIZER_TYPE_COUNT
+    };
+
     // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
     struct ggml_opt_optimizer_params {
-        // AdamW optimizer parameters
         struct {
             float alpha; // learning rate
-            float beta1;
-            float beta2;
+            float beta1; // first AdamW momentum
+            float beta2; // second AdamW momentum
             float eps;   // epsilon for numerical stability
-            float wd;    // weight decay for AdamW, use 0.0f to disable
+            float wd;    // weight decay - 0.0f to disable
         } adamw;
+        struct {
+            float alpha; // learning rate
+            float wd;    // weight decay
+        } sgd;
     };
 
     // callback to calculate optimizer parameters prior to a backward pass
@@ -112,8 +122,11 @@ extern "C" {
 
         int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done
 
-        ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
-        void * get_opt_pars_ud;                     // userdata for calculating optimizer parameters
+        ggml_opt_get_optimizer_params get_opt_pars;    // callback for calculating optimizer parameters
+        void *                        get_opt_pars_ud; // userdata for calculating optimizer parameters
+
+        // only GGML_OPT_OPTIMIZER_TYPE_ADAMW needs m, v momenta per parameter tensor
+        enum ggml_opt_optimizer_type optimizer;
     };
 
     // get parameters for an optimization context with defaults set where possible
@@ -142,6 +155,10 @@ extern "C" {
     // get the gradient accumulator for a node from the forward graph
     GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
 
+    GGML_API enum ggml_opt_optimizer_type ggml_opt_context_optimizer_type(ggml_opt_context_t); //TODO consistent naming scheme
+
+    GGML_API const char * ggml_opt_optimizer_name(enum ggml_opt_optimizer_type);
+
     // ====== Optimization Result ======
 
     GGML_API ggml_opt_result_t ggml_opt_result_init(void);
@@ -226,12 +243,14 @@ extern "C" {
             struct ggml_tensor            * outputs,        // output tensor, must have shape [ne_label, ndata_batch] if labels are used
             ggml_opt_dataset_t              dataset,        // dataset with data and optionally also labels
             enum ggml_opt_loss_type         loss_type,      // loss to minimize
+            enum ggml_opt_optimizer_type    optimizer,      // sgd or adamw
             ggml_opt_get_optimizer_params   get_opt_pars,   // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)
             int64_t                         nepoch,         // how many times the dataset should be iterated over
             int64_t                         nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs
             float                           val_split,      // fraction of the dataset to use for validation, must be in [0.0f, 1.0f)
             bool                            silent);        // whether or not info prints to stderr should be suppressed
 
+
 #ifdef  __cplusplus
 }
 #endif

ml/backend/ggml/ggml/include/ggml-zdnn.h

+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// device buffer
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zdnn_reg(void);
+
+#ifdef __cplusplus
+}
+#endif

ml/backend/ggml/ggml/include/ggml.h

@@ -241,7 +241,16 @@
 #define GGML_ROPE_TYPE_MROPE  8
 #define GGML_ROPE_TYPE_VISION 24
 
+#define GGML_MROPE_SECTIONS   4
+
 #define GGML_UNUSED(x) (void)(x)
+#ifdef __CUDACC__
+template<typename... Args>
+__host__ __device__ constexpr inline void ggml_unused_vars_impl(Args&&...) noexcept {}
+#define GGML_UNUSED_VARS(...) ggml_unused_vars_impl(__VA_ARGS__)
+#else
+#define GGML_UNUSED_VARS(...) do { (void)sizeof((__VA_ARGS__, 0)); } while(0)
+#endif // __CUDACC__
 
 #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
 
@@ -275,19 +284,19 @@
 //    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
 //
 #define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
-    const type prefix##0 = (pointer)->array[0]; \
+    const type prefix##0 = (pointer) ? (pointer)->array[0] : 0; \
     GGML_UNUSED(prefix##0);
 #define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
     GGML_TENSOR_LOCALS_1    (type, prefix, pointer, array) \
-    const type prefix##1 = (pointer)->array[1]; \
+    const type prefix##1 = (pointer) ? (pointer)->array[1] : 0; \
     GGML_UNUSED(prefix##1);
 #define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
     GGML_TENSOR_LOCALS_2    (type, prefix, pointer, array) \
-    const type prefix##2 = (pointer)->array[2]; \
+    const type prefix##2 = (pointer) ? (pointer)->array[2] : 0; \
     GGML_UNUSED(prefix##2);
 #define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
     GGML_TENSOR_LOCALS_3  (type, prefix, pointer, array) \
-    const type prefix##3 = (pointer)->array[3]; \
+    const type prefix##3 = (pointer) ? (pointer)->array[3] : 0; \
     GGML_UNUSED(prefix##3);
 
 #define GGML_TENSOR_UNARY_OP_LOCALS \
@@ -502,7 +511,9 @@ extern "C" {
         GGML_OP_CONV_TRANSPOSE_1D,
         GGML_OP_IM2COL,
         GGML_OP_IM2COL_BACK,
+        GGML_OP_IM2COL_3D,
         GGML_OP_CONV_2D,
+        GGML_OP_CONV_3D,
         GGML_OP_CONV_2D_DW,
         GGML_OP_CONV_TRANSPOSE_2D,
         GGML_OP_POOL_1D,
@@ -540,6 +551,7 @@ extern "C" {
         GGML_OP_CROSS_ENTROPY_LOSS,
         GGML_OP_CROSS_ENTROPY_LOSS_BACK,
         GGML_OP_OPT_STEP_ADAMW,
+        GGML_OP_OPT_STEP_SGD,
 
         GGML_OP_GLU,
 
@@ -1392,6 +1404,7 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // note: casting from f32 to i32 will discard the fractional part
     GGML_API struct ggml_tensor * ggml_cast(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1516,7 +1529,11 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // supports 3D: a->ne[2] == b->ne[1]
+    // supports 4D a:
+    // a     [n_embd, ne1, ne2, ne3]
+    // b I32 [n_rows, ne2, ne3, 1]
+    //
+    // return [n_embd, n_rows, ne2, ne3]
     GGML_API struct ggml_tensor * ggml_get_rows(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,  // data
@@ -1660,7 +1677,7 @@ extern "C" {
             struct ggml_tensor  * b,
             struct ggml_tensor  * c,
             int                   n_dims,
-            int                   sections[4],
+            int                   sections[GGML_MROPE_SECTIONS],
             int                   mode,
             int                   n_ctx_orig,
             float                 freq_base,
@@ -1686,6 +1703,22 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
+    GGML_API struct ggml_tensor * ggml_rope_multi_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[GGML_MROPE_SECTIONS],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
     GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1843,6 +1876,41 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
+    GGML_API struct ggml_tensor * ggml_im2col_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int64_t               IC,
+            int                   s0, // stride width
+            int                   s1, // stride height
+            int                   s2, // stride depth
+            int                   p0, // padding width
+            int                   p1, // padding height
+            int                   p2, // padding depth
+            int                   d0, // dilation width
+            int                   d1, // dilation height
+            int                   d2, // dilation depth
+            enum ggml_type        dst_type);
+
+    // a: [OC*IC, KD, KH, KW]
+    // b: [N*IC, ID, IH, IW]
+    // result: [N*OC, OD, OH, OW]
+    GGML_API struct ggml_tensor * ggml_conv_3d(
+                struct ggml_context * ctx,
+                struct ggml_tensor  * a,
+                struct ggml_tensor  * b,
+                int64_t               IC,
+                int                   s0, // stride width
+                int                   s1, // stride height
+                int                   s2, // stride depth
+                int                   p0, // padding width
+                int                   p1, // padding height
+                int                   p2, // padding depth
+                int                   d0, // dilation width
+                int                   d1, // dilation height
+                int                   d2  // dilation depth
+        );
+
     // kernel size is a->ne[0] x a->ne[1]
     // stride is equal to kernel size
     // padding is zero
@@ -1914,6 +1982,23 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
+    GGML_API struct ggml_tensor * ggml_conv_3d_direct(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // kernel [KW, KH, KD, IC * OC]
+            struct ggml_tensor  * b,   // input  [W, H, D, C * N]
+            int                   s0,  // stride
+            int                   s1,
+            int                   s2,
+            int                   p0,  // padding
+            int                   p1,
+            int                   p2,
+            int                   d0,  // dilation
+            int                   d1,
+            int                   d2,
+            int                   n_channels,
+            int                   n_batch,
+            int                   n_channels_out);
+
     enum ggml_op_pool {
         GGML_OP_POOL_MAX,
         GGML_OP_POOL_AVG,
@@ -2004,6 +2089,19 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    GGML_API struct ggml_tensor * ggml_pad_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  lp0,
+            int                  rp0,
+            int                  lp1,
+            int                  rp1,
+            int                  lp2,
+            int                  rp2,
+            int                  lp3,
+            int                  rp3
+            );
+
     // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
     GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
             struct ggml_context * ctx,
@@ -2293,7 +2391,14 @@ extern "C" {
             struct ggml_tensor  * grad,
             struct ggml_tensor  * m,
             struct ggml_tensor  * v,
-            struct ggml_tensor  * adamw_params); // parameters such a the learning rate
+            struct ggml_tensor  * adamw_params); // parameters such as the learning rate
+
+    // stochastic gradient descent step (with weight decay)
+    GGML_API struct ggml_tensor * ggml_opt_step_sgd(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  grad,
+        struct ggml_tensor *  sgd_params); // alpha, weight decay
 
     //
     // automatic differentiation

ml/backend/ggml/ggml/src/CMakeLists.txt

@@ -114,6 +114,9 @@ message(STATUS "GGML_SYSTEM_ARCH: ${GGML_SYSTEM_ARCH}")
 
 if (NOT MSVC)
     if (GGML_STATIC)
+        if (UNIX AND NOT APPLE)
+            set(CMAKE_FIND_LIBRARY_SUFFIXES ".a;.so")
+        endif()
         add_link_options(-static)
         if (MINGW)
             add_link_options(-static-libgcc -static-libstdc++)
@@ -382,6 +385,7 @@ ggml_add_backend(RPC)
 ggml_add_backend(SYCL)
 ggml_add_backend(Vulkan)
 ggml_add_backend(WebGPU)
+ggml_add_backend(zDNN)
 ggml_add_backend(OpenCL)
 
 foreach (target ggml-base ggml)

ml/backend/ggml/ggml/src/ggml-alloc.c

@@ -23,7 +23,7 @@ static bool ggml_is_view(const struct ggml_tensor * t) {
 }
 
 // ops that return true for this function must not use restrict pointers for their backend implementations
-static bool ggml_op_can_inplace(enum ggml_op op) {
+bool ggml_op_can_inplace(enum ggml_op op) {
     switch (op) {
         case GGML_OP_SCALE:
         case GGML_OP_DIAG_MASK_ZERO:
@@ -95,39 +95,104 @@ enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_te
 
 // dynamic tensor allocator
 
+#define GGML_VBUFFER_MAX_CHUNKS 16
+
+// relative memory address within an allocation that can be split into multiple buffers (chunks)
+struct buffer_address {
+    int chunk;     // index of a backend buffer
+    size_t offset; // local memory offset within the buffer
+};
+
+static const struct buffer_address GGML_BUFFER_ADDRESS_INVALID = { -1, SIZE_MAX };
+
+static bool ggml_buffer_address_less(struct buffer_address a, struct buffer_address b) {
+    return a.chunk != b.chunk ? a.chunk < b.chunk : a.offset < b.offset;
+}
+
 struct free_block {
     size_t offset;
     size_t size;
 };
 
-struct ggml_dyn_tallocr {
-    size_t alignment;
-    int n_free_blocks;
+struct tallocr_chunk {
     struct free_block free_blocks[MAX_FREE_BLOCKS];
+    int n_free_blocks;
     size_t max_size;
+};
+
+struct ggml_dyn_tallocr {
+    size_t alignment;
+    size_t max_chunk_size;
+    struct tallocr_chunk * chunks[GGML_VBUFFER_MAX_CHUNKS];
+    int n_chunks;
 
 #ifdef GGML_ALLOCATOR_DEBUG
     struct {
         const struct ggml_tensor * tensor;
-        size_t offset;
+        struct buffer_address addr;
     } allocated_tensors[1024];
 #endif
 };
 
+static void ggml_dyn_tallocr_insert_block(struct tallocr_chunk * chunk, size_t offset, size_t size) {
+    GGML_ASSERT(chunk->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
+    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
+    int insert_pos = 0;
+    while (insert_pos < chunk->n_free_blocks && chunk->free_blocks[insert_pos].offset < offset) {
+        insert_pos++;
+    }
+    // shift all blocks from insert_pos onward to make room for the new block
+    for (int i = chunk->n_free_blocks; i > insert_pos; i--) {
+        chunk->free_blocks[i] = chunk->free_blocks[i-1];
+    }
+    // insert the new block
+    chunk->free_blocks[insert_pos].offset = offset;
+    chunk->free_blocks[insert_pos].size = size;
+    chunk->n_free_blocks++;
+}
+
+static void ggml_dyn_tallocr_remove_block(struct tallocr_chunk * chunk, int idx) {
+    // shift all elements after idx by 1 to the left, overwriting the element at idx
+    for (int i = idx; i < chunk->n_free_blocks; i++) {
+        chunk->free_blocks[i] = chunk->free_blocks[i+1];
+    }
+    chunk->n_free_blocks--;
+}
+
+static int ggml_dyn_tallocr_new_chunk(struct ggml_dyn_tallocr * alloc, size_t min_size) {
+    if (alloc->n_chunks >= GGML_VBUFFER_MAX_CHUNKS) {
+        return -1;
+    }
+    struct tallocr_chunk * chunk = calloc(1, sizeof(struct tallocr_chunk));
+    chunk->n_free_blocks = 1;
+    chunk->free_blocks[0].offset = 0;
+    // available space in a chunk is limited to max_chunk_size, but can be higher if:
+    // 1. a single tensor exceeds the maximum, and cannot fit any other way
+    // 2. we are running out of chunks
+    // backends will either manage to allocate the larger size, or report an error.
+    chunk->free_blocks[0].size = MAX(min_size, alloc->max_chunk_size);
+    if (alloc->n_chunks == GGML_VBUFFER_MAX_CHUNKS - 1) {
+        chunk->free_blocks[0].size = SIZE_MAX/2;
+    }
+    alloc->chunks[alloc->n_chunks] = chunk;
+    alloc->n_chunks++;
+    return alloc->n_chunks - 1;
+}
+
 #ifdef GGML_ALLOCATOR_DEBUG
-static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
+static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, const struct ggml_tensor * tensor) {
     for (int i = 0; i < 1024; i++) {
         if (alloc->allocated_tensors[i].tensor == NULL) {
             alloc->allocated_tensors[i].tensor = tensor;
-            alloc->allocated_tensors[i].offset = offset;
+            alloc->allocated_tensors[i].addr = addr;
             return;
         }
     }
     GGML_ABORT("out of allocated_tensors");
 }
-static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
+static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, const struct ggml_tensor * tensor) {
     for (int i = 0; i < 1024; i++) {
-        if (alloc->allocated_tensors[i].offset == offset) {
+        if (alloc->allocated_tensors[i].addr.chunk == addr.chunk && alloc->allocated_tensors[i].addr.offset == addr.offset) {
             alloc->allocated_tensors[i].tensor = NULL;
             return;
         }
@@ -136,76 +201,94 @@ static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offs
 }
 #endif
 
-static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) {
+static struct buffer_address ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) {
     size = aligned_offset(NULL, size, alloc->alignment);
 
     AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
 
+    int best_fit_chunk = -1;
+    int best_fit_block = -1;
     size_t max_avail = 0;
 
-    // find the best fitting free block besides the last block
-    int best_fit_block = -1;
-    size_t best_fit_size = SIZE_MAX;
-    for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
-        struct free_block * block = &alloc->free_blocks[i];
-        max_avail = MAX(max_avail, block->size);
-        if (block->size >= size && block->size <= best_fit_size) {
-            best_fit_block = i;
-            best_fit_size = block->size;
+    // find the best fitting free block besides the last block, within any chunk
+    for (int c = 0; c < alloc->n_chunks; ++c) {
+        struct tallocr_chunk * chunk = alloc->chunks[c];
+        size_t best_fit_size = SIZE_MAX;
+        for (int i = 0; i < chunk->n_free_blocks - 1; i++) {
+            struct free_block * block = &chunk->free_blocks[i];
+            max_avail = MAX(max_avail, block->size);
+            if (block->size >= size && block->size <= best_fit_size) {
+                best_fit_chunk = c;
+                best_fit_block = i;
+                best_fit_size = block->size;
+            }
         }
     }
 
     if (best_fit_block == -1) {
-        // the last block is our last resort
-        struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
-        max_avail = MAX(max_avail, block->size);
-        if (block->size >= size) {
-            best_fit_block = alloc->n_free_blocks - 1;
-        } else {
-            // this should never happen
-            GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
-                    __func__, size, max_avail);
-            GGML_ABORT("not enough space in the buffer");
+        // no suitable block found, try the last block (this will grow a chunks size)
+        for (int c = 0; c < alloc->n_chunks; ++c) {
+            struct tallocr_chunk * chunk = alloc->chunks[c];
+            if (chunk->n_free_blocks > 0) {
+                struct free_block * block = &chunk->free_blocks[chunk->n_free_blocks - 1];
+                max_avail = MAX(max_avail, block->size);
+                if (block->size >= size) {
+                    best_fit_chunk = c;
+                    best_fit_block = chunk->n_free_blocks - 1;
+                    break;
+                }
+            }
         }
     }
 
-    struct free_block * block = &alloc->free_blocks[best_fit_block];
-    size_t offset = block->offset;
-    block->offset = offset + size;
+    if (best_fit_block == -1) {
+        // none of the existing chunks have enough space left
+        best_fit_chunk = ggml_dyn_tallocr_new_chunk(alloc, size);
+        best_fit_block = 0;
+    }
+    if (best_fit_chunk == -1) {
+        // since the last chunk always has virtually endless memory, this should never happen
+        GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
+            __func__, size, max_avail);
+        GGML_ABORT("graph allocation: failed to reserve memory");
+    }
+
+    struct tallocr_chunk * chunk = alloc->chunks[best_fit_chunk];
+    struct free_block    * block = &chunk->free_blocks[best_fit_block];
+    struct buffer_address  addr  = {.chunk = best_fit_chunk, .offset = block->offset };
+    block->offset += size;
     block->size -= size;
     if (block->size == 0) {
         // remove block if empty
-        alloc->n_free_blocks--;
-        for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
-            alloc->free_blocks[j] = alloc->free_blocks[j+1];
-        }
+        ggml_dyn_tallocr_remove_block(chunk, best_fit_block);
     }
 
-    AT_PRINTF("block %d, offset %zu\n", best_fit_block, offset);
+    AT_PRINTF("block %d, offset %zu, chunk %d\n", best_fit_block, addr.offset, addr.chunk);
 
 #ifdef GGML_ALLOCATOR_DEBUG
-    add_allocated_tensor(alloc, offset, tensor);
-    size_t cur_max = offset + size;
-    if (cur_max > alloc->max_size) {
-        // sort allocated_tensors by offset
+    add_allocated_tensor(alloc, addr, tensor);
+    size_t cur_max = addr.offset + size;
+    if (cur_max > alloc->max_size[addr.chunk]) {
+        // sort allocated_tensors by chunk/offset
         for (int i = 0; i < 1024; i++) {
             for (int j = i + 1; j < 1024; j++) {
-                if (alloc->allocated_tensors[i].offset > alloc->allocated_tensors[j].offset) {
+                if (ggml_buffer_address_less(alloc->allocated_tensors[j].addr, alloc->allocated_tensors[i].addr)) {
                     const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor;
-                    size_t tmp_offset = alloc->allocated_tensors[i].offset;
+                    struct buffer_address tmp_addr = alloc->allocated_tensors[i].addr;
                     alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor;
-                    alloc->allocated_tensors[i].offset = alloc->allocated_tensors[j].offset;
+                    alloc->allocated_tensors[i].addr = alloc->allocated_tensors[j].addr;
                     alloc->allocated_tensors[j].tensor = tmp_tensor;
-                    alloc->allocated_tensors[j].offset = tmp_offset;
+                    alloc->allocated_tensors[j].addr = tmp_addr;
                 }
             }
         }
-        GGML_LOG_DEBUG("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
+        GGML_LOG_DEBUG("max_size[%d] = %.2f MB: tensors: ", addr.chunk, cur_max / 1024.0 / 1024.0);
         for (int i = 0; i < 1024; i++) {
             if (alloc->allocated_tensors[i].tensor) {
-                GGML_LOG_DEBUG("%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
-                    alloc->allocated_tensors[i].offset,
-                    alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
+                GGML_LOG_DEBUG("%s [%d: %zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
+                    alloc->allocated_tensors[i].addr.chunk,
+                    alloc->allocated_tensors[i].addr.offset,
+                    alloc->allocated_tensors[i].addr.offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
                     ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0);
             }
         }
@@ -213,78 +296,69 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
     }
 #endif
 
-    alloc->max_size = MAX(alloc->max_size, offset + size);
+    chunk->max_size = MAX(chunk->max_size, addr.offset + size);
 
-    return offset;
+    return addr;
 
     GGML_UNUSED(tensor);
 }
 
 // this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, size_t size, const struct ggml_tensor * tensor) {
+static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size, const struct ggml_tensor * tensor) {
     size = aligned_offset(NULL, size, alloc->alignment);
 
-    AT_PRINTF("%s: freeing %s at %zu (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, offset, size, alloc->n_free_blocks);
+    AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n",
+        __func__, tensor->name, addr.chunk, addr.offset, size, alloc->chunks[addr.chunk]->n_free_blocks);
 
 #ifdef GGML_ALLOCATOR_DEBUG
-    remove_allocated_tensor(alloc, offset, tensor);
+    remove_allocated_tensor(alloc, addr, tensor);
 #endif
 
+    struct tallocr_chunk * chunk = alloc->chunks[addr.chunk];
+
     // see if we can merge with an existing block
-    for (int i = 0; i < alloc->n_free_blocks; i++) {
-        struct free_block * block = &alloc->free_blocks[i];
+    for (int i = 0; i < chunk->n_free_blocks; i++) {
+        struct free_block * block = &chunk->free_blocks[i];
         // check if ptr is at the end of the block
-        if (block->offset + block->size == offset) {
+        if (block->offset + block->size == addr.offset) {
             block->size += size;
             // check if we can merge with the next block
-            if (i < alloc->n_free_blocks - 1 && block->offset + block->size == alloc->free_blocks[i+1].offset) {
-                block->size += alloc->free_blocks[i+1].size;
-                alloc->n_free_blocks--;
-                for (int j = i+1; j < alloc->n_free_blocks; j++) {
-                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+            if (i < chunk->n_free_blocks - 1) {
+                struct free_block * next = &chunk->free_blocks[i+1];
+                if (block->offset + block->size == next->offset) {
+                    block->size += next->size;
+                    ggml_dyn_tallocr_remove_block(chunk, i+1);
                 }
             }
             return;
         }
         // check if ptr is at the beginning of the block
-        if (offset + size == block->offset) {
-            block->offset = offset;
+        if (addr.offset + size == block->offset) {
+            block->offset = addr.offset;
             block->size += size;
             // check if we can merge with the previous block
-            if (i > 0 && alloc->free_blocks[i-1].offset + alloc->free_blocks[i-1].size == block->offset) {
-                alloc->free_blocks[i-1].size += block->size;
-                alloc->n_free_blocks--;
-                for (int j = i; j < alloc->n_free_blocks; j++) {
-                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+            if (i > 0) {
+                struct free_block * prev = &chunk->free_blocks[i-1];
+                if (prev->offset + prev->size == block->offset) {
+                    prev->size += block->size;
+                    ggml_dyn_tallocr_remove_block(chunk, i);
                 }
             }
             return;
         }
     }
     // otherwise, add a new block
-    GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
-    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
-    int insert_pos = 0;
-    while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].offset < offset) {
-        insert_pos++;
-    }
-    // shift all blocks from insert_pos onward to make room for the new block
-    for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
-        alloc->free_blocks[i] = alloc->free_blocks[i-1];
-    }
-    // insert the new block
-    alloc->free_blocks[insert_pos].offset = offset;
-    alloc->free_blocks[insert_pos].size = size;
-    alloc->n_free_blocks++;
+    ggml_dyn_tallocr_insert_block(chunk, addr.offset, size);
 
     GGML_UNUSED(tensor);
 }
 
 static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
-    alloc->n_free_blocks = 1;
-    alloc->free_blocks[0].offset = 0;
-    alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows
-    alloc->max_size = 0;
+    for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS; i++) {
+        free(alloc->chunks[i]);
+        alloc->chunks[i] = NULL;
+    }
+    alloc->n_chunks = 0;
 
 #ifdef GGML_ALLOCATOR_DEBUG
     for (int i = 0; i < 1024; i++) {
@@ -293,14 +367,14 @@ static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
 #endif
 }
 
-static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) {
+static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment, size_t max_buffer_size) {
     struct ggml_dyn_tallocr * alloc = (struct ggml_dyn_tallocr *)malloc(sizeof(struct ggml_dyn_tallocr));
 
     *alloc = (struct ggml_dyn_tallocr) {
-        /*.alignment     = */ alignment,
-        /*.n_free_blocks = */ 0,
-        /*.free_blocks   = */ {{0}},
-        /*.max_size      = */ 0,
+        /*.alignment      = */ alignment,
+        /*.max_chunk_size = */ MIN(max_buffer_size, SIZE_MAX/2), // clamp to avoid overflows
+        /*.chunks         = */ {NULL},
+        /*.n_chunks       = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
         /*.allocated_tensors = */ {{0}},
 #endif
@@ -312,11 +386,79 @@ static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) {
 }
 
 static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) {
+    for (int i = 0; i < alloc->n_chunks; ++i) {
+        free(alloc->chunks[i]);
+    }
     free(alloc);
 }
 
 static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc) {
-    return alloc->max_size;
+    size_t max_size = 0;
+    for (int i = 0; i < alloc->n_chunks; i++) {
+        max_size += alloc->chunks[i]->max_size;
+    }
+    return max_size;
+}
+
+
+// virtual buffer with contiguous memory range, split into multiple backend buffers (chunks)
+
+struct vbuffer {
+    ggml_backend_buffer_t chunks[GGML_VBUFFER_MAX_CHUNKS];
+};
+
+static void ggml_vbuffer_free(struct vbuffer * buf) {
+    if (buf == NULL) {
+        return;
+    }
+    for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS; ++i) {
+        ggml_backend_buffer_free(buf->chunks[i]);
+    }
+    free(buf);
+}
+
+static int ggml_vbuffer_n_chunks(struct vbuffer * buf) {
+    int n = 0;
+    while (n < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[n]) n++;
+    return n;
+}
+
+static size_t ggml_vbuffer_size(struct vbuffer * buf) {
+    size_t size = 0;
+    for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[i]; ++i) {
+        size += ggml_backend_buffer_get_size(buf->chunks[i]);
+    }
+    return size;
+}
+
+static struct vbuffer * ggml_vbuffer_alloc(ggml_backend_buffer_type_t buft, const struct ggml_dyn_tallocr * talloc, enum ggml_backend_buffer_usage usage) {
+    struct vbuffer * buf = (struct vbuffer *)calloc(1, sizeof(struct vbuffer));
+    if (buf == NULL) {
+        return NULL;
+    }
+
+    for (int n = 0; n < talloc->n_chunks; n++) {
+        size_t chunk_size = talloc->chunks[n]->max_size;
+        buf->chunks[n] = ggml_backend_buft_alloc_buffer(buft, chunk_size);
+        if (buf->chunks[n] == NULL) {
+            ggml_vbuffer_free(buf);
+            return NULL;
+        }
+        ggml_backend_buffer_set_usage(buf->chunks[n], usage);
+    }
+    return buf;
+}
+
+static void ggml_vbuffer_tensor_alloc(struct vbuffer * buf, struct ggml_tensor * tensor, struct buffer_address buf_addr) {
+    void * base = ggml_backend_buffer_get_base(buf->chunks[buf_addr.chunk]);
+    void * addr = (char *)base + buf_addr.offset;
+    ggml_backend_tensor_alloc(buf->chunks[buf_addr.chunk], tensor, addr);
+}
+
+static void ggml_vbuffer_reset(struct vbuffer * buf) {
+    for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[i]; ++i) {
+        ggml_backend_buffer_reset(buf->chunks[i]);
+    }
 }
 
 
@@ -328,13 +470,13 @@ struct hash_node {
     int n_children;
     int n_views;
     int buffer_id;
-    size_t offset; // offset within the buffer
+    struct buffer_address addr;
     bool allocated;
 };
 
 struct tensor_alloc {
     int buffer_id;
-    size_t offset;
+    struct buffer_address addr;
     size_t size_max; // 0 = pre-allocated, unused, or view
 };
 
@@ -349,7 +491,7 @@ struct node_alloc {
 
 struct ggml_gallocr {
     ggml_backend_buffer_type_t * bufts; // [n_buffers]
-    ggml_backend_buffer_t * buffers; // [n_buffers]
+    struct vbuffer ** buffers; // [n_buffers]
     size_t *buffer_sizes; // [n_buffers]
     struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers]
     int n_buffers;
@@ -371,7 +513,7 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
     galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
     GGML_ASSERT(galloc->bufts != NULL);
 
-    galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t));
+    galloc->buffers = calloc(n_bufs, sizeof(struct vbuffer *));
     GGML_ASSERT(galloc->buffers != NULL);
 
     galloc->buffer_sizes = calloc(n_bufs, sizeof(size_t));
@@ -394,7 +536,8 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
 
         if (galloc->buf_tallocs[i] == NULL) {
             size_t alignment = ggml_backend_buft_get_alignment(bufts[i]);
-            galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment);
+            size_t max_size = ggml_backend_buft_get_max_size(bufts[i]);
+            galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment, max_size);
         }
     }
     galloc->n_buffers = n_bufs;
@@ -422,7 +565,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
                 }
             }
             if (!freed) {
-                ggml_backend_buffer_free(galloc->buffers[i]);
+                ggml_vbuffer_free(galloc->buffers[i]);
             }
         }
         if (galloc->buf_tallocs != NULL) {
@@ -472,7 +615,7 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
 
     if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) {
         hn->allocated = true;
-        assert(hn->offset == 0);
+        assert(hn->addr.offset == 0);
 
         // try to reuse a parent's buffer (inplace)
         if (ggml_op_can_inplace(node->op)) {
@@ -506,9 +649,9 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
                         struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src);
                         if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
                             AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
-                            assert(view_src_hn->offset == p_hn->offset);
+                            assert(view_src_hn->addr.chunk == p_hn->addr.chunk && view_src_hn->addr.offset == p_hn->addr.offset);
                             hn->buffer_id = p_hn->buffer_id;
-                            hn->offset = p_hn->offset;
+                            hn->addr = p_hn->addr;
                             p_hn->allocated = false; // avoid freeing the parent
                             view_src_hn->allocated = false;
                             return;
@@ -516,7 +659,7 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
                     } else {
                         AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
                         hn->buffer_id = p_hn->buffer_id;
-                        hn->offset = p_hn->offset;
+                        hn->addr = p_hn->addr;
                         p_hn->allocated = false; // avoid freeing the parent
                         return;
                     }
@@ -527,9 +670,8 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
         struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
         ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
         size_t size = ggml_backend_buft_get_alloc_size(buft, node);
-        size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
         hn->buffer_id = buffer_id;
-        hn->offset = offset;
+        hn->addr = ggml_dyn_tallocr_alloc(alloc, size, node);
     }
 }
 
@@ -541,12 +683,11 @@ static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * n
     }
 
     struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
-    size_t offset = hn->offset;
     int buffer_id = hn->buffer_id;
     struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
     ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
     size_t size = ggml_backend_buft_get_alloc_size(buft, node);
-    ggml_dyn_tallocr_free_tensor(alloc, offset, size, node);
+    ggml_dyn_tallocr_free_tensor(alloc, hn->addr, size, node);
     hn->allocated = false;
 }
 
@@ -697,24 +838,24 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         struct node_alloc * node_alloc = &galloc->node_allocs[i];
         if (node->view_src || node->data) {
             node_alloc->dst.buffer_id = -1;
-            node_alloc->dst.offset = SIZE_MAX;
+            node_alloc->dst.addr = GGML_BUFFER_ADDRESS_INVALID;
             node_alloc->dst.size_max = 0;
         } else {
             struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
             node_alloc->dst.buffer_id = hn->buffer_id;
-            node_alloc->dst.offset    = hn->offset;
+            node_alloc->dst.addr = hn->addr;
             node_alloc->dst.size_max  = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node);
         }
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
             if (!src || src->view_src || src->data) {
                 node_alloc->src[j].buffer_id = -1;
-                node_alloc->src[j].offset = SIZE_MAX;
+                node_alloc->src[j].addr = GGML_BUFFER_ADDRESS_INVALID;
                 node_alloc->src[j].size_max = 0;
             } else {
                 struct hash_node * hn = ggml_gallocr_hash_get(galloc, src);
                 node_alloc->src[j].buffer_id = hn->buffer_id;
-                node_alloc->src[j].offset   = hn->offset;
+                node_alloc->src[j].addr = hn->addr;
                 node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src);
             }
         }
@@ -730,11 +871,11 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
         if (leaf->view_src || leaf->data) {
             galloc->leaf_allocs[i].leaf.buffer_id = -1;
-            galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
+            galloc->leaf_allocs[i].leaf.addr = GGML_BUFFER_ADDRESS_INVALID;
             galloc->leaf_allocs[i].leaf.size_max = 0;
         } else {
             galloc->leaf_allocs[i].leaf.buffer_id = hn->buffer_id;
-            galloc->leaf_allocs[i].leaf.offset = hn->offset;
+            galloc->leaf_allocs[i].leaf.addr = hn->addr;
             galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
         }
     }
@@ -751,7 +892,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
             }
         }
 
-        size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
+        size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
         size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
 
         // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
@@ -760,18 +901,17 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
             GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
 
-            ggml_backend_buffer_free(galloc->buffers[i]);
-            galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
+            ggml_vbuffer_free(galloc->buffers[i]);
+            galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
             if (galloc->buffers[i]) {
-                galloc->buffer_sizes[i] = ggml_backend_buffer_get_size(galloc->buffers[i]);
-                ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
+                galloc->buffer_sizes[i] = ggml_vbuffer_size(galloc->buffers[i]);
             } else {
                 GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
                 galloc->buffer_sizes[i] = new_size;
                 success = false;
             }
         } else {
-            galloc->buffer_sizes[i] = ggml_backend_buffer_get_size(galloc->buffers[i]);
+            galloc->buffer_sizes[i] = ggml_vbuffer_size(galloc->buffers[i]);
         }
     }
 
@@ -784,11 +924,11 @@ bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
 
 static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) {
     int buffer_id = tensor_alloc->buffer_id;
-    assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
+    assert(tensor->data || tensor->view_src || ggml_backend_buft_get_alloc_size(galloc->bufts[buffer_id], tensor) <= tensor_alloc->size_max);
 
     if (tensor->view_src != NULL) {
         if (tensor->buffer == NULL) {
-            assert(tensor_alloc->offset == SIZE_MAX);
+            assert(tensor_alloc->addr.offset == SIZE_MAX);
             if (tensor->view_src->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
@@ -797,11 +937,9 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
         }
     } else {
         if (tensor->data == NULL) {
-            assert(tensor_alloc->offset != SIZE_MAX);
-            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
-            void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
-            void * addr = (char *)base + tensor_alloc->offset;
-            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
+            assert(tensor_alloc->addr.offset != SIZE_MAX);
+            assert(ggml_backend_buft_get_alloc_size(galloc->bufts[buffer_id], tensor) <= tensor_alloc->size_max);
+            ggml_vbuffer_tensor_alloc(galloc->buffers[buffer_id], tensor, tensor_alloc->addr);
         } else {
             if (tensor->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
@@ -886,7 +1024,7 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
     // reset buffers
     for (int i = 0; i < galloc->n_buffers; i++) {
         if (galloc->buffers[i] != NULL) {
-            ggml_backend_buffer_reset(galloc->buffers[i]);
+            ggml_vbuffer_reset(galloc->buffers[i]);
         }
     }
 
@@ -929,7 +1067,7 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
         }
     }
 
-    return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]);
+    return ggml_vbuffer_size(galloc->buffers[buffer_id]);
 }
 
 size_t ggml_gallocr_get_attempted_buffer_size(ggml_gallocr_t galloc, int buffer_id) {

ml/backend/ggml/ggml/src/ggml-backend-impl.h

@@ -8,7 +8,7 @@
 extern "C" {
 #endif
 
-    #define GGML_BACKEND_API_VERSION 1
+    #define GGML_BACKEND_API_VERSION 2
 
     //
     // Backend buffer type
@@ -121,6 +121,9 @@ extern "C" {
         // wait for an event on on a different stream
         void (*event_wait)  (ggml_backend_t backend, ggml_backend_event_t event);
 
+        // (optional) sort/optimize the nodes in the graph
+        void                      (*graph_optimize)    (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+
         // (optional) reserves intermediate buffers needed for the compution
         // if alloc is true, memory is actually allocated, otherwise the required amount is just returned by buffer_size
         enum ggml_status          (*graph_reserve)     (ggml_backend_t backend, struct ggml_cgraph * cgraph, bool alloc);

ml/backend/ggml/ggml/src/ggml-backend-reg.cpp

@@ -49,6 +49,10 @@
 #include "ggml-webgpu.h"
 #endif
 
+#ifdef GGML_USE_ZDNN
+#include "ggml-zdnn.h"
+#endif
+
 #ifdef GGML_USE_OPENCL
 #include "ggml-opencl.h"
 #endif
@@ -131,6 +135,10 @@ static void * dl_get_sym(dl_handle * handle, const char * name) {
     return p;
 }
 
+static const char * dl_error() {
+    return "";
+}
+
 #else
 
 using dl_handle = void;
@@ -151,6 +159,11 @@ static void * dl_get_sym(dl_handle * handle, const char * name) {
     return dlsym(handle, name);
 }
 
+static const char * dl_error() {
+    const char *rslt = dlerror();
+    return rslt != nullptr ? rslt : "";
+}
+
 #endif
 
 using dl_handle_ptr = std::unique_ptr<dl_handle, dl_handle_deleter>;
@@ -180,6 +193,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_WEBGPU
         register_backend(ggml_backend_webgpu_reg());
 #endif
+#ifdef GGML_USE_ZDNN
+        register_backend(ggml_backend_zdnn_reg());
+#endif
 #ifdef GGML_USE_OPENCL
         register_backend(ggml_backend_opencl_reg());
 #endif
@@ -238,7 +254,7 @@ struct ggml_backend_registry {
         dl_handle_ptr handle { dl_load_library(path) };
         if (!handle) {
             if (!silent) {
-                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_str(path).c_str());
+                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_str(path).c_str(), dl_error());
             }
             return nullptr;
         }
@@ -398,9 +414,8 @@ ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const
 
 ggml_backend_t ggml_backend_init_best(void) {
     ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
-    if (!dev) {
-        dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-    }
+    dev = dev ? dev : ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU);
+    dev = dev ? dev : ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
     if (!dev) {
         return nullptr;
     }
@@ -529,7 +544,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
                 if (filename.native().find(file_prefix) == 0 && ext == file_extension) {
                     dl_handle_ptr handle { dl_load_library(entry) };
                     if (!handle && !silent) {
-                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_str(entry.path()).c_str());
+                        GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_str(entry.path()).c_str(), dl_error());
                     }
                     if (handle) {
                         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");

ml/backend/ggml/ggml/src/ggml-backend.cpp

@@ -19,9 +19,8 @@
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include <string>
-#include <vector>
 #include <algorithm>
+#include <vector>
 
 #ifdef __APPLE__
 #include <sys/types.h>
@@ -32,6 +31,7 @@
 // backend buffer type
 
 const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     return buft->iface.get_name(buft);
 }
 
@@ -54,14 +54,17 @@ ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t
         return buf;
     }
 
+    GGML_ASSERT(buft);
     return buft->iface.alloc_buffer(buft, size);
 }
 
 size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     return buft->iface.get_alignment(buft);
 }
 
 size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     // get_max_size is optional, defaults to SIZE_MAX
     if (buft->iface.get_max_size) {
         return buft->iface.get_max_size(buft);
@@ -70,6 +73,7 @@ size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
 }
 
 size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) {
+    GGML_ASSERT(buft);
     // get_alloc_size is optional, defaults to ggml_nbytes
     if (buft->iface.get_alloc_size) {
         size_t size = buft->iface.get_alloc_size(buft, tensor);
@@ -80,6 +84,7 @@ size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const s
 }
 
 bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     if (buft->iface.is_host) {
         return buft->iface.is_host(buft);
     }
@@ -87,6 +92,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
 }
 
 ggml_backend_dev_t ggml_backend_buft_get_device(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     return buft->device;
 }
 
@@ -124,10 +130,12 @@ void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
 }
 
 size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->size;
 }
 
 void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     // get_base is optional if the buffer is zero-sized
     if (buffer->size == 0) {
         return NULL;
@@ -147,6 +155,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 enum ggml_status ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    GGML_ASSERT(buffer);
     // init_tensor is optional
     if (buffer->iface.init_tensor) {
         return buffer->iface.init_tensor(buffer, tensor);
@@ -155,6 +164,7 @@ enum ggml_status ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, s
 }
 
 void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
     // clear is optional if the buffer is zero-sized
     if (buffer->size == 0) {
         return;
@@ -180,6 +190,7 @@ bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
 }
 
 void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
+    GGML_ASSERT(buffer);
     buffer->usage = usage;
 
     // FIXME: add a generic callback to the buffer interface
@@ -189,14 +200,17 @@ void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backe
 }
 
 enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->usage;
 }
 
 ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->buft;
 }
 
 void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     if (buffer->iface.reset) {
         buffer->iface.reset(buffer);
     }
@@ -235,6 +249,7 @@ void ggml_backend_free(ggml_backend_t backend) {
 }
 
 ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_buffer_type(backend->device);
 }
 
@@ -251,6 +266,8 @@ size_t ggml_backend_get_max_size(ggml_backend_t backend) {
 }
 
 void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
@@ -262,6 +279,8 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor *
 }
 
 void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
@@ -303,6 +322,7 @@ void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, siz
 }
 
 void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     if (size == 0) {
@@ -318,6 +338,7 @@ void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size
 }
 
 void ggml_backend_synchronize(ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     if (backend->iface.synchronize == NULL) {
         return;
     }
@@ -326,18 +347,21 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
 }
 
 ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_create != NULL);
 
     return backend->iface.graph_plan_create(backend, cgraph);
 }
 
 void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_free != NULL);
 
     backend->iface.graph_plan_free(backend, plan);
 }
 
 enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
 
     return backend->iface.graph_plan_compute(backend, plan);
@@ -350,22 +374,27 @@ enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_
 }
 
 enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend);
     return backend->iface.graph_compute(backend, cgraph);
 }
 
 bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_supports_op(backend->device, op);
 }
 
 bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_supports_buft(backend->device, buft);
 }
 
 bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_offload_op(backend->device, op);
 }
 
 ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     return backend->device;
 }
 
@@ -401,6 +430,7 @@ void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t b
         return;
     }
 
+    GGML_ASSERT(backend_dst);
     if (backend_dst->iface.cpy_tensor_async != NULL) {
         if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
             return;
@@ -432,38 +462,52 @@ void ggml_backend_event_free(ggml_backend_event_t event) {
 }
 
 void ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.event_record != NULL);
 
     backend->iface.event_record(backend, event);
 }
 
 void ggml_backend_event_synchronize(ggml_backend_event_t event) {
+    GGML_ASSERT(event);
     GGML_ASSERT(event->device->iface.event_synchronize);
 
     event->device->iface.event_synchronize(event->device, event);
 }
 
 void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.event_wait != NULL);
 
     backend->iface.event_wait(backend, event);
 }
 
+static void ggml_backend_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend);
+    if (backend->iface.graph_optimize != NULL) {
+        backend->iface.graph_optimize(backend, cgraph);
+    }
+}
+
 // Backend device
 
 const char * ggml_backend_dev_name(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_name(device);
 }
 
 const char * ggml_backend_dev_description(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_description(device);
 }
 
 void ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total) {
+    GGML_ASSERT(device);
     device->iface.get_memory(device, free, total);
 }
 
 enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_type(device);
 }
 
@@ -473,10 +517,12 @@ void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_d
 }
 
 ggml_backend_reg_t ggml_backend_dev_backend_reg(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->reg;
 }
 
 ggml_backend_t ggml_backend_dev_init(ggml_backend_dev_t device, const char * params) {
+    GGML_ASSERT(device);
     return device->iface.init_backend(device, params);
 }
 
@@ -489,10 +535,12 @@ void ggml_backend_dev_reset(ggml_backend_dev_t device) {
 }
 
 ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_buffer_type(device);
 }
 
 ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     if (device->iface.get_host_buffer_type == NULL) {
         return NULL;
     }
@@ -501,18 +549,22 @@ ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t
 }
 
 ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size) {
+    GGML_ASSERT(device);
     return device->iface.buffer_from_host_ptr(device, ptr, size, max_tensor_size);
 }
 
 bool ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
+    GGML_ASSERT(device);
     return device->iface.supports_op(device, op);
 }
 
 bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(device);
     return device->iface.supports_buft(device, buft);
 }
 
 bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
+    GGML_ASSERT(device);
     if (device->iface.offload_op != NULL) {
         return device->iface.offload_op(device, op);
     }
@@ -523,18 +575,22 @@ bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_te
 // Backend (reg)
 
 const char * ggml_backend_reg_name(ggml_backend_reg_t reg) {
+    GGML_ASSERT(reg);
     return reg->iface.get_name(reg);
 }
 
 size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg) {
+    GGML_ASSERT(reg);
     return reg->iface.get_device_count(reg);
 }
 
 ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(reg);
     return reg->iface.get_device(reg, index);
 }
 
 void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+    GGML_ASSERT(reg);
     if (!reg->iface.get_proc_address) {
         return NULL;
     }
@@ -549,6 +605,7 @@ struct ggml_backend_multi_buffer_context {
 };
 
 static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
         ggml_backend_buffer_free(ctx->buffers[i]);
@@ -560,6 +617,7 @@ static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer)
 }
 
 static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
         ggml_backend_buffer_clear(ctx->buffers[i], value);
@@ -595,10 +653,12 @@ ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer
 }
 
 bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer;
 }
 
 void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
+    GGML_ASSERT(buffer);
     GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
@@ -626,7 +686,7 @@ static bool ggml_is_view_op(enum ggml_op op) {
 #endif
 
 #ifndef GGML_SCHED_MAX_SPLIT_INPUTS
-#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
+#define GGML_SCHED_MAX_SPLIT_INPUTS 30
 #endif
 
 #ifndef GGML_SCHED_MAX_COPIES
@@ -883,7 +943,7 @@ static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, stru
 }
 
 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
-static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     // reset splits
     sched->n_splits = 0;
     sched->n_graph_inputs = 0;
@@ -1279,6 +1339,10 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         struct ggml_backend_sched_split * split = &sched->splits[i];
         split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
 
+        // Optimize this split of the graph. This needs to happen before we make graph_copy,
+        // so they are in sync.
+        ggml_backend_graph_optimize(sched->backends[split->backend_id], &split->graph);
+
         // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
         for (int j = 0; j < split->n_inputs; j++) {
             assert(graph_copy->size > (graph_copy->n_nodes + 1));
@@ -1384,17 +1448,22 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
 }
 
 static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     struct ggml_backend_sched_split * splits = sched->splits;
 
-    for (int i = 0; i < sched->n_splits; i++) {
-        struct ggml_backend_sched_split * split = &splits[i];
+    ggml_tensor * prev_ids_tensor = nullptr;
+    std::vector<int32_t> ids;
+    std::vector<ggml_bitset_t> used_ids;
+
+    for (int split_id = 0; split_id < sched->n_splits; split_id++) {
+        struct ggml_backend_sched_split * split = &splits[split_id];
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
         // copy the input tensors to the split backend
-        for (int j = 0; j < split->n_inputs; j++) {
-            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
-            struct ggml_tensor * input = split->inputs[j];
+        for (int input_id = 0; input_id < split->n_inputs; input_id++) {
+            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
+            struct ggml_tensor * input = split->inputs[input_id];
             struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy);
 
             if (input->flags & GGML_TENSOR_FLAG_INPUT) {
@@ -1412,16 +1481,104 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 } else {
                     ggml_backend_synchronize(split_backend);
                 }
-                // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
-                // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
-                if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
+
+                // when offloading MoE weights, we can reduce the amount of data copied by copying only the experts that are used
+                ggml_tensor * node = split->graph.nodes[0];
+                if (split->graph.n_nodes > 0 &&
+                    ggml_backend_buffer_get_usage(input->buffer) == GGML_BACKEND_BUFFER_USAGE_WEIGHTS &&
+                    ggml_backend_buffer_is_host(input->buffer) && (
+                    (node->src[0] == input_cpy && node->op == GGML_OP_MUL_MAT_ID)
+                    //|| (node->src[1] == input_cpy && node->op == GGML_OP_ADD_ID) /* GGML_OP_ADD_ID weights are small and not worth splitting */
+                    )) {
+
+                    const int64_t n_expert   = node->op == GGML_OP_MUL_MAT_ID ? input->ne[2] : input->ne[1];
+                    const size_t expert_size = node->op == GGML_OP_MUL_MAT_ID ? input->nb[2] : input->nb[1];
+
                     ggml_backend_synchronize(input_backend);
-                    if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                        ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                    } else {
-                        ggml_backend_synchronize(split_backend);
+
+                    // get the ids
+                    ggml_tensor * ids_tensor = node->src[2];
+                    ggml_backend_t ids_backend = split_backend;
+
+                    // if the ids tensor is also an input of the split, it may not have been copied yet to the split backend
+                    // in that case, we use the original ids tensor
+                    for (int i = input_id + 1; i < split->n_inputs; i++) {
+                        if (ids_tensor == tensor_copy(split->inputs[i], split_backend_id, sched->cur_copy)) {
+                            ids_tensor = split->inputs[i];
+                            ids_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[i]);
+                            break;
+                        }
+                    }
+
+                    if (ids_tensor != prev_ids_tensor) {
+                        ids.resize(ggml_nbytes(ids_tensor) / sizeof(int32_t));
+                        ggml_backend_tensor_get_async(ids_backend, ids_tensor, ids.data(), 0, ggml_nbytes(ids_tensor));
+                        ggml_backend_synchronize(ids_backend);
+
+                        // find the used experts
+                        used_ids.clear();
+                        used_ids.resize(ggml_bitset_size(n_expert));
+                        for (int64_t i1 = 0; i1 < ids_tensor->ne[1]; i1++) {
+                            for (int64_t i0 = 0; i0 < ids_tensor->ne[0]; i0++) {
+                                int32_t id = ids[i1 * ids_tensor->nb[1]/sizeof(int32_t) + i0 * ids_tensor->nb[0]/sizeof(int32_t)];
+                                GGML_ASSERT(id >= 0 && id < n_expert);
+                                ggml_bitset_set(used_ids.data(), id);
+                            }
+                        }
+
+                        prev_ids_tensor = ids_tensor;
+                    }
+
+                    // group consecutive experts and copy them together
+                    auto copy_experts = [&](int32_t first_id, int32_t last_id) {
+                        const size_t expert_offset = first_id * expert_size;
+                        const size_t expert_size_copy =  (last_id - first_id + 1) * expert_size;
+                        const size_t padding = std::min<size_t>(expert_size, 512);
+                        const size_t padding_end = last_id < n_expert - 1 ? padding : 0;
+
+                        ggml_backend_tensor_set_async(split_backend,
+                            input_cpy,
+                            (const uint8_t *)input->data + expert_offset, expert_offset,
+                            // copy a bit extra at the to ensure there are no NaNs in the padding of the last expert
+                            // this is necessary for MMQ in the CUDA backend
+                            expert_size_copy + padding_end);
+                    };
+
+                    int id = 0;
+                    while (!ggml_bitset_get(used_ids.data(), id)) {
+                        id++;
+                    }
+                    int32_t first_id = id;
+                    int32_t last_id = first_id;
+
+                    for (++id; id < n_expert; ++id) {
+                        if (!ggml_bitset_get(used_ids.data(), id)) {
+                            continue;
+                        }
+
+                        if (id == last_id + 1) {
+                            last_id = id;
+                            continue;
+                        }
+
+                        copy_experts(first_id, last_id);
+
+                        first_id = id;
+                        last_id = id;
+                    }
+                    copy_experts(first_id, last_id);
+                } else {
+                    // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
+                    // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
+                    if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
+                        ggml_backend_synchronize(input_backend);
+                        if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                            ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
+                        } else {
+                            ggml_backend_synchronize(split_backend);
+                        }
+                        ggml_backend_tensor_copy(input, input_cpy);
                     }
-                    ggml_backend_tensor_copy(input, input_cpy);
                 }
             }
         }
@@ -1578,6 +1735,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
 }
 
 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     // reset state for the next run
     if (!sched->is_reset) {
         ggml_hash_set_reset(&sched->hash_set);
@@ -1589,8 +1747,11 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
 }
 
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
+    GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
+    ggml_backend_sched_reset(sched);
+
     ggml_backend_sched_synchronize(sched);
 
     ggml_backend_sched_split_graph(sched, measure_graph);
@@ -1623,6 +1784,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
 }
 
 bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
     GGML_ASSERT(!sched->is_alloc);
 
@@ -1647,6 +1809,7 @@ enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, st
 }
 
 enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT(sched);
     if (!sched->is_reset && !sched->is_alloc) {
         ggml_backend_sched_reset(sched);
     }
@@ -1661,6 +1824,7 @@ enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sch
 }
 
 void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     for (int i = 0; i < sched->n_backends; i++) {
         ggml_backend_synchronize(sched->backends[i]);
     }
@@ -1673,28 +1837,42 @@ void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
 }
 
 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
+    GGML_ASSERT(sched);
     sched->callback_eval = callback;
     sched->callback_eval_user_data = user_data;
 }
 
 int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     return sched->n_splits;
 }
 
 int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     return sched->n_copies;
 }
 
 int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     return sched->n_backends;
 }
 
 ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
+    GGML_ASSERT(sched);
     GGML_ASSERT(i >= 0 && i < sched->n_backends);
     return sched->backends[i];
 }
 
+ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend) {
+    GGML_ASSERT(sched);
+    int backend_index = ggml_backend_sched_backend_id(sched, backend);
+    GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
+
+    return sched->bufts[backend_index];
+}
+
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
+    GGML_ASSERT(sched);
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
 
@@ -1715,6 +1893,7 @@ size_t ggml_backend_sched_get_attempted_buffer_size(ggml_backend_sched_t sched,
 }
 
 void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
+    GGML_ASSERT(sched);
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
     tensor_backend_id(node) = backend_index;
@@ -1723,6 +1902,7 @@ void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct gg
 }
 
 ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
+    GGML_ASSERT(sched);
     int backend_index = tensor_backend_id(node);
     if (backend_index == -1) {
         return NULL;
@@ -1733,6 +1913,7 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched,
 // utils
 
 enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->view_src != NULL);
     GGML_ASSERT(tensor->view_src->buffer != NULL);
@@ -1744,6 +1925,7 @@ enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
 }
 
 enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->data == NULL);
     GGML_ASSERT(tensor->view_src == NULL);
@@ -1817,6 +1999,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set * hash_set, struct ggml_
 }
 
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
+    GGML_ASSERT(graph);
     struct ggml_hash_set hash_set = ggml_hash_set_new(graph->visited_hash_set.size);
     struct ggml_tensor ** node_copies = (ggml_tensor **) calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
     bool * node_init = (bool *) calloc(hash_set.size, sizeof(node_init[0]));
@@ -1961,6 +2144,7 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
 // CPU backend - buffer
 
 static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     uintptr_t data = (uintptr_t)buffer->context;
 
     // align the buffer
@@ -1972,6 +2156,7 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     ggml_aligned_free(buffer->context, buffer->size);
     delete buffer;
 }
@@ -1981,24 +2166,28 @@ static void ggml_backend_cpu_ptr_buffer_free_buffer(ggml_backend_buffer_t buffer
 }
 
 static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     memset((char *)tensor->data + offset, value, size);
 
     GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     memcpy((char *)tensor->data + offset, data, size);
 
     GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     memcpy(data, (const char *)tensor->data + offset, size);
 
     GGML_UNUSED(buffer);
 }
 
 static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    GGML_ASSERT(src);
     if (ggml_backend_buffer_is_host(src->buffer)) {
         memcpy(dst->data, src->data, ggml_nbytes(src));
         return true;
@@ -2009,6 +2198,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con
 }
 
 static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
     memset(buffer->context, value, buffer->size);
 }
 

ml/backend/ggml/ggml/src/ggml-blas/CMakeLists.txt

@@ -74,7 +74,7 @@ if (BLAS_FOUND)
 
     target_compile_options(ggml-blas PRIVATE ${BLAS_LINKER_FLAGS})
 
-    if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel"))
+    if ("${BLAS_INCLUDE_DIRS}" MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel"))
         add_compile_definitions(GGML_BLAS_USE_MKL)
     endif()
 

ml/backend/ggml/ggml/src/ggml-blas/ggml-blas.cpp

@@ -270,6 +270,7 @@ static struct ggml_backend_i blas_backend_i = {
     /* .graph_compute           = */ ggml_backend_blas_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_blas_guid(void) {

ml/backend/ggml/ggml/src/ggml-cpu/CMakeLists.txt

@@ -224,7 +224,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC SME)
                     string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
                     if (NOT ${feature_pos} EQUAL -1)
-                        message(STATUS "ARM feature ${feature} enabled")
+                        # Special handling for MATMUL_INT8 when machine doesn't support i8mm
+                        if ("${feature}" STREQUAL "MATMUL_INT8" AND GGML_MACHINE_SUPPORTS_noi8mm)
+                            message(STATUS "ARM feature ${feature} detected but unsetting due to machine not supporting i8mm")
+                            list(APPEND ARCH_FLAGS -U__ARM_FEATURE_MATMUL_INT8)
+                        else()
+                            message(STATUS "ARM feature ${feature} enabled")
+                        endif()
                     endif()
                 endforeach()
             endif()
@@ -433,15 +439,31 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             ggml-cpu/arch/riscv/quants.c
             ggml-cpu/arch/riscv/repack.cpp
             )
-        if (GGML_RVV)
-            if (GGML_XTHEADVECTOR)
-                list(APPEND ARCH_FLAGS -march=rv64gc_xtheadvector -mabi=lp64d)
-            elseif (GGML_RV_ZFH)
-                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -mabi=lp64d)
-            else()
-                list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d)
+        if (GGML_CPU_RISCV64_SPACEMIT)
+            target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_CPU_RISCV64_SPACEMIT ${RISCV64_SPACEMIT_IME_SPEC})
+            list(APPEND GGML_CPU_SOURCES
+                ggml-cpu/spacemit/ime.cpp
+                ggml-cpu/spacemit/ime.h
+                ggml-cpu/spacemit/ime1_kernels.cpp
+                ggml-cpu/spacemit/ime_kernels.h
+            )
+        endif()
+        set(MARCH_STR "rv64gc")
+        if (GGML_RV_ZFH)
+            string(APPEND MARCH_STR "_zfh")
+        endif()
+        if (GGML_XTHEADVECTOR)
+            string(APPEND MARCH_STR "_xtheadvector")
+        elseif (GGML_RVV)
+            string(APPEND MARCH_STR "_v")
+            if (GGML_RV_ZVFH)
+                string(APPEND MARCH_STR "_zvfh")
             endif()
         endif()
+        if (GGML_RV_ZICBOP)
+            string(APPEND MARCH_STR "_zicbop")
+        endif()
+        list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
     elseif (GGML_SYSTEM_ARCH STREQUAL "s390x")
         message(STATUS "s390x detected")
         list(APPEND GGML_CPU_SOURCES ggml-cpu/arch/s390/quants.c)
@@ -450,7 +472,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # TODO: Separation to determine activation of VX/VXE/VXE2
         if (${S390X_M} MATCHES "8561|8562")
-            set(GGML_NNPA OFF)
             message(STATUS "z15 target")
             list(APPEND ARCH_FLAGS -march=z15)
         elseif (${S390X_M} MATCHES "3931")
@@ -460,7 +481,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version.
             #       binutils must also be updated to the latest for the -march=z17 flag to work. Otherwise, use -march=arch15.
             message(STATUS "z17 target")
-            list(APPEND ARCH_FLAGS -march=z17)
+            list(APPEND ARCH_FLAGS -march=arch15)
         else()
             message(STATUS "Unknown target")
             message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.")
@@ -472,11 +493,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             list(APPEND ARCH_FLAGS -mvx -mzvector)
             list(APPEND ARCH_DEFINITIONS GGML_VXE)
         endif()
-
-        if (GGML_NNPA)
-            message(STATUS "NNPA enabled")
-            list(APPEND ARCH_DEFINITIONS GGML_NNPA)
-        endif()
     elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "wasm")
         message(STATUS "Wasm detected")
         list (APPEND GGML_CPU_SOURCES ggml-cpu/arch/wasm/quants.c)
@@ -497,9 +513,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.11.0")
+        set(KLEIDIAI_COMMIT_TAG "v1.14.0")
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "3fe9e5ab964c375c53839296eb71eaa2")
+        set(KLEIDIAI_ARCHIVE_MD5  "45e110675d93f99f82c23a1afcca76bc")
 
         if (POLICY CMP0135)
             cmake_policy(SET CMP0135 NEW)
@@ -555,6 +571,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         list(APPEND GGML_KLEIDIAI_SOURCES
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32.c
+            ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c)
@@ -575,8 +592,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.c
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.c
+                ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa_asm.S
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_bf16p2vlx2_f32_sme.c
-                ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c)
+                ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c
+                ${KLEIDIAI_SRC}/kai/kai_common_sme_asm.S)
             set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2")
         endif()
 

ml/backend/ggml/ggml/src/ggml-cpu/amx/amx.cpp

@@ -7,7 +7,7 @@
 #include "ggml-cpu.h"
 #include "traits.h"
 
-#if defined(__gnu_linux__)
+#if defined(__linux__)
 #include <sys/syscall.h>
 #include <unistd.h>
 #endif
@@ -186,7 +186,7 @@ static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_ty
 #define XFEATURE_XTILEDATA      18
 
 static bool ggml_amx_init() {
-#if defined(__gnu_linux__)
+#if defined(__linux__)
     if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) {
         fprintf(stderr, "AMX is not ready to be used!\n");
         return false;
@@ -194,6 +194,8 @@ static bool ggml_amx_init() {
     return true;
 #elif defined(_WIN32)
     return true;
+#else
+    return false;
 #endif
 }
 

ml/backend/ggml/ggml/src/ggml-cpu/arch-fallback.h

@@ -40,18 +40,22 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
 // repack.cpp
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // repack.cpp
@@ -69,7 +73,6 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -80,12 +83,14 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__loongarch64)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
@@ -103,12 +108,14 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__riscv)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
@@ -133,16 +140,16 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__s390x__)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
-#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
-#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -153,7 +160,6 @@
 #define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
 #define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -164,12 +170,14 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__wasm__)
 // quants.c
 #define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
@@ -195,10 +203,12 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #endif

ml/backend/ggml/ggml/src/ggml-cpu/common.h

@@ -28,6 +28,14 @@ static inline float bf16_to_f32(ggml_bf16_t x) {
     return GGML_BF16_TO_FP32(x);
 }
 
+static inline float i32_to_f32(int32_t x) {
+    return x;
+}
+
+static inline int32_t f32_to_i32(float x) {
+    return x;
+}
+
 static inline float f32_to_f32(float x) {
     return x;
 }
@@ -54,6 +62,12 @@ struct type_conversion_table<ggml_bf16_t> {
     static constexpr ggml_bf16_t (*from_f32)(float) = f32_to_bf16;
 };
 
+template <>
+struct type_conversion_table<int32_t> {
+    static constexpr float (*to_f32)(int32_t) = i32_to_f32;
+    static constexpr int32_t (*from_f32)(float) = f32_to_i32;
+};
+
 static std::pair<int64_t, int64_t> get_thread_range(const struct ggml_compute_params * params, const struct ggml_tensor * src0) {
     const int64_t ith = params->ith;
     const int64_t nth = params->nth;

ml/backend/ggml/ggml/src/ggml-cpu/ggml-cpu-impl.h

@@ -68,12 +68,6 @@ struct ggml_compute_params {
 #endif  // __VXE2__
 #endif  // __s390x__ && __VEC__
 
-#if defined(__s390x__) && defined(GGML_NNPA)
-#ifndef __NNPA__
-#define __NNPA__
-#endif  // __NNPA__
-#endif  // __s390x__ && GGML_NNPA
-
 #if defined(__ARM_FEATURE_SVE)
 #include <sys/prctl.h>
 #endif
@@ -486,6 +480,19 @@ inline static int16x8_t vec_padd_s16(int16x8_t a, int16x8_t b) {
     return v_abo + v_abe;
 }
 
+/**
+ * @see https://github.com/ggml-org/llama.cpp/pull/14037
+ */
+inline static float vec_hsum_f32x4(float32x4_t v) {
+    float32x4_t v_temp = v + vec_reve(v);
+    return v_temp[0] + v_temp[1];
+}
+
+inline static int32_t vec_hsum_i32x4(int32x4_t v) {
+    int32x4_t v_temp = v + vec_reve(v);
+    return v_temp[0] + v_temp[1];
+}
+
 inline static int32x4_t ggml_vec_dot(int32x4_t acc, int8x16_t a, int8x16_t b) {
     const int16x8_t p = vec_mule(a, b) + vec_mulo(a, b);
     return acc + (vec_unpackh(p) + vec_unpackl(p));

ml/backend/ggml/ggml/src/ggml-cpu/ggml-cpu.c

@@ -375,6 +375,9 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
     },
+    [GGML_TYPE_I32] = {
+        .from_float               = (ggml_from_float_t) ggml_cpu_fp32_to_i32,
+    },
 };
 
 const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) {
@@ -472,10 +475,10 @@ struct ggml_threadpool {
 struct ggml_compute_state {
 #ifndef GGML_USE_OPENMP
     ggml_thread_t thrd;
-    bool cpumask[GGML_MAX_N_THREADS];
     int  last_graph;
     bool pending;
 #endif
+    bool cpumask[GGML_MAX_N_THREADS];
     struct ggml_threadpool * threadpool;
     int ith;
 };
@@ -1878,10 +1881,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_im2col_back_f32(params, tensor);
             } break;
+        case GGML_OP_IM2COL_3D:
+            {
+                ggml_compute_forward_im2col_3d(params, tensor);
+            } break;
         case GGML_OP_CONV_2D:
             {
                 ggml_compute_forward_conv_2d(params, tensor);
             } break;
+        case GGML_OP_CONV_3D:
+            {
+                ggml_compute_forward_conv_3d(params, tensor);
+            } break;
         case GGML_OP_CONV_2D_DW:
             {
                 ggml_compute_forward_conv_2d_dw(params, tensor);
@@ -2024,6 +2035,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
                 ggml_compute_forward_opt_step_adamw(params, tensor);
             }
             break;
+        case GGML_OP_OPT_STEP_SGD:
+            {
+                ggml_compute_forward_opt_step_sgd(params, tensor);
+            }
+            break;
         case GGML_OP_NONE:
             {
                 // nop
@@ -2248,7 +2264,9 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_IM2COL:
         case GGML_OP_IM2COL_BACK:
+        case GGML_OP_IM2COL_3D:
         case GGML_OP_CONV_2D:
+        case GGML_OP_CONV_3D:
         case GGML_OP_CONV_2D_DW:
         case GGML_OP_CONV_TRANSPOSE_1D:
         case GGML_OP_CONV_TRANSPOSE_2D:
@@ -2327,6 +2345,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:
+        case GGML_OP_OPT_STEP_SGD:
             {
                 n_tasks = n_threads;
             } break;
@@ -2682,7 +2701,10 @@ struct ggml_cplan ggml_graph_plan(
                         if (ggml_is_quantized(node->type) ||
                             // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
                             (node->src[0]->type == GGML_TYPE_F16  && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
-                            (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
+                            (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16) ||
+                            // conversion between F32 and I32
+                            (node->src[0]->type == GGML_TYPE_F32 && node->src[1] && node->src[1]->type == GGML_TYPE_I32) ||
+                            (node->src[0]->type == GGML_TYPE_I32 && node->src[1] && node->src[1]->type == GGML_TYPE_F32)) {
                             cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                         }
                     } break;
@@ -2769,6 +2791,7 @@ struct ggml_cplan ggml_graph_plan(
                         }
                     } break;
                 case GGML_OP_CONV_2D:
+                case GGML_OP_CONV_3D:
                     {
                         cur = GGML_IM2COL_WORK_SIZE;
                     } break;
@@ -3064,7 +3087,14 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
 
     threadpool->workers = workers;
 
-#ifndef GGML_USE_OPENMP
+#ifdef GGML_USE_OPENMP
+    int32_t cpumask_iter = 0;
+
+    // Compute CPU masks for each thread
+    for (int j = 0; j < tpp->n_threads; j++) {
+        ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter);
+    }
+#else // GGML_USE_OPENMP
     ggml_mutex_init(&threadpool->mutex);
     ggml_cond_init(&threadpool->cond);
 
@@ -3137,7 +3167,14 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
                 atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
             }
 
-            ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]);
+            // Apply thread CPU mask and priority
+            int ith = omp_get_thread_num();
+
+            ggml_thread_apply_priority(threadpool->prio);
+            if (ggml_thread_cpumask_is_valid(threadpool->workers[ith].cpumask)) {
+                ggml_thread_apply_affinity(threadpool->workers[ith].cpumask);
+            }
+            ggml_graph_compute_thread(&threadpool->workers[ith]);
         }
     } else {
         atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed);
@@ -3200,20 +3237,12 @@ void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) {
         __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
         _mm_storel_epi64((__m128i *)(y + i), y_vec);
     }
-#elif defined(__NNPA__)
-    for (; i + 7 < n; i += 8) {
-        float32x4_t v_xh = vec_xl(0, (const float *)(x + i + 0));
-        float32x4_t v_xl = vec_xl(0, (const float *)(x + i + 4));
-        uint16x8_t v_yd = vec_round_from_fp32(v_xh, v_xl, 0);
-        uint16x8_t v_y = vec_convert_to_fp16(v_yd, 0);
-        vec_xst(v_y, 0, (ggml_fp16_t *)(y + i));
-    }
-    for (; i + 3 < n; i += 4) {
-        float32x4_t v_x = vec_xl(0, (const float *)(x + i));
-        float32x4_t v_zero = vec_splats(0.0f);
-        uint16x8_t v_yd = vec_round_from_fp32(v_x, v_zero, 0);
-        uint16x8_t v_y = vec_convert_to_fp16(v_yd, 0);
-        vec_xst(v_y, 0, (ggml_fp16_t *)(y + i));
+#elif defined(__riscv_zvfh)
+    for (int vl; i < n; i += vl) {
+        vl = __riscv_vsetvl_e32m2(n - i);
+        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
+        vfloat16m1_t vy = __riscv_vfncvt_f_f_w_f16m1(vx, vl);
+        __riscv_vse16_v_f16m1((_Float16 *)&y[i], vy, vl);
     }
 #endif
     for (; i < n; ++i) {
@@ -3241,21 +3270,6 @@ void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
         __m128 y_vec = _mm_cvtph_ps(x_vec);
         _mm_storeu_ps(y + i, y_vec);
     }
-#elif defined(__NNPA__)
-    for (; i + 7 < n; i += 8) {
-        uint16x8_t v_x = vec_xl(0, (const ggml_fp16_t *)(x + i));
-        uint16x8_t v_yd = vec_convert_from_fp16(v_x, 0);
-        float32x4_t v_yh = vec_extend_to_fp32_hi(v_yd, 0);
-        float32x4_t v_yl = vec_extend_to_fp32_lo(v_yd, 0);
-        vec_xst(v_yh, 0, (float *)(y + i + 0));
-        vec_xst(v_yl, 0, (float *)(y + i + 4));
-    }
-    for (; i + 3 < n; i += 4) {
-        uint16x8_t v_x = vec_xl(0, (const ggml_fp16_t *)(x + i));
-        uint16x8_t v_yd = vec_convert_from_fp16(v_x, 0);
-        float32x4_t v_yh = vec_extend_to_fp32_hi(v_yd, 0);
-        vec_xst(v_yh, 0, (float *)(y + i));
-    }
 #endif
 
     for (; i < n; ++i) {
@@ -3270,6 +3284,13 @@ void ggml_cpu_fp32_to_bf16(const float * x, ggml_bf16_t * y, int64_t n) {
     }
 }
 
+void ggml_cpu_fp32_to_i32(const float * x, int32_t * y, int64_t n) {
+    int64_t i = 0;
+    for (; i < n; ++i) {
+        y[i] = x[i];
+    }
+}
+
 void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) {
     int64_t i = 0;
 #if defined(__AVX2__)
@@ -3459,14 +3480,6 @@ int ggml_cpu_has_vxe(void) {
 #endif
 }
 
-int ggml_cpu_has_nnpa(void) {
-#if defined(GGML_NNPA)
-    return 1;
-#else
-    return 0;
-#endif
-}
-
 int ggml_cpu_has_neon(void) {
 #if defined(__ARM_ARCH) && defined(__ARM_NEON)
     return 1;