whisper : adapt to latest ggml (skip) (#0)

Makefile

@@ -904,10 +904,10 @@ ggml/src/ggml-alloc.o: \
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
 ggml/src/ggml-backend.o: \
-	ggml/src/ggml-backend.c \
+	ggml/src/ggml-backend.cpp \
 	ggml/include/ggml.h \
 	ggml/include/ggml-backend.h
-	$(CC)  $(CFLAGS)   -c $< -o $@
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 ggml/src/ggml-quants.o: \
 	ggml/src/ggml-quants.c \

Package.swift

@@ -34,7 +34,7 @@ let package = Package(
                 "src/whisper.cpp",
                 "ggml/src/ggml-aarch64.c",
                 "ggml/src/ggml-alloc.c",
-                "ggml/src/ggml-backend.c",
+                "ggml/src/ggml-backend.cpp",
                 "ggml/src/ggml-quants.c",
                 "ggml/src/ggml-metal.m"
             ],

bindings/ruby/ext/extconf.rb

@@ -11,7 +11,7 @@ system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-alloc.h')} ."
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-alloc.c')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend-impl.h')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.h')} .")
-system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.c')} .")
+system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.cpp')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-common.h')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-quants.h')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-quants.c')} .")

examples/talk-llama/llama.cpp

@@ -12,9 +12,7 @@
 #  include "ggml-rpc.h"
 #endif
 
-#ifdef GGML_USE_CUDA
-#  include "ggml-cuda.h"
-#elif defined(GGML_USE_VULKAN)
+#if defined(GGML_USE_VULKAN)
 #  include "ggml-vulkan.h"
 #elif defined(GGML_USE_SYCL)
 #  include "ggml-sycl.h"
@@ -610,7 +608,7 @@ enum llm_tensor {
     LLM_TENSOR_CLS_OUT,
 };
 
-static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
+static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
     {
         LLM_ARCH_LLAMA,
         {
@@ -1566,32 +1564,32 @@ struct LLM_TN {
         return LLM_TENSOR_NAMES.at(arch).at(tensor);
     }
 
-    std::string operator()(llm_tensor tensor, const std::string & suffix) const {
+    std::string operator()(llm_tensor tensor, const char * suffix) const {
         if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
+        return std::string(LLM_TENSOR_NAMES.at(arch).at(tensor)) + "." + suffix;
     }
 
     std::string operator()(llm_tensor tensor, int bid) const {
         if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid);
     }
 
-    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
+    std::string operator()(llm_tensor tensor, const char * suffix, int bid) const {
         if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid) + "." + suffix;
     }
 
-    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
+    std::string operator()(llm_tensor tensor, const char * suffix, int bid, int xid) const {
         if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
             return "__missing__";
         }
-        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid) + "." + suffix;
     }
 };
 
@@ -2264,59 +2262,16 @@ static std::string llama_token_to_piece(const struct llama_model * model, llama_
     return piece;
 }
 
-static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
-    ggml_backend_buffer_type_t buft = nullptr;
-
-#if defined(GGML_USE_CUDA)
-    // host buffers should only be used when data is expected to be copied to/from the GPU
-    if (host_buffer) {
-        buft = ggml_backend_cuda_host_buffer_type();
-    }
-#elif defined(GGML_USE_SYCL)
-    if (host_buffer) {
-        buft = ggml_backend_sycl_host_buffer_type();
-    }
-#elif defined(GGML_USE_CANN)
-    if (host_buffer) {
-        buft = ggml_backend_cann_host_buffer_type();
-    }
-#elif defined(GGML_USE_CPU_HBM)
-    buft = ggml_backend_cpu_hbm_buffer_type();
-#elif defined(GGML_USE_VULKAN)
-    if (host_buffer) {
-        buft = ggml_backend_vk_host_buffer_type();
-    }
-#endif
-
-    if (buft == nullptr) {
-        buft = ggml_backend_cpu_buffer_type();
-    }
-    return buft;
-
-    GGML_UNUSED(host_buffer);
-}
-
 //
 // globals
 //
 
-struct llama_state {
-    llama_state() {
-#ifdef GGML_USE_METAL
-        ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
-#elif defined(GGML_USE_CUDA)
-        ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data);
-#elif defined(GGML_USE_CANN)
-        ggml_backend_cann_log_set_callback(log_callback, log_callback_user_data);
-#endif
-    }
-
-    // We save the log callback globally
+struct llama_logger_state {
     ggml_log_callback log_callback = llama_log_callback_default;
     void * log_callback_user_data = nullptr;
 };
 
-static llama_state g_state;
+static llama_logger_state g_logger_state;
 
 // available llama models
 enum e_model {
@@ -2920,14 +2875,17 @@ struct llama_model {
 
     std::vector<llama_layer> layers;
 
+    // gguf metadata
+    std::unordered_map<std::string, std::string> gguf_kv;
+
     llama_split_mode split_mode;
     int main_gpu;
     int n_gpu_layers;
 
-    std::vector<std::string> rpc_servers;
+    // list of devices used in this model
+    std::vector<ggml_backend_dev_t> devices;
 
-    // gguf metadata
-    std::unordered_map<std::string, std::string> gguf_kv;
+    std::vector<std::string> rpc_servers;
 
     // layer -> buffer type mapping
     struct layer_buft {
@@ -2970,11 +2928,6 @@ struct llama_model {
             ggml_free(ctx);
         }
         for (ggml_backend_buffer_t buf : bufs) {
-#ifdef GGML_USE_CUDA
-            if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
-                ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
-            }
-#endif
             ggml_backend_buffer_free(buf);
         }
         while (!lora_adapters.empty()) {
@@ -3460,72 +3413,116 @@ struct llama_lora_adapter {
     }
 };
 
-static size_t llama_get_device_count(const llama_model & model) {
-    size_t count = 1;
-#if defined(GGML_USE_CUDA)
-    count = ggml_backend_cuda_get_device_count();
+static int llama_get_device_count(const llama_model & model) {
+    int count = (int) model.devices.size();
+
+#if defined(GGML_USE_RPC)
+    count += (int) model.rpc_servers.size();
+#endif
+
+#if defined(GGML_USE_METAL)
+    count += 1;
 #elif defined(GGML_USE_SYCL)
-    count = ggml_backend_sycl_get_device_count();
+    count += ggml_backend_sycl_get_device_count();
 #elif defined(GGML_USE_VULKAN)
-    count = ggml_backend_vk_get_device_count();
+    count += ggml_backend_vk_get_device_count();
 #elif defined(GGML_USE_CANN)
-    return ggml_backend_cann_get_device_count();
-#endif
-#if defined(GGML_USE_RPC)
-    count += model.rpc_servers.size();
+    count += ggml_backend_cann_get_device_count();
 #endif
+
     return count;
+
     GGML_UNUSED(model);
 }
 
-static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) {
+static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_model & model, bool host_buffer) {
     ggml_backend_buffer_type_t buft = nullptr;
 
-#ifdef GGML_USE_RPC
-    int rpc_count = (int)model.rpc_servers.size();
-#else
-    int rpc_count = 0;
+    if (host_buffer) {
+        for (auto * dev : model.devices) {
+            buft = ggml_backend_dev_host_buffer_type(dev);
+            if (buft != nullptr) {
+                break;
+            }
+        }
+    }
+
+#if defined(GGML_USE_SYCL)
+    if (host_buffer) {
+        buft = ggml_backend_sycl_host_buffer_type();
+    }
+#elif defined(GGML_USE_CANN)
+    if (host_buffer) {
+        buft = ggml_backend_cann_host_buffer_type();
+    }
+#elif defined(GGML_USE_CPU_HBM)
+    buft = ggml_backend_cpu_hbm_buffer_type();
+#elif defined(GGML_USE_VULKAN)
+    if (host_buffer) {
+        buft = ggml_backend_vk_host_buffer_type();
+    }
 #endif
-    int local_gpu = gpu - rpc_count;
+
+    if (buft == nullptr) {
+        buft = ggml_backend_cpu_buffer_type();
+    }
+    return buft;
+
+    GGML_UNUSED(host_buffer);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int device) {
+    ggml_backend_buffer_type_t buft = nullptr;
+
 #if defined(GGML_USE_RPC)
-    if (gpu < rpc_count) {
-        const char * endpoint = model.rpc_servers[gpu].c_str();
+    int rpc_count = (int)model.rpc_servers.size();
+    if (device < rpc_count) {
+        const char * endpoint = model.rpc_servers[device].c_str();
         return ggml_backend_rpc_buffer_type(endpoint);
     }
+    device -= rpc_count;
 #endif
+
+    if (device < (int)model.devices.size()) {
+        return ggml_backend_dev_buffer_type(model.devices[device]);
+    }
+    device -= (int)model.devices.size();
+
 #if defined(GGML_USE_METAL)
     buft = ggml_backend_metal_buffer_type();
-#elif defined(GGML_USE_CUDA)
-    buft = ggml_backend_cuda_buffer_type(local_gpu);
 #elif defined(GGML_USE_VULKAN)
-    buft = ggml_backend_vk_buffer_type(local_gpu);
+    buft = ggml_backend_vk_buffer_type(device);
 #elif defined(GGML_USE_SYCL)
-    buft = ggml_backend_sycl_buffer_type(local_gpu);
+    buft = ggml_backend_sycl_buffer_type(device);
 #elif defined(GGML_USE_KOMPUTE)
-    buft = ggml_backend_kompute_buffer_type(local_gpu);
-    if (buft == nullptr) {
-        LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, local_gpu);
-    }
+    buft = ggml_backend_kompute_buffer_type(device);
 #elif defined(GGML_USE_CANN)
-    buft = ggml_backend_cann_buffer_type(local_gpu);
+    buft = ggml_backend_cann_buffer_type(device);
 #endif
 
     if (buft == nullptr) {
-        buft = llama_default_buffer_type_cpu(true);
+        buft = llama_default_buffer_type_cpu(model, true);
     }
     return buft;
+
     GGML_UNUSED(model);
-    GGML_UNUSED(local_gpu);
 }
 
 static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) {
     ggml_backend_buffer_type_t buft = nullptr;
 
-#ifdef GGML_USE_CUDA
-    if (ggml_backend_cuda_get_device_count() > 1) {
-        buft = ggml_backend_cuda_split_buffer_type(tensor_split);
+    // find a backend that supports split buffers
+    for (size_t i = 0; i < ggml_backend_reg_count(); ++i) {
+        ggml_backend_reg_t reg = ggml_backend_reg_get(i);
+
+        auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
+        if (ggml_backend_split_buffer_type_fn) {
+            buft = ggml_backend_split_buffer_type_fn(tensor_split);
+            if (buft != nullptr) {
+                break;
+            }
+        }
     }
-#endif
 
 #ifdef GGML_USE_SYCL
     if (ggml_backend_sycl_get_device_count() > 1) {
@@ -3542,13 +3539,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_mo
 }
 
 static size_t llama_get_device_memory(const llama_model & model, int device) {
-#ifdef GGML_USE_RPC
-    int rpc_count = (int)model.rpc_servers.size();
-#else
-    int rpc_count = 0;
-#endif
-    int local_device = device - rpc_count;
 #if defined(GGML_USE_RPC)
+    int rpc_count = (int)model.rpc_servers.size();
     if (device < rpc_count) {
         size_t total;
         size_t free;
@@ -3556,32 +3548,37 @@ static size_t llama_get_device_memory(const llama_model & model, int device) {
         ggml_backend_rpc_get_device_memory(endpoint, &free, &total);
         return free;
     }
+    device = device - rpc_count;
 #endif
-#if defined(GGML_USE_CUDA)
-    size_t total;
-    size_t free;
-    ggml_backend_cuda_get_device_memory(local_device, &free, &total);
-    return free;
-#elif defined(GGML_USE_SYCL)
+
+    if (device < (int)model.devices.size()) {
+        ggml_backend_dev_t dev = model.devices[device];
+        size_t total;
+        size_t free;
+        ggml_backend_dev_memory(dev, &free, &total);
+        return free;
+    }
+
+#if defined(GGML_USE_SYCL)
     size_t total;
     size_t free;
-    ggml_backend_sycl_get_device_memory(local_device, &free, &total);
+    ggml_backend_sycl_get_device_memory(device, &free, &total);
     return free;
 #elif defined(GGML_USE_VULKAN)
     size_t total;
     size_t free;
-    ggml_backend_vk_get_device_memory(local_device, &free, &total);
+    ggml_backend_vk_get_device_memory(device, &free, &total);
     return free;
 #elif defined(GGML_USE_CANN)
     size_t total;
     size_t free;
-    ggml_backend_cann_get_device_memory(local_device, &free, &total);
+    ggml_backend_cann_get_device_memory(device, &free, &total);
     return free;
 #else
     return 1;
 #endif
     GGML_UNUSED(model);
-    GGML_UNUSED(local_device);
+    GGML_UNUSED(device);
 }
 
 //
@@ -3624,7 +3621,7 @@ static bool llama_kv_cache_init(
             buft_layer_count[model.buft_layer[i].buft]++;
         }
     } else {
-        buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer;
+        buft_layer_count[llama_default_buffer_type_cpu(model, true)] = n_layer;
     }
 
     // create a context for each buffer type
@@ -4916,7 +4913,7 @@ struct llama_model_loader {
     static const int TENSOR_NOT_REQUIRED = 1;
     static const int TENSOR_DUPLICATED   = 2;
 
-    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, int flags = 0) {
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list<int64_t> & ne, int flags = 0) {
         const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
 
         if (cur == NULL) {
@@ -4926,7 +4923,7 @@ struct llama_model_loader {
         return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED);
     }
 
-    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
+    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true) {
         const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
 
         if (cur == NULL) {
@@ -4939,7 +4936,7 @@ struct llama_model_loader {
 
         std::array<int64_t, GGML_MAX_DIMS> dims;
         for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
-            dims[i] = i < ne.size() ? ne[i] : 1;
+            dims[i] = i < ne.size() ? ne.begin()[i] : 1;
         }
 
         struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
@@ -5037,7 +5034,7 @@ struct llama_model_loader {
     // Returns false if cancelled by progress_callback
     bool load_all_data(
             struct ggml_context * ctx,
-            llama_buf_map & bufs_mmap,
+            llama_buf_map & bufs,
             llama_mlocks * lmlocks,
             llama_progress_callback progress_callback,
             void * progress_callback_user_data) {
@@ -5046,43 +5043,94 @@ struct llama_model_loader {
         std::vector<no_init<uint8_t>> read_buf;
         std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
 
-#if defined(GGML_USE_CUDA)
         // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
         // NVMe raid configurations might require more / larger buffers.
         constexpr size_t n_buffers = 4;
         constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
 
         std::vector<ggml_backend_buffer_t> host_buffers;
-        std::vector<void*> host_ptrs;
         std::vector<ggml_backend_event_t> events;
+        std::vector<void *> host_ptrs;
         size_t buffer_idx = 0; // buffer to use for async loads
-
-        ggml_backend_t cuda_backend = nullptr;
-        if (!use_mmap && !check_tensors) {
+        ggml_backend_t upload_backend = [&](const char * fn) -> ggml_backend_t {
+            if (use_mmap || check_tensors) {
+                return nullptr;
+            }
             // When not using mmaped io use async uploads from pinned memory to GPU memory.
-            // First determine if the CUDA backend is active, and if so, determine the device ID.
-            ggml_backend_buffer_t buf = bufs_mmap.count(0) ? bufs_mmap.at(0) : nullptr;
-            if (buf) {
-                ggml_backend_buffer_type_t buffer_type = ggml_backend_buffer_get_type(buf);
-                for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
-                    auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i);
-                    if (buffer_type == cuda_buffer_type) {
-                        cuda_backend = ggml_backend_cuda_init(i);
-                        break;
-                    }
-                }
+            // First determine if the backend supports the necessary features for async uploads.
+            auto * buf = bufs.count(0) ? bufs.at(0) : nullptr;
+            if (!buf) {
+                LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", fn);
+                return nullptr;
+            }
+
+            auto * buft = ggml_backend_buffer_get_type(buf);
+            auto * dev = ggml_backend_buft_get_device(buft);
+            if (!dev) {
+                LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", fn,
+                    ggml_backend_buft_name(buft));
+                return nullptr;
+            }
+
+            if (buft != ggml_backend_dev_buffer_type(dev)) {
+                LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", fn,
+                    ggml_backend_buft_name(buft), ggml_backend_dev_name(dev));
+                return nullptr;
+            }
+
+            ggml_backend_dev_props props;
+            ggml_backend_dev_get_props(dev, &props);
+            if (!props.caps.async || !props.caps.host_buffer || !props.caps.events) {
+                LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", fn,
+                    ggml_backend_dev_name(dev));
+                return nullptr;
+            }
+
+            auto * host_buft = ggml_backend_dev_host_buffer_type(dev);
+            if (!host_buft) {
+                LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", fn,
+                    ggml_backend_dev_name(dev));
+                return nullptr;
             }
 
-            // If the cuda backend is active create pinned memory buffers and events for synchronisation.
-            if (cuda_backend) {
-                for (size_t idx = 0; idx < n_buffers; ++idx) {
-                    host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size));
-                    host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx]));
-                    events.emplace_back(ggml_backend_event_new(cuda_backend));
+            // If the backend is supported, create pinned memory buffers and events for synchronisation.
+            for (size_t idx = 0; idx < n_buffers; ++idx) {
+                auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size);
+                if (!buf) {
+                    LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", fn,
+                        ggml_backend_dev_name(dev));
+                    return nullptr;
+                }
+
+                host_buffers.emplace_back(buf);
+                host_ptrs.emplace_back(ggml_backend_buffer_get_base(buf));
+
+                auto * event = ggml_backend_event_new(dev);
+                if (!event) {
+                    LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", fn,
+                        ggml_backend_dev_name(dev));
+                    return nullptr;
                 }
+
+                events.emplace_back(event);
             }
+
+            ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+            if (!backend) {
+                LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", fn,
+                    ggml_backend_dev_name(dev));
+                return nullptr;
+            }
+
+            return backend;
+        }(__func__);
+
+        if (upload_backend) {
+            LLAMA_LOG_DEBUG("%s: using async uploads for device %s, buffer type %s, backend %s\n", __func__,
+                ggml_backend_dev_name(ggml_backend_get_device(upload_backend)),
+                ggml_backend_buft_name(ggml_backend_buffer_get_type(bufs.at(0))),
+                ggml_backend_name(upload_backend));
         }
-#endif
 
         for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
             const auto * weight = get_weight(ggml_get_name(cur));
@@ -5102,8 +5150,8 @@ struct llama_model_loader {
             if (use_mmap) {
                 const auto & mapping = mappings.at(weight->idx);
                 ggml_backend_buffer_t buf_mmap = nullptr;
-                if (bufs_mmap.count(weight->idx)) {
-                    buf_mmap = bufs_mmap.at(weight->idx);
+                if (bufs.count(weight->idx)) {
+                    buf_mmap = bufs.at(weight->idx);
                 }
                 uint8_t * data = (uint8_t *) mapping->addr + weight->offs;
 
@@ -5139,9 +5187,8 @@ struct llama_model_loader {
                         }));
                     }
                 } else {
-#if defined(GGML_USE_CUDA)
-                    // If cuda_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
-                    if (cuda_backend) {
+                    // If upload_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
+                    if (upload_backend) {
                         file->seek(weight->offs, SEEK_SET);
 
                         size_t bytes_read = 0;
@@ -5151,17 +5198,14 @@ struct llama_model_loader {
 
                             ggml_backend_event_synchronize(events[buffer_idx]);
                             file->read_raw(host_ptrs[buffer_idx], read_iteration);
-                            ggml_backend_tensor_set_async(cuda_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
-                            ggml_backend_event_record(events[buffer_idx]);
+                            ggml_backend_tensor_set_async(upload_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
+                            ggml_backend_event_record(events[buffer_idx], upload_backend);
 
                             bytes_read += read_iteration;
                             ++buffer_idx;
                             buffer_idx %= n_buffers;
                         }
-                    }
-                    else
-#endif
-                    {
+                    } else {
                         read_buf.resize(n_size);
                         file->seek(weight->offs, SEEK_SET);
                         file->read_raw(read_buf.data(), n_size);
@@ -5176,17 +5220,15 @@ struct llama_model_loader {
             size_done += n_size;
         }
 
-#if defined(GGML_USE_CUDA)
-        // free temporary resources used for async cuda uploads
-        if (cuda_backend) {
-            for (size_t idx = 0; idx < n_buffers;++idx) {
-                ggml_backend_event_synchronize(events[idx]);
-                ggml_backend_event_free(events[idx]);
-                ggml_backend_buffer_free(host_buffers[idx]);
-            }
-            ggml_backend_free(cuda_backend);
+        // free temporary resources used for async uploads
+        for (auto * event : events) {
+            ggml_backend_event_synchronize(event);
+            ggml_backend_event_free(event);
         }
-#endif
+        for (auto * buf : host_buffers) {
+            ggml_backend_buffer_free(buf);
+        }
+        ggml_backend_free(upload_backend);
 
         // check validation results
         bool validation_failed = false;
@@ -6922,6 +6964,13 @@ static bool llm_load_tensors(
         void * progress_callback_user_data) {
     auto & hparams = model.hparams;
 
+    // check if the value of main_gpu is valid
+    if (llama_get_device_count(model) > 0 &&
+        split_mode != LLAMA_SPLIT_MODE_LAYER &&
+        (main_gpu < 0 || main_gpu >= llama_get_device_count(model))) {
+        throw std::runtime_error(format("invalid value for main_gpu: %d (available devices: %d)", main_gpu, llama_get_device_count(model)));
+    }
+
     model.split_mode   = split_mode;
     model.main_gpu     = main_gpu;
     model.n_gpu_layers = n_gpu_layers;
@@ -6931,14 +6980,14 @@ static bool llm_load_tensors(
     bool use_mmap_buffer = true;
 
     // there is very little benefit to offloading the input layer, so always keep it on the CPU
-    model.buft_input = llama_default_buffer_type_cpu(true);
+    model.buft_input = llama_default_buffer_type_cpu(model, true);
     //model.buft_input = llama_default_buffer_type_offload(main_gpu);
 
     model.buft_layer.resize(n_layer);
 
     // assign cpu layers
     for (int i = 0; i < i_gpu_start; ++i) {
-        model.buft_layer[i] = llama_default_buffer_type_cpu(true);
+        model.buft_layer[i] = llama_default_buffer_type_cpu(model, true);
     }
 
     if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
@@ -6976,7 +7025,7 @@ static bool llm_load_tensors(
             int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
             model.buft_output = llama_default_buffer_type_offload(model, layer_gpu);
         } else {
-            model.buft_output = llama_default_buffer_type_cpu(true);
+            model.buft_output = llama_default_buffer_type_cpu(model, true);
         }
     } else {
         ggml_backend_buffer_type_t split_buft;
@@ -7000,7 +7049,7 @@ static bool llm_load_tensors(
                 llama_default_buffer_type_offload(model, main_gpu)
             };
         } else {
-            model.buft_output = llama_default_buffer_type_cpu(true);
+            model.buft_output = llama_default_buffer_type_cpu(model, true);
         }
     }
 
@@ -8872,7 +8921,7 @@ static bool llm_load_tensors(
         // only the mmap region containing the tensors in the model is mapped to the backend buffer
         // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
         // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
-        if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
+        if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(model, true)) {
             for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
                 void * addr = nullptr;
                 size_t first, last;
@@ -8886,13 +8935,6 @@ static bool llm_load_tensors(
                 }
                 model.bufs.push_back(buf);
                 bufs.emplace(idx, buf);
-#ifdef GGML_USE_CUDA
-                if (n_layer >= n_gpu_layers) {
-                    ggml_backend_cuda_register_host_buffer(
-                        ggml_backend_buffer_get_base(buf),
-                        ggml_backend_buffer_get_size(buf));
-                }
-#endif
             }
         }
 #ifdef GGML_USE_METAL
@@ -16956,7 +16998,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
             lctx.embd = nullptr;
         }
 
-        lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
+        lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(lctx.model, true), new_size);
         if (lctx.buf_output == nullptr) {
             LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
             return 0;
@@ -18987,21 +19029,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
 }
 
 size_t llama_max_devices(void) {
-#if defined(GGML_USE_RPC)
-    return GGML_RPC_MAX_SERVERS;
-#elif defined(GGML_USE_METAL)
-    return 1;
-#elif defined(GGML_USE_CUDA)
-    return GGML_CUDA_MAX_DEVICES;
-#elif defined(GGML_USE_SYCL)
-    return GGML_SYCL_MAX_DEVICES;
-#elif defined(GGML_USE_VULKAN)
-    return GGML_VK_MAX_DEVICES;
-#elif defined(GGML_USE_CANN)
-    return GGML_CANN_MAX_DEVICES;
-#else
-    return 1;
-#endif
+    return 16;
 }
 
 bool llama_supports_mmap(void) {
@@ -19013,12 +19041,13 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL)   || defined(GGML_USE_VULKAN) || \
+#if defined(GGML_USE_METAL)   || defined(GGML_USE_VULKAN) || \
     defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
     // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
     return true;
 #else
-    return false;
+    return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr ||
+        ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL) != nullptr;
 #endif
 }
 
@@ -19083,17 +19112,30 @@ struct llama_model * llama_load_model_from_file(
             return true;
         };
     }
+
     if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
         // split the servers set them into model->rpc_servers
         std::string servers(params.rpc_servers);
         size_t pos = 0;
-        while ((pos = servers.find(",")) != std::string::npos) {
+        while ((pos = servers.find(',')) != std::string::npos) {
             std::string server = servers.substr(0, pos);
             model->rpc_servers.push_back(server);
             servers.erase(0, pos + 1);
         }
         model->rpc_servers.push_back(servers);
     }
+
+    // create list of devices to use with this model
+    // currently, we use all available devices
+    // TODO: rework API to give user more control over device selection
+    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        // skip the CPU backend since it is handled separately
+        if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU_FULL) {
+            model->devices.push_back(dev);
+        }
+    }
+
     int status = llama_model_load(path_model, *model, params);
     GGML_ASSERT(status <= 0);
     if (status < 0) {
@@ -19255,6 +19297,36 @@ struct llama_context * llama_new_context_with_model(
 
     if (!hparams.vocab_only) {
         // initialize backends
+        int main_gpu = model->main_gpu;
+
+        // with registry
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            if (main_gpu >= 0 && main_gpu < (int)model->devices.size()) {
+                ggml_backend_dev_t main_dev = model->devices[main_gpu];
+                ggml_backend_t backend = ggml_backend_dev_init(main_dev, nullptr);
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(main_dev));
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        } else {
+            // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+            for (auto * dev : model->devices) {
+                ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev));
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        }
+        if (main_gpu >= (int)model->devices.size()) {
+            main_gpu -= (int)model->devices.size();
+        }
+
 #if defined(GGML_USE_RPC)
         if (model->n_gpu_layers > 0) {
             for (const auto & endpoint : model->rpc_servers) {
@@ -19267,6 +19339,9 @@ struct llama_context * llama_new_context_with_model(
                 ctx->backends.push_back(backend);
             }
         }
+        if (main_gpu >= (int)model->rpc_servers.size()) {
+            main_gpu -= (int)model->rpc_servers.size();
+        }
 #endif
 
 #if defined(GGML_USE_METAL)
@@ -19279,28 +19354,6 @@ struct llama_context * llama_new_context_with_model(
             }
             ctx->backends.push_back(ctx->backend_metal);
         }
-#elif defined(GGML_USE_CUDA)
-        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
-            // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
-            ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
-            if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
-                llama_free(ctx);
-                return nullptr;
-            }
-            ctx->backends.push_back(backend);
-        } else {
-            // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
-            for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
-                ggml_backend_t backend = ggml_backend_cuda_init(device);
-                if (backend == nullptr) {
-                    LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
-                    llama_free(ctx);
-                    return nullptr;
-                }
-                ctx->backends.push_back(backend);
-            }
-        }
 #elif defined(GGML_USE_VULKAN)
         if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
             LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
@@ -19308,7 +19361,7 @@ struct llama_context * llama_new_context_with_model(
             return nullptr;
         }
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_t backend = ggml_backend_vk_init(model->main_gpu);
+            ggml_backend_t backend = ggml_backend_vk_init(main_gpu);
             if (backend == nullptr) {
                 LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
                 llama_free(ctx);
@@ -19329,9 +19382,9 @@ struct llama_context * llama_new_context_with_model(
 #elif defined(GGML_USE_SYCL)
         // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
-            ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
+            ggml_backend_t backend = ggml_backend_sycl_init(main_gpu);
             if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu);
+                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, main_gpu);
                 llama_free(ctx);
                 return nullptr;
             }
@@ -19350,7 +19403,7 @@ struct llama_context * llama_new_context_with_model(
         }
 #elif defined(GGML_USE_KOMPUTE)
         if (model->n_gpu_layers > 0) {
-            auto * backend = ggml_backend_kompute_init(model->main_gpu);
+            auto * backend = ggml_backend_kompute_init(main_gpu);
             if (backend == nullptr) {
                 LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__);
                 llama_free(ctx);
@@ -19359,29 +19412,29 @@ struct llama_context * llama_new_context_with_model(
             ctx->backends.push_back(backend);
         }
 #elif defined(GGML_USE_CANN)
-    // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
-    // TODO: ggml_backend_cann is not support split tensor now, just leave code here.
-    if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
-        ggml_backend_t backend = ggml_backend_cann_init(model->main_gpu);
-        if (backend == nullptr) {
-            LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, model->main_gpu);
-            llama_free(ctx);
-            return nullptr;
-        }
-        ctx->backends.push_back(backend);
-    } else {
-        // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
-        // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version.
-        for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) {
-            ggml_backend_t backend = ggml_backend_cann_init(device);
+        // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+        // TODO: ggml_backend_cann is not support split tensor now, just leave code here.
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            ggml_backend_t backend = ggml_backend_cann_init(main_gpu);
             if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device);
+                LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, main_gpu);
                 llama_free(ctx);
                 return nullptr;
             }
             ctx->backends.push_back(backend);
+        } else {
+            // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+            // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version.
+            for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) {
+                ggml_backend_t backend = ggml_backend_cann_init(device);
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device);
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
         }
-    }
 #endif
 
 #ifdef GGML_USE_BLAS
@@ -19446,7 +19499,7 @@ struct llama_context * llama_new_context_with_model(
             for (auto * backend : ctx->backends) {
                 if (ggml_backend_is_cpu(backend)) {
                     // use host buffers for the CPU backend compute buffer
-                    backend_buft.push_back(llama_default_buffer_type_cpu(true));
+                    backend_buft.push_back(llama_default_buffer_type_cpu(*model, true));
                 } else {
                     backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
                 }
@@ -19457,17 +19510,37 @@ struct llama_context * llama_new_context_with_model(
             // buffer used to store the computation graph and the tensor meta data
             ctx->buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
 
+            // TODO: move these checks to ggml_backend_sched
             // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
             bool pipeline_parallel =
                 llama_get_device_count(*model) > 1 &&
                 model->n_gpu_layers > (int)model->hparams.n_layer &&
                 model->split_mode == LLAMA_SPLIT_MODE_LAYER &&
                 params.offload_kqv;
-#ifndef GGML_USE_CUDA
-            // pipeline parallelism requires support for async compute and events
-            // currently this is only implemented in the CUDA backend
-            pipeline_parallel = false;
-#endif
+
+            // pipeline parallelism requires support for async compute and events in all devices
+            if (pipeline_parallel) {
+                for (auto * backend : ctx->backends) {
+                    if (ggml_backend_is_cpu(backend)) {
+                        // ignore CPU backend
+                        continue;
+                    }
+                    auto * dev = ggml_backend_get_device(backend);
+                    if (!dev) {
+                        // backend is using old interface, not supported
+                        pipeline_parallel = false;
+                        break;
+                    }
+                    ggml_backend_dev_props props;
+                    ggml_backend_dev_get_props(dev, &props);
+                    if (!props.caps.async || !props.caps.events) {
+                        // device does not support async compute or events
+                        pipeline_parallel = false;
+                        break;
+                    }
+                }
+            }
+
             ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), max_nodes, pipeline_parallel);
 
             if (pipeline_parallel) {
@@ -21772,15 +21845,9 @@ const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal
 }
 
 void llama_log_set(ggml_log_callback log_callback, void * user_data) {
-    g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
-    g_state.log_callback_user_data = user_data;
-#ifdef GGML_USE_METAL
-    ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
-#elif defined(GGML_USE_CUDA)
-    ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
-#elif defined(GGML_USE_CANN)
-    ggml_backend_cann_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
-#endif
+    ggml_log_set(log_callback, user_data);
+    g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
+    g_logger_state.log_callback_user_data = user_data;
 }
 
 static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
@@ -21789,12 +21856,12 @@ static void llama_log_internal_v(ggml_log_level level, const char * format, va_l
     char buffer[128];
     int len = vsnprintf(buffer, 128, format, args);
     if (len < 128) {
-        g_state.log_callback(level, buffer, g_state.log_callback_user_data);
+        g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data);
     } else {
         char * buffer2 = new char[len + 1];
         vsnprintf(buffer2, len + 1, format, args_copy);
         buffer2[len] = 0;
-        g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
+        g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data);
         delete[] buffer2;
     }
     va_end(args_copy);

examples/talk-llama/unicode-data.cpp

@@ -7,7 +7,7 @@
 #include <unordered_map>
 #include <unordered_set>
 
-const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // start, flags // last=next_start-1
+const std::initializer_list<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // start, flags // last=next_start-1
 {0x000000, 0x0080},
 {0x000020, 0x0008},
 {0x000021, 0x0020},
@@ -2311,7 +2311,8 @@ const std::unordered_set<uint32_t> unicode_set_whitespace = {
 0x003000,
 };
 
-const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
+// list is always in ascending order, to enable binary searh
+const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_lowercase = {
 {0x000041, 0x000061},
 {0x000042, 0x000062},
 {0x000043, 0x000063},
@@ -3747,7 +3748,8 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
 {0x01E921, 0x01E943},
 };
 
-const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
+// list is always in ascending order, to enable binary searh
+const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_uppercase = {
 {0x000061, 0x000041},
 {0x000062, 0x000042},
 {0x000063, 0x000043},
@@ -5200,7 +5202,7 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x01E943, 0x01E921},
 };
 
-const std::vector<range_nfd> unicode_ranges_nfd = {  // start, last, nfd
+const std::initializer_list<range_nfd> unicode_ranges_nfd = {  // start, last, nfd
 {0x000000, 0x000000, 0x000000},
 {0x0000C0, 0x0000C5, 0x000041},
 {0x0000C7, 0x0000C7, 0x000043},

examples/talk-llama/unicode-data.h

@@ -13,8 +13,8 @@ struct range_nfd {
 
 static const uint32_t MAX_CODEPOINTS = 0x110000;
 
-extern const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags;
+extern const std::initializer_list<std::pair<uint32_t, uint16_t>> unicode_ranges_flags;
 extern const std::unordered_set<uint32_t> unicode_set_whitespace;
-extern const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase;
-extern const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase;
-extern const std::vector<range_nfd> unicode_ranges_nfd;
+extern const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_lowercase;
+extern const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_uppercase;
+extern const std::initializer_list<range_nfd> unicode_ranges_nfd;

examples/talk-llama/unicode.cpp

@@ -123,11 +123,11 @@ uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
 static std::vector<codepoint_flags> unicode_cpt_flags_array() {
     std::vector<codepoint_flags> cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED);
 
-    assert (unicode_ranges_flags.front().first == 0);
-    assert (unicode_ranges_flags.back().first == MAX_CODEPOINTS);
+    assert (unicode_ranges_flags.begin()[0].first == 0);
+    assert (unicode_ranges_flags.begin()[unicode_ranges_flags.size()-1].first == MAX_CODEPOINTS);
     for (size_t i = 1; i < unicode_ranges_flags.size(); ++i) {
-        const auto range_ini = unicode_ranges_flags[i-1];  // codepoint_ini, flags
-        const auto range_end = unicode_ranges_flags[i];    // codepoint_end, flags
+        const auto range_ini = unicode_ranges_flags.begin()[i-1];  // codepoint_ini, flags
+        const auto range_end = unicode_ranges_flags.begin()[i];    // codepoint_end, flags
         for (uint32_t cpt = range_ini.first; cpt < range_end.first; ++cpt) {
             cpt_flags[cpt] = range_ini.second;
         }
@@ -597,7 +597,7 @@ std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & c
     std::vector<uint32_t> result(cpts.size());
     for (size_t i = 0; i < cpts.size(); ++i) {
         const uint32_t cpt = cpts[i];
-        auto it = std::upper_bound(unicode_ranges_nfd.cbegin(), unicode_ranges_nfd.cend(), cpt, comp) - 1;
+        auto it = std::upper_bound(unicode_ranges_nfd.begin(), unicode_ranges_nfd.end(), cpt, comp) - 1;
         result[i] = (it->first <= cpt && cpt <= it->last) ? it->nfd : cpt;
     }
     return result;
@@ -639,8 +639,15 @@ uint8_t unicode_utf8_to_byte(const std::string & utf8) {
 }
 
 uint32_t unicode_tolower(uint32_t cp) {
-    auto it = unicode_map_lowercase.find(cp);
-    return it == unicode_map_lowercase.end() ? cp : it->second;
+    // binary search
+    auto it = std::lower_bound(unicode_map_lowercase.begin(), unicode_map_lowercase.end(), cp,
+        [](const std::pair<uint32_t, uint32_t> & pair, uint32_t value) {
+            return pair.first < value;
+        });
+    if (it != unicode_map_lowercase.end() && it->first == cp) {
+        return it->second;
+    }
+    return cp;  // Return the original code point if no lowercase mapping is found
 }
 
 std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {

examples/whisper.android.java/app/src/main/jni/whisper/CMakeLists.txt

@@ -9,7 +9,7 @@ set(SOURCE_FILES
     ${WHISPER_LIB_DIR}/ggml/src/ggml.c
     ${WHISPER_LIB_DIR}/ggml/src/ggml-aarch64.c
     ${WHISPER_LIB_DIR}/ggml/src/ggml-alloc.c
-    ${WHISPER_LIB_DIR}/ggml/src/ggml-backend.c
+    ${WHISPER_LIB_DIR}/ggml/src/ggml-backend.cpp
     ${WHISPER_LIB_DIR}/ggml/src/ggml-quants.c
     ${WHISPER_LIB_DIR}/src/whisper.cpp
     ${CMAKE_SOURCE_DIR}/jni.c

examples/whisper.android/lib/src/main/jni/whisper/CMakeLists.txt

@@ -21,7 +21,7 @@ if (NOT GGML_HOME)
         ${WHISPER_LIB_DIR}/ggml/src/ggml.c
         ${WHISPER_LIB_DIR}/ggml/src/ggml-aarch64.c
         ${WHISPER_LIB_DIR}/ggml/src/ggml-alloc.c
-        ${WHISPER_LIB_DIR}/ggml/src/ggml-backend.c
+        ${WHISPER_LIB_DIR}/ggml/src/ggml-backend.cpp
         ${WHISPER_LIB_DIR}/ggml/src/ggml-quants.c
         )
 endif()

examples/whisper.objc/whisper.objc.xcodeproj/project.pbxproj

@@ -22,7 +22,7 @@
 		18627C9B29052CFF00BD2A04 /* ggml-base.en.bin in Resources */ = {isa = PBXBuildFile; fileRef = 18627C9A29052CFF00BD2A04 /* ggml-base.en.bin */; };
 		18A276062C2A98A5001C8D37 /* ggml-metal.metal in Copy Files */ = {isa = PBXBuildFile; fileRef = 1844471D2AB2195F007D6BFE /* ggml-metal.metal */; };
 		18A2760B2C2A9B43001C8D37 /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 1844471D2AB2195F007D6BFE /* ggml-metal.metal */; };
-		18ABE15A2AF556340044A204 /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 18ABE1572AF556340044A204 /* ggml-backend.c */; };
+		18ABE15A2AF556340044A204 /* ggml-backend.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 18ABE1572AF556340044A204 /* ggml-backend.cpp */; };
 		18ABE15B2AF556340044A204 /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 18ABE1592AF556340044A204 /* ggml-quants.c */; };
 		7FE3424B2A0C3FA20015A058 /* whisper-encoder-impl.m in Sources */ = {isa = PBXBuildFile; fileRef = 7FE342452A0C3FA20015A058 /* whisper-encoder-impl.m */; };
 		7FE3424C2A0C3FA20015A058 /* whisper-encoder.mm in Sources */ = {isa = PBXBuildFile; fileRef = 7FE342472A0C3FA20015A058 /* whisper-encoder.mm */; };
@@ -73,7 +73,7 @@
 		18ABE1542AF556340044A204 /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../../ggml/src/ggml-quants.h"; sourceTree = "<group>"; };
 		18ABE1552AF556340044A204 /* ggml-backend.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-backend.h"; path = "../../../ggml/include/ggml-backend.h"; sourceTree = "<group>"; };
 		18ABE1562AF556340044A204 /* ggml-backend-impl.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-backend-impl.h"; path = "../../../ggml/src/ggml-backend-impl.h"; sourceTree = "<group>"; };
-		18ABE1572AF556340044A204 /* ggml-backend.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.c"; path = "../../../ggml/src/ggml-backend.c"; sourceTree = "<group>"; };
+		18ABE1572AF556340044A204 /* ggml-backend.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.cpp"; path = "../../../ggml/src/ggml-backend.cpp"; sourceTree = "<group>"; };
 		18ABE1582AF556340044A204 /* ggml-impl.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-impl.h"; path = "../../../ggml/src/ggml-impl.h"; sourceTree = "<group>"; };
 		18ABE1592AF556340044A204 /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../../ggml/src/ggml-quants.c"; sourceTree = "<group>"; };
 		7FE342452A0C3FA20015A058 /* whisper-encoder-impl.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = "whisper-encoder-impl.m"; sourceTree = "<group>"; };
@@ -120,7 +120,7 @@
 				18A275FF2C2A9563001C8D37 /* ggml-common.h */,
 				18A275FE2C2A94DE001C8D37 /* ggml-metal.h */,
 				18ABE1562AF556340044A204 /* ggml-backend-impl.h */,
-				18ABE1572AF556340044A204 /* ggml-backend.c */,
+				18ABE1572AF556340044A204 /* ggml-backend.cpp */,
 				18ABE1552AF556340044A204 /* ggml-backend.h */,
 				18ABE1582AF556340044A204 /* ggml-impl.h */,
 				18ABE1592AF556340044A204 /* ggml-quants.c */,
@@ -248,7 +248,7 @@
 				18627C7B29052BDF00BD2A04 /* AppDelegate.m in Sources */,
 				7FE3424D2A0C3FA20015A058 /* whisper-decoder-impl.m in Sources */,
 				1844471A2AB211A2007D6BFE /* ggml-alloc.c in Sources */,
-				18ABE15A2AF556340044A204 /* ggml-backend.c in Sources */,
+				18ABE15A2AF556340044A204 /* ggml-backend.cpp in Sources */,
 				18627C8C29052BE000BD2A04 /* main.m in Sources */,
 				18627C7E29052BDF00BD2A04 /* SceneDelegate.m in Sources */,
 				1844471C2AB21655007D6BFE /* ggml-metal.m in Sources */,

ggml/src/ggml-backend.c

@@ -1,2294 +0,0 @@
-#include "ggml-backend-impl.h"
-#include "ggml-alloc.h"
-#include "ggml-impl.h"
-
-#include <assert.h>
-#include <limits.h>
-#include <stdarg.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-
-// backend buffer type
-
-const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
-    return buft->iface.get_name(buft);
-}
-
-GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    return buft->iface.alloc_buffer(buft, size);
-}
-
-size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
-    return buft->iface.get_alignment(buft);
-}
-
-size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
-    // get_max_size is optional, defaults to SIZE_MAX
-    if (buft->iface.get_max_size) {
-        return buft->iface.get_max_size(buft);
-    }
-    return SIZE_MAX;
-}
-
-GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
-    // 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);
-        assert(size >= ggml_nbytes(tensor));
-        return size;
-    }
-    return ggml_nbytes(tensor);
-}
-
-bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
-    if (buft->iface.is_host) {
-        return buft->iface.is_host(buft);
-    }
-    return false;
-}
-
-// backend buffer
-
-GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
-               ggml_backend_buffer_type_t      buft,
-        struct ggml_backend_buffer_i           iface,
-               ggml_backend_buffer_context_t   context,
-               size_t                          size) {
-    ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));
-
-    (*buffer) = (struct ggml_backend_buffer) {
-        /* .interface = */ iface,
-        /* .buft      = */ buft,
-        /* .context   = */ context,
-        /* .size      = */ size,
-        /* .usage     = */ GGML_BACKEND_BUFFER_USAGE_ANY
-    };
-
-    return buffer;
-}
-
-const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) {
-    return buffer->iface.get_name(buffer);
-}
-
-void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
-    if (buffer == NULL) {
-        return;
-    }
-
-    if (buffer->iface.free_buffer != NULL) {
-        buffer->iface.free_buffer(buffer);
-    }
-    free(buffer);
-}
-
-size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
-    return buffer->size;
-}
-
-void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
-    void * base = buffer->iface.get_base(buffer);
-
-    GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
-
-    return base;
-}
-
-GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    // init_tensor is optional
-    if (buffer->iface.init_tensor) {
-        buffer->iface.init_tensor(buffer, tensor);
-    }
-}
-
-size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer));
-}
-
-size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer));
-}
-
-size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor);
-}
-
-void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    buffer->iface.clear(buffer, value);
-}
-
-bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer));
-}
-
-void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
-    buffer->usage = usage;
-
-    // FIXME: add a generic callback to the buffer interface
-    if (ggml_backend_buffer_is_multi_buffer(buffer)) {
-        ggml_backend_multi_buffer_set_usage(buffer, usage);
-    }
-}
-
-enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
-    return buffer->usage;
-}
-
-ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
-    return buffer->buft;
-}
-
-void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
-    if (buffer->iface.reset) {
-        buffer->iface.reset(buffer);
-    }
-}
-
-bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) {
-    ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer;
-    if (dst_buf->iface.cpy_tensor) {
-        return dst_buf->iface.cpy_tensor(dst_buf, src, dst);
-    }
-    return false;
-}
-
-// backend
-
-ggml_guid_t ggml_backend_guid(ggml_backend_t backend) {
-    if (backend == NULL) {
-        return NULL;
-    }
-    return backend->guid;
-}
-
-const char * ggml_backend_name(ggml_backend_t backend) {
-    if (backend == NULL) {
-        return "NULL";
-    }
-    return backend->iface.get_name(backend);
-}
-
-void ggml_backend_free(ggml_backend_t backend) {
-    if (backend == NULL) {
-        return;
-    }
-
-    backend->iface.free(backend);
-}
-
-ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
-    return backend->iface.get_default_buffer_type(backend);
-}
-
-ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
-    return ggml_backend_buft_alloc_buffer(ggml_backend_get_default_buffer_type(backend), size);
-}
-
-size_t ggml_backend_get_alignment(ggml_backend_t backend) {
-    return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend));
-}
-
-size_t ggml_backend_get_max_size(ggml_backend_t backend) {
-    return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(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(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
-
-    if (backend->iface.set_tensor_async == NULL) {
-        ggml_backend_tensor_set(tensor, data, offset, size);
-    } else {
-        backend->iface.set_tensor_async(backend, tensor, data, offset, size);
-    }
-}
-
-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(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
-
-    if (backend->iface.get_tensor_async == NULL) {
-        ggml_backend_tensor_get(tensor, data, offset, size);
-    } else {
-        backend->iface.get_tensor_async(backend, tensor, data, offset, size);
-    }
-}
-
-GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
-
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
-    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
-
-    if (!size) {
-        return;
-    }
-
-    buf->iface.set_tensor(buf, tensor, data, offset, size);
-}
-
-GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
-
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
-    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
-
-    if (!size) {
-        return;
-    }
-
-    buf->iface.get_tensor(buf, tensor, data, offset, size);
-}
-
-GGML_API GGML_CALL void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
-    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
-
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
-    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
-
-    if (!size) {
-        return;
-    }
-
-    GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not supported by backend buffer");
-
-    buf->iface.memset_tensor(buf, tensor, value, offset, size);
-}
-
-void ggml_backend_synchronize(ggml_backend_t backend) {
-    if (backend->iface.synchronize == NULL) {
-        return;
-    }
-
-    backend->iface.synchronize(backend);
-}
-
-ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    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->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->iface.graph_plan_compute != NULL);
-
-    return backend->iface.graph_plan_compute(backend, plan);
-}
-
-enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
-    ggml_backend_synchronize(backend);
-    return err;
-}
-
-enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    return backend->iface.graph_compute(backend, cgraph);
-}
-
-bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    return backend->iface.supports_op(backend, op);
-}
-
-bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
-    return backend->iface.supports_buft(backend, buft);
-}
-
-bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    if (backend->iface.offload_op != NULL) {
-        return backend->iface.offload_op(backend, op);
-    }
-    return false;
-}
-
-// backend copy
-
-static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
-    if (a->type != b->type) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (a->ne[i] != b->ne[i]) {
-            return false;
-        }
-        if (a->nb[i] != b->nb[i]) {
-            return false;
-        }
-    }
-    return true;
-}
-
-void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
-    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
-
-    if (src == dst) {
-        return;
-    }
-
-    if (ggml_backend_buffer_is_host(src->buffer)) {
-        ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
-    } else if (ggml_backend_buffer_is_host(dst->buffer)) {
-        ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
-    } else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
-#ifndef NDEBUG
-        fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
-#endif
-        size_t nbytes = ggml_nbytes(src);
-        void * data = malloc(nbytes);
-        ggml_backend_tensor_get(src, data, 0, nbytes);
-        ggml_backend_tensor_set(dst, data, 0, nbytes);
-        free(data);
-    }
-}
-
-void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
-
-    if (src == dst) {
-        return;
-    }
-
-    if (backend_dst->iface.cpy_tensor_async != NULL) {
-        if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
-            return;
-        }
-    }
-
-    // an async copy would normally happen after all the queued operations on both backends are completed
-    // to simulate the same behavior, we need to synchronize both backends first, and do a blocking copy
-    ggml_backend_synchronize(backend_src);
-    ggml_backend_synchronize(backend_dst);
-    ggml_backend_tensor_copy(src, dst);
-}
-
-// events
-
-ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) {
-    if (backend->iface.event_new == NULL) {
-        return NULL;
-    }
-    return backend->iface.event_new(backend);
-}
-
-void ggml_backend_event_free(ggml_backend_event_t event) {
-    if (event == NULL) {
-        return;
-    }
-    event->backend->iface.event_free(event);
-}
-
-void ggml_backend_event_record(ggml_backend_event_t event) {
-    GGML_ASSERT(event->backend->iface.event_record != NULL);
-
-    event->backend->iface.event_record(event);
-}
-
-void ggml_backend_event_synchronize(ggml_backend_event_t event) {
-    GGML_ASSERT(event->backend->iface.event_synchronize != NULL);
-
-    event->backend->iface.event_synchronize(event);
-}
-
-void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
-    GGML_ASSERT(backend->iface.event_wait != NULL);
-
-    backend->iface.event_wait(backend, event);
-}
-
-// backend registry
-
-#define GGML_REG_MAX_BACKENDS 64
-
-struct ggml_backend_reg {
-    char name[128];
-    ggml_backend_init_fn init_fn;
-    ggml_backend_buffer_type_t default_buffer_type;
-    void * user_data;
-};
-
-static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS];
-static size_t ggml_backend_registry_count = 0;
-
-GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
-
-GGML_CALL static void ggml_backend_registry_init(void) {
-    static bool initialized = false;
-
-    if (initialized) {
-        return;
-    }
-
-    initialized = true;
-
-    ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL);
-
-    // add forward decls here to avoid including the backend headers
-#ifdef GGML_USE_CUDA
-    extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
-    ggml_backend_cuda_reg_devices();
-#endif
-
-#ifdef GGML_USE_SYCL
-    extern void ggml_backend_sycl_reg_devices(void);
-    ggml_backend_sycl_reg_devices();
-#endif
-
-#ifdef GGML_USE_METAL
-    extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
-    extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
-    ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
-#endif
-
-#ifdef GGML_USE_VULKAN
-    extern GGML_CALL int ggml_backend_vk_reg_devices(void);
-    ggml_backend_vk_reg_devices();
-#endif
-
-#ifdef GGML_USE_KOMPUTE
-    extern GGML_CALL void ggml_backend_kompute_reg_devices(void);
-    ggml_backend_kompute_reg_devices();
-#endif
-
-#ifdef GGML_USE_CANN
-    extern GGML_CALL int ggml_backend_cann_reg_devices(void);
-    ggml_backend_cann_reg_devices();
-#endif
-}
-
-GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
-    GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS);
-
-    size_t id = ggml_backend_registry_count;
-
-    ggml_backend_registry[id] = (struct ggml_backend_reg) {
-        /* .name                = */ {0},
-        /* .fn                  = */ init_fn,
-        /* .default_buffer_type = */ default_buffer_type,
-        /* .user_data           = */ user_data,
-    };
-
-    snprintf(ggml_backend_registry[id].name, sizeof(ggml_backend_registry[id].name), "%s", name);
-
-#ifndef NDEBUG
-    fprintf(stderr, "%s: registered backend %s\n", __func__, name);
-#endif
-
-    ggml_backend_registry_count++;
-}
-
-size_t ggml_backend_reg_get_count(void) {
-    ggml_backend_registry_init();
-
-    return ggml_backend_registry_count;
-}
-
-size_t ggml_backend_reg_find_by_name(const char * name) {
-    ggml_backend_registry_init();
-
-    for (size_t i = 0; i < ggml_backend_registry_count; i++) {
-        // TODO: case insensitive in a portable way
-        if (strcmp(ggml_backend_registry[i].name, name) == 0) {
-            return i;
-        }
-    }
-
-    // not found
-    return SIZE_MAX;
-}
-
-// init from backend:params string
-ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str) {
-    ggml_backend_registry_init();
-
-    const char * params = strchr(backend_str, ':');
-    char backend_name[128];
-    if (params == NULL) {
-        snprintf(backend_name, sizeof(backend_name), "%s", backend_str);
-        params = "";
-    } else {
-        snprintf(backend_name, sizeof(backend_name), "%.*s", (int)(params - backend_str), backend_str);
-        params++;
-    }
-
-    size_t backend_i = ggml_backend_reg_find_by_name(backend_name);
-
-    if (backend_i == SIZE_MAX) {
-        fprintf(stderr, "%s: backend %s not found\n", __func__, backend_name);
-        return NULL;
-    }
-
-    return ggml_backend_reg_init_backend(backend_i, params);
-}
-
-const char * ggml_backend_reg_get_name(size_t i) {
-    ggml_backend_registry_init();
-
-    GGML_ASSERT(i < ggml_backend_registry_count);
-    return ggml_backend_registry[i].name;
-}
-
-ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params) {
-    ggml_backend_registry_init();
-
-    GGML_ASSERT(i < ggml_backend_registry_count);
-    return ggml_backend_registry[i].init_fn(params, ggml_backend_registry[i].user_data);
-}
-
-ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i) {
-    ggml_backend_registry_init();
-
-    GGML_ASSERT(i < ggml_backend_registry_count);
-    return ggml_backend_registry[i].default_buffer_type;
-}
-
-ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
-    ggml_backend_registry_init();
-
-    GGML_ASSERT(i < ggml_backend_registry_count);
-    return ggml_backend_buft_alloc_buffer(ggml_backend_registry[i].default_buffer_type, size);
-}
-
-// backend CPU
-
-static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment
-
-GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
-    return "CPU";
-
-    GGML_UNUSED(buffer);
-}
-
-GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
-    uintptr_t data = (uintptr_t)buffer->context;
-
-    // align the buffer
-    if (data % TENSOR_ALIGNMENT != 0) {
-        data = GGML_PAD(data, TENSOR_ALIGNMENT);
-    }
-
-    return (void *)data;
-}
-
-GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    free(buffer->context);
-}
-
-GGML_CALL 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) {
-    memset((char *)tensor->data + offset, value, size);
-
-    GGML_UNUSED(buffer);
-}
-
-GGML_CALL 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) {
-    memcpy((char *)tensor->data + offset, data, size);
-
-    GGML_UNUSED(buffer);
-}
-
-GGML_CALL 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) {
-    memcpy(data, (const char *)tensor->data + offset, size);
-
-    GGML_UNUSED(buffer);
-}
-
-GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
-    if (ggml_backend_buffer_is_host(src->buffer)) {
-        memcpy(dst->data, src->data, ggml_nbytes(src));
-        return true;
-    }
-    return false;
-
-    GGML_UNUSED(buffer);
-}
-
-GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    memset(buffer->context, value, buffer->size);
-}
-
-static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
-    /* .get_name        = */ ggml_backend_cpu_buffer_name,
-    /* .free_buffer     = */ ggml_backend_cpu_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
-    /* .init_tensor     = */ NULL, // no initialization required
-    /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
-    /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
-    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
-    /* .clear           = */ ggml_backend_cpu_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-// for buffers from ptr, free is not called
-static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
-    /* .get_name        = */ ggml_backend_cpu_buffer_name,
-    /* .free_buffer     = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
-    /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
-    /* .init_tensor     = */ NULL, // no initialization required
-    /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
-    /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
-    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
-    /* .clear           = */ ggml_backend_cpu_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
-    return "CPU";
-
-    GGML_UNUSED(buft);
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
-    void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h)
-    if (data == NULL) {
-        fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
-        return NULL;
-    }
-
-    return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
-}
-
-GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return TENSOR_ALIGNMENT;
-
-    GGML_UNUSED(buft);
-}
-
-GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
-    return true;
-
-    GGML_UNUSED(buft);
-}
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
-    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
-        /* .iface = */ {
-            /* .get_name         = */ ggml_backend_cpu_buffer_type_get_name,
-            /* .alloc_buffer     = */ ggml_backend_cpu_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
-            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
-            /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
-        },
-        /* .context = */ NULL,
-    };
-
-    return &ggml_backend_cpu_buffer_type;
-}
-
-#ifdef GGML_USE_CPU_HBM
-
-// buffer type HBM
-
-#include <hbwmalloc.h>
-
-GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
-    return "CPU_HBM";
-
-    GGML_UNUSED(buft);
-}
-
-GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
-    return "CPU_HBM";
-
-    GGML_UNUSED(buf);
-}
-
-GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    hbw_free(buffer->context);
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    //void * ptr = hbw_malloc(size);
-    void * ptr;
-    int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
-    if (result != 0) {
-        fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size);
-        return NULL;
-    }
-
-    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
-    buffer->buft = buft;
-    buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name;
-    buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;
-
-    return buffer;
-}
-
-ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
-    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = {
-        /* .iface    = */ {
-            /* .get_name         = */ ggml_backend_cpu_hbm_buffer_type_get_name,
-            /* .alloc_buffer     = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
-            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
-            /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
-        },
-        /* .context  = */ NULL,
-    };
-
-    return &ggml_backend_cpu_buffer_type_hbm;
-}
-#endif
-
-struct ggml_backend_cpu_context {
-    int                 n_threads;
-    ggml_threadpool_t   threadpool;
-
-    void *              work_data;
-    size_t              work_size;
-
-    ggml_abort_callback abort_callback;
-    void *              abort_callback_data;
-};
-
-GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
-    return "CPU";
-
-    GGML_UNUSED(backend);
-}
-
-GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) {
-    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
-    free(cpu_ctx->work_data);
-    free(cpu_ctx);
-    free(backend);
-}
-
-GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
-    return ggml_backend_cpu_buffer_type();
-
-    GGML_UNUSED(backend);
-}
-
-struct ggml_backend_plan_cpu {
-    struct ggml_cplan cplan;
-    struct ggml_cgraph cgraph;
-};
-
-GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
-    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
-
-    struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
-
-    cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);
-    cpu_plan->cgraph = *cgraph; // FIXME: deep copy
-
-    if (cpu_plan->cplan.work_size > 0) {
-        cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
-        if (cpu_plan->cplan.work_data == NULL) {
-            free(cpu_plan);
-            return NULL;
-        }
-    }
-
-    cpu_plan->cplan.abort_callback      = cpu_ctx->abort_callback;
-    cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data;
-
-    return cpu_plan;
-}
-
-GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
-
-    free(cpu_plan->cplan.work_data);
-    free(cpu_plan);
-
-    GGML_UNUSED(backend);
-}
-
-GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
-
-    return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
-
-    GGML_UNUSED(backend);
-}
-
-GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
-
-    struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);
-
-    if (cpu_ctx->work_size < cplan.work_size) {
-        free(cpu_ctx->work_data);
-        cpu_ctx->work_data = malloc(cplan.work_size);
-        if (cpu_ctx->work_data == NULL) {
-            cpu_ctx->work_size = 0;
-            return GGML_STATUS_ALLOC_FAILED;
-        }
-        cpu_ctx->work_size = cplan.work_size;
-    }
-    cplan.work_data = cpu_ctx->work_data;
-
-    cplan.abort_callback      = cpu_ctx->abort_callback;
-    cplan.abort_callback_data = cpu_ctx->abort_callback_data;
-
-    return ggml_graph_compute(cgraph, &cplan);
-}
-
-GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    switch (op->op) {
-        case GGML_OP_CPY:
-            return
-                op->type != GGML_TYPE_IQ2_XXS &&
-                op->type != GGML_TYPE_IQ2_XS  &&
-                op->type != GGML_TYPE_IQ1_S   &&
-                op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
-        case GGML_OP_MUL_MAT:
-            return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
-        case GGML_OP_ROPE_BACK:
-            return op->src[2] == NULL && (op->op_params[2] & 4) == 0;
-        case GGML_OP_IM2COL_BACK:
-            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
-        default:
-            return true;
-    }
-
-    GGML_UNUSED(backend);
-}
-
-GGML_CALL static bool ggml_backend_cpu_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
-    return ggml_backend_buft_is_host(buft);
-
-    GGML_UNUSED(backend);
-}
-
-static struct ggml_backend_i cpu_backend_i = {
-    /* .get_name                = */ ggml_backend_cpu_name,
-    /* .free                    = */ ggml_backend_cpu_free,
-    /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type,
-    /* .set_tensor_async        = */ NULL,
-    /* .get_tensor_async        = */ NULL,
-    /* .cpy_tensor_async        = */ NULL,
-    /* .synchronize             = */ NULL,
-    /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,
-    /* .graph_plan_free         = */ ggml_backend_cpu_graph_plan_free,
-    /* .graph_plan_update       = */ NULL,
-    /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
-    /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
-    /* .supports_op             = */ ggml_backend_cpu_supports_op,
-    /* .supports_buft           = */ ggml_backend_cpu_supports_buft,
-    /* .offload_op              = */ NULL,
-    /* .event_new               = */ NULL,
-    /* .event_free              = */ NULL,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
-    /* .event_synchronize       = */ NULL,
-};
-
-static ggml_guid_t ggml_backend_cpu_guid(void) {
-    static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 };
-    return &guid;
-}
-
-ggml_backend_t ggml_backend_cpu_init(void) {
-    struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
-    if (ctx == NULL) {
-        return NULL;
-    }
-
-    ctx->n_threads           = GGML_DEFAULT_N_THREADS;
-    ctx->threadpool          = NULL;
-    ctx->work_data           = NULL;
-    ctx->work_size           = 0;
-    ctx->abort_callback      = NULL;
-    ctx->abort_callback_data = NULL;
-
-    ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
-    if (cpu_backend == NULL) {
-        free(ctx);
-        return NULL;
-    }
-
-    *cpu_backend = (struct ggml_backend) {
-        /* .guid      = */ ggml_backend_cpu_guid(),
-        /* .interface = */ cpu_backend_i,
-        /* .context   = */ ctx
-    };
-    return cpu_backend;
-}
-
-GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
-    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid());
-}
-
-void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
-    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
-
-    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
-    ctx->n_threads = n_threads;
-}
-
-void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) {
-    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
-
-    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
-
-    if (ctx->threadpool && ctx->threadpool != threadpool) {
-        // already had a different threadpool, pause/suspend it before switching
-        ggml_threadpool_pause(ctx->threadpool);
-    }
-    ctx->threadpool = threadpool;
-}
-
-void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) {
-    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
-
-    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
-    ctx->abort_callback = abort_callback;
-    ctx->abort_callback_data = abort_callback_data;
-}
-
-GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
-    GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned");
-    return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size);
-}
-
-GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
-    return ggml_backend_cpu_init();
-
-    GGML_UNUSED(params);
-    GGML_UNUSED(user_data);
-}
-
-// multi-buffer buffer
-
-struct ggml_backend_multi_buffer_context {
-    ggml_backend_buffer_t * buffers;
-    size_t n_buffers;
-};
-
-typedef struct ggml_backend_multi_buffer_context * ggml_backend_multi_buffer_context_t;
-
-GGML_CALL static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) {
-    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
-
-    return ctx->buffers[0]->iface.get_name(ctx->buffers[0]);
-}
-
-GGML_CALL static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
-    for (size_t i = 0; i < ctx->n_buffers; i++) {
-        ggml_backend_buffer_free(ctx->buffers[i]);
-    }
-
-    free(ctx->buffers);
-    free(ctx);
-}
-
-GGML_CALL static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
-    for (size_t i = 0; i < ctx->n_buffers; i++) {
-        ggml_backend_buffer_clear(ctx->buffers[i], value);
-    }
-}
-
-static struct ggml_backend_buffer_i ggml_backend_multi_buffer_context_interface(void) {
-    static struct ggml_backend_buffer_i multi_backend_buffer_i = {
-        /* .get_name        = */ ggml_backend_multi_buffer_get_name,
-        /* .free_buffer     = */ ggml_backend_multi_buffer_free_buffer,
-        /* .get_base        = */ NULL,
-        /* .init_tensor     = */ NULL,
-        /* .memset_tensor   = */ NULL,
-        /* .set_tensor      = */ NULL,
-        /* .get_tensor      = */ NULL,
-        /* .cpy_tensor      = */ NULL,
-        /* .clear           = */ ggml_backend_multi_buffer_clear,
-        /* .reset           = */ NULL,
-    };
-
-    return multi_backend_buffer_i;
-}
-
-GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) {
-    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) malloc(sizeof(struct ggml_backend_multi_buffer_context));
-    ctx->n_buffers = n_buffers;
-    ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t));
-
-    GGML_ASSERT(ctx->buffers != NULL);
-
-    size_t total_size = 0;
-    for (size_t i = 0; i < n_buffers; i++) {
-        ctx->buffers[i] = buffers[i];
-        total_size += ggml_backend_buffer_get_size(buffers[i]);
-    }
-
-    return ggml_backend_buffer_init(buffers[0]->buft, ggml_backend_multi_buffer_context_interface(), ctx, total_size);
-}
-
-GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
-    return buffer->iface.get_name == ggml_backend_multi_buffer_get_name;
-}
-
-GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
-    GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
-    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
-    for (size_t i = 0; i < ctx->n_buffers; i++) {
-        ggml_backend_buffer_set_usage(ctx->buffers[i], usage);
-    }
-}
-
-// creates a copy of the tensor with the same memory layout
-static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) {
-    struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor);
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        dup->nb[i] = tensor->nb[i];
-    }
-    return dup;
-}
-
-static bool ggml_is_view_op(enum ggml_op op) {
-    return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
-}
-
-// scheduler
-
-#ifndef GGML_SCHED_MAX_BACKENDS
-#define GGML_SCHED_MAX_BACKENDS 16
-#endif
-
-#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
-#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
-#endif
-
-#ifndef GGML_SCHED_MAX_COPIES
-#define GGML_SCHED_MAX_COPIES 4
-#endif
-
-struct ggml_backend_sched_split {
-    int backend_id;
-    int i_start;
-    int i_end;
-    struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
-    int n_inputs;
-    // graph view of this split
-    struct ggml_cgraph graph;
-};
-
-struct ggml_backend_sched {
-    bool is_reset; // true if the scheduler has been reset since the last graph split
-    bool is_alloc;
-
-    int n_backends;
-
-    ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
-    ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
-    ggml_gallocr_t galloc;
-
-    // hash map of the nodes in the graph
-    struct ggml_hash_set  hash_set;
-    int                 * hv_tensor_backend_ids; // [hash_set.size]
-    struct ggml_tensor ** hv_tensor_copies;      // [hash_set.size][n_backends][n_copies]
-
-    int * node_backend_ids; // [graph_size]
-    int * leaf_backend_ids; // [graph_size]
-
-    int * prev_node_backend_ids; // [graph_size]
-    int * prev_leaf_backend_ids; // [graph_size]
-
-    // copy of the graph with modified inputs
-    struct ggml_cgraph graph;
-
-    // graph splits
-    struct ggml_backend_sched_split * splits;
-    int n_splits;
-    int splits_capacity;
-
-    // pipeline parallelism support
-    int n_copies;
-    int cur_copy;
-    ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
-    struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
-    int n_graph_inputs;
-
-    struct ggml_context * ctx;
-
-    ggml_backend_sched_eval_callback callback_eval;
-    void * callback_eval_user_data;
-
-    char * context_buffer;
-    size_t context_buffer_size;
-
-    bool debug;
-};
-
-#define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor)
-#define tensor_backend_id(tensor) sched->hv_tensor_backend_ids[hash_id(tensor)]
-#define tensor_id_copy(id, backend_id, copy_id) sched->hv_tensor_copies[(id) * sched->n_backends * sched->n_copies + (backend_id) * sched->n_copies + (copy_id)]
-#define tensor_copy(tensor, backend_id, copy_id) tensor_id_copy(hash_id(tensor), backend_id, copy_id)
-
-// returns the priority of the backend, lower id is higher priority
-static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
-    for (int i = 0; i < sched->n_backends; i++) {
-        if (sched->backends[i] == backend) {
-            return i;
-        }
-    }
-    return -1;
-}
-
-static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) {
-    ggml_backend_buffer_t buffer = tensor->buffer;
-    if (buffer == NULL) {
-        return -1;
-    }
-
-    // find highest prio backend that supports the buffer type and the op
-    for (int i = 0; i < sched->n_backends; i++) {
-        if (ggml_backend_supports_buft(sched->backends[i], buffer->buft) &&
-            ggml_backend_supports_op(sched->backends[i], op)) {
-            return i;
-        }
-    }
-
-#ifndef NDEBUG
-    fprintf(stderr, "%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
-        __func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name);
-#endif
-
-    return -1;
-}
-
-#if 0
-#define GGML_SCHED_MAX_SPLITS_DEBUG 4096
-static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
-#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
-#define GET_CAUSE(node) causes[hash_id(node)]
-#else
-#define SET_CAUSE(node, ...)
-#define GET_CAUSE(node) ""
-#endif
-
-// returns the backend that should be used for the node based on the current locations
-static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) {
-    // TODO: use supports_op to check if the backend supports the op
-
-    // assign pre-allocated nodes to their backend
-    int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor);
-    if (cur_backend_id != -1) {
-        SET_CAUSE(tensor, "1.dst");
-        return cur_backend_id;
-    }
-
-    // view_src
-    if (tensor->view_src != NULL) {
-        cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src, tensor);
-        if (cur_backend_id != -1) {
-            SET_CAUSE(tensor, "1.vsrc");
-            return cur_backend_id;
-        }
-    }
-
-    if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) {
-        // since the tensor is pre-allocated, it cannot be moved to another backend
-        GGML_ABORT("pre-allocated tensor in a backend that cannot run the operation");
-    }
-
-    // graph input
-    if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
-        cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU)
-        SET_CAUSE(tensor, "1.inp");
-        return cur_backend_id;
-    }
-
-    // operations with weights are preferably run on the same backend as the weights
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        const struct ggml_tensor * src = tensor->src[i];
-        if (src == NULL) {
-            continue;
-        }
-        if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
-            int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
-            // check if a backend with higher prio wants to offload the op
-            if (src_backend_id == sched->n_backends - 1) {
-                for (int b = 0; b < src_backend_id; b++) {
-                    if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
-                        SET_CAUSE(tensor, "1.off");
-                        return b;
-                    }
-                }
-            }
-            SET_CAUSE(tensor, "1.wgt%d", i);
-            return src_backend_id;
-        }
-    }
-
-    return -1;
-}
-
-static char * fmt_size(size_t size) {
-    static char buffer[128];
-    if (size >= 1024*1024) {
-        snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024);
-    } else {
-        snprintf(buffer, sizeof(buffer), "%zuK", size/1024);
-    }
-    return buffer;
-}
-
-static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    int cur_split = 0;
-    for (int i = 0; i < graph->n_nodes; i++) {
-        if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
-            ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
-            fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
-                sched->splits[cur_split].n_inputs);
-            for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
-                fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
-                    fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
-            }
-            fprintf(stderr, "\n");
-            cur_split++;
-        }
-        struct ggml_tensor * node = graph->nodes[i];
-        if (ggml_is_view_op(node->op)) {
-            continue;
-        }
-        ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
-        fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
-            fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
-                continue;
-            }
-            ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
-            fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
-                fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
-        }
-        fprintf(stderr, "\n");
-    }
-}
-
-static bool ggml_backend_sched_buffer_supported(ggml_backend_sched_t sched, struct ggml_tensor * t, int backend_id) {
-    ggml_backend_buffer_t buf = t->view_src ? t->view_src->buffer : t->buffer;
-    ggml_backend_buffer_type_t buft = NULL;
-
-    if (buf) {
-        // the tensor is already allocated
-        buft = buf->buft;
-    } else {
-        // see if the tensor already has a backend assigned, and use the buffer type of that backend
-        int tensor_backend_id = tensor_backend_id(t);
-        if (tensor_backend_id == -1 && t->view_src) {
-            tensor_backend_id = tensor_backend_id(t->view_src);
-        }
-        if (tensor_backend_id != -1) {
-            buft = sched->bufts[tensor_backend_id];
-        }
-    }
-
-    return buft != NULL && ggml_backend_supports_buft(sched->backends[backend_id], buft);
-}
-
-static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, struct ggml_tensor * node, int cur_backend_id, int * node_backend_id) {
-    if (ggml_backend_supports_op(sched->backends[cur_backend_id], node)) {
-        *node_backend_id = cur_backend_id;
-        SET_CAUSE(node, "2.sup");
-    }
-}
-
-// 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) {
-    // reset splits
-    sched->n_splits = 0;
-    sched->n_graph_inputs = 0;
-    sched->is_reset = false;
-
-    struct ggml_init_params params = {
-        /* .mem_size =   */ sched->context_buffer_size,
-        /* .mem_buffer = */ sched->context_buffer,
-        /* .no_alloc =   */ true
-    };
-
-    ggml_free(sched->ctx);
-
-    sched->ctx = ggml_init(params);
-    if (sched->ctx == NULL) {
-        GGML_ABORT("%s: failed to initialize context\n", __func__);
-    }
-
-    // pass 1: assign backends to ops with pre-allocated inputs
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        int * leaf_backend_id = &tensor_backend_id(leaf);
-        // do not overwrite user assignments
-        if (*leaf_backend_id == -1) {
-            *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
-        }
-    }
-
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        int * node_backend_id = &tensor_backend_id(node);
-        // do not overwrite user assignments
-        if (*node_backend_id == -1) {
-            *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);
-
-#if 0
-            // src
-            if (node->op == GGML_OP_NONE) {
-                continue;
-            }
-
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * src = node->src[j];
-                if (src == NULL) {
-                    continue;
-                }
-                int * src_backend_id = &tensor_backend_id(src);
-                if (*src_backend_id == -1) {
-                    *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
-                }
-            }
-#endif
-        }
-    }
-
-    // pass 2: expand current backend assignments
-    // assign the same backend to adjacent nodes
-    // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
-    // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
-    // ops unsupported by the backend being expanded will be left unassigned so that they can be assigned later when the locations of its inputs are known
-    // expand gpu down
-    {
-        int cur_backend_id = -1;
-        for (int i = 0; i < graph->n_nodes; i++) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int * node_backend_id = &tensor_backend_id(node);
-            if (*node_backend_id != -1) {
-                if (*node_backend_id == sched->n_backends - 1) {
-                    // skip cpu (lowest prio backend)
-                    cur_backend_id = -1;
-                } else {
-                    cur_backend_id = *node_backend_id;
-                }
-            } else if (cur_backend_id != -1) {
-                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
-            }
-        }
-    }
-    // expand gpu up
-    {
-        int cur_backend_id = -1;
-        for (int i = graph->n_nodes - 1; i >= 0; i--) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int * node_backend_id = &tensor_backend_id(node);
-            if (*node_backend_id != -1) {
-                if (*node_backend_id == sched->n_backends - 1) {
-                    // skip cpu (lowest prio backend)
-                    cur_backend_id = -1;
-                } else {
-                    cur_backend_id = *node_backend_id;
-                }
-            } else if (cur_backend_id != -1) {
-                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
-            }
-        }
-    }
-    // expand rest down
-    {
-        int cur_backend_id = -1;
-        for (int i = 0; i < graph->n_nodes; i++) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int * node_backend_id = &tensor_backend_id(node);
-            if (*node_backend_id != -1) {
-                cur_backend_id = *node_backend_id;
-            } else if (cur_backend_id != -1) {
-                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
-            }
-        }
-    }
-    // expand rest up
-    {
-        int cur_backend_id = -1;
-        for (int i = graph->n_nodes - 1; i >= 0; i--) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int * node_backend_id = &tensor_backend_id(node);
-            if (*node_backend_id != -1) {
-                cur_backend_id = *node_backend_id;
-            } else if (cur_backend_id != -1) {
-                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
-            }
-        }
-    }
-
-    // pass 3: upgrade nodes to higher prio backends with compatible buffer types
-    // if the tensor is already in the same buffer type (*) as another higher priority backend, we should move it there
-    // however, we also need to verify that the sources are in compatible buffer types
-    // (*) the actual requirement is more relaxed, the buffer type of the backend should be supported by all the users of this tensor further down the graph
-    // however, this is slow to verify, so we have a more strict requirement that the buffer type is the same
-    // this is not uncommon since multiple backends can use host memory, with the same buffer type (eg. BLAS and CPU)
-    // additionally, set remaining unassigned nodes to the backend with the most supported inputs
-    // only nodes that could not be assigned during expansion due to the backend not supporting the op should be unassigned at this point
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        if (ggml_is_view_op(node->op)) {
-            continue;
-        }
-        int * node_backend_id = &tensor_backend_id(node);
-        if (*node_backend_id == -1) {
-            // unassigned node: find the backend with the most supported inputs
-            int n_supported_best = -1;
-            for (int b = 0; b < sched->n_backends; b++) {
-                if (ggml_backend_supports_op(sched->backends[b], node)) {
-                    int n_supported = 0;
-                    for (int j = 0; j < GGML_MAX_SRC; j++) {
-                        struct ggml_tensor * src = node->src[j];
-                        if (src == NULL) {
-                            continue;
-                        }
-                        if ((tensor_backend_id(src) != -1 || tensor_backend_id(src->view_src) != -1) && ggml_backend_sched_buffer_supported(sched, src, b)) {
-                            n_supported++;
-                        }
-                    }
-                    if (n_supported > n_supported_best) {
-                        n_supported_best = n_supported;
-                        *node_backend_id = b;
-                        SET_CAUSE(node, "3.best");
-                    }
-                }
-            }
-        } else {
-            // assigned node: upgrade to higher prio backend if possible
-            for (int b = 0; b < *node_backend_id; b++) {
-                if (sched->bufts[b] == sched->bufts[*node_backend_id] && ggml_backend_supports_op(sched->backends[b], node)) {
-                    bool supported = true;
-                    for (int j = 0; j < GGML_MAX_SRC; j++) {
-                        struct ggml_tensor * src = node->src[j];
-                        if (src == NULL) {
-                            continue;
-                        }
-                        if (!ggml_backend_sched_buffer_supported(sched, src, b)) {
-                            supported = false;
-                            break;
-                        }
-                    }
-                    if (supported) {
-                        *node_backend_id = b;
-                        SET_CAUSE(node, "3.upg");
-                        break;
-                    }
-                }
-            }
-        }
-    }
-
-    // pass 4: assign backends to remaining src from dst and view_src
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        int * cur_backend_id = &tensor_backend_id(node);
-        if (node->view_src != NULL && *cur_backend_id == -1) {
-            *cur_backend_id = tensor_backend_id(node->view_src);
-            SET_CAUSE(node, "4.vsrc");
-        }
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
-                continue;
-            }
-            int * src_backend_id = &tensor_backend_id(src);
-            if (*src_backend_id == -1) {
-                if (src->view_src != NULL) {
-                    // views are always on the same backend as the source
-                    *src_backend_id = tensor_backend_id(src->view_src);
-                    SET_CAUSE(src, "4.vsrc");
-                } else {
-                    *src_backend_id = *cur_backend_id;
-                    SET_CAUSE(src, "4.cur");
-                }
-            }
-        }
-    }
-
-    // pass 5: split graph, find tensors that need to be copied
-    {
-        int i_split = 0;
-        struct ggml_backend_sched_split * split = &sched->splits[0];
-        // find the backend of the first split, skipping view ops
-        int i = 0;
-        for (; i < graph->n_nodes; i++) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (!ggml_is_view_op(node->op)) {
-                split->backend_id = tensor_backend_id(node);
-                break;
-            }
-        }
-        split->i_start = 0;
-        split->n_inputs = 0;
-        int cur_backend_id = split->backend_id;
-        for (; i < graph->n_nodes; i++) {
-            struct ggml_tensor * node = graph->nodes[i];
-
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-
-            const int node_backend_id = tensor_backend_id(node);
-
-            assert(node_backend_id != -1); // all nodes should be assigned by now
-
-            // check if we should start a new split based on the sources of the current node
-            bool need_new_split = false;
-            if (node_backend_id == cur_backend_id && split->n_inputs > 0) {
-                for (int j = 0; j < GGML_MAX_SRC; j++) {
-                    struct ggml_tensor * src = node->src[j];
-                    if (src == NULL) {
-                        continue;
-                    }
-                    // check if a weight is on a different backend
-                    // by starting a new split, the memory of the previously offloaded weights can be reused
-                    if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
-                        int src_backend_id = tensor_backend_id(src);
-                        if (src_backend_id != cur_backend_id) {
-                            need_new_split = true;
-                            break;
-                        }
-                    }
-                    // check if the split has too many inputs
-                    // FIXME: count the number of inputs instead of only checking when full
-                    if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
-                        const size_t id = hash_id(src);
-                        int src_backend_id = sched->hv_tensor_backend_ids[id];
-                        bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id);
-                        if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) {
-                            //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name);
-                            need_new_split = true;
-                            break;
-                        }
-                    }
-                }
-            }
-
-            if (node_backend_id != cur_backend_id || need_new_split) {
-                split->i_end = i;
-                i_split++;
-                if (i_split >= sched->splits_capacity) {
-                    sched->splits_capacity *= 2;
-                    sched->splits = realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split));
-                    GGML_ASSERT(sched->splits != NULL);
-                }
-                split = &sched->splits[i_split];
-                split->backend_id = node_backend_id;
-                split->i_start = i;
-                split->n_inputs = 0;
-                cur_backend_id = node_backend_id;
-            }
-
-            // find inputs that are not on the same backend
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * src = node->src[j];
-                if (src == NULL) {
-                    continue;
-                }
-
-                size_t src_id = hash_id(src);
-                const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
-                assert(src_backend_id != -1); // all inputs should be assigned by now
-
-                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
-                    if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
-                        ggml_backend_t backend = sched->backends[src_backend_id];
-                        for (int c = 0; c < sched->n_copies; c++) {
-                            struct ggml_tensor * tensor_copy;
-                            if (c == sched->cur_copy) {
-                                tensor_copy = src; // use the original tensor as the current copy
-                            } else {
-                                tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                                ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
-                            }
-                            if (sched->n_copies > 1) {
-                                ggml_set_input(tensor_copy);
-                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
-                            }
-                            tensor_id_copy(src_id, src_backend_id, c) = tensor_copy;
-                            SET_CAUSE(tensor_copy, "4.cpy");
-                        }
-                        int n_graph_inputs = sched->n_graph_inputs++;
-                        GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
-                        sched->graph_inputs[n_graph_inputs] = src;
-                    }
-                }
-
-                if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) {
-                    // create a copy of the input in the split's backend
-                    if (tensor_id_copy(src_id, cur_backend_id, 0) == NULL) {
-                        ggml_backend_t backend = sched->backends[cur_backend_id];
-                        for (int c = 0; c < sched->n_copies; c++) {
-                            struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                            ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
-                            if (sched->n_copies > 1) {
-                                ggml_set_input(tensor_copy);
-                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
-                            }
-                            tensor_id_copy(src_id, cur_backend_id, c) = tensor_copy;
-                            SET_CAUSE(tensor_copy, "4.cpy");
-                        }
-                        int n_inputs = split->n_inputs++;
-                        GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
-                        split->inputs[n_inputs] = src;
-                    }
-                    node->src[j] = tensor_id_copy(src_id, cur_backend_id, sched->cur_copy);
-                }
-            }
-        }
-        split->i_end = graph->n_nodes;
-        sched->n_splits = i_split + 1;
-    }
-
-    if (sched->debug) {
-        ggml_backend_sched_print_assignments(sched, graph);
-    }
-
-    // swap node_backend_ids and leaf _backend_ids with prevs
-    {
-        int * tmp = sched->node_backend_ids;
-        sched->node_backend_ids = sched->prev_node_backend_ids;
-        sched->prev_node_backend_ids = tmp;
-
-        tmp = sched->leaf_backend_ids;
-        sched->leaf_backend_ids = sched->prev_leaf_backend_ids;
-        sched->prev_leaf_backend_ids = tmp;
-    }
-
-    int graph_size = MAX(graph->n_nodes, graph->n_leafs) + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sched->n_copies;
-    if (sched->graph.size < graph_size) {
-        sched->graph.size = graph_size;
-        sched->graph.nodes = realloc(sched->graph.nodes, graph_size * sizeof(struct ggml_tensor *));
-        sched->graph.leafs = realloc(sched->graph.leafs, graph_size * sizeof(struct ggml_tensor *));
-        GGML_ASSERT(sched->graph.nodes != NULL);
-        GGML_ASSERT(sched->graph.leafs != NULL);
-    }
-    sched->graph.n_nodes = 0;
-    sched->graph.n_leafs = 0;
-
-    struct ggml_cgraph * graph_copy = &sched->graph;
-
-    for (int i = 0; i < sched->n_splits; i++) {
-        struct ggml_backend_sched_split * split = &sched->splits[i];
-        split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
-
-        // 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));
-
-            struct ggml_tensor * input = split->inputs[j];
-            const size_t input_id = hash_id(input);
-            struct ggml_tensor * input_cpy = tensor_id_copy(input_id, split->backend_id, sched->cur_copy);
-
-            // add a dependency to the input source so that it is not freed before the copy is done
-            struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
-            input_dep->src[0] = input;
-            sched->node_backend_ids[graph_copy->n_nodes] = sched->hv_tensor_backend_ids[input_id];
-            graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
-
-            // add a dependency to the input copy so that it is allocated at the start of the split
-            sched->node_backend_ids[graph_copy->n_nodes] = split->backend_id;
-            graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
-        }
-
-        for (int j = split->i_start; j < split->i_end; j++) {
-            assert(graph_copy->size > graph_copy->n_nodes);
-            sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]);
-            graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
-        }
-    }
-
-    if (sched->n_copies > 1) {
-        // add input copies as leafs so that they are allocated first
-        for (int i = 0; i < sched->n_graph_inputs; i++) {
-            struct ggml_tensor * input = sched->graph_inputs[i];
-            size_t id = hash_id(input);
-            int backend_id = tensor_backend_id(input);
-            for (int c = 0; c < sched->n_copies; c++) {
-                struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
-                sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
-                assert(graph_copy->size > graph_copy->n_leafs);
-                graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
-            }
-        }
-
-        for (int i = 0; i < sched->n_splits; i++) {
-            struct ggml_backend_sched_split * split = &sched->splits[i];
-            int backend_id = split->backend_id;
-            for (int j = 0; j < split->n_inputs; j++) {
-                struct ggml_tensor * input = split->inputs[j];
-                size_t id = hash_id(input);
-                for (int c = 0; c < sched->n_copies; c++) {
-                    struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
-                    sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
-                    assert(graph_copy->size > graph_copy->n_leafs);
-                    graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
-                }
-            }
-        }
-    }
-
-    // add leafs from the original graph
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
-        assert(graph_copy->size > graph_copy->n_leafs);
-        graph_copy->leafs[graph_copy->n_leafs++] = leaf;
-    }
-}
-
-static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
-    bool backend_ids_changed = false;
-    for (int i = 0; i < sched->graph.n_nodes; i++) {
-        if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i] &&
-            sched->bufts[sched->node_backend_ids[i]] != sched->bufts[sched->prev_node_backend_ids[i]]) {
-            backend_ids_changed = true;
-            break;
-        }
-    }
-    if (!backend_ids_changed) {
-        for (int i = 0; i < sched->graph.n_leafs; i++) {
-            if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i] &&
-                sched->bufts[sched->leaf_backend_ids[i]] != sched->bufts[sched->prev_leaf_backend_ids[i]]) {
-                backend_ids_changed = true;
-                break;
-            }
-        }
-    }
-
-    // allocate graph
-    if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
-        // the re-allocation may cause the split inputs to be moved to a different address
-        ggml_backend_sched_synchronize(sched);
-#ifndef NDEBUG
-        fprintf(stderr, "%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
-#endif
-        ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
-        if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
-            fprintf(stderr, "%s: failed to allocate graph\n", __func__);
-            return false;
-        }
-    }
-
-    return true;
-}
-
-static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t 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];
-        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];
-            struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy);
-
-            if (input->flags & GGML_TENSOR_FLAG_INPUT) {
-                // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
-                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);
-            } else {
-                // wait for the split backend to finish using the input before overwriting it
-                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                    ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } 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)) {
-                    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);
-                }
-            }
-        }
-
-        if (!sched->callback_eval) {
-            enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
-            if (ec != GGML_STATUS_SUCCESS) {
-                return ec;
-            }
-        } else {
-            // similar to ggml_backend_compare_graph_backend
-            for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
-                struct ggml_tensor * t = split->graph.nodes[j0];
-
-                // check if the user needs data from this node
-                bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);
-
-                int j1 = j0;
-
-                // determine the range [j0, j1] of nodes that can be computed together
-                while (!need && j1 < split->graph.n_nodes - 1) {
-                    t = split->graph.nodes[++j1];
-                    need = sched->callback_eval(t, true, sched->callback_eval_user_data);
-                }
-
-                struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
-
-                enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
-                if (ec != GGML_STATUS_SUCCESS) {
-                    return ec;
-                }
-
-                // TODO: pass backend to the callback, then the user can decide if they want to synchronize
-                ggml_backend_synchronize(split_backend);
-
-                if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
-                    break;
-                }
-
-                j0 = j1;
-            }
-        }
-
-        // record the event of this copy
-        if (split->n_inputs > 0) {
-            if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]);
-            }
-        }
-    }
-
-    sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
-
-    return GGML_STATUS_SUCCESS;
-}
-
-ggml_backend_sched_t ggml_backend_sched_new(
-        ggml_backend_t * backends,
-        ggml_backend_buffer_type_t * bufts,
-        int n_backends,
-        size_t graph_size,
-        bool parallel) {
-    GGML_ASSERT(n_backends > 0);
-    GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
-    GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
-
-    struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));
-
-    sched->debug = getenv("GGML_SCHED_DEBUG") != NULL;
-    sched->n_backends = n_backends;
-    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
-
-    // initialize hash table
-    // FIXME: needs to be size*2 to account for leafs (do it in graph_split instead)
-    sched->hash_set    = ggml_hash_set_new(graph_size);
-    sched->hv_tensor_backend_ids = malloc(sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
-    sched->hv_tensor_copies      = malloc(sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
-
-    const size_t ggml_sched_max_splits = graph_size; // at most there is one split for each node in the graph
-    const size_t nodes_size = graph_size + ggml_sched_max_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2;
-    sched->node_backend_ids = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
-    sched->leaf_backend_ids = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
-    sched->prev_node_backend_ids = calloc(nodes_size, sizeof(sched->prev_node_backend_ids[0]));
-    sched->prev_leaf_backend_ids = calloc(nodes_size, sizeof(sched->prev_leaf_backend_ids[0]));
-
-    sched->context_buffer_size = ggml_sched_max_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + ggml_graph_overhead_custom(graph_size, false);
-    sched->context_buffer = malloc(sched->context_buffer_size);
-
-    const int initial_splits_capacity = 16;
-    sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
-    sched->splits_capacity = initial_splits_capacity;
-
-    for (int b = 0; b < n_backends; b++) {
-        sched->backends[b] = backends[b];
-        sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
-        GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b]));
-        if (sched->n_copies > 1) {
-            for (int c = 0; c < sched->n_copies; c++) {
-                sched->events[b][c] = ggml_backend_event_new(backends[b]);
-            }
-        }
-    }
-
-    sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
-
-    ggml_backend_sched_reset(sched);
-
-    return sched;
-}
-
-void ggml_backend_sched_free(ggml_backend_sched_t sched) {
-    if (sched == NULL) {
-        return;
-    }
-    for (int b = 0; b < sched->n_backends; b++) {
-        for (int c = 0; c < sched->n_copies; c++) {
-            ggml_backend_event_free(sched->events[b][c]);
-        }
-    }
-    ggml_gallocr_free(sched->galloc);
-    ggml_free(sched->ctx);
-    ggml_hash_set_free(&sched->hash_set);
-    free(sched->splits);
-    free(sched->hv_tensor_backend_ids);
-    free(sched->hv_tensor_copies);
-    free(sched->node_backend_ids);
-    free(sched->leaf_backend_ids);
-    free(sched->prev_node_backend_ids);
-    free(sched->prev_leaf_backend_ids);
-    free(sched->context_buffer);
-    free(sched->graph.nodes);
-    free(sched->graph.leafs);
-    free(sched);
-}
-
-void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
-    // reset state for the next run
-    if (!sched->is_reset) {
-        ggml_hash_set_reset(&sched->hash_set);
-        memset(sched->hv_tensor_backend_ids, -1, sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
-        memset(sched->hv_tensor_copies,       0, sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
-        sched->is_reset = true;
-    }
-    sched->is_alloc = false;
-}
-
-bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
-    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
-
-    ggml_backend_sched_split_graph(sched, measure_graph);
-
-    if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
-        return false;
-    }
-
-    ggml_backend_sched_reset(sched);
-    ggml_backend_sched_synchronize(sched);
-
-    return true;
-}
-
-bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
-
-    ggml_backend_sched_split_graph(sched, graph);
-
-
-    if (!ggml_backend_sched_alloc_splits(sched)) {
-        return false;
-    }
-
-    sched->is_alloc = true;
-
-    return true;
-}
-
-enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
-    ggml_backend_sched_synchronize(sched);
-    return err;
-}
-
-enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    if (!sched->is_reset && !sched->is_alloc) {
-        ggml_backend_sched_reset(sched);
-    }
-
-    if (!sched->is_alloc) {
-        if (!ggml_backend_sched_alloc_graph(sched, graph)) {
-            return GGML_STATUS_ALLOC_FAILED;
-        }
-    }
-
-    return ggml_backend_sched_compute_splits(sched);
-}
-
-void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
-    for (int i = 0; i < sched->n_backends; i++) {
-        ggml_backend_synchronize(sched->backends[i]);
-    }
-}
-
-void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
-    sched->callback_eval = callback;
-    sched->callback_eval_user_data = user_data;
-}
-
-int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
-    return sched->n_splits;
-}
-
-int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
-    return sched->n_copies;
-}
-
-int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
-    return sched->n_backends;
-}
-
-ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
-    GGML_ASSERT(i >= 0 && i < sched->n_backends);
-    return sched->backends[i];
-}
-
-size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
-    int backend_index = ggml_backend_sched_backend_id(sched, backend);
-    GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
-
-    return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
-}
-
-void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
-    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;
-    SET_CAUSE(node, "usr");
-    sched->is_reset = false;
-}
-
-ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
-    int backend_index = tensor_backend_id(node);
-    if (backend_index == -1) {
-        return NULL;
-    }
-    return sched->backends[backend_index];
-}
-
-// utils
-
-void ggml_backend_view_init(struct ggml_tensor * tensor) {
-    GGML_ASSERT(tensor->buffer == NULL);
-    GGML_ASSERT(tensor->view_src != NULL);
-    GGML_ASSERT(tensor->view_src->buffer != NULL);
-    GGML_ASSERT(tensor->view_src->data != NULL);
-
-    tensor->buffer = tensor->view_src->buffer;
-    tensor->data = (char *)tensor->view_src->data + tensor->view_offs;
-    ggml_backend_buffer_init_tensor(tensor->buffer, tensor);
-}
-
-void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
-    GGML_ASSERT(tensor->buffer == NULL);
-    GGML_ASSERT(tensor->data == NULL);
-    GGML_ASSERT(tensor->view_src == NULL);
-    GGML_ASSERT(addr >= ggml_backend_buffer_get_base(buffer));
-    GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
-                (char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));
-
-    tensor->buffer = buffer;
-    tensor->data = addr;
-    ggml_backend_buffer_init_tensor(buffer, tensor);
-}
-
-static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies,
-    struct ggml_context * ctx_allocated, struct ggml_context * ctx_unallocated, struct ggml_tensor * src) {
-
-    GGML_ASSERT(src != NULL);
-    GGML_ASSERT(src->data && "graph must be allocated");
-
-    size_t id = ggml_hash_insert(&hash_set, src);
-    if (id == GGML_HASHSET_ALREADY_EXISTS) {
-        return node_copies[ggml_hash_find(&hash_set, src)];
-    }
-
-    struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src);
-    if (src->view_src != NULL) {
-        dst->view_src = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src);
-        dst->view_offs = src->view_offs;
-    }
-    dst->op = src->op;
-    memcpy(dst->op_params, src->op_params, sizeof(dst->op_params));
-    ggml_set_name(dst, src->name);
-
-    // copy src
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        struct ggml_tensor * s = src->src[i];
-        if (s == NULL) {
-            continue;
-        }
-        dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s);
-    }
-
-    node_copies[id] = dst;
-    return dst;
-}
-
-static void graph_copy_init_tensor(struct ggml_hash_set * hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
-    size_t id = ggml_hash_find(hash_set, src);
-    if (node_init[id]) {
-        return;
-    }
-    node_init[id] = true;
-
-    struct ggml_tensor * dst = node_copies[id];
-    if (dst->view_src != NULL) {
-        graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src);
-        ggml_backend_view_init(dst);
-    }
-    else {
-        ggml_backend_tensor_copy(src, dst);
-    }
-
-    // init src
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        struct ggml_tensor * s = src->src[i];
-        if (s == NULL) {
-            continue;
-        }
-        graph_copy_init_tensor(hash_set, node_copies, node_init, s);
-    }
-}
-
-struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
-    struct ggml_hash_set hash_set = ggml_hash_set_new(graph->visited_hash_set.size);
-    struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
-    bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));
-
-    struct ggml_init_params params = {
-        /* .mem_size   = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
-        /* .mem_buffer = */ NULL,
-        /* .no_alloc   = */ true
-    };
-
-    struct ggml_context * ctx_allocated = ggml_init(params);
-    struct ggml_context * ctx_unallocated = ggml_init(params);
-
-    if (ctx_allocated == NULL || ctx_unallocated == NULL) {
-        fprintf(stderr, "failed to allocate context for graph copy\n");
-        ggml_hash_set_free(&hash_set);
-        free(node_copies);
-        free(node_init);
-        ggml_free(ctx_allocated);
-        ggml_free(ctx_unallocated);
-        return (struct ggml_backend_graph_copy) {
-            /* .buffer           = */ NULL,
-            /* .ctx_allocated    = */ NULL,
-            /* .ctx_unallocated  = */ NULL,
-            /* .graph            = */ NULL,
-        };
-    }
-
-    // dup nodes
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node);
-    }
-
-    // allocate nodes
-    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
-    if (buffer == NULL) {
-        fprintf(stderr, "failed to allocate buffer for graph copy\n");
-        ggml_hash_set_free(&hash_set);
-        free(node_copies);
-        free(node_init);
-        ggml_free(ctx_allocated);
-        ggml_free(ctx_unallocated);
-        return (struct ggml_backend_graph_copy) {
-            /* .buffer           = */ NULL,
-            /* .ctx_allocated    = */ NULL,
-            /* .ctx_unallocated  = */ NULL,
-            /* .graph            = */ NULL,
-        };
-    }
-
-    //printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024);
-
-    // copy data and init views
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        graph_copy_init_tensor(&hash_set, node_copies, node_init, node);
-    }
-
-    // build graph copy
-    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(ctx_allocated, graph->size, false);
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        struct ggml_tensor * node_copy = node_copies[ggml_hash_find(&hash_set, node)];
-        graph_copy->nodes[i] = node_copy;
-    }
-    graph_copy->n_nodes = graph->n_nodes;
-
-    ggml_hash_set_free(&hash_set);
-    free(node_copies);
-    free(node_init);
-
-    return (struct ggml_backend_graph_copy) {
-        /* .buffer           = */ buffer,
-        /* .ctx_allocated    = */ ctx_allocated,
-        /* .ctx_unallocated  = */ ctx_unallocated,
-        /* .graph            = */ graph_copy,
-    };
-}
-
-void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) {
-    ggml_backend_buffer_free(copy.buffer);
-    ggml_free(copy.ctx_allocated);
-    ggml_free(copy.ctx_unallocated);
-}
-
-bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) {
-    struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph);
-    if (copy.buffer == NULL) {
-        return false;
-    }
-
-    struct ggml_cgraph * g1 = graph;
-    struct ggml_cgraph * g2 = copy.graph;
-
-    assert(g1->n_nodes == g2->n_nodes);
-
-    for (int i = 0; i < g1->n_nodes; i++) {
-        //printf("eval %d/%d\n", i, g1->n_nodes);
-        struct ggml_tensor * t1 = g1->nodes[i];
-        struct ggml_tensor * t2 = g2->nodes[i];
-
-        assert(t1->op == t2->op && ggml_are_same_layout(t1, t2));
-
-        struct ggml_cgraph g1v = ggml_graph_view(g1, i, i + 1);
-        struct ggml_cgraph g2v = ggml_graph_view(g2, i, i + 1);
-
-        ggml_backend_graph_compute(backend1, &g1v);
-        ggml_backend_graph_compute(backend2, &g2v);
-
-        if (ggml_is_view_op(t1->op)) {
-            continue;
-        }
-
-        // compare results, calculate rms etc
-        if (!callback(i, t1, t2, user_data)) {
-            break;
-        }
-    }
-
-    ggml_backend_graph_copy_free(copy);
-
-    return true;
-}

scripts/sync-ggml.last

@@ -1 +1 @@
-6ebf0cf75db1739b6c8b26ccca3f5029ab35fe4a
+e7fd7deec20ef1ced3eebe38802f3c2126fddfa4

src/whisper.cpp

@@ -1239,6 +1239,8 @@ static size_t aheads_masks_nbytes(struct whisper_aheads_masks & aheads_masks) {
 static ggml_backend_t whisper_backend_init_gpu(const whisper_context_params & params) {
     ggml_backend_t result = NULL;
 
+    ggml_log_set(g_state.log_callback, g_state.log_callback_user_data);
+
 #ifdef GGML_USE_CUDA
     if (params.use_gpu) {
         WHISPER_LOG_INFO("%s: using CUDA backend\n", __func__);
@@ -1252,7 +1254,6 @@ static ggml_backend_t whisper_backend_init_gpu(const whisper_context_params & pa
 #ifdef GGML_USE_METAL
     if (params.use_gpu) {
         WHISPER_LOG_INFO("%s: using Metal backend\n", __func__);
-        ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
         result = ggml_backend_metal_init();
         if (!result) {
             WHISPER_LOG_ERROR("%s: ggml_backend_metal_init() failed\n", __func__);