diff --git a/CMakeLists.txt b/CMakeLists.txt index 8c1f91f..e9bdeef 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -5,6 +5,9 @@ cmake_policy(SET CMP0042 NEW) project (llama-node) set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED true) +set(CMAKE_C_STANDARD 11) +set(CMAKE_C_STANDARD_REQUIRED true) if(NOT DEFINED napi_build_version) set(napi_build_version 6) @@ -77,6 +80,73 @@ file( "src/SaveSessionWorker.h" ) +if (LLAMA_QNN) + if (PLATFORM STREQUAL "linux" AND ARCH STREQUAL "x64") + set(QNN_PLATFORM "x86_64-linux-clang") + elseif (PLATFORM STREQUAL "linux" AND ARCH STREQUAL "arm64") + set(QNN_PLATFORM "aarch64-ubuntu-gcc7.5") + elseif (PLATFORM STREQUAL "win32" AND ARCH STREQUAL "x64") + set(QNN_PLATFORM "x86_64-windows-msvc") + elseif (PLATFORM STREQUAL "win32" AND ARCH STREQUAL "arm64") + set(QNN_PLATFORM "aarch64-windows-msvc") + endif() + + if (NOT QNN_PLATFORM) + message(FATAL_ERROR "QNN is not supported on this platform") + endif() + set(QNN_LIB_PATH ${QNN_ROOT}/lib/${QNN_PLATFORM}) + + file( + GLOB QNN_SO_FILES + "${QNN_LIB_PATH}/libc++*" + "${QNN_LIB_PATH}/libQnn*.so" + "${QNN_LIB_PATH}/Htp*.dll" + "${QNN_LIB_PATH}/Qnn*" + ) + + file(COPY ${QNN_SO_FILES} DESTINATION ${PLATFORM_BINARY_DIR}) + + file( + GLOB QNN_EXTRA_FILES + "${QNN_ROOT}/lib/hexagon-v*/unsigned/libQnn*Skel.so" + "${QNN_ROOT}/lib/hexagon-v*/unsigned/*.cat" + ) + + file(COPY ${QNN_EXTRA_FILES} DESTINATION ${PLATFORM_BINARY_DIR}) + + list(APPEND LINKS ${QNN_SO_FILES}) + + file( + GLOB QNN_HEADER_FILES + "src/ggml-qnn.h" + ) + + file( + GLOB QNN_SOURCE_FILES + "src/ggml-qnn.cpp" + ) + + target_compile_definitions(ggml PUBLIC GGML_USE_QNN) + target_include_directories(ggml PUBLIC ${QNN_ROOT}/include/QNN) + target_sources(ggml PRIVATE ${QNN_SOURCE_FILES} ${QNN_HEADER_FILES}) + target_include_directories(llama PRIVATE "src") + set_target_properties(ggml PROPERTIES CXX_STANDARD 17) + + # apply patches/qnn.patch to ggml + add_custom_command( + OUTPUT ${CMAKE_BUILD_DIR}/patch.log + COMMAND git apply ${CMAKE_SOURCE_DIR}/patches/qnn.patch + WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/src/llama.cpp + ) +else() + # undo patches/qnn.patch to ggml + add_custom_command( + OUTPUT ${CMAKE_BUILD_DIR}/patch.log + COMMAND git apply -R ${CMAKE_SOURCE_DIR}/patches/qnn.patch + WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/src/llama.cpp + ) +endif() + add_library(${PROJECT_NAME} SHARED ${SOURCE_FILES} ${CMAKE_JS_SRC}) set_target_properties(${PROJECT_NAME} PROPERTIES PREFIX "" SUFFIX ".node") target_link_libraries(${PROJECT_NAME} ${CMAKE_JS_LIB} llama ggml common) diff --git a/package.json b/package.json index 47b938b..fae134a 100644 --- a/package.json +++ b/package.json @@ -41,7 +41,8 @@ "bin/**/*", "scripts/*.js", "scripts/*.ts", - "src/**/*.{c,cc,cpp,h,hh,hpp,txt,cmake}", + "src/*", + "externals/**/*.{c,cc,cpp,h,hh,hpp,txt,cmake}", "lib/*.js", "lib/*.ts", "CMakeLists.txt" diff --git a/patches/qnn.patch b/patches/qnn.patch new file mode 100644 index 0000000..3bbe1e1 --- /dev/null +++ b/patches/qnn.patch @@ -0,0 +1,74 @@ +diff --git a/ggml-backend.c b/ggml-backend.c +index f5bdcf07..536a5767 100644 +--- a/ggml-backend.c ++++ b/ggml-backend.c +@@ -416,7 +416,7 @@ GGML_CALL static void ggml_backend_registry_init(void) { + } + + initialized = true; +- ++ printf("GGML_USE_CPU\n"); + 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 +@@ -445,6 +445,10 @@ GGML_CALL static void ggml_backend_registry_init(void) { + extern GGML_CALL void ggml_backend_kompute_reg_devices(void); + ggml_backend_kompute_reg_devices(); + #endif ++#ifdef GGML_USE_QNN ++ extern GGML_CALL void ggml_backend_qnn_reg_devices(void); ++ ggml_backend_qnn_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) { +diff --git a/llama.cpp b/llama.cpp +index 18d6297c..f2a39613 100644 +--- a/llama.cpp ++++ b/llama.cpp +@@ -17,6 +17,8 @@ + # include "ggml-sycl.h" + #elif defined(GGML_USE_KOMPUTE) + # include "ggml-kompute.h" ++#elif defined(GGML_USE_QNN) ++# include "ggml-qnn.h" + #endif + + #ifdef GGML_USE_METAL +@@ -1679,6 +1681,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { + buft = ggml_backend_opencl_buffer_type(); + #elif defined(GGML_USE_KOMPUTE) + buft = ggml_backend_kompute_buffer_type(gpu); ++#elif defined(GGML_USE_QNN) ++ buft = ggml_backend_qnn_buffer_type(gpu); + if (buft == nullptr) { + LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); + } +@@ -15293,8 +15297,9 @@ bool llama_supports_mlock(void) { + + bool llama_supports_gpu_offload(void) { + #if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ +- defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) ++ defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_QNN) + // Defined when llama.cpp is compiled with support for offloading model layers to GPU. ++ printf("llama_supports_gpu_offload: true\n"); + return true; + #else + return false; +@@ -15607,6 +15612,16 @@ struct llama_context * llama_new_context_with_model( + } + ctx->backends.push_back(backend); + } ++#elif defined(GGML_USE_QNN) ++ if (model->n_gpu_layers > 0) { ++ auto * backend = ggml_backend_qnn_init(model->main_gpu); ++ if (backend == nullptr) { ++ LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__); ++ llama_free(ctx); ++ return nullptr; ++ } ++ ctx->backends.push_back(backend); ++ } + #endif + ctx->backend_cpu = ggml_backend_cpu_init(); + if (ctx->backend_cpu == nullptr) { diff --git a/src/LlamaContext.cpp b/src/LlamaContext.cpp index d905de0..f63ae8b 100644 --- a/src/LlamaContext.cpp +++ b/src/LlamaContext.cpp @@ -57,6 +57,7 @@ LlamaContext::LlamaContext(const Napi::CallbackInfo &info) params.use_mmap = get_option(options, "use_mmap", true); params.numa = static_cast(get_option(options, "numa", 0)); + params.main_gpu = get_option(options, "main_gpu", 0); llama_backend_init(); llama_numa_init(params.numa); diff --git a/src/ggml-qnn.cpp b/src/ggml-qnn.cpp new file mode 100644 index 0000000..58b8607 --- /dev/null +++ b/src/ggml-qnn.cpp @@ -0,0 +1,5332 @@ +/* + * MIT license + * Copyright (C) 2024 Project KanTV + * SPDX-License-Identifier: MIT + * + * this is implementation of "PoC: Add Qualcomm mobile SoC native backend for GGML", https://github.com/zhouwg/kantv/issues/121 + * + * this is also the implementation of ggml QNN(Qualcomm Neural Network, aka AI Engine Direct) backend + * + * and will be submitted to upstream GGML/whisper.cpp/llama.cpp and modify from + * + * Copyright (C) 2024 Project KanTV + * + * to + * + * Copyright (C) 2024 GGML authors + * + * accordingly + * + * + * status: + * + * 1. core implementation(data path works fine as expected with whisper.cpp using QNN CPU/GPU backend on Qualcomm's SoC based low-end phone + * + * 2. core implementation(data path works fine as expected with whisper.cpp using QNN HTP(aka DSP) backend on Qualcomm's soC based high-end phone + * + * 3. GGML_OP_MUL_MAT & GGML_OP_MUL & GGML_OP_ADD using QNN API has been completed + * + * todo: + * + * 1. lack of implementation of other GGML-OPs using QNN API(only support GGML_OP_MUL_MAT, + * GGML_OP_MUL, GGML_OP_ADD, would be done by community in upstream GGML community) + * + * 2. only support FP32 / FP16 (other data type not used currently, would be done by community in upstream GGML community) + * + * 3. data type of input tensors and output tensor must be same(this is a big limitation) + * + * 4. QNN's RPC feature(which useful for QNN HTP(aka DSP) backend) not used + * + * 5. multi QNN backend(CPU/GPU/DSP) simultaneously not support + * + * 6. multithreading not work with QNN GPU/HTP(aka DSP) backend + * + */ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef __cplusplus + #include + using std::atomic_int; + using std::atomic_bool; + using std::atomic_load; + using std::atomic_fetch_sub; + using std::atomic_store; +#else /* not __cplusplus */ + #include +#endif /* __cplusplus */ + +#include "QnnTypes.h" +#include "QnnCommon.h" +#include "QnnContext.h" +#include "QnnBackend.h" +#include "QnnGraph.h" +#include "QnnProperty.h" +#include "QnnTensor.h" +#include "QnnInterface.h" +#include "Saver/QnnSaver.h" +#include "System/QnnSystemInterface.h" +#include "HTP/QnnHtpDevice.h" + +#include "ggml-qnn.h" + +#include "ggml-backend-impl.h" +// ================================================================================================= +// +// forward/external declaration +// +// ================================================================================================= +class qnn_instance; + +//TODO: should be removed because this is a workaround method during development stage +extern "C" void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +//static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); + +static void ggml_qnn_log_internal(ggml_log_level level, const char * file, const char * func, int line, const char * format, ...); + +#if (defined __ANDROID__) || (defined ANDROID) //Qualcomm's QNN could running on Windows over ARM(aka WoA) +extern "C" int __android_log_print(int prio, const char * tag, const char * fmt, ...) +__attribute__((__format__(printf, 3, 4))); +#endif + + + +// ================================================================================================= +// +// self-defined macro / data structure +// +// ================================================================================================= +#define RPCMEM_DEFAULT_FLAGS 1 +#define RPCMEM_HEAP_ID_SYSTEM 25 + +#define GGML_DUMP_TENSOR(tensor) ggml_tensor_dump(tensor, #tensor) + +#define GGML_QNN_LOGBUF_LEN 4096 +#define GGML_QNN_MAX_BUFFERS 128 +#define MATRIX_ROW_PADDING 512 + +#define BUF_MAJOR_MASK 0xFF000000 +#define BUF_CONTROL_BASE 0xEE000000 + +#define GGML_QNN_DEBUG 0 +#define GGML_QNN_TRACE 0 + +#define QNN_LOG_ERROR(...) ggml_qnn_log_internal(GGML_LOG_LEVEL_DEBUG, __FILE__, __FUNCTION__, __LINE__, __VA_ARGS__) +#define QNN_LOG_WARN(...) ggml_qnn_log_internal(GGML_LOG_LEVEL_DEBUG , __FILE__, __FUNCTION__, __LINE__, __VA_ARGS__) +#define QNN_LOG_INFO(...) ggml_qnn_log_internal(GGML_LOG_LEVEL_DEBUG , __FILE__, __FUNCTION__, __LINE__, __VA_ARGS__) + +#if GGML_QNN_DEBUG +#define QNN_LOG_DEBUG(...) ggml_qnn_log_internal(GGML_LOG_LEVEL_DEBUG, __FILE__, __FUNCTION__, __LINE__, __VA_ARGS__) +#else +#define QNN_LOG_DEBUG(...) +#endif + +#if GGML_QNN_TRACE +#define ENTER_FUNC() ggml_qnn_log_internal(GGML_LOG_LEVEL_DEBUG, __FILE__, __FUNCTION__, __LINE__, "enter %s", __FUNCTION__) + +#define LEAVE_FUNC() ggml_qnn_log_internal(GGML_LOG_LEVEL_DEBUG, __FILE__, __FUNCTION__, __LINE__, "leave %s", __FUNCTION__) + +#else + +#define ENTER_FUNC() + +#define LEAVE_FUNC() +#endif + + +#define VALIDATE(value, status) \ + do { \ + status = value; \ + if (status != QNN_SUCCESS) { \ + QNN_LOG_WARN("%s expected QNN_SUCCESS\n", #value); \ + return status; \ + } \ + } while (0) + +#define VALIDATE_TENSOR_VERSION(tensor, err) VALIDATE(validate_tensor_version(tensor), err) + +#define VALIDATE_OP_CONFIG_VERSION(op, err) VALIDATE(validate_opconfig_version(op), err) + +#define QNN_VER_PTR(x) (&((x).v1)) +#define QNN_OP_CFG_VALID(opConfig) ((opConfig).version == QNN_OPCONFIG_VERSION_1) + +#define QNN_OP_CFG_GET_NAME(opConfig) get_qnn_oponfig_name(opConfig) +#define QNN_OP_CFG_GET_PACKAGE_NAME(opConfig) get_qnn_opconfig_packagename(opConfig) +#define QNN_OP_CFG_GET_TYPE_NAME(opConfig) get_qnn_opconfig_typename(opConfig) +#define QNN_OP_CFG_GET_NUM_PARAMS(opConfig) get_qnn_opconfig_numparams(opConfig) +#define QNN_OP_CFG_GET_PARAMS(opConfig) get_qnn_opconfig_params(opConfig) +#define QNN_OP_CFG_GET_NUM_INPUTS(opConfig) get_qnn_opconfig_numinputs(opConfig) +#define QNN_OP_CFG_GET_INPUTS(opConfig) get_qnn_opconfig_inputs(opConfig) +#define QNN_OP_CFG_GET_NUM_OUTPUTS(opConfig) get_qnn_opconfig_numoutputs(opConfig) +#define QNN_OP_CFG_GET_OUTPUTS(opConfig) get_qnn_opconfig_outputs(opConfig) + +#define QNN_OP_CFG_SET_NAME(opConfig, value) set_qnn_opconfig_name(opConfig, value) +#define QNN_OP_CFG_SET_PACKAGE_NAME(opConfig, value) set_qnn_opconfig_packagename(opConfig, value) +#define QNN_OP_CFG_SET_TYPE_NAME(opConfig, value) set_qnn_opconfig_typename(opConfig, value) + +#define QNN_OP_CFG_SET_PARAMS(opConfig, numOfParams, params) \ + set_qnn_opconfig_params(opConfig, numOfParams, params) + +#define QNN_OP_CFG_SET_INPUTS(opConfig, numOfInputs, inputTensors) \ + set_qnn_opconfig_inputs(opConfig, numOfInputs, inputTensors) + +#define QNN_OP_CFG_SET_OUTPUTS(opConfig, numOfOutputs, outputTensors) \ + set_qnn_opconfig_outputs(opConfig, numOfOutputs, outputTensors) + +#define QNN_TENSOR_GET_ID(tensor) get_qnn_tensorid(tensor) +#define QNN_TENSOR_GET_NAME(tensor) get_qnn_tensorname(tensor) +#define QNN_TENSOR_GET_TYPE(tensor) get_qnn_tensortype(tensor) +#define QNN_TENSOR_GET_DATA_FORMAT(tensor) get_qnn_tensor_dataformat(tensor) +#define QNN_TENSOR_GET_DATA_TYPE(tensor) get_qnn_tensor_datatype(tensor) +#define QNN_TENSOR_GET_QUANT_PARAMS(tensor) get_qnn_tensor_quantparams(tensor) +#define QNN_TENSOR_GET_RANK(tensor) get_qnn_tensor_rank(tensor) +#define QNN_TENSOR_GET_DIMENSIONS(tensor) get_qnn_tensor_dimensions(tensor) +#define QNN_TENSOR_GET_MEM_TYPE(tensor) get_qnn_tensor_memtype(tensor) +#define QNN_TENSOR_GET_CLIENT_BUF(tensor) get_qnn_tensor_clientbuf(tensor) +#define QNN_TENSOR_GET_MEM_HANDLE(tensor) get_qnn_tensor_memhandle(tensor) + +#define QNN_TENSOR_SET_ID(tensor, value) set_qnn_tensor_id(tensor, value) +#define QNN_TENSOR_SET_NAME(tensor, value) set_qnn_tensor_name(tensor, value) +#define QNN_TENSOR_SET_TYPE(tensor, value) set_qnn_tensor_type(tensor, value) +#define QNN_TENSOR_SET_DATA_FORMAT(tensor, value) set_qnn_tensor_dataformat(tensor, value) +#define QNN_TENSOR_SET_DATA_TYPE(tensor, value) set_qnn_tensor_datatype(tensor, value) +#define QNN_TENSOR_SET_QUANT_PARAMS(tensor, value) set_qnn_tensor_quantparams(tensor, value) +#define QNN_TENSOR_SET_RANK(tensor, value) set_qnn_tensor_rank(tensor, value) +#define QNN_TENSOR_SET_DIMENSIONS(tensor, value) set_qnn_tensor_dimensions(tensor, value) +#define QNN_TENSOR_SET_MEM_TYPE(tensor, value) set_qnn_tensor_memtype(tensor, value) +#define QNN_TENSOR_SET_CLIENT_BUF(tensor, value) set_qnn_tensor_clientbuf(tensor, value) +#define QNN_TENSOR_SET_MEM_HANDLE(tensor, value) set_qnn_tensor_memhandle(tensor, value) + + + +using pfn_rpc_mem_init = void (*)(void); +using pfn_rpc_mem_deinit = void (*)(void); +using pfn_rpc_mem_alloc = void *(*)(int, uint32_t, int); +using pfn_rpc_mem_free = void (*)(void *); +using pfn_rpc_mem_to_fd = int (*)(void *); + +using _pfn_QnnSaver_initialize = decltype(QnnSaver_initialize); +using _pfn_QnnInterface_getProviders = decltype(QnnInterface_getProviders); +using _pfn_QnnSystemInterface_getProviders = decltype(QnnSystemInterface_getProviders); + + + +typedef struct qnn_buf_s qnn_buf_t; +typedef struct qnn_buf_s qnn_buf_buffer_t; +typedef struct buf_element_s buf_element_t; +typedef void (*ggml_qnn_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); +typedef void (*ggml_qnn_func_common_t)(const ggml_op ggmlop, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +enum class ggml_qnn_profile_level { + profile_off = 0, + profile_basic = 1, + profile_detail = 2 +}; + + +struct buf_element_s { + buf_element_t * next; + + unsigned char * mem; + unsigned char * content; /* start of raw content in mem */ + + uint32_t size ; /* size of content */ + int32_t max_size; /* size of pre-allocated memory pointed to by mem */ + uint32_t type; + void (*free_buffer) (buf_element_t * buf); + void * source; /* CPU, GPU, DSP, ... */ + int id; +} ; + + +struct qnn_buf_s { + buf_element_t * first, * last; + + size_t qnn_buf_size; + uint32_t qnn_buf_data_size; + void * qnn_buf_empty_cb_data; + const char * name; + + pthread_mutex_t mutex; + pthread_cond_t not_empty; + + void (*put) (qnn_buf_t * fifo, buf_element_t * buf); + + buf_element_t *(*get) (qnn_buf_t * fifo); + + void (*clear) (qnn_buf_t * fifo) ; + + int (*size) (qnn_buf_t * fifo); + + int (*num_free) (qnn_buf_t * fifo); + + uint32_t (*data_size) (qnn_buf_t * fifo); + + void (*destroy) (qnn_buf_t * fifo); + + buf_element_t * (*buffer_alloc) (qnn_buf_t * self); + + buf_element_t * (*buffer_try_alloc) (qnn_buf_t * self); + + buf_element_t * buffer_pool_top; + pthread_mutex_t buffer_pool_mutex; + pthread_cond_t buffer_pool_cond_not_empty; + int buffer_pool_num_free; + int buffer_pool_capacity; + int buffer_pool_buf_size; + void * buffer_pool_base; /* used to free mem pool */ +} ; + + +struct ggml_backend_qnn_context { + int device; + int threads; + char name[GGML_MAX_NAME]; + char lib[GGML_MAX_NAME]; + qnn_instance * instance; + qnn_buf_t * buffer_pool; + struct ggml_backend * backend; + QNN_INTERFACE_VER_TYPE raw_interface; + QNN_SYSTEM_INTERFACE_VER_TYPE raw_system_interface; +} ; + + +// ================================================================================================= +// +// static global variables +// +// ================================================================================================= +//TODO: should be removed for support multi QNN backend simultaneously +static ggml_backend_t g_qnn_backend = nullptr; + +//TODO: should be removed for support multi QNN backend simultaneously +static int g_current_device = 3; // 3 is the default ggml backend + +static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; +static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; +static void ggml_setup_op_has_task_pass(void) { + { // INIT + bool * p = GGML_OP_HAS_INIT; + + p[GGML_OP_ACC ] = true; + p[GGML_OP_MUL_MAT ] = true; + p[GGML_OP_MUL_MAT_ID ] = true; + p[GGML_OP_OUT_PROD ] = true; + p[GGML_OP_SET ] = true; + p[GGML_OP_GET_ROWS_BACK ] = true; + p[GGML_OP_DIAG_MASK_INF ] = true; + p[GGML_OP_DIAG_MASK_ZERO ] = true; + p[GGML_OP_CONV_TRANSPOSE_1D ] = true; + p[GGML_OP_CONV_TRANSPOSE_2D ] = true; + p[GGML_OP_FLASH_ATTN_BACK ] = true; + p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + p[GGML_OP_ADD_REL_POS ] = true; + } + + { // FINALIZE + bool * p = GGML_OP_HAS_FINALIZE; + + p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + } +} + +//use a prebuild static memory layout to avoid complex resource management, this method also used +//in GGML internal or FFmpeg + +//QNN cDSP and HTA backend would not be used currently, just focus on QNN CPU/GPU/HTP(aka DSP) backend currently +static struct ggml_backend_qnn_context g_qnn_mgr[GGML_QNN_MAX_DEVICES] = { + [QNN_CPU] = {.device = 0, .threads = 1, .name = "qnn-cpu", .lib = "libQnnCpu.so", .instance = nullptr, .buffer_pool = nullptr, .backend = nullptr, .raw_interface = nullptr, .raw_system_interface = nullptr}, + [QNN_GPU] = {.device = 1, .threads = 1, .name = "qnn-gpu", .lib = "libQnnGpu.so", .instance = nullptr, .buffer_pool = nullptr, .backend = nullptr, .raw_interface = nullptr, .raw_system_interface = nullptr}, + [QNN_HTP] = {.device = 2, .threads = 1, .name = "qnn-htp(aka dsp)", .lib = "libQnnHtp.so", .instance = nullptr, .buffer_pool = nullptr, .backend = nullptr, .raw_interface = nullptr, .raw_system_interface = nullptr}, +}; + + + +// ================================================================================================= +// +// internal helper functions +// +// ================================================================================================= +static inline int validate_tensor_version(Qnn_Tensor_t tensor) { + if (tensor.version != QNN_TENSOR_VERSION_1) { + QNN_LOG_WARN("validate_tensor_version() tensor %s, got unsupported version %d\n", + tensor.v1.name, + tensor.version); + return 1; + } + return 0; +} + + +static inline int validate_opconfig_version(Qnn_OpConfig_t opConfig) { + if (opConfig.version != QNN_OPCONFIG_VERSION_1) { + QNN_LOG_WARN("validate_opconfig_version() op %s, got unsupported version %d\n", + opConfig.v1.name, + opConfig.version); + return 1; + } + return 0; +} + + +static inline const char * get_qnn_oponfig_name(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.name; + } + return nullptr; +} + + +static inline const char * get_qnn_oponfig_name(const Qnn_OpConfig_t * opConfig) { + return get_qnn_oponfig_name(*opConfig); +} + + +static inline const char * get_qnn_opconfig_packagename(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.packageName; + } + return nullptr; +} + + +static inline const char * get_qnn_opconfig_packagename(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_packagename(*opConfig); +} + + +static inline const char * get_qnn_opconfig_typename(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.typeName; + } + return nullptr; +} + + +static inline const char * get_qnn_opconfig_typename(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_typename(*opConfig); +} + + +static inline uint32_t get_qnn_opconfig_numparams(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.numOfParams; + } + return 0u; +} + + +static inline uint32_t get_qnn_opconfig_numparams(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_numparams(*opConfig); +} + + +static inline const Qnn_Param_t * get_qnn_opconfig_params(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.params; + } + return nullptr; +} + + +static inline const Qnn_Param_t * get_qnn_opconfig_params(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_params(*opConfig); +} + + +static inline uint32_t get_qnn_opconfig_numinputs(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.numOfInputs; + } + return 0u; +} + + +static inline uint32_t get_qnn_opconfig_numinputs(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_numinputs(*opConfig); +} + + +static inline const Qnn_Tensor_t * get_qnn_opconfig_inputs(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.inputTensors; + } + return nullptr; +} + + +static inline const Qnn_Tensor_t * get_qnn_opconfig_inputs(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_inputs(*opConfig); +} + + +static inline uint32_t get_qnn_opconfig_numoutputs(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.numOfOutputs; + } + return 0u; +} + + +static inline uint32_t get_qnn_opconfig_numoutputs(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_numoutputs(*opConfig); +} + + +static inline const Qnn_Tensor_t * get_qnn_opconfig_outputs(const Qnn_OpConfig_t & opConfig) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + return opConfig.v1.outputTensors; + } + return nullptr; +} + + +static inline const Qnn_Tensor_t * get_qnn_opconfig_outputs(const Qnn_OpConfig_t * opConfig) { + return get_qnn_opconfig_outputs(*opConfig); +} + + +static inline void set_qnn_opconfig_name(Qnn_OpConfig_t & opConfig, const char * name) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + opConfig.v1.name = name; + } +} + + +static inline void set_qnn_opconfig_name(Qnn_OpConfig_t * opConfig, const char * name) { + set_qnn_opconfig_name(*opConfig, name); +} + + +static inline void set_qnn_opconfig_packagename(Qnn_OpConfig_t & opConfig, const char * packageName) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + opConfig.v1.packageName = packageName; + } +} + + +static inline void set_qnn_opconfig_packagename(Qnn_OpConfig_t * opConfig, const char * packageName) { + set_qnn_opconfig_packagename(*opConfig, packageName); +} + + +static inline void set_qnn_opconfig_typename(Qnn_OpConfig_t & opConfig, const char * typeName) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + opConfig.v1.typeName = typeName; + } +} + + +static inline void set_qnn_opconfig_typename(Qnn_OpConfig_t * opConfig, const char * typeName) { + set_qnn_opconfig_typename(*opConfig, typeName); +} + + +static inline void set_qnn_opconfig_params(Qnn_OpConfig_t & opConfig, + uint32_t numOfParams, + Qnn_Param_t * params) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + opConfig.v1.numOfParams = numOfParams; + opConfig.v1.params = params; + } +} + + +static inline void set_qnn_opconfig_params(Qnn_OpConfig_t * opConfig, + uint32_t numOfParams, + Qnn_Param_t * params) { + set_qnn_opconfig_params(*opConfig, numOfParams, params); +} + + +static inline void set_qnn_opconfig_inputs(Qnn_OpConfig_t & opConfig, + uint32_t numOfInputs, + Qnn_Tensor_t * inputTensors) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + opConfig.v1.numOfInputs = numOfInputs; + opConfig.v1.inputTensors = inputTensors; + } +} + + +static inline void set_qnn_opconfig_inputs(Qnn_OpConfig_t * opConfig, + uint32_t numOfInputs, + Qnn_Tensor_t * inputTensors) { + set_qnn_opconfig_inputs(*opConfig, numOfInputs, inputTensors); +} + + +static inline void set_qnn_opconfig_outputs(Qnn_OpConfig_t & opConfig, + uint32_t numOfOutputs, + Qnn_Tensor_t * outputTensors) { + if (opConfig.version == QNN_OPCONFIG_VERSION_1) { + opConfig.v1.numOfOutputs = numOfOutputs; + opConfig.v1.outputTensors = outputTensors; + } +} + + +static inline void set_qnn_opconfig_outputs(Qnn_OpConfig_t * opConfig, + uint32_t numOfOutputs, + Qnn_Tensor_t * outputTensors) { + set_qnn_opconfig_outputs(*opConfig, numOfOutputs, outputTensors); +} + + +static inline uint32_t get_qnn_tensorid(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.id; + } + return 0u; +} + + +static inline uint32_t get_qnn_tensorid(const Qnn_Tensor_t * tensor) { return get_qnn_tensorid(*tensor); } + + +static inline const char * get_qnn_tensorname(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.name; + } + return nullptr; +} + + +static inline const char * get_qnn_tensorname(const Qnn_Tensor_t * tensor) { + return get_qnn_tensorname(*tensor); +} + + +static inline Qnn_TensorType_t get_qnn_tensortype(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.type; + } + return QNN_TENSOR_TYPE_UNDEFINED; +} + + +static inline Qnn_TensorType_t get_qnn_tensortype(const Qnn_Tensor_t * tensor) { + return get_qnn_tensortype(*tensor); +} + + +static inline Qnn_TensorDataFormat_t get_qnn_tensor_dataformat(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.dataFormat; + } + return QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER; +} + + +static inline Qnn_TensorDataFormat_t get_qnn_tensor_dataformat(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_dataformat(*tensor); +} + + +static inline Qnn_DataType_t get_qnn_tensor_datatype(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.dataType; + } + return QNN_DATATYPE_UNDEFINED; +} + + +static inline Qnn_DataType_t get_qnn_tensor_datatype(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_datatype(*tensor); +} + + +static inline Qnn_QuantizeParams_t get_qnn_tensor_quantparams(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.quantizeParams; + } + return QNN_QUANTIZE_PARAMS_INIT; +} + + +static inline Qnn_QuantizeParams_t get_qnn_tensor_quantparams(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_quantparams(*tensor); +} + + +static inline uint32_t get_qnn_tensor_rank(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.rank; + } + return 0u; +} + + +static inline uint32_t get_qnn_tensor_rank(const Qnn_Tensor_t * tensor) { return get_qnn_tensor_rank(*tensor); } + + +static inline uint32_t * get_qnn_tensor_dimensions(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.dimensions; + } + return nullptr; +} + + +static inline uint32_t * get_qnn_tensor_dimensions(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_dimensions(*tensor); +} + + +static inline Qnn_TensorMemType_t get_qnn_tensor_memtype(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.memType; + } + return QNN_TENSORMEMTYPE_UNDEFINED; +} + + +static inline Qnn_TensorMemType_t get_qnn_tensor_memtype(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_memtype(*tensor); +} + + +static inline Qnn_ClientBuffer_t get_qnn_tensor_clientbuf(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.clientBuf; + } + return QNN_CLIENT_BUFFER_INIT; +} + + +static inline Qnn_ClientBuffer_t get_qnn_tensor_clientbuf(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_clientbuf(*tensor); +} + + +static inline Qnn_MemHandle_t get_qnn_tensor_memhandle(const Qnn_Tensor_t & tensor) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + return tensor.v1.memHandle; + } + return nullptr; +} + + +static inline Qnn_MemHandle_t get_qnn_tensor_memhandle(const Qnn_Tensor_t * tensor) { + return get_qnn_tensor_memhandle(*tensor); +} + + +static inline void set_qnn_tensor_id(Qnn_Tensor_t & tensor, uint32_t id) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.id = id; + } +} + + +static inline void set_qnn_tensor_id(Qnn_Tensor_t * tensor, uint32_t id) { set_qnn_tensor_id(*tensor, id); } + + +static inline void set_qnn_tensor_name(Qnn_Tensor_t & tensor, const char * name) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.name = name; + } +} + + +static inline void set_qnn_tensor_name(Qnn_Tensor_t * tensor, const char * name) { + set_qnn_tensor_name(*tensor, name); +} + + +static inline void set_qnn_tensor_type(Qnn_Tensor_t & tensor, Qnn_TensorType_t type) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.type = type; + } +} + + +static inline void set_qnn_tensor_type(Qnn_Tensor_t * tensor, Qnn_TensorType_t type) { + set_qnn_tensor_type(*tensor, type); +} + + +static inline void set_qnn_tensor_dataformat(Qnn_Tensor_t & tensor, Qnn_TensorDataFormat_t format) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.dataFormat = format; + } +} + + +static inline void set_qnn_tensor_dataformat(Qnn_Tensor_t * tensor, Qnn_TensorDataFormat_t format) { + set_qnn_tensor_dataformat(*tensor, format); +} + + +static inline void set_qnn_tensor_datatype(Qnn_Tensor_t & tensor, Qnn_DataType_t dataType) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.dataType = dataType; + } +} + + +static inline void set_qnn_tensor_datatype(Qnn_Tensor_t * tensor, Qnn_DataType_t dataType) { + set_qnn_tensor_datatype(*tensor, dataType); +} + + +static inline void set_qnn_tensor_quantparams(Qnn_Tensor_t & tensor, Qnn_QuantizeParams_t params) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.quantizeParams = params; + } +} + + +static inline void set_qnn_tensor_quantparams(Qnn_Tensor_t * tensor, Qnn_QuantizeParams_t params) { + set_qnn_tensor_quantparams(*tensor, params); +} + + +static inline void set_qnn_tensor_rank(Qnn_Tensor_t & tensor, uint32_t rank) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.rank = rank; + } +} + + +static inline void set_qnn_tensor_rank(Qnn_Tensor_t * tensor, uint32_t rank) { + set_qnn_tensor_rank(*tensor, rank); +} + + +static inline void set_qnn_tensor_dimensions(Qnn_Tensor_t & tensor, uint32_t * dims) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.dimensions = dims; + } +} + + +static inline void set_qnn_tensor_dimensions(Qnn_Tensor_t * tensor, uint32_t * dims) { + set_qnn_tensor_dimensions(*tensor, dims); +} + + +static inline void set_qnn_tensor_memtype(Qnn_Tensor_t & tensor, Qnn_TensorMemType_t memType) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.memType = memType; + } +} + + +static inline void set_qnn_tensor_memtype(Qnn_Tensor_t * tensor, Qnn_TensorMemType_t memType) { + set_qnn_tensor_memtype(*tensor, memType); +} + + +static inline void set_qnn_tensor_clientbuf(Qnn_Tensor_t & tensor, Qnn_ClientBuffer_t clientBuf) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.clientBuf = clientBuf; + } +} + + +static inline void set_qnn_tensor_clientbuf(Qnn_Tensor_t * tensor, Qnn_ClientBuffer_t clientBuf) { + set_qnn_tensor_clientbuf(*tensor, clientBuf); +} + + +static inline void set_qnn_tensor_memhandle(Qnn_Tensor_t & tensor, Qnn_MemHandle_t handle) { + if (tensor.version == QNN_TENSOR_VERSION_1) { + tensor.v1.memHandle = handle; + } +} + + +static inline void set_qnn_tensor_memhandle(Qnn_Tensor_t * tensor, Qnn_MemHandle_t handle) { + set_qnn_tensor_memhandle(*tensor, handle); +} + + + +static size_t memscpy(void * dst, size_t dstSize, const void * src, size_t copySize) { + if (!dst || !src || !dstSize || !copySize) + return 0; + + size_t minSize = dstSize < copySize ? dstSize : copySize; + + memcpy(dst, src, minSize); + + return minSize; +} + + +static char * ggml_qnn_strndup(const char * source, size_t maxlen) { + return ::strndup(source, maxlen); +} + + +static int deep_copy_qnn_tensors(Qnn_Tensor_t & src, Qnn_Tensor_t & dst) { + int err = 0; + VALIDATE_TENSOR_VERSION(src, err); + + dst.version = src.version; + QNN_TENSOR_SET_NAME( + dst, ggml_qnn_strndup(QNN_TENSOR_GET_NAME(src), std::string(QNN_TENSOR_GET_NAME(src)).size())); + if (QNN_TENSOR_GET_NAME(dst) == nullptr) { + return 1; + } + QNN_TENSOR_SET_ID(dst, QNN_TENSOR_GET_ID(src)); + QNN_TENSOR_SET_TYPE(dst, QNN_TENSOR_GET_TYPE(src)); + QNN_TENSOR_SET_DATA_FORMAT(dst, QNN_TENSOR_GET_DATA_FORMAT(src)); + QNN_TENSOR_SET_DATA_TYPE(dst, QNN_TENSOR_GET_DATA_TYPE(src)); + QNN_TENSOR_SET_MEM_TYPE(dst, QNN_TENSOR_GET_MEM_TYPE(src)); + + // Only metadata (i.e. non-static data) is copied from source to destination. The union still + // must be initialized so that the clientBuf/memHandle do not contain garbage data + if (QNN_TENSOR_GET_MEM_TYPE(src) == QNN_TENSORMEMTYPE_RAW) { + Qnn_ClientBuffer_t clientBuf = {nullptr, 0}; + QNN_TENSOR_SET_CLIENT_BUF(dst, clientBuf); + } else if (QNN_TENSOR_GET_MEM_TYPE(src) == QNN_TENSORMEMTYPE_MEMHANDLE) { + QNN_TENSOR_SET_MEM_HANDLE(dst, nullptr); + } else { + return 1; + } + + Qnn_QuantizeParams_t srcQParam = QNN_TENSOR_GET_QUANT_PARAMS(src); + Qnn_QuantizationEncoding_t encoding = srcQParam.quantizationEncoding; + if (encoding == QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET) { + // need to allocate and copy memory for scaleOffset as it is a pointer array + Qnn_QuantizeParams_t srcQParamCpy = srcQParam; + Qnn_AxisScaleOffset_t &axisScaleOffset = srcQParamCpy.axisScaleOffsetEncoding; + Qnn_ScaleOffset_t **scaleOffset = &axisScaleOffset.scaleOffset; + size_t scaleOffsetSize = axisScaleOffset.numScaleOffsets * sizeof(Qnn_ScaleOffset_t); + *scaleOffset = (Qnn_ScaleOffset_t *)malloc(scaleOffsetSize); + memscpy(*scaleOffset, + scaleOffsetSize, + srcQParam.axisScaleOffsetEncoding.scaleOffset, + scaleOffsetSize); + QNN_TENSOR_SET_QUANT_PARAMS(dst, srcQParamCpy); + } else if (encoding == QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET) { + // need to allocate and copy memory for scaleOffset as it is a pointer array + Qnn_QuantizeParams_t srcQParamCpy = srcQParam; + Qnn_BwAxisScaleOffset_t &bwAxisScaleOffset = srcQParamCpy.bwAxisScaleOffsetEncoding; + size_t scaleSize = bwAxisScaleOffset.numElements * sizeof(float); + float **scales = &bwAxisScaleOffset.scales; + int32_t **offsets = &bwAxisScaleOffset.offsets; + *scales = (float *)malloc(scaleSize); + memscpy(*scales, scaleSize, srcQParam.bwAxisScaleOffsetEncoding.scales, scaleSize); + + // Only copy offsets if present, nullptr implies all offsets are 0 + if (bwAxisScaleOffset.offsets != nullptr) { + size_t offsetSize = bwAxisScaleOffset.numElements * sizeof(int32_t); + *offsets = (int32_t *)malloc(offsetSize); + memscpy(*offsets, offsetSize, srcQParam.bwAxisScaleOffsetEncoding.offsets, offsetSize); + } + QNN_TENSOR_SET_QUANT_PARAMS(dst, srcQParamCpy); + } else { + QNN_TENSOR_SET_QUANT_PARAMS(dst, srcQParam); + } + + // need to allocate and copy memory for all the pointer members + uint32_t rank = QNN_TENSOR_GET_RANK(src); + //QNN_LOG_DEBUG("QNN tensor rank %d", rank); + QNN_TENSOR_SET_RANK(dst, rank); + size_t dim_size = rank * sizeof(uint32_t); + uint32_t * dimensions = (uint32_t *)malloc(dim_size); + if (dimensions == nullptr) { + QNN_LOG_WARN("deep_copy_qnn_tensors() allocation error while copying tensor %s\n", QNN_TENSOR_GET_NAME(src)); + return 1; + } + //QNN_LOG_DEBUG("%p", dimensions); + memscpy(dimensions, dim_size, QNN_TENSOR_GET_DIMENSIONS(src), dim_size); + QNN_TENSOR_SET_DIMENSIONS(dst, dimensions); + + return err; +} + + +static int free_qnn_tensor(Qnn_Tensor_t & tensor) { + //ENTER_FUNC(); + int err = 0; + VALIDATE_TENSOR_VERSION(tensor, err); + + //QNN_LOG_INFO("here"); + if (nullptr == QNN_TENSOR_GET_NAME(tensor)) { + QNN_LOG_INFO("it should not happen, pls check"); + } else { + //QNN_LOG_DEBUG("QNN tensor name %s", QNN_TENSOR_GET_NAME(tensor)); + free((void *) QNN_TENSOR_GET_NAME(tensor)); + } + if (nullptr == QNN_TENSOR_GET_DIMENSIONS(tensor)) { + QNN_LOG_INFO("it should not happen, pls check"); + } else { + //QNN_LOG_DEBUG("%p", QNN_TENSOR_GET_DIMENSIONS(tensor)); + //TODO:why crash in here? why pointer changed with mul_mat? + //memory leak after comment below line + //free(QNN_TENSOR_GET_DIMENSIONS(tensor)); + } + //LEAVE_FUNC(); + + return err; +} + + +static int free_qnn_tensors(Qnn_Tensor_t *& tensors, uint32_t numTensors) { + int err = 0; + + // free all pointer allocations in struct + for (size_t i = 0; i < numTensors; i++) { + free_qnn_tensor(tensors[i]); + } + free(tensors); + + return err; +} + + +static float ggml_tensor_sum_elements(const ggml_tensor * tensor) { + double sum = 0; + float value = 0; + std::ostringstream tmposs; + if (tensor->type == GGML_TYPE_F32) { + for (int h = 0; h < tensor->ne[3]; h++) { + for (int i = 0; i < tensor->ne[2]; i++) { + for (int j = 0; j < tensor->ne[1]; j++) { + for (int k = 0; k < tensor->ne[0]; k++) { + value = ((float *) tensor->data)[h * tensor->ne[2] + i * tensor->ne[1] + j * tensor->ne[0] + k]; + sum += value; + //QNN_LOG_DEBUG("[%d][%d][%d][%d]%.2f \t", h, i, j, k, value); + tmposs << std::setw(8) << std::fixed << std::setprecision(2) << value << "\t"; + } + if (strlen(tmposs.str().c_str()) > 4000) { + + } else { + QNN_LOG_DEBUG("%s", tmposs.str().c_str()); + } + tmposs.clear(); + tmposs.str(""); + QNN_LOG_DEBUG("\n"); + } + } + } + } + QNN_LOG_DEBUG("\n"); + return sum; +} + + +static void ggml_dump_tensor(const ggml_tensor * tensor, const char * name) { + QNN_LOG_DEBUG("dump ggml tensor %s\n", name); + QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", name, + tensor->type, ggml_type_name(tensor->type), + tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]); + float sum = ggml_tensor_sum_elements(tensor); + + //QNN_LOG_DEBUG("\n"); + //QNN_LOG_DEBUG("Sum of tensor %s is %6.2f\n", name, sum); +} + + +static uint32_t ggml_get_tensor_rank(const ggml_tensor * tensor) { + uint32_t rank = 0; + for (int i = 0; i < GGML_MAX_DIMS; i++) { + if ((0 != tensor->ne[i]) && (1 != tensor->ne[i])) { + rank++; + } + } + return rank; +} + + +//TODO: +//ref:explanation of k-quants, https://github.com/ggerganov/llama.cpp/pull/1684 +static Qnn_DataType_t qnn_datatype_from_ggml_datatype(enum ggml_type ggmltype) { + switch (ggmltype) { + case GGML_TYPE_Q4_0: + return QNN_DATATYPE_UFIXED_POINT_4; + case GGML_TYPE_Q4_1: + return QNN_DATATYPE_SFIXED_POINT_4; + case GGML_TYPE_Q8_0: + return QNN_DATATYPE_UFIXED_POINT_8; + case GGML_TYPE_Q8_1: + return QNN_DATATYPE_SFIXED_POINT_8; + case GGML_TYPE_F16: + return QNN_DATATYPE_FLOAT_16; + case GGML_TYPE_F32: + return QNN_DATATYPE_FLOAT_32; + + } + return QNN_DATATYPE_FLOAT_32; +} + + +//TODO: +static const char * qnn_opname_from_ggmlop(enum ggml_op ggmlop) { + switch (ggmlop) { + case GGML_OP_ADD: + return QNN_OP_ELEMENT_WISE_ADD; + case GGML_OP_MUL: + return QNN_OP_ELEMENT_WISE_MULTIPLY; + case GGML_OP_MUL_MAT: + return QNN_OP_MAT_MUL; + } + + return nullptr; +} + +static uint32_t ggml_get_tensor_data_size(const ggml_tensor * tensor) { + /* + size_t data_size = ggml_row_size(tensor->type, tensor->ne[0]); + size_t n_dims = ggml_get_tensor_rank(tensor); + for (int i = 1; i < n_dims; i++) { + data_size *= tensor->ne[i]; + } + + return data_size; + */ + return ggml_nbytes(tensor); +} + + +template +Fn load_qnn_functionpointers(void * handle, const char * function_name) { + return reinterpret_cast(dlsym(handle, function_name)); +} + + +static void qnn_xfree(void * ptr) { + if (nullptr != ptr) { + free(ptr); + ptr = nullptr; + } +} + + +static void * qnn_xmalloc(size_t size) { + void * ptr; + + if (!size) + size++; + + if ((ptr = calloc(1, size)) == nullptr) { + QNN_LOG_WARN("malloc(%d) failed: %s\n",size, strerror(errno)); + return nullptr; + } + + return ptr; +} + + +static void * qnn_xmalloc_aligned(size_t alignment, size_t size, void ** base) { + char * ptr; + + *base = ptr = static_cast(qnn_xmalloc(size + alignment)); + + while ((size_t) ptr % alignment) + ptr++; + + return ptr; +} + + +static void buffer_pool_free (buf_element_t * element) { + qnn_buf_t * self = (qnn_buf_t *) element->source; + + pthread_mutex_lock(&self->buffer_pool_mutex); + + element->next = self->buffer_pool_top; + self->buffer_pool_top = element; + + self->buffer_pool_num_free++; + if (self->buffer_pool_num_free > self->buffer_pool_capacity) { + QNN_LOG_DEBUG("TOO MANY FREE\n"); + } + + pthread_cond_signal (&self->buffer_pool_cond_not_empty); + + pthread_mutex_unlock (&self->buffer_pool_mutex); +} + + +static buf_element_t * buffer_pool_alloc (qnn_buf_t * self) { + buf_element_t * buf = nullptr; + int i; + + pthread_mutex_lock (&self->buffer_pool_mutex); + + while (self->buffer_pool_num_free < 2) { + pthread_cond_wait (&self->buffer_pool_cond_not_empty, &self->buffer_pool_mutex); + } + + buf = self->buffer_pool_top; + self->buffer_pool_top = self->buffer_pool_top->next; + self->buffer_pool_num_free--; + + buf->content = buf->mem; + buf->size = 0; + buf->type = 0; + + pthread_mutex_unlock (&self->buffer_pool_mutex); + + return buf; +} + + +static buf_element_t * buffer_pool_try_alloc (qnn_buf_t * self) { + buf_element_t * buf = nullptr; + + pthread_mutex_lock (&self->buffer_pool_mutex); + + if (self->buffer_pool_top) { + buf = self->buffer_pool_top; + self->buffer_pool_top = self->buffer_pool_top->next; + self->buffer_pool_num_free--; + } else { + buf = nullptr; + } + + pthread_mutex_unlock (&self->buffer_pool_mutex); + + if (buf) { + buf->content = buf->mem; + buf->size = 0; + } + + return buf; +} + + +static void qnn_buf_buffer_put(qnn_buf_t * fifo, buf_element_t * element) { + pthread_mutex_lock (&fifo->mutex); + + if (fifo->last) + fifo->last->next = element; + else + fifo->first = element; + + fifo->last = element; + element->next = nullptr; + fifo->qnn_buf_size++; + fifo->qnn_buf_data_size += element->size; + + QNN_LOG_DEBUG("put:index %d, fifo->size is %d, self->buffer_pool_num_free %d\n", element->id, fifo->qnn_buf_size, fifo->buffer_pool_num_free); + pthread_cond_signal (&fifo->not_empty); + + pthread_mutex_unlock (&fifo->mutex); +} + + +static buf_element_t * qnn_buf_buffer_get (qnn_buf_t * fifo) { + buf_element_t * buf = nullptr; + + pthread_mutex_lock (&fifo->mutex); +#if 0 + while (fifo->first == nullptr) { + pthread_cond_wait (&fifo->not_empty, &fifo->mutex); + } +#else + if (fifo->first == nullptr) { + pthread_mutex_unlock (&fifo->mutex); + return nullptr; + } +#endif + + buf = fifo->first; + + fifo->first = fifo->first->next; + if (fifo->first==nullptr) + fifo->last = nullptr; + + fifo->qnn_buf_size--; + fifo->qnn_buf_data_size -= buf->size; + + pthread_mutex_unlock (&fifo->mutex); + + return buf; +} + + +static void qnn_buf_buffer_clear (qnn_buf_t * fifo) { + buf_element_t * buf, * next, * prev; + + pthread_mutex_lock (&fifo->mutex); + + buf = fifo->first; + prev = nullptr; + + while (buf != nullptr) { + next = buf->next; + if ((buf->type & BUF_MAJOR_MASK) != BUF_CONTROL_BASE) { + if (prev) + prev->next = next; + else + fifo->first = next; + + if (!next) + fifo->last = prev; + + fifo->qnn_buf_size--; + fifo->qnn_buf_data_size -= buf->size; + + buf->free_buffer(buf); + } else { + prev = buf; + } + + buf = next; + } + + QNN_LOG_DEBUG("free buffers after clear: %d\n", fifo->buffer_pool_num_free); + pthread_mutex_unlock (&fifo->mutex); +} + + +static int qnn_buf_buffer_size (qnn_buf_t * self) { + int size = 0; + + pthread_mutex_lock(&self->mutex); + size = self->qnn_buf_size; + pthread_mutex_unlock(&self->mutex); + + return size; +} + + +static uint32_t qnn_buf_buffer_data_size (qnn_buf_t * self) { + uint32_t data_size; + + pthread_mutex_lock(&self->mutex); + data_size = self->qnn_buf_data_size; + pthread_mutex_unlock(&self->mutex); + + return data_size; +} + + +static int qnn_buf_buffer_num_free (qnn_buf_t * self) { + int buffer_pool_num_free = 0; + + pthread_mutex_lock(&self->mutex); + buffer_pool_num_free = self->buffer_pool_num_free; + pthread_mutex_unlock(&self->mutex); + + return buffer_pool_num_free; +} + + +static void qnn_buf_buffer_dispose (qnn_buf_t * self) { + ENTER_FUNC(); + buf_element_t * buf, * next; + int received = 0; + + self->clear( self ); + buf = self->buffer_pool_top; + + while (buf != nullptr) { + next = buf->next; + qnn_xfree(buf); + received++; + + buf = next; + } + + while (received < self->buffer_pool_capacity) { + buf = self->get(self); + qnn_xfree(buf); + received++; + } + + qnn_xfree(self->buffer_pool_base); + pthread_mutex_destroy(&self->mutex); + pthread_cond_destroy(&self->not_empty); + pthread_mutex_destroy(&self->buffer_pool_mutex); + pthread_cond_destroy(&self->buffer_pool_cond_not_empty); + qnn_xfree((void *)self->name); + qnn_xfree (self); + + LEAVE_FUNC(); +} + + +static qnn_buf_t * qnn_buf_new(const char * name, int num_buffers, uint32_t buf_size) { + int i = 0; + int alignment = 4; + qnn_buf_t * self = nullptr; + uint8_t * multi_buffer = nullptr; + + self = (qnn_buf_t*)qnn_xmalloc(sizeof(qnn_buf_t)); + if (nullptr == self) { + QNN_LOG_WARN("malloc memory failed\n"); + return nullptr; + } + + self->name = strdup(name); + self->first = nullptr; + self->last = nullptr; + self->qnn_buf_size = 0; + self->put = qnn_buf_buffer_put; + self->get = qnn_buf_buffer_get; + self->clear = qnn_buf_buffer_clear; + self->size = qnn_buf_buffer_size; + self->num_free = qnn_buf_buffer_num_free; + self->data_size = qnn_buf_buffer_data_size; + self->destroy = qnn_buf_buffer_dispose; + pthread_mutex_init (&self->mutex, nullptr); + pthread_cond_init (&self->not_empty, nullptr); + + + if (buf_size % alignment != 0) + buf_size += alignment - (buf_size % alignment); + + QNN_LOG_INFO("[%s]allocating %d Mbytes memory(alignment = %d)\n", name, (num_buffers * buf_size) / (1 << 20), alignment); + + multi_buffer = (uint8_t *)qnn_xmalloc_aligned (alignment, num_buffers * buf_size, &self->buffer_pool_base); + if (nullptr == multi_buffer) { + QNN_LOG_WARN("malloc memory failed\n"); + free(self); + return nullptr; + } + + self->buffer_pool_top = nullptr; + + pthread_mutex_init (&self->buffer_pool_mutex, nullptr); + pthread_cond_init (&self->buffer_pool_cond_not_empty, nullptr); + + self->buffer_pool_num_free = 0; + self->buffer_pool_capacity = num_buffers; + self->buffer_pool_buf_size = buf_size; + self->buffer_alloc = buffer_pool_alloc; + self->buffer_try_alloc = buffer_pool_try_alloc; + + for (i = 0; i < num_buffers; i++) { + buf_element_t * buf = nullptr; + + buf = (buf_element_t *)qnn_xmalloc(sizeof (buf_element_t)); + if (nullptr == buf) { + QNN_LOG_WARN("malloc memory failed"); + free(multi_buffer); + free(self); + return nullptr; + } + + buf->id = i; + buf->mem = multi_buffer; + multi_buffer += buf_size; + + buf->max_size = buf_size; + buf->free_buffer = buffer_pool_free; + buf->source = self; + + buffer_pool_free(buf); + } + + return self; +} + + +static const char * get_qnn_backend_name(int n_backend_type) { + switch (n_backend_type) { + case 0: + return "QNN-CPU"; + case 1: + return "QNN-GPU"; + case 2: + return "QNN-HTP(DSP)"; + case 3: + return "ggml"; //the default GGML backend, used to compare performance between QNN backend and the default GGML backend + +#if 0 //QNN cDSP and HTA backend would not be used currently, focus on QNN CPU/GPU/HTP(aka DSP) backend currently + case 3: + return "QNN-cDSP"; + case 4: + return "QNN-HTA"; +#endif + + default: + return "unknown"; + } +} + + +static intptr_t align_to(size_t alignment, intptr_t offset) { + return offset % alignment == 0 ? offset + : offset + + (static_cast(alignment) - + offset % static_cast(alignment)); +} + + +static void ggml_qnn_log_internal(ggml_log_level level, const char * file, const char * func, int line, const char * format, ...) { + static std::mutex ggml_qnn_log_internal_mutex; + static char s_ggml_qnn_log_internal_buf[GGML_QNN_LOGBUF_LEN]; + + { + std::lock_guard lock(ggml_qnn_log_internal_mutex); + va_list args; + va_start(args, format); + int len_prefix = snprintf(s_ggml_qnn_log_internal_buf, GGML_QNN_LOGBUF_LEN, "[%s, %d]: ", func, line); + int len = vsnprintf(s_ggml_qnn_log_internal_buf + len_prefix, GGML_QNN_LOGBUF_LEN - len_prefix, format, args); + if (len < (GGML_QNN_LOGBUF_LEN - len_prefix)) { +#if (defined __ANDROID__) || (defined ANDROID) + __android_log_print(level, "KANTV", "%s", s_ggml_qnn_log_internal_buf); //TODO:modify to llama.cpp before submit to upstream +#else + printf("%s", s_ggml_qnn_log_internal_buf); //Qualcomm's QNN could running on Window over ARM +#endif + } + va_end(args); + } +} + + +// ================================================================================================= +// +// wrapper class of Qualcomm QNN(Qualcomm Neural Network, aka Qualcomm AI Engine Direct) SDK +// +// ================================================================================================= +class qnn_interface { + +#define DEFINE_SHIM_FUNCTION_INTERFACE(F, pointer_name) \ + template \ + inline auto qnn_##F(Args... args) const { \ + return (_qnn_interface->QNN_INTERFACE_VER_NAME.pointer_name)( \ + std::forward(args)...); \ + } + + +#define DEFINE_SHIM_FUNCTION_SYS_INTERFACE(F, pointer_name) \ + template \ + inline auto qnn_##F(Args... args) const { \ + return (_qnn_sys_interface->QNN_SYSTEM_INTERFACE_VER_NAME.pointer_name)( \ + std::forward(args)...); \ + } + + friend class qnn_instance; + +public: + qnn_interface() = default; + + // QnnBackend + DEFINE_SHIM_FUNCTION_INTERFACE(backend_create, backendCreate); + + DEFINE_SHIM_FUNCTION_INTERFACE(backend_free, backendFree); + + DEFINE_SHIM_FUNCTION_INTERFACE(backend_register_op_package, backendRegisterOpPackage); + + DEFINE_SHIM_FUNCTION_INTERFACE(backend_validate_op_config, backendValidateOpConfig); + + DEFINE_SHIM_FUNCTION_INTERFACE(backend_get_api_version, backendGetApiVersion); + + // QnnDevice + DEFINE_SHIM_FUNCTION_INTERFACE(device_create, deviceCreate); + + DEFINE_SHIM_FUNCTION_INTERFACE(device_free, deviceFree); + + DEFINE_SHIM_FUNCTION_INTERFACE(device_get_infrastructure, deviceGetInfrastructure); + + DEFINE_SHIM_FUNCTION_INTERFACE(device_get_platform_info, deviceGetPlatformInfo); + + DEFINE_SHIM_FUNCTION_INTERFACE(device_get_info, deviceGetInfo); + + // QnnContext + DEFINE_SHIM_FUNCTION_INTERFACE(context_create, contextCreate); + + DEFINE_SHIM_FUNCTION_INTERFACE(context_get_binary_size, contextGetBinarySize); + + DEFINE_SHIM_FUNCTION_INTERFACE(context_get_binary, contextGetBinary); + + DEFINE_SHIM_FUNCTION_INTERFACE(context_create_from_binary, contextCreateFromBinary); + + DEFINE_SHIM_FUNCTION_INTERFACE(context_free, contextFree); + + // QnnGraph + DEFINE_SHIM_FUNCTION_INTERFACE(graph_create, graphCreate); + + DEFINE_SHIM_FUNCTION_INTERFACE(graph_add_node, graphAddNode); + + DEFINE_SHIM_FUNCTION_INTERFACE(graph_finalize, graphFinalize); + + DEFINE_SHIM_FUNCTION_INTERFACE(graph_execute, graphExecute); + + DEFINE_SHIM_FUNCTION_INTERFACE(graph_retrieve, graphRetrieve); + + // QnnLog + DEFINE_SHIM_FUNCTION_INTERFACE(log_create, logCreate); + + DEFINE_SHIM_FUNCTION_INTERFACE(log_free, logFree); + + DEFINE_SHIM_FUNCTION_INTERFACE(log_set_log_level, logSetLogLevel); + + // QnnProfile + DEFINE_SHIM_FUNCTION_INTERFACE(profile_create, profileCreate); + + DEFINE_SHIM_FUNCTION_INTERFACE(profile_get_events, profileGetEvents); + + DEFINE_SHIM_FUNCTION_INTERFACE(profile_get_sub_events, profileGetSubEvents); + + DEFINE_SHIM_FUNCTION_INTERFACE(profile_get_event_data, profileGetEventData); + + DEFINE_SHIM_FUNCTION_INTERFACE(profile_free, profileFree); + + // QnnMem + DEFINE_SHIM_FUNCTION_INTERFACE(mem_register, memRegister); + + DEFINE_SHIM_FUNCTION_INTERFACE(mem_de_register, memDeRegister); + + // QnnProperty + DEFINE_SHIM_FUNCTION_INTERFACE(property_has_capability, propertyHasCapability); + + // QnnTensor + DEFINE_SHIM_FUNCTION_INTERFACE(tensor_create_context_tensor, tensorCreateContextTensor); + + DEFINE_SHIM_FUNCTION_INTERFACE(tensor_create_graph_tensor, tensorCreateGraphTensor); + + // QnnSystem + DEFINE_SHIM_FUNCTION_SYS_INTERFACE(system_context_create, systemContextCreate); + + DEFINE_SHIM_FUNCTION_SYS_INTERFACE(system_context_get_binary_info, systemContextGetBinaryInfo); + + DEFINE_SHIM_FUNCTION_SYS_INTERFACE(system_context_free, systemContextFree); + + void set_qnn_interface(const QnnInterface_t * qnn_interface) { + _qnn_interface = qnn_interface; + } + + void set_qnn_system_interface(const QnnSystemInterface_t * qnn_sys_interface) { + _qnn_sys_interface = qnn_sys_interface; + } + + uint32_t get_backend_id() const { + return _qnn_interface->backendId; + } + + bool is_loaded() const { + return ((_qnn_sys_interface != nullptr) && (_qnn_interface != nullptr)); + } + +private: + const QnnInterface_t *_qnn_interface = nullptr; + + const QnnSystemInterface_t *_qnn_sys_interface = nullptr; +}; + + + +// ================================================================================================= +// +// wrapper class of Qualcomm QNN(Qualcomm Neural Network, aka Qualcomm AI Engine Direct) SDK +// +// and +// +// resource management of QNN resources for GGML's QNN backend +// ================================================================================================= +class qnn_instance { +public: + using BackendIdType = decltype(QnnInterface_t{}.backendId); + + explicit qnn_instance(const std::string & lib_path, const std::string & backend_name, + const std::string & model_name) : + _lib_path(std::move(lib_path)), + _backend_name(std::move(backend_name)), + _model_name(std::move(model_name)) {}; + + ~qnn_instance() { + } + + int qnn_init(const QnnSaver_Config_t ** saver_config); + + int qnn_finalize(); + + const qnn_interface &get_qnn_interface() { + if (!_qnn_interface.is_loaded()) { + QNN_LOG_WARN("pls check why _qnn_interface is not loaded\n"); + } + return _qnn_interface; + } + + + const QNN_INTERFACE_VER_TYPE &get_qnn_raw_interface() { + if (!_qnn_interface.is_loaded()) { + QNN_LOG_WARN("pls check why _qnn_interface is not loaded\n"); + } + return _qnn_raw_interface; + } + + const QNN_SYSTEM_INTERFACE_VER_TYPE &get_qnn_raw_system_interface() { + if (!_qnn_interface.is_loaded()) { + QNN_LOG_WARN("pls check why _qnn_interface is not loaded\n"); + } + return _qnn_raw_system_interface; + } + + const Qnn_LogHandle_t get_qnn_log_handle() { return _qnn_log_handle; } + + const Qnn_ProfileHandle_t get_qnn_profile_handle() { return _qnn_profile_handle; } + + const Qnn_DeviceHandle_t get_qnn_device_handle() { return _qnn_device_handle; } + + const Qnn_BackendHandle_t get_qnn_backend_handle() { return _qnn_backend_handle; } + + const Qnn_ContextHandle_t get_qnn_context_handle() { return _qnn_context_handle; } + + const QnnSystemContext_Handle_t get_qnn_system_handle() { return _qnn_system_handle; } + + const Qnn_GraphHandle_t get_qnn_graph_handle() { return _qnn_graph_handle; } + + + int init_qnn_graph(const char * graph_name, + bool debug, + uint8_t do_node_validation = 1, + const QnnGraph_Config_t ** graph_configs = nullptr + ); + + int finalize_qnn_graph(); + + int init_htp_perfinfra() { + QnnDevice_Infrastructure_t device_infra = nullptr; + int error = _qnn_raw_interface.deviceGetInfrastructure(&device_infra); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to get qnn device infra\n"); + return 1; + } + + QnnHtpDevice_Infrastructure_t *htp_infra = static_cast(device_infra); + QnnHtpDevice_PerfInfrastructure_t *htp_perfinfra = &htp_infra->perfInfra; + uint32_t power_configid = 1; + uint32_t device_id = 0; + uint32_t core_id = 0; + htp_perfinfra->createPowerConfigId(device_id, core_id, &power_configid); + _qnn_htp_perfinfra = htp_perfinfra; + _qnn_power_configid = power_configid; + + return 0; + } + + + int set_rpc_polling() { + if (_qnn_rpc_pollingtime > 0) { + QnnHtpPerfInfrastructure_PowerConfig_t rpc_pollingTime; + memset(&rpc_pollingTime, 0, sizeof(rpc_pollingTime)); + rpc_pollingTime.option = + QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_RPC_POLLING_TIME; + rpc_pollingTime.rpcPollingTimeConfig = _qnn_rpc_pollingtime; + const QnnHtpPerfInfrastructure_PowerConfig_t *powerConfigs[] = {&rpc_pollingTime, nullptr}; + if (_qnn_htp_perfinfra) { + _qnn_htp_perfinfra->setPowerConfig(_qnn_power_configid, powerConfigs); + } + } + return 0; + } + + + int set_high_performance_mode() { + if (nullptr == _qnn_htp_perfinfra) { + QNN_LOG_DEBUG("perf intra is null\n"); + return 1; + } + + QnnHtpPerfInfrastructure_PowerConfig_t powerConfig; + memset(&powerConfig, 0, sizeof(powerConfig)); + powerConfig.option = QNN_HTP_PERF_INFRASTRUCTURE_POWER_CONFIGOPTION_DCVS_V3; + powerConfig.dcvsV3Config.dcvsEnable = 0; + powerConfig.dcvsV3Config.setDcvsEnable = 1; + powerConfig.dcvsV3Config.contextId = _qnn_power_configid; + powerConfig.dcvsV3Config.powerMode = QNN_HTP_PERF_INFRASTRUCTURE_POWERMODE_PERFORMANCE_MODE; + powerConfig.dcvsV3Config.setSleepLatency = 1; // True to consider Latency parameter otherwise False + powerConfig.dcvsV3Config.setBusParams = 1; // True to consider Bus parameter otherwise False + powerConfig.dcvsV3Config.setCoreParams = 1; // True to consider Core parameter otherwise False + powerConfig.dcvsV3Config.sleepDisable = 0; // True to consider sleep/LPM modes, False to enable + powerConfig.dcvsV3Config.setSleepDisable = 0; // True to consider sleep disable/enable parameter otherwise False + // set Sleep latency parameter + uint32_t latencyValue = 40; + powerConfig.dcvsV3Config.sleepLatency = latencyValue; // range 40-2000 micro sec + // set Bus Clock Parameters (refer QnnHtpPerfInfrastructure_VoltageCorner_t enum) + powerConfig.dcvsV3Config.busVoltageCornerMin = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; + powerConfig.dcvsV3Config.busVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; + powerConfig.dcvsV3Config.busVoltageCornerMax = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; + // set Core Clock Parameters (refer QnnHtpPerfInfrastructure_VoltageCorner_t enum) + powerConfig.dcvsV3Config.coreVoltageCornerMin = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; + powerConfig.dcvsV3Config.coreVoltageCornerTarget = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; + powerConfig.dcvsV3Config.coreVoltageCornerMax = DCVS_VOLTAGE_VCORNER_MAX_VOLTAGE_CORNER; + // set power config with different performance parameters + const QnnHtpPerfInfrastructure_PowerConfig_t *powerConfigs[] = {&powerConfig, nullptr}; + + _qnn_htp_perfinfra->setPowerConfig(_qnn_power_configid, powerConfigs); + + return 0; + } + + std::string &get_qnn_graph_name() { return _graph_name; } + + bool is_rpcmem_initialized() { + return _rpcmem_initialized; + } + + void set_rpcmem_initialized(bool initialized) { + _rpcmem_initialized = initialized; + } + + int32_t rpcmem_to_fd(void * buf); + + int register_rpcmem(void * p_data, Qnn_Tensor_t * p_tensor); + + void unregister_rpcmem(); + + void *alloc_rpcmem(size_t bytes, size_t alignment); + + void free_rpcmem(void * buf); + + bool is_rpcmem_allocated(void * buf); + + bool is_rpcmem_registered(Qnn_MemHandle_t handle) { + return _qnn_mem_set.count(handle) != 0U; + } + +public: + //TODO:refine + std::map> _qnn_graph_map; + +private: + int load_system(); + + int unload_system(); + + int load_backend(std::string &lib_path, const QnnSaver_Config_t ** saver_config); + + int unload_backend(); + + void set_qnn_raw_interface(QNN_INTERFACE_VER_TYPE & raw_interface) { + _qnn_raw_interface = raw_interface; + } + + void set_qnn_raw_system_interface(QNN_SYSTEM_INTERFACE_VER_TYPE &raw_interface) { + _qnn_raw_system_interface = raw_interface; + } + +private: + static constexpr const int _required_num_providers = 1; + +private: + std::string _lib_path; + std::string _backend_name; + std::string _model_name; // prebuilt QNN model name, not used in currently + BackendIdType _backend_id; + + bool _debug_tensor = false; // flag to indicate if requested graph is to be run in debug mode + bool _do_node_validations = true; // flag to indicate whether all add_node calls need to be validated + QnnLog_Level_t _qnn_log_level = QNN_LOG_LEVEL_DEBUG; + + ggml_qnn_profile_level _profile_level = ggml_qnn_profile_level::profile_detail; + + qnn_interface _qnn_interface; + + void *_system_lib_handle = nullptr; + void *_model_lib_handle = nullptr; + + Qnn_GraphHandle_t _qnn_graph_handle = nullptr; + + Qnn_LogHandle_t _qnn_log_handle = nullptr; + + Qnn_ProfileHandle_t _qnn_profile_handle = nullptr; + + Qnn_DeviceHandle_t _qnn_device_handle = nullptr; + + Qnn_BackendHandle_t _qnn_backend_handle = nullptr; + + Qnn_ContextHandle_t _qnn_context_handle = nullptr; + + QnnSystemContext_Handle_t _qnn_system_handle = nullptr; + + QnnHtpDevice_PerfInfrastructure_t *_qnn_htp_perfinfra = nullptr; + uint32_t _qnn_power_configid = 1; + uint32_t _qnn_rpc_pollingtime = 9999; // 0-10000 us for high performing + + QNN_INTERFACE_VER_TYPE _qnn_raw_interface; + QNN_SYSTEM_INTERFACE_VER_TYPE _qnn_raw_system_interface; + + std::unordered_set _qnn_mem_set; + + static std::mutex _init_mutex; + static std::unordered_map _loaded_lib_handle; + static std::unordered_map _lib_path_to_backend_id; + static std::unordered_map _loaded_backend; + + void *_rpc_lib_handle = nullptr; + atomic_bool _rpcmem_initialized{false}; + pfn_rpc_mem_alloc _pfn_rpc_mem_alloc; + pfn_rpc_mem_free _pfn_rpc_mem_free; + pfn_rpc_mem_to_fd _pfn_rpc_mem_to_fd; + pfn_rpc_mem_init _pfn_rpc_mem_init; + pfn_rpc_mem_deinit _pfn_rpc_mem_deinit; + std::unordered_map _rpcmem_store_map; + + + std::string _graph_name; +}; + + + +// ================================================================================================= +// +// implementation of wrapper class +// +// ================================================================================================= +std::mutex qnn_instance::_init_mutex; + +std::unordered_map qnn_instance::_loaded_lib_handle; + +std::unordered_map qnn_instance::_lib_path_to_backend_id; + +std::unordered_map qnn_instance::_loaded_backend; + + +void * qnn_instance::alloc_rpcmem(size_t bytes, size_t alignment) { + if (!_rpcmem_initialized) { + QNN_LOG_WARN("rpc memory not initialized\n"); + return nullptr; + } + + auto allocate_bytes = static_cast(bytes + alignment); + void *buf = _pfn_rpc_mem_alloc(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS, allocate_bytes); + if (buf == nullptr) { + QNN_LOG_WARN("failed to allocate rpc memory\n"); + return nullptr; + } + + auto aligned_buf = reinterpret_cast(align_to(alignment, + reinterpret_cast(buf))); + bool status = _rpcmem_store_map.insert(std::pair(aligned_buf, buf)).second; + if (!status) { + QNN_LOG_WARN("failed to allocate rpc memory\n"); + _pfn_rpc_mem_free(buf); + } + + return aligned_buf; +} + + +void qnn_instance::free_rpcmem(void * buf) { + if (!is_rpcmem_initialized()) { + QNN_LOG_WARN("rpc memory not initialized\n"); + } else if (0 == _rpcmem_store_map.count(buf)) { + QNN_LOG_WARN("no allocated tensor\n"); + } else { + _pfn_rpc_mem_free(_rpcmem_store_map[buf]); + _rpcmem_store_map.erase(buf); + } +} + + +int32_t qnn_instance::rpcmem_to_fd(void *buf) { + int32_t mem_fd = -1; + if (!is_rpcmem_initialized()) { + QNN_LOG_WARN("rpc memory not initialized\n"); + } else { + mem_fd = _pfn_rpc_mem_to_fd(buf); + } + + return mem_fd; +} + + +int qnn_instance::register_rpcmem(void * p_data, Qnn_Tensor_t * p_tensor) { + if (nullptr == p_data || (nullptr == p_tensor)) { + QNN_LOG_WARN("invalid param\n"); + return 1; + } + + if (!is_rpcmem_initialized()) { + QNN_LOG_WARN("rpc memory not initialized\n"); + return 2; + } + + if (is_rpcmem_allocated(p_data)) { + QNN_LOG_WARN("rpc memory already allocated\n"); + //return 3; + } + if (is_rpcmem_registered((QNN_VER_PTR(*p_tensor)->memHandle))) { + QNN_LOG_WARN("tensor %s has been registered shared memory\n", (QNN_VER_PTR(*p_tensor)->name)); + return 4; + } + + int32_t mem_fd = rpcmem_to_fd(p_data); + if (-1 == mem_fd) { + QNN_LOG_WARN("failed to get file descriptor\n"); + return 5; + } + QNN_LOG_DEBUG("mem_fd %d\n", mem_fd); + Qnn_MemDescriptor_t descriptor = { + {QNN_VER_PTR(*p_tensor)->rank, QNN_VER_PTR(*p_tensor)->dimensions, nullptr}, + QNN_VER_PTR(*p_tensor)->dataType, + QNN_MEM_TYPE_ION, + {{mem_fd}}}; + Qnn_MemHandle_t handle = nullptr; + int error = QNN_SUCCESS; + error = _qnn_interface.qnn_mem_register( + _qnn_context_handle, + &descriptor, + /*numDescriptors=*/1, + &handle); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to register shared memory, error %d, %s\n", QNN_GET_ERROR_CODE(error), + strerror(error)); + return 6; + } else { + QNN_LOG_INFO("tensor %s successfully register shared memory\n", (QNN_VER_PTR(*p_tensor)->name)); + } + QNN_VER_PTR(*p_tensor)->memHandle = handle; + _qnn_mem_set.insert(handle); + + return 0; +} + + +void qnn_instance::unregister_rpcmem() { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + + if (_qnn_mem_set.empty()) { + QNN_LOG_WARN("no rpcmem registered\n"); + } + + for (auto &mem_handle : _qnn_mem_set) { + error = _qnn_interface.qnn_mem_de_register(&mem_handle, 1); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to unregister shared memory, error %d\n", QNN_GET_ERROR_CODE(error)); + } + } + _qnn_mem_set.clear(); +} + + +bool qnn_instance::is_rpcmem_allocated(void * buf) { + return _rpcmem_store_map.count(buf) != 0U; +} + + +int qnn_instance::load_backend(std::string & lib_path, const QnnSaver_Config_t ** saver_config) { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + QNN_LOG_DEBUG("lib_path:%s\n", lib_path.c_str()); + + void *lib_handle = dlopen(lib_path.c_str(), RTLD_NOW | RTLD_GLOBAL); + if (nullptr == lib_handle) { + QNN_LOG_WARN("can not open QNN library %s, with error: %s", lib_path.c_str(), dlerror()); + return 1; + } + + // load get_provider function + auto get_providers = load_qnn_functionpointers<_pfn_QnnInterface_getProviders *>(lib_handle, + "QnnInterface_getProviders"); + if (nullptr == get_providers) { + QNN_LOG_WARN("can not load symbol QnnInterface_getProviders : %s", dlerror()); + return 2; + } + + // get QnnInterface Providers + std::uint32_t num_providers = 0; + const QnnInterface_t **provider_list = nullptr; + error = get_providers(&provider_list, &num_providers); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to get providers, error %d", QNN_GET_ERROR_CODE(error)); + return 3; + } + QNN_LOG_DEBUG("num_providers=%d\n", num_providers); + if (num_providers != _required_num_providers) { + QNN_LOG_WARN("providers is %d instead of required %d", num_providers, _required_num_providers); + return 4; + } + + if (nullptr == provider_list) { + QNN_LOG_WARN("failed to get qnn interface providers\n"); + return 5; + } + bool found_valid_interface = false; + QNN_INTERFACE_VER_TYPE qnn_interface; + for (size_t idx = 0; idx < num_providers; idx++) { + if (QNN_API_VERSION_MAJOR == provider_list[idx]->apiVersion.coreApiVersion.major && + QNN_API_VERSION_MINOR <= provider_list[idx]->apiVersion.coreApiVersion.minor) { + found_valid_interface = true; + qnn_interface = provider_list[idx]->QNN_INTERFACE_VER_NAME; + break; + } + } + + if (!found_valid_interface) { + QNN_LOG_WARN("unable to find a valid qnn interface\n"); + return 6; + } else { + QNN_LOG_INFO("find a valid qnn interface\n"); + } + set_qnn_raw_interface(qnn_interface); + + BackendIdType backend_id = provider_list[0]->backendId; + _lib_path_to_backend_id[lib_path] = backend_id; + if (_loaded_backend.count(backend_id) > 0) { + QNN_LOG_WARN("lib_path %s is loaded, but backend %d already exists\n", + lib_path.c_str(), backend_id); + } + _loaded_backend[backend_id] = provider_list[0]; + if (_loaded_lib_handle.count(backend_id) > 0) { + QNN_LOG_WARN("closing %p\n", _loaded_lib_handle[backend_id]); + int dlclose_error = dlclose(_loaded_lib_handle[backend_id]); + if (dlclose_error != 0) { + QNN_LOG_WARN("fail to close %p with error %s\n", _loaded_lib_handle[backend_id], dlerror()); + } + } + _loaded_lib_handle[backend_id] = lib_handle; + _backend_id = backend_id; + +#if 0 + QnnSaver_Config_t outputdir_cfg; + outputdir_cfg.option = QNN_SAVER_CONFIG_OPTION_OUTPUT_DIRECTORY; + outputdir_cfg.outputDirectory = "/data/data/com.cdeos.kantv/qnn/"; + + QnnSaver_Config_t backendid_cfg; + backendid_cfg.option = QNN_SAVER_CONFIG_OPTION_BACKEND_ID; + backendid_cfg.backendId = _backend_id; + const QnnSaver_Config_t *saverCfg[] = {&outputdir_cfg, &backendid_cfg, nullptr}; + if (0 == QnnSaver_initialize(saverCfg)) { + QNN_LOG_INFO("QnnSaver_initialize successfully"); + } else { + QNN_LOG_INFO("QnnSaver_initialize failure"); + } +#endif + + auto saver_initialize = load_qnn_functionpointers<_pfn_QnnSaver_initialize *>( + _loaded_lib_handle[backend_id], "QnnSaver_initialize"); + if (nullptr != saver_initialize) { + error = saver_initialize(saver_config); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to saver_initialize，error %d", QNN_GET_ERROR_CODE(error)); + return 7; + } + } else { + QNN_LOG_WARN("saver_initialize is null\n"); + } + + return 0; +} + + +int qnn_instance::unload_backend() { + ENTER_FUNC(); + int dlclose_error = 0; + for (auto &it : _loaded_lib_handle) { + dlclose_error = dlclose(it.second); + if (dlclose_error != 0) { + QNN_LOG_WARN("failed to close QNN backend %d, error %s\n", it.first, dlerror()); + } + } + + _loaded_lib_handle.clear(); + _lib_path_to_backend_id.clear(); + _loaded_backend.clear(); + + LEAVE_FUNC(); + + return 0; +} + + +int qnn_instance::load_system() { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + + QNN_LOG_WARN("lib_path:%s\n", _lib_path.c_str()); + std::string system_lib_path = _lib_path + "libQnnSystem.so"; + QNN_LOG_DEBUG("system_lib_path:%s\n", system_lib_path.c_str()); + + _system_lib_handle = dlopen(system_lib_path.c_str(), RTLD_NOW | RTLD_LOCAL); + if (nullptr == _system_lib_handle) { + QNN_LOG_WARN("can not open QNN library %s, error: %s\n", system_lib_path.c_str(), dlerror()); + return 1; + } + + auto * get_providers = reinterpret_cast<_pfn_QnnSystemInterface_getProviders *>(dlsym( + _system_lib_handle, "QnnSystemInterface_getProviders")); + if (nullptr == get_providers) { + QNN_LOG_WARN("can not load QNN symbol QnnSystemInterface_getProviders: %s\n", dlerror()); + return 2; + } + + uint32_t num_providers = 0; + const QnnSystemInterface_t ** provider_list = nullptr; + error = get_providers(&provider_list, &num_providers); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to get providers, error %d\n", QNN_GET_ERROR_CODE(error)); + return 3; + } + + if (num_providers != _required_num_providers) { + QNN_LOG_WARN("providers is %d instead of required %d\n", num_providers, _required_num_providers); + return 4; + } + + if (nullptr == provider_list) { + QNN_LOG_WARN("can not get providers\n"); + return 5; + } + + QNN_SYSTEM_INTERFACE_VER_TYPE qnn_system_interface; + bool found_valid_system_interface = false; + for (size_t idx = 0; idx < num_providers; idx++) { + if (QNN_SYSTEM_API_VERSION_MAJOR == + provider_list[idx]->systemApiVersion.major && + QNN_SYSTEM_API_VERSION_MINOR <= + provider_list[idx]->systemApiVersion.minor) { + found_valid_system_interface = true; + qnn_system_interface = provider_list[idx]->QNN_SYSTEM_INTERFACE_VER_NAME; + break; + } + } + if (!found_valid_system_interface) { + QNN_LOG_WARN("unable to find a valid qnn system interface\n"); + return 6; + } else { + QNN_LOG_INFO("find a valid qnn system interface\n"); + } + set_qnn_raw_system_interface(qnn_system_interface); + + _qnn_interface.set_qnn_system_interface(provider_list[0]); + + _qnn_interface.qnn_system_context_create(&_qnn_system_handle); + if (nullptr == _qnn_system_handle) { + QNN_LOG_WARN("can not create QNN system contenxt\n"); + } else { + QNN_LOG_DEBUG("initialize qnn system successfully\n"); + } + + return 0; +} + + +int qnn_instance::unload_system() { + ENTER_FUNC(); + + int result = 0; + + if (nullptr == _system_lib_handle) { + QNN_LOG_DEBUG("system lib handle is null\n"); + return 1; + } + + if (nullptr != _qnn_system_handle) { + result = _qnn_interface.qnn_system_context_free(_qnn_system_handle); + if (result != QNN_SUCCESS) { + QNN_LOG_WARN("failed to free QNN system context\n"); + } + _qnn_system_handle = nullptr; + } + + int dlclose_error = dlclose(_system_lib_handle); + if (dlclose_error != 0) { + QNN_LOG_WARN("failed to close QnnSystem library, error %s\n", dlerror()); + return 2; + } + + _system_lib_handle = nullptr; + LEAVE_FUNC(); + + return 0; +} + + +static void ggml_qnn_logcallback(const char * fmt, + QnnLog_Level_t level, + uint64_t timestamp, + va_list argp) { + + static std::mutex log_mutex; + static unsigned char s_ggml_qnn_logbuf[GGML_QNN_LOGBUF_LEN]; + + const char * levelStr = ""; + switch (level) { + case QNN_LOG_LEVEL_ERROR: + levelStr = " ERROR "; + break; + case QNN_LOG_LEVEL_WARN: + levelStr = "WARNING"; + break; + case QNN_LOG_LEVEL_INFO: + levelStr = " INFO "; + break; + case QNN_LOG_LEVEL_DEBUG: + levelStr = " DEBUG "; + break; + case QNN_LOG_LEVEL_VERBOSE: + levelStr = "VERBOSE"; + break; + case QNN_LOG_LEVEL_MAX: + levelStr = "UNKNOWN"; + break; + } + + double ms = (double) timestamp / 1000000.0; + + { + std::lock_guard lock(log_mutex); + + int len_content = 0; + memset(s_ggml_qnn_logbuf, 0, GGML_QNN_LOGBUF_LEN); + len_content = vsnprintf(reinterpret_cast(s_ggml_qnn_logbuf), GGML_QNN_LOGBUF_LEN, fmt, argp); + //QNN_LOG_DEBUG("%8.1fms [%-7s] %s ", ms, levelStr, s_ggml_qnn_logbuf); + } +} + + +int qnn_instance::qnn_init(const QnnSaver_Config_t ** saver_config) { + BackendIdType backend_id = QNN_BACKEND_ID_NULL; + QNN_LOG_DEBUG("enter qni_init\n"); + + const std::lock_guard lock(_init_mutex); + + if (0 != load_system()) { + QNN_LOG_WARN("can not load QNN system lib, pls check why?\n"); + return 1; + } else { + QNN_LOG_DEBUG("load QNN system lib successfully\n"); + } + + std::string bakend_lib_path = _lib_path + _backend_name; + if (0 == _lib_path_to_backend_id.count(bakend_lib_path)) { + int is_load_ok = load_backend(bakend_lib_path, saver_config); + if (0 != is_load_ok) { + QNN_LOG_WARN("failed to load QNN backend\n"); + return 2; + } + } + + backend_id = _lib_path_to_backend_id[bakend_lib_path]; + if (0 == _loaded_backend.count(backend_id) || + 0 == _loaded_lib_handle.count(backend_id)) { + QNN_LOG_WARN("library %s is loaded but loaded backend count=%zu, loaded lib_handle count=%zu\n", + bakend_lib_path.c_str(), + _loaded_backend.count(backend_id), + _loaded_lib_handle.count(backend_id)); + return 3; + } + + _qnn_interface.set_qnn_interface(_loaded_backend[backend_id]); + +#if 1 + _qnn_interface.qnn_log_create(ggml_qnn_logcallback, _qnn_log_level, &_qnn_log_handle); +#else + _qnn_raw_interface.logCreate(ggml_qnn_logcallback, _qnn_log_level, &_qnn_log_handle); +#endif + if (nullptr == _qnn_log_handle) { + QNN_LOG_WARN("why failed to initialize qnn log\n"); //DSP backend not work on Qualcomm SoC based low-end phone + return 4; + } else { + QNN_LOG_DEBUG("initialize qnn log successfully\n"); + } + + + std::vector temp_backend_config; + _qnn_interface.qnn_backend_create(_qnn_log_handle, temp_backend_config.empty() ? nullptr + : temp_backend_config.data(), + &_qnn_backend_handle); + if (nullptr == _qnn_backend_handle) { + QNN_LOG_WARN("why failed to initialize qnn backend\n"); + return 5; + } else { + QNN_LOG_DEBUG("initialize qnn backend successfully\n"); + } + + if (nullptr != _qnn_raw_interface.propertyHasCapability) { + auto qnnStatus = _qnn_raw_interface.propertyHasCapability(QNN_PROPERTY_GROUP_DEVICE); + if (QNN_PROPERTY_NOT_SUPPORTED == qnnStatus) { + QNN_LOG_WARN("device property is not supported\n"); + } + if (QNN_PROPERTY_ERROR_UNKNOWN_KEY == qnnStatus) { + QNN_LOG_WARN("device property is not known to backend\n"); + } + } + + auto qnnStatus = _qnn_raw_interface.deviceCreate(_qnn_log_handle, nullptr, &_qnn_device_handle); + if (QNN_SUCCESS != qnnStatus && QNN_DEVICE_ERROR_UNSUPPORTED_FEATURE != qnnStatus) { + QNN_LOG_WARN("failed to create QNN device\n"); + } else { + QNN_LOG_INFO("create device successfully\n"); + } + + /* + std::vector temp_device_config; + _qnn_interface.qnn_device_create(_qnn_log_handle, temp_device_config.empty() ? nullptr : temp_device_config.data(), &_qnn_device_handle); + if (nullptr == _qnn_device_handle) { + QNN_LOG_WARN("why failed to initialize qnn device\n"); + //return 6; + } + */ + + if (ggml_qnn_profile_level::profile_off != _profile_level) { + QNN_LOG_INFO("profiling turned on; level = %d", _profile_level); + if (ggml_qnn_profile_level::profile_basic == _profile_level) { + QNN_LOG_INFO("basic profiling requested. creating Qnn Profile object\n"); + if (QNN_PROFILE_NO_ERROR != _qnn_raw_interface.profileCreate( + _qnn_backend_handle, QNN_PROFILE_LEVEL_BASIC, &_qnn_profile_handle)) { + QNN_LOG_WARN("unable to create profile handle in the backend\n"); + return 7; + } else { + QNN_LOG_DEBUG("initialize qnn profile successfully\n"); + } + } else if (ggml_qnn_profile_level::profile_detail == _profile_level) { + QNN_LOG_INFO("detailed profiling requested. Creating Qnn Profile object\n"); + if (QNN_PROFILE_NO_ERROR != _qnn_raw_interface.profileCreate( + _qnn_backend_handle, QNN_PROFILE_LEVEL_DETAILED, &_qnn_profile_handle)) { + QNN_LOG_WARN("unable to create profile handle in the backend\n"); + return 7; + } else { + QNN_LOG_DEBUG("initialize qnn profile successfully\n"); + } + } + } + +#ifdef __ANDROID__ + _rpc_lib_handle = dlopen("libcdsprpc.so", RTLD_NOW | RTLD_LOCAL); + if (nullptr == _rpc_lib_handle) { + QNN_LOG_WARN("failed to load qualcomm's rpc lib, error:%s\n", dlerror()); + return 9; + } else { + QNN_LOG_DEBUG("load rpcmem lib successfully\n"); + set_rpcmem_initialized(true); + } + _pfn_rpc_mem_init = reinterpret_cast(dlsym(_rpc_lib_handle, "rpcmem_init")); + _pfn_rpc_mem_deinit = reinterpret_cast(dlsym(_rpc_lib_handle, "rpcmem_deinit")); + _pfn_rpc_mem_alloc = reinterpret_cast(dlsym(_rpc_lib_handle,"rpcmem_alloc")); + _pfn_rpc_mem_free = reinterpret_cast(dlsym(_rpc_lib_handle, "rpcmem_free")); + _pfn_rpc_mem_to_fd = reinterpret_cast(dlsym(_rpc_lib_handle,"rpcmem_to_fd")); + if (nullptr == _pfn_rpc_mem_alloc || nullptr == _pfn_rpc_mem_free + || nullptr == _pfn_rpc_mem_to_fd) { + QNN_LOG_WARN("unable to access symbols in QNN RPC lib. dlerror(): %s", dlerror()); + dlclose(_rpc_lib_handle); + return 10; + } + + if (nullptr != _pfn_rpc_mem_init) // make Qualcomm's SoC based low-end phone happy + _pfn_rpc_mem_init(); +#endif + + std::vector temp_context_config; + _qnn_interface.qnn_context_create(_qnn_backend_handle, _qnn_device_handle, + temp_context_config.empty() ? nullptr + : temp_context_config.data(), + &_qnn_context_handle); + if (nullptr == _qnn_context_handle) { + QNN_LOG_WARN("why failed to initialize qnn context\n"); + return 8; + } else { + QNN_LOG_DEBUG("initialize qnn context successfully\n"); + } + + QNN_LOG_DEBUG("leave qni_init\n"); + + return 0; +} + + +//QNN SDK would/might/should release all allocated resource in SDK's internal +int qnn_instance::qnn_finalize() { + int ret_status = 0; + Qnn_ErrorHandle_t error = QNN_SUCCESS; + ENTER_FUNC(); + + if (nullptr != _pfn_rpc_mem_deinit) // make Qualcomm's SoC based low-end phone happy + _pfn_rpc_mem_deinit(); + +#ifdef __ANDROID__ + if (dlclose(_rpc_lib_handle) != 0) { + QNN_LOG_WARN("failed to unload qualcomm's rpc lib, error:%s\n", dlerror()); + } else { + QNN_LOG_DEBUG("succeed to close rpcmem lib\n"); + } +#endif + + if (nullptr != _qnn_context_handle) { + error = _qnn_interface.qnn_context_free(_qnn_context_handle, _qnn_profile_handle); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to free QNN context_handle: ID %u, error %d\n", + _qnn_interface.get_backend_id(), QNN_GET_ERROR_CODE(error)); + + } + _qnn_context_handle = nullptr; + } + + if (nullptr != _qnn_profile_handle) { + error = _qnn_interface.qnn_profile_free(_qnn_profile_handle); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to free QNN profile_handle: ID %u, error %d\n", + _qnn_interface.get_backend_id(), QNN_GET_ERROR_CODE(error)); + + } + _qnn_profile_handle = nullptr; + } + + if (nullptr != _qnn_device_handle) { + error = _qnn_interface.qnn_device_free(_qnn_device_handle); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to free QNN device_handle: ID %u, error %d\n", + _qnn_interface.get_backend_id(), QNN_GET_ERROR_CODE(error)); + + } + _qnn_device_handle = nullptr; + } + + if (nullptr != _qnn_backend_handle) { + error = _qnn_interface.qnn_backend_free(_qnn_backend_handle); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to free QNN backend_handle: ID %u, error %d\n", + _qnn_interface.get_backend_id(), QNN_GET_ERROR_CODE(error)); + } + _qnn_backend_handle = nullptr; + + } + + if (nullptr != _qnn_log_handle) { + error = _qnn_interface.qnn_log_free(_qnn_log_handle); + if (error != QNN_SUCCESS) { + QNN_LOG_WARN("failed to free QNN log_handle: ID %u, error %d\n", + _qnn_interface.get_backend_id(), QNN_GET_ERROR_CODE(error)); + } + _qnn_log_handle = nullptr; + } + + unload_backend(); + + unload_system(); + + LEAVE_FUNC(); + + return ret_status; +} + + +int qnn_instance::init_qnn_graph(const char * graph_name, bool debug, uint8_t do_node_validation, + const QnnGraph_Config_t ** graph_configs) { + int result = 0; + + ENTER_FUNC(); + + if (nullptr == graph_name) { + QNN_LOG_WARN("graph name is null\n"); + return 1; + } + + if (!_graph_name.empty()) { + QNN_LOG_WARN("qnn model for graph %s already initialized\n", graph_name); + return 2; + } + + if (!do_node_validation) { + QNN_LOG_WARN("node validation disabled, backend will not perform op validation prior to adding node\n"); + } + + _graph_name = graph_name; + _debug_tensor = debug; + _do_node_validations = do_node_validation; + + result = _qnn_raw_interface.graphCreate(_qnn_context_handle, graph_name, graph_configs, + &_qnn_graph_handle); + if (result != QNN_GRAPH_NO_ERROR || nullptr == _qnn_graph_handle) { + QNN_LOG_WARN("failed to create graph in qnn context\n"); + return 3; + } else { + QNN_LOG_INFO("succeed to create graph %s, %p\n", graph_name, _qnn_graph_handle); + } + + LEAVE_FUNC(); + return 0; +} + + +int qnn_instance::finalize_qnn_graph() { + ENTER_FUNC(); + if (nullptr != _qnn_graph_handle) { + if (_qnn_raw_interface.graphFinalize(_qnn_graph_handle, _qnn_profile_handle, nullptr) != + QNN_GRAPH_NO_ERROR) { + QNN_LOG_WARN("finalizing graph failure\n"); + //return 1; + } + } else { + QNN_LOG_DEBUG("qnn graph handle is null\n"); + } + + LEAVE_FUNC(); + + return 0; +} + + + +// ================================================================================================= +// +// implementation of GGML's QNN backend +// +// ================================================================================================= +//TODO: refine / improvement +static bool ggml_qnn_can_handle_op(const struct ggml_tensor * src0, const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + + //double check + bool supported_op = ((dst->op == GGML_OP_ADD) || (dst->op == GGML_OP_MUL) || (dst->op == GGML_OP_MUL_MAT)); + if (!supported_op) { + QNN_LOG_DEBUG("op %d(%s)not support", dst->op, ggml_op_name(dst->op)); + return false; + } + + + //make QNN SDK happy + if (dst->op == GGML_OP_ADD) { + return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) && + (dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16) && ((ne00 > 1 && ne01 > 1 && ne10 > 1 && ne11 > 1)) && + (ggml_get_tensor_rank(src0) == ggml_get_tensor_rank(src1)); + + } + + if (dst->op == GGML_OP_MUL_MAT) { +#if 0 // log output have significant effect to performance but useful during development stage + QNN_LOG_DEBUG("GGML_OP_MUL_MAT"); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, ggml_get_tensor_rank(src0), + src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2], + src0->nb[0], src0->nb[1], src0->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, ggml_get_tensor_rank(src1), + src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2], + src1->nb[0], src1->nb[1], src1->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, ggml_get_tensor_rank(dst), + dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0], + dst->nb[1], dst->nb[2]); +#endif + } + + //make QNN SDK happy + return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) && + (src0->type == src1->type) && (src0->type == dst->type) && ((ne00 > 1 && ne01 > 1 && ne10 > 1 && ne11 > 1)); + + +} + + + +#if 0 //this is dirty implementation before 04-21-2024, reserve it for purpose of reference +//ref: PoC-S26: offload simple f32 2x2 matrix addition operation to QNN CPU +// https://github.com/zhouwg/kantv/blob/kantv-poc-with-qnn/core/ggml/jni/ggml-jni-impl-external.cpp#L6736 +static void ggml_qnn_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, ggml_get_tensor_rank(src0), + src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2], + src0->nb[0], src0->nb[1], src0->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, ggml_get_tensor_rank(src1), + src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2], + src1->nb[0], src1->nb[1], src1->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, ggml_get_tensor_rank(dst), + dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0], + dst->nb[1], dst->nb[2]); + //GGML_DUMP_TENSOR(src0); + + int error = 0; + + int64_t n_begin_time = 0LL; + int64_t n_end_time = 0LL; + int64_t n_durtion = 0LL; + + qnn_instance * instance = nullptr; + + static Qnn_GraphHandle_t graph_handle = nullptr; + static Qnn_Tensor_t tensor_0 = QNN_TENSOR_INIT; + static Qnn_Tensor_t tensor_1 = QNN_TENSOR_INIT; + static Qnn_Tensor_t tensor_2 = QNN_TENSOR_INIT; + + Qnn_QuantizeParams_t quantize_param = QNN_QUANTIZE_PARAMS_INIT; + Qnn_OpConfig_t qnn_opconfig = QNN_OPCONFIG_INIT; + Qnn_Param_t qnn_params[] = {}; + + enum ggml_op ggmlop = GGML_OP_ADD; + Qnn_DataType_t src0_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t src1_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t dst_qnn_type = QNN_DATATYPE_FLOAT_32; + + ggml_time_init(); + n_begin_time = ggml_time_us(); + + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + + struct ggml_backend_qnn_context * ctx = (struct ggml_backend_qnn_context *) g_qnn_backend->context; + instance = ctx->instance; + + if (src0->type == GGML_TYPE_F16) + src0_qnn_type = QNN_DATATYPE_FLOAT_16; + if (src1->type == GGML_TYPE_F16) + src1_qnn_type = QNN_DATATYPE_FLOAT_16; + if (dst->type == GGML_TYPE_F16) + dst_qnn_type = QNN_DATATYPE_FLOAT_16; + + + QNN_INTERFACE_VER_TYPE qnn_raw_interface = ctx->raw_interface; + QNN_SYSTEM_INTERFACE_VER_TYPE qnn_raw_system_interface = ctx->raw_system_interface; + + bool graph_initialized = false; + std::string map_entry = std::string(ggml_op_name(ggmlop)); + if (qnn_graph_map.find(map_entry) != qnn_graph_map.end()) { + graph_initialized = true; + } + + if (!graph_initialized) { + error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), + ggml_op_name(ggmlop), nullptr, &graph_handle); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("can't create qnn graph handle, error = %d\n", error); + return; + } + tensor_0 = { + .version= QNN_TENSOR_VERSION_1, + {.v1= { + .id=0, + .name= "ggml_op_add_tensor_0", + .type= QNN_TENSOR_TYPE_APP_WRITE, + .dataFormat= QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER, + .dataType= src0_qnn_type, + .quantizeParams= {QNN_DEFINITION_UNDEFINED, + QNN_QUANTIZATION_ENCODING_UNDEFINED, + {.scaleOffsetEncoding= {.scale= 0.0000000000000000f, .offset= 0}}}, + .rank= ggml_get_tensor_rank(src0), + .dimensions=dimensions_input_0, + .memType= QNN_TENSORMEMTYPE_RAW, + {.clientBuf= {.data=nullptr, + .dataSize=0}}}} + }; + + tensor_1 = { + .version= QNN_TENSOR_VERSION_1, + {.v1= { + .id=0, + .name= "ggml_op_add_tensor_1", + .type= QNN_TENSOR_TYPE_APP_WRITE, + .dataFormat= QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER, + .dataType= src1_qnn_type, + .quantizeParams= {QNN_DEFINITION_UNDEFINED, + QNN_QUANTIZATION_ENCODING_UNDEFINED, + {.scaleOffsetEncoding= {.scale= 0.0000000000000000f, .offset= 0}}}, + .rank= ggml_get_tensor_rank(src1), + .dimensions=dimensions_input_1, + .memType= QNN_TENSORMEMTYPE_RAW, + {.clientBuf= {.data=nullptr, + .dataSize=0}}}} + }; + + tensor_2 = (Qnn_Tensor_t) { + .version= QNN_TENSOR_VERSION_1, + {.v1= { + .id=0, + .name= "ggml_op_add_tensor_2", + .type= QNN_TENSOR_TYPE_APP_READ, + .dataFormat= QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER, + .dataType= dst_qnn_type, + .quantizeParams= {QNN_DEFINITION_UNDEFINED, + QNN_QUANTIZATION_ENCODING_UNDEFINED, + {.scaleOffsetEncoding= {.scale= 0.0000000000000000f, .offset= 0}}}, + .rank= ggml_get_tensor_rank(dst), + .dimensions= dimensions_output, + .memType= QNN_TENSORMEMTYPE_RAW, + {.clientBuf= {.data=nullptr, + .dataSize=0}}}}}; + + + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, &tensor_0); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, &tensor_1); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, &tensor_2); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + + QNN_VER_PTR(tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + tensor_0, + tensor_1 + }; + + Qnn_Tensor_t tensor_outputs[] = { + tensor_2 + }; + + + Qnn_OpConfig_t opconfig = { + (Qnn_OpConfigVersion_t) 1, .v1 = { + "ggml_op_add", + QNN_OP_PACKAGE_NAME_QTI_AISW, + QNN_OP_ELEMENT_WISE_ADD, + 0, + qnn_params, + 2, + tensor_inputs, + 1, + tensor_outputs + } + }; + error = qnn_raw_interface.graphAddNode(graph_handle, opconfig); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphFinalize(graph_handle, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, + nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + qnn_graph_map[map_entry] = graph_handle; + } else { + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + QNN_VER_PTR(tensor_0)->dimensions = dimensions_input_0; + QNN_VER_PTR(tensor_0)->rank = ggml_get_tensor_rank(src0); + QNN_VER_PTR(tensor_0)->dataType = src0_qnn_type; + QNN_VER_PTR(tensor_1)->dimensions = dimensions_input_1; + QNN_VER_PTR(tensor_1)->rank = ggml_get_tensor_rank(src1); + QNN_VER_PTR(tensor_1)->dataType = src1_qnn_type; + QNN_VER_PTR(tensor_2)->dimensions = dimensions_output; + QNN_VER_PTR(tensor_2)->rank = ggml_get_tensor_rank(dst); + QNN_VER_PTR(tensor_2)->dataType = dst_qnn_type; + + QNN_VER_PTR(tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + tensor_0, + tensor_1 + }; + + Qnn_Tensor_t tensor_outputs[] = { + tensor_2 + }; + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + } + + n_end_time = ggml_time_us(); + n_durtion = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ggml_qnn_add : %lld milliseconds\n", n_durtion); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} +#endif +//ref: PoC-S26: offload simple f32 2x2 matrix addition operation to QNN CPU +// https://github.com/zhouwg/kantv/blob/kantv-poc-with-qnn/core/ggml/jni/ggml-jni-impl-external.cpp#L6736 +static void ggml_qnn_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + bool graph_initialized = false; + int64_t n_begin_time = 0LL; + int64_t n_end_time = 0LL; + int64_t n_durtion = 0LL; + + qnn_instance * instance = nullptr; + struct ggml_backend_qnn_context * ctx = nullptr; + + std::string graph_name = "ggml_op_qnn_add"; + Qnn_GraphHandle_t graph_handle = nullptr; + Qnn_Tensor_t * tensor_0 = nullptr; + Qnn_Tensor_t * tensor_1 = nullptr; + Qnn_Tensor_t * tensor_2 = nullptr; + + Qnn_QuantizeParams_t quantize_param = QNN_QUANTIZE_PARAMS_INIT; + Qnn_OpConfig_t qnn_opconfig = QNN_OPCONFIG_INIT; + //Qnn_Param_t qnn_params[] = {}; + + enum ggml_op ggmlop = GGML_OP_ADD; + Qnn_DataType_t src0_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t src1_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t dst_qnn_type = QNN_DATATYPE_FLOAT_32; + + + if ((nullptr == src0) || (nullptr == src1) || (nullptr == dst)) { + QNN_LOG_WARN("pls check why GGML tensor is null"); + return; + } + tensor_0 = (Qnn_Tensor_t *)src0->extra; + tensor_1 = (Qnn_Tensor_t *)src1->extra; + tensor_2 = (Qnn_Tensor_t *)dst->extra; + if ((nullptr == tensor_0) || (nullptr == tensor_1) || (nullptr == tensor_2)) { + QNN_LOG_WARN("pls check why QNN tensor is null"); + return; + } + ctx = (struct ggml_backend_qnn_context *)g_qnn_backend->context; + if (nullptr == ctx) { + QNN_LOG_WARN("pls check why backend ctx is null"); + return; + } + instance = ctx->instance; + if (nullptr == instance) { + QNN_LOG_WARN("pls check why qnn instance is null"); + return; + } + QNN_INTERFACE_VER_TYPE qnn_raw_interface = ctx->raw_interface; + + n_begin_time = ggml_time_us(); +#if 0 //it works fine with whisper.cpp and llama.cpp. comment them because focus on mulmat in llama.cpp inference since 04-23-2024 + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, ggml_get_tensor_rank(src0), + src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2], + src0->nb[0], src0->nb[1], src0->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, ggml_get_tensor_rank(src1), + src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2], + src1->nb[0], src1->nb[1], src1->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, ggml_get_tensor_rank(dst), + dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0], + dst->nb[1], dst->nb[2]); + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + QNN_LOG_DEBUG("tensor0 name %s", QNN_TENSOR_GET_NAME(tensor_0)); + QNN_LOG_DEBUG("tensor1 name %s", QNN_TENSOR_GET_NAME(tensor_1)); + QNN_LOG_DEBUG("tensor2 name %s", QNN_TENSOR_GET_NAME(tensor_2)); +#endif + + QNN_VER_PTR(*tensor_0)->type = QNN_TENSOR_TYPE_APP_WRITE; + QNN_VER_PTR(*tensor_1)->type = QNN_TENSOR_TYPE_APP_WRITE; + QNN_VER_PTR(*tensor_2)->type = QNN_TENSOR_TYPE_APP_READ; + + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + + + src0_qnn_type = qnn_datatype_from_ggml_datatype(src0->type); + src1_qnn_type = qnn_datatype_from_ggml_datatype(src1->type); + dst_qnn_type = qnn_datatype_from_ggml_datatype(dst->type); + + std::string map_entry = std::string(ggml_op_name(ggmlop)); + if (instance->_qnn_graph_map.find(map_entry) != instance->_qnn_graph_map.end()) { + graph_initialized = true; + auto & graph_item = instance->_qnn_graph_map[map_entry]; + graph_handle = std::get<0>(graph_item); + } + + if (!graph_initialized) { + graph_name = graph_name + "_" + std::to_string(ctx->threads) + src0->name + "_" + src1->name; + QNN_LOG_DEBUG("graph name %s", graph_name.c_str()); + //QnnGraph_Config_t graph_config; + //graph_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM; + //graph_config.customConfig = strdup(graph_name.c_str()); + //const QnnGraph_Config_t * p_graph_config = &graph_config; + error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), graph_name.c_str(), nullptr, &graph_handle); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("can't create qnn graph handle with graph name %s, error = %d\n", graph_name.c_str(), error); + return; + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_0); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_1); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_2); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + + QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + *tensor_0, + *tensor_1 + }; + Qnn_Tensor_t tensor_outputs[] = { + *tensor_2 + }; + Qnn_OpConfig_t opconfig = { + (Qnn_OpConfigVersion_t) 1, .v1 = { + "ggml_op_add", + QNN_OP_PACKAGE_NAME_QTI_AISW, + QNN_OP_ELEMENT_WISE_ADD, + 0, + nullptr, + 2, + tensor_inputs, + 1, + tensor_outputs + } + }; + error = qnn_raw_interface.graphAddNode(graph_handle, opconfig); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphFinalize(graph_handle, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + auto graph_item = std::make_tuple(graph_handle, tensor_0, tensor_1, tensor_2); + instance->_qnn_graph_map[map_entry] = graph_item; + } else { + auto & graph_item = instance->_qnn_graph_map[map_entry]; + graph_handle = std::get<0>(graph_item); + tensor_0 = std::get<1>(graph_item); + tensor_1 = std::get<2>(graph_item); + tensor_2 = std::get<3>(graph_item); + + //comment them because focus on mulmat in llama.cpp inference since 04-23-2024 + //QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + QNN_VER_PTR(*tensor_0)->dimensions = dimensions_input_0; + QNN_VER_PTR(*tensor_0)->rank = ggml_get_tensor_rank(src0); + QNN_VER_PTR(*tensor_0)->dataType = src0_qnn_type; + QNN_VER_PTR(*tensor_1)->dimensions = dimensions_input_1; + QNN_VER_PTR(*tensor_1)->rank = ggml_get_tensor_rank(src1); + QNN_VER_PTR(*tensor_1)->dataType = src1_qnn_type; + QNN_VER_PTR(*tensor_2)->dimensions = dimensions_output; + QNN_VER_PTR(*tensor_2)->rank = ggml_get_tensor_rank(dst); + QNN_VER_PTR(*tensor_2)->dataType = dst_qnn_type; + + QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + *tensor_0, + *tensor_1 + }; + Qnn_Tensor_t tensor_outputs[] = { + *tensor_2 + }; + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + } + n_end_time = ggml_time_us(); + n_durtion = (n_end_time - n_begin_time) / 1000; + //comment them because focus on mulmat in llama.cpp inference since 04-23-2024 + //QNN_LOG_DEBUG("duration of ggml_qnn_add : %lld milliseconds\n", n_durtion); + //QNN_LOG_DEBUG("call %s done\n", __func__); +} + + + +/* + * ggml_qnn_mul_mat was re-added as a standalone function because + * the following comments came from https://github.com/ggerganov/llama.cpp/pull/1632 + * MUL_MAT take most of the compute time (about 95%). So to speed up llama, we have to focus on MUL_MAT. + * We have three kinds of MUL_MAT to compute: + * mul_mat_f32: both src0 and src1 are F32. + * mul_mat_f16_f32: src0 is F16 and src1 is F32. + * mul_mat_q_f32: src0 is quantized (Q4_0, Q4_1, ...), and src1 is F32. +*/ + +static void ggml_qnn_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + bool graph_initialized = false; + int64_t n_begin_time = 0LL; + int64_t n_end_time = 0LL; + int64_t n_durtion = 0LL; + + qnn_instance * instance = nullptr; + struct ggml_backend_qnn_context * ctx = nullptr; + + std::string graph_name = "ggml_op_qnn_mul_mat"; + Qnn_GraphHandle_t graph_handle = nullptr; + Qnn_Tensor_t * tensor_0 = nullptr; + Qnn_Tensor_t * tensor_1 = nullptr; + Qnn_Tensor_t * tensor_2 = nullptr; + + Qnn_QuantizeParams_t quantize_param = QNN_QUANTIZE_PARAMS_INIT; + Qnn_OpConfig_t qnn_opconfig = QNN_OPCONFIG_INIT; + //Qnn_Param_t qnn_params[] = {}; + + enum ggml_op ggmlop = GGML_OP_ADD; + Qnn_DataType_t src0_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t src1_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t dst_qnn_type = QNN_DATATYPE_FLOAT_32; + + + if ((nullptr == src0) || (nullptr == src1) || (nullptr == dst)) { + QNN_LOG_WARN("pls check why GGML tensor is null"); + return; + } + tensor_0 = (Qnn_Tensor_t *)src0->extra; + tensor_1 = (Qnn_Tensor_t *)src1->extra; + tensor_2 = (Qnn_Tensor_t *)dst->extra; + if ((nullptr == tensor_0) || (nullptr == tensor_1) || (nullptr == tensor_2)) { + QNN_LOG_WARN("pls check why QNN tensor is null"); + return; + } + ctx = (struct ggml_backend_qnn_context *)g_qnn_backend->context; + if (nullptr == ctx) { + QNN_LOG_WARN("pls check why backend ctx is null"); + return; + } + instance = ctx->instance; + if (nullptr == instance) { + QNN_LOG_WARN("pls check why qnn instance is null"); + return; + } + QNN_INTERFACE_VER_TYPE qnn_raw_interface = ctx->raw_interface; + + n_begin_time = ggml_time_us(); + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, ggml_get_tensor_rank(src0), + src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2], + src0->nb[0], src0->nb[1], src0->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, ggml_get_tensor_rank(src1), + src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2], + src1->nb[0], src1->nb[1], src1->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, ggml_get_tensor_rank(dst), + dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0], + dst->nb[1], dst->nb[2]); + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + QNN_LOG_DEBUG("tensor0 name %s", QNN_TENSOR_GET_NAME(tensor_0)); + QNN_LOG_DEBUG("tensor1 name %s", QNN_TENSOR_GET_NAME(tensor_1)); + QNN_LOG_DEBUG("tensor2 name %s", QNN_TENSOR_GET_NAME(tensor_2)); + + QNN_VER_PTR(*tensor_0)->type = QNN_TENSOR_TYPE_APP_WRITE; + QNN_VER_PTR(*tensor_1)->type = QNN_TENSOR_TYPE_APP_WRITE; + QNN_VER_PTR(*tensor_2)->type = QNN_TENSOR_TYPE_APP_READ; + + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + + src0_qnn_type = qnn_datatype_from_ggml_datatype(src0->type); + src1_qnn_type = qnn_datatype_from_ggml_datatype(src1->type); + dst_qnn_type = qnn_datatype_from_ggml_datatype(dst->type); + + std::string map_entry = std::string(ggml_op_name(ggmlop)); + if (instance->_qnn_graph_map.find(map_entry) != instance->_qnn_graph_map.end()) { + graph_initialized = true; + auto & graph_item = instance->_qnn_graph_map[map_entry]; + graph_handle = std::get<0>(graph_item); + } + + if (!graph_initialized) { + graph_name = graph_name + "_" + std::to_string(ctx->threads) + src0->name + "_" + src1->name; + QNN_LOG_DEBUG("graph name %s", graph_name.c_str()); + error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), graph_name.c_str(), nullptr, &graph_handle); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("can't create qnn graph handle with graph name %s, error = %d\n", graph_name.c_str(), error); + return; + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_0); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_1); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_2); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + + QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + *tensor_0, + *tensor_1 + }; + Qnn_Tensor_t tensor_outputs[] = { + *tensor_2 + }; + Qnn_OpConfig_t opconfig = { + (Qnn_OpConfigVersion_t) 1, .v1 = { + "ggml_op_mul_mat", + QNN_OP_PACKAGE_NAME_QTI_AISW, + QNN_OP_MAT_MUL, + 0, + nullptr, + 2, + tensor_inputs, + 1, + tensor_outputs + } + }; + error = qnn_raw_interface.graphAddNode(graph_handle, opconfig); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphFinalize(graph_handle, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + auto graph_item = std::make_tuple(graph_handle, tensor_0, tensor_1, tensor_2); + instance->_qnn_graph_map[map_entry] = graph_item; + } else { + auto & graph_item = instance->_qnn_graph_map[map_entry]; + graph_handle = std::get<0>(graph_item); + tensor_0 = std::get<1>(graph_item); + tensor_1 = std::get<2>(graph_item); + tensor_2 = std::get<3>(graph_item); + + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + QNN_VER_PTR(*tensor_0)->dimensions = dimensions_input_0; + QNN_VER_PTR(*tensor_0)->rank = ggml_get_tensor_rank(src0); + QNN_VER_PTR(*tensor_0)->dataType = src0_qnn_type; + QNN_VER_PTR(*tensor_1)->dimensions = dimensions_input_1; + QNN_VER_PTR(*tensor_1)->rank = ggml_get_tensor_rank(src1); + QNN_VER_PTR(*tensor_1)->dataType = src1_qnn_type; + QNN_VER_PTR(*tensor_2)->dimensions = dimensions_output; + QNN_VER_PTR(*tensor_2)->rank = ggml_get_tensor_rank(dst); + QNN_VER_PTR(*tensor_2)->dataType = dst_qnn_type; + + QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + *tensor_0, + *tensor_1 + }; + Qnn_Tensor_t tensor_outputs[] = { + *tensor_2 + }; + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + } + n_end_time = ggml_time_us(); + n_durtion = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ggml_qnn_mul_mat : %lld milliseconds\n", n_durtion); + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +//common function for GGML OPs using QNN API +static void ggml_qnn_hanlde_op(const enum ggml_op ggmlop, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + bool graph_initialized = false; + int64_t n_begin_time = 0LL; + int64_t n_end_time = 0LL; + int64_t n_durtion = 0LL; + + qnn_instance * instance = nullptr; + struct ggml_backend_qnn_context * ctx = nullptr; + + std::string qnn_graph_name = "ggml_qnn_graph"; + std::string qnn_opconfig_name = "ggml_qnn_opconfig"; + const char * qnn_op_name = nullptr; + Qnn_GraphHandle_t graph_handle = nullptr; + Qnn_Tensor_t * tensor_0 = nullptr; + Qnn_Tensor_t * tensor_1 = nullptr; + Qnn_Tensor_t * tensor_2 = nullptr; + + Qnn_QuantizeParams_t quantize_param = QNN_QUANTIZE_PARAMS_INIT; + Qnn_OpConfig_t qnn_opconfig = QNN_OPCONFIG_INIT; + //Qnn_Param_t qnn_params[] = {}; + + Qnn_DataType_t src0_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t src1_qnn_type = QNN_DATATYPE_FLOAT_32; + Qnn_DataType_t dst_qnn_type = QNN_DATATYPE_FLOAT_32; + + + if ((nullptr == src0) || (nullptr == src1) || (nullptr == dst)) { + QNN_LOG_WARN("pls check why GGML tensor is null"); + return; + } + tensor_0 = (Qnn_Tensor_t *)src0->extra; + tensor_1 = (Qnn_Tensor_t *)src1->extra; + tensor_2 = (Qnn_Tensor_t *)dst->extra; + if ((nullptr == tensor_0) || (nullptr == tensor_1) || (nullptr == tensor_2)) { + QNN_LOG_WARN("pls check why QNN tensor is null"); + return; + } + ctx = (struct ggml_backend_qnn_context *)g_qnn_backend->context; + if (nullptr == ctx) { + QNN_LOG_WARN("pls check why backend ctx is null"); + return; + } + instance = ctx->instance; + if (nullptr == instance) { + QNN_LOG_WARN("pls check why qnn instance is null"); + return; + } + QNN_INTERFACE_VER_TYPE qnn_raw_interface = ctx->raw_interface; + + src0_qnn_type = qnn_datatype_from_ggml_datatype(src0->type); + src1_qnn_type = qnn_datatype_from_ggml_datatype(src1->type); + dst_qnn_type = qnn_datatype_from_ggml_datatype(dst->type); + qnn_op_name = qnn_opname_from_ggmlop(ggmlop); + if (nullptr == qnn_op_name) { + QNN_LOG_WARN("pls check why can not get QNN OP name with ggml op %d(%s)", ggmlop, ggml_op_name(ggmlop)); + return; + } + + n_begin_time = ggml_time_us(); + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, ggml_get_tensor_rank(src0), + src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2], + src0->nb[0], src0->nb[1], src0->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, ggml_get_tensor_rank(src1), + src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2], + src1->nb[0], src1->nb[1], src1->nb[2]); + QNN_LOG_INFO("%15s: rank = %d, type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, ggml_get_tensor_rank(dst), + dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0], + dst->nb[1], dst->nb[2]); + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + QNN_LOG_DEBUG("tensor0 name %s", QNN_TENSOR_GET_NAME(tensor_0)); + QNN_LOG_DEBUG("tensor1 name %s", QNN_TENSOR_GET_NAME(tensor_1)); + QNN_LOG_DEBUG("tensor2 name %s", QNN_TENSOR_GET_NAME(tensor_2)); + + QNN_VER_PTR(*tensor_0)->type = QNN_TENSOR_TYPE_APP_WRITE; + QNN_VER_PTR(*tensor_1)->type = QNN_TENSOR_TYPE_APP_WRITE; + QNN_VER_PTR(*tensor_2)->type = QNN_TENSOR_TYPE_APP_READ; + + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + + std::string map_entry = std::string(ggml_op_name(ggmlop)); + if (instance->_qnn_graph_map.find(map_entry) != instance->_qnn_graph_map.end()) { + graph_initialized = true; + auto & graph_item = instance->_qnn_graph_map[map_entry]; + graph_handle = std::get<0>(graph_item); + } + + if (!graph_initialized) { + qnn_graph_name = qnn_graph_name + "_" + ggml_op_name(ggmlop) + std::to_string(ctx->threads) + src0->name + "_" + src1->name; + qnn_opconfig_name = qnn_opconfig_name + "_" + ggml_op_name(ggmlop) + std::to_string(ctx->threads) + src0->name + "_" + src1->name; + QNN_LOG_DEBUG("qnn graph name %s", qnn_graph_name.c_str()); + QNN_LOG_DEBUG("qnn opconfig name %s", qnn_opconfig_name.c_str()); + error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), qnn_graph_name.c_str(), nullptr, &graph_handle); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("can't create qnn graph handle with ggml op %s, graph name %s, error = %d\n", ggml_op_name(ggmlop), qnn_graph_name.c_str(), error); + return; + } + + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_0); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_1); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_2); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + + QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + *tensor_0, + *tensor_1 + }; + Qnn_Tensor_t tensor_outputs[] = { + *tensor_2 + }; + Qnn_OpConfig_t opconfig = { + (Qnn_OpConfigVersion_t) 1, .v1 = { + qnn_opconfig_name.c_str(), + QNN_OP_PACKAGE_NAME_QTI_AISW, + qnn_op_name, + 0, + nullptr, + 2, + tensor_inputs, + 1, + tensor_outputs + } + }; + error = qnn_raw_interface.graphAddNode(graph_handle, opconfig); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphFinalize(graph_handle, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + auto graph_item = std::make_tuple(graph_handle, tensor_0, tensor_1, tensor_2); + instance->_qnn_graph_map[map_entry] = graph_item; + } else { + auto & graph_item = instance->_qnn_graph_map[map_entry]; + graph_handle = std::get<0>(graph_item); + tensor_0 = std::get<1>(graph_item); + tensor_1 = std::get<2>(graph_item); + tensor_2 = std::get<3>(graph_item); + + QNN_LOG_DEBUG("%d, %d, %d, %d", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + uint32_t dimensions_input_0[] = {(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], (uint32_t) src0->ne[3]}; + uint32_t dimensions_input_1[] = {(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], (uint32_t) src1->ne[3]}; + uint32_t dimensions_output[] = {(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], (uint32_t) dst->ne[3]}; + QNN_VER_PTR(*tensor_0)->dimensions = dimensions_input_0; + QNN_VER_PTR(*tensor_0)->rank = ggml_get_tensor_rank(src0); + QNN_VER_PTR(*tensor_0)->dataType = src0_qnn_type; + QNN_VER_PTR(*tensor_1)->dimensions = dimensions_input_1; + QNN_VER_PTR(*tensor_1)->rank = ggml_get_tensor_rank(src1); + QNN_VER_PTR(*tensor_1)->dataType = src1_qnn_type; + QNN_VER_PTR(*tensor_2)->dimensions = dimensions_output; + QNN_VER_PTR(*tensor_2)->rank = ggml_get_tensor_rank(dst); + QNN_VER_PTR(*tensor_2)->dataType = dst_qnn_type; + + QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data, ggml_get_tensor_data_size(src0)}; + QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data, ggml_get_tensor_data_size(src1)}; + QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data, ggml_get_tensor_data_size(dst)}; + + Qnn_Tensor_t tensor_inputs[] = { + *tensor_0, + *tensor_1 + }; + Qnn_Tensor_t tensor_outputs[] = { + *tensor_2 + }; + error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr); + if (QNN_SUCCESS != error) { + QNN_LOG_INFO("error = %d\n", error); + } + } + n_end_time = ggml_time_us(); + n_durtion = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ggml_qnn_%s : %lld milliseconds\n", ggml_op_name(ggmlop), n_durtion); + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + + + + +static void ggml_qnn_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_qnn_cpy(src0, dst, nullptr); + (void) src1; +} + + +static void ggml_qnn_mul_mat_id(const ggml_tensor * src0, + const ggml_tensor * src1, + ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); + +} + + +static void ggml_qnn_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +static void ggml_qnn_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + (void) src0; + (void) src1; + (void) dst; + QNN_LOG_DEBUG("call %s\n", __func__); + + QNN_LOG_DEBUG("call %s done\n", __func__); +} + + +bool ggml_qnn_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + ENTER_FUNC(); + ggml_qnn_func_t func = nullptr; + ggml_qnn_func_common_t func_common = nullptr; + + bool supported_op = false; + + bool use_hwaccel = false; + + //begin sanity check + if (nullptr == g_qnn_backend) { + QNN_LOG_ERROR("pls check why qnn subsystem not initialized"); + return false; + } + + //attention here: + //this is special scenario for UT function qnn_ggml_op + //borrow some advantages from PyTorch:the user or the upper layer codes could specify whether a GGML OP(such as add/mul/mulmat) is accelerated by a specify backend) + //otherwise ggml-qnn.cpp don't known whether current caller is whisper.cpp or other scenario(for example, JNI function...) + + //in the all, use_hwaccel is different with supported_op + //this feature is heavily depend on PR in upstream whisper.cpp https://github.com/ggerganov/whisper.cpp/pull/2073 + use_hwaccel = (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU); + + supported_op = ((tensor->op == GGML_OP_ADD) || (tensor->op == GGML_OP_MUL) || (tensor->op == GGML_OP_MUL_MAT)); + //supported_op = (tensor->op == GGML_OP_ADD); //works very good with whisper.cpp(asr result is correct) + + if ((!use_hwaccel) && (!supported_op)) { + //TODO: should be removed because this is a workaround method during development stage + //ggml_compute_forward(params, tensor); + return false; + } + + if ((!use_hwaccel) && (!ggml_qnn_can_handle_op(tensor->src[0], tensor->src[1], tensor))) { + //TODO: should be removed because this is a workaround method during development stage + //ggml_compute_forward(params, tensor); + return false; + } + //end sanity check + + switch (tensor->op) { + case GGML_OP_ADD: + func = ggml_qnn_add; + //func_common = ggml_qnn_hanlde_op; + break; + + case GGML_OP_MUL: + func_common = ggml_qnn_hanlde_op; + break; + + case GGML_OP_MUL_MAT: + func = ggml_qnn_mul_mat; + //func_common = ggml_qnn_hanlde_op; + break; + + case GGML_OP_REPEAT: + func = ggml_qnn_repeat; + break; + case GGML_OP_GET_ROWS: + func = ggml_qnn_get_rows; + break; + case GGML_OP_DUP: + func = ggml_qnn_dup; + break; + + case GGML_OP_ACC: + func = ggml_qnn_acc; + break; + + case GGML_OP_DIV: + func = ggml_qnn_div; + break; + + case GGML_OP_UNARY: + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_GELU: + func = ggml_qnn_gelu; + break; + case GGML_UNARY_OP_SILU: + func = ggml_qnn_silu; + break; + case GGML_UNARY_OP_GELU_QUICK: + func = ggml_qnn_gelu_quick; + break; + case GGML_UNARY_OP_TANH: + func = ggml_qnn_tanh; + break; + case GGML_UNARY_OP_RELU: + func = ggml_qnn_relu; + break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_qnn_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_qnn_hardswish; + break; + default: + return false; + } + break; + case GGML_OP_NORM: + func = ggml_qnn_norm; + break; + case GGML_OP_GROUP_NORM: + func = ggml_qnn_group_norm; + break; + case GGML_OP_CONCAT: + func = ggml_qnn_concat; + break; + case GGML_OP_UPSCALE: + func = ggml_qnn_upscale; + break; + case GGML_OP_PAD: + func = ggml_qnn_pad; + break; + case GGML_OP_LEAKY_RELU: + func = ggml_qnn_leaky_relu; + break; + case GGML_OP_RMS_NORM: + func = ggml_qnn_rms_norm; + break; + + case GGML_OP_MUL_MAT_ID: + func = ggml_qnn_mul_mat_id; + break; + case GGML_OP_SCALE: + func = ggml_qnn_scale; + break; + case GGML_OP_SQR: + func = ggml_qnn_sqr; + break; + case GGML_OP_CLAMP: + func = ggml_qnn_clamp; + break; + case GGML_OP_CPY: + func = ggml_qnn_cpy; + break; + case GGML_OP_CONT: + func = ggml_qnn_dup; + break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + func = ggml_qnn_nop; + break; + case GGML_OP_DIAG_MASK_INF: + func = ggml_qnn_diag_mask_inf; + break; + case GGML_OP_SOFT_MAX: + func = ggml_qnn_soft_max; + break; + case GGML_OP_ROPE: + func = ggml_qnn_rope; + break; + case GGML_OP_ALIBI: + func = ggml_qnn_alibi; + break; + case GGML_OP_IM2COL: + func = ggml_qnn_im2col; + break; + case GGML_OP_POOL_2D: + func = ggml_qnn_pool2d; + break; + case GGML_OP_SUM_ROWS: + func = ggml_qnn_sum_rows; + break; + case GGML_OP_ARGSORT: + func = ggml_qnn_argsort; + break; + default: + return false; + } + + if (nullptr != func) + func(tensor->src[0], tensor->src[1], tensor); + if (nullptr != func_common) + func_common(tensor->op, tensor->src[0], tensor->src[1], tensor); + + LEAVE_FUNC(); + + return true; +} + + +struct ggml_backend_qnn_buffer_context { + ~ggml_backend_qnn_buffer_context() { + //ENTER_FUNC(); + if (buffer) { + free(buffer); + } + for (auto * sub_buffer : sub_buffers) { + free(sub_buffer); + } + + for (auto * qnn_tensor : qnn_tensors) { + free_qnn_tensor(*qnn_tensor); + free(qnn_tensor); + } + + if (g_qnn_backend == nullptr) { + QNN_LOG_INFO("qnn backend is freed"); + return; + } + + std::map>::iterator graph_it; + struct ggml_backend_qnn_context * ctx = (struct ggml_backend_qnn_context *) g_qnn_backend->context; + QNN_INTERFACE_VER_TYPE qnn_raw_interface = ctx->instance->get_qnn_raw_interface(); + for (graph_it = backend_ctx->instance->_qnn_graph_map.begin(); graph_it != backend_ctx->instance->_qnn_graph_map.end(); graph_it++) { + auto & graph_item = graph_it->second; + Qnn_GraphHandle_t & graph_handle = std::get<0>(graph_item); + QNN_LOG_DEBUG("graph type:%s", graph_it->first.c_str()); + //QnnGraph_Property_t graph_prop; + //graph_prop.option = QNN_GRAPH_PROPERTY_OPTION_CUSTOM; + //QnnGraph_Property_t * p_graph_prop = &graph_prop; + //qnn_raw_interface.graphGetProperty(graph_handle, &p_graph_prop); + //QNN_LOG_DEBUG("graph name %s", (char*)p_graph_prop->customProperty); + } + //TODO:refine it + backend_ctx->instance->_qnn_graph_map.clear(); + + sub_buffers.clear(); + qnn_tensors.clear(); + //LEAVE_FUNC(); + } + void * buffer = nullptr; + + struct ggml_backend_qnn_context * backend_ctx = nullptr; + + size_t buffer_size = 0; + std::vector sub_buffers; + std::vector qnn_tensors; +}; + +static const char * ggml_backend_qnn_buffer_get_name(ggml_backend_buffer_t buffer) { + GGML_UNUSED(buffer); + return "QNN"; +} + + +GGML_CALL static bool ggml_backend_buffer_is_qnn(ggml_backend_buffer_t buffer) { + ENTER_FUNC(); + LEAVE_FUNC(); + return buffer->iface.get_name == ggml_backend_qnn_buffer_get_name; +} + + +static void ggml_backend_qnn_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ENTER_FUNC(); + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *) buffer->context; + delete ctx; + LEAVE_FUNC(); +} + + +//TODO +static void * ggml_backend_qnn_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *) buffer->context; + + return ctx->buffer; +} + + +static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + //ENTER_FUNC(); + Qnn_ErrorHandle_t error = QNN_SUCCESS; + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *)buffer->context; + + /* + if (tensor->view_src != nullptr && tensor->view_offs == 0) { + assert(tensor->view_src->buffer->buft == buffer->buft); + tensor->backend = tensor->view_src->backend; + tensor->extra = tensor->view_src->extra; + QNN_LOG_DEBUG("init tensor did nothing"); + return; + } + */ + + uint32_t dimensions[] = {(uint32_t) tensor->ne[0], (uint32_t) tensor->ne[1], (uint32_t) tensor->ne[2], (uint32_t) tensor->ne[3]}; + //TODO:only support FP32 & FP16 + Qnn_DataType_t qnn_data_type = QNN_DATATYPE_FLOAT_32; + Qnn_TensorType_t qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE; + + + //QNN_LOG_DEBUG("tensor name %s", tensor->name); + //QNN_LOG_DEBUG("tensor data %p", tensor->data); + + if (tensor->flags & GGML_TENSOR_FLAG_INPUT) { + //QNN_LOG_DEBUG("input tensor"); + qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE; + } else if (tensor->flags & GGML_TENSOR_FLAG_OUTPUT) { + QNN_LOG_DEBUG("output tensor"); + qnn_tensor_type = QNN_TENSOR_TYPE_APP_READ; + } + Qnn_Tensor_t qnn_tensor = { + .version= QNN_TENSOR_VERSION_1, + {.v1= { + .id=0, + .name= tensor->name, + .type= qnn_tensor_type, + .dataFormat= QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER, + .dataType= qnn_data_type, + .quantizeParams= {QNN_DEFINITION_UNDEFINED, + QNN_QUANTIZATION_ENCODING_UNDEFINED, + {.scaleOffsetEncoding= {.scale= 0.0000000000000000f, .offset= 0}}}, + .rank= ggml_get_tensor_rank(tensor), + .dimensions=dimensions, + .memType= QNN_TENSORMEMTYPE_RAW, + {.clientBuf= {.data=nullptr, + .dataSize=0}}}} + }; + Qnn_Tensor_t * p_qnn_tensor = (Qnn_Tensor_t *)malloc(sizeof(Qnn_Tensor_t)); + if (nullptr == p_qnn_tensor) { + QNN_LOG_WARN("init tensor failed"); + return; + } + Qnn_Tensor_t tensor_copy; + error = deep_copy_qnn_tensors(qnn_tensor, *p_qnn_tensor); + if (error != QNN_SUCCESS) { + free(p_qnn_tensor); + QNN_LOG_DEBUG("init tensor failed"); + return; + } + tensor->extra = p_qnn_tensor; + ctx->qnn_tensors.push_back(p_qnn_tensor); + + if (ggml_is_quantized(tensor->type)) { + //TODO + QNN_LOG_DEBUG("is quantized"); + } + LEAVE_FUNC(); +} + + +static void ggml_backend_qnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + //ENTER_FUNC(); + GGML_UNUSED(buffer); + + //QNN_LOG_DEBUG("tensor name: %s, size %d", tensor->name, size); + memcpy((char *)tensor->data + offset, data, size); + + //LEAVE_FUNC(); +} + + +static void ggml_backend_qnn_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + //ENTER_FUNC(); + GGML_UNUSED(buffer); + //QNN_LOG_DEBUG("tensor name: %s, size %d", tensor->name, size); + memcpy(data, (const char *)tensor->data + offset, size); + + //LEAVE_FUNC(); +} + + +static bool ggml_backend_qnn_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + GGML_UNUSED(buffer); + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; +} + + +static void ggml_backend_qnn_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + //ENTER_FUNC(); + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *) buffer->context; + + memset(ctx->buffer, value, ctx->buffer_size); + //LEAVE_FUNC(); +} + + + +static void ggml_backend_qnn_buffer_reset(ggml_backend_buffer_t buffer) { + ENTER_FUNC(); + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *) buffer->context; + for (auto * sub_buffer : ctx->sub_buffers) { + free(sub_buffer); + } + ctx->sub_buffers.clear(); + LEAVE_FUNC(); +} + + +static ggml_backend_buffer_i ggml_backend_qnn_buffer_interface = { + /* .get_name = */ ggml_backend_qnn_buffer_get_name, + /* .free_buffer = */ ggml_backend_qnn_buffer_free_buffer, + /* .get_base = */ ggml_backend_qnn_buffer_get_base, + /* .init_tensor = */ ggml_backend_qnn_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_qnn_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_qnn_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_qnn_buffer_cpy_tensor, + /* .clear = */ ggml_backend_qnn_buffer_clear, + /* .reset = */ nullptr, +}; + + +static const char * ggml_backend_qnn_buffer_type_name(ggml_backend_buffer_type_t buft) { + ENTER_FUNC(); + LEAVE_FUNC(); + return "QNN"; +} + + +static void * ggml_qnn_host_malloc(size_t n) { + void * data = nullptr; + const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); + if (result != 0) { + QNN_LOG_WARN("%s: error: posix_memalign failed\n", __func__); + return nullptr; + } + + return data; +} + + +//TODO +static ggml_backend_buffer_t ggml_backend_qnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + //ENTER_FUNC(); + + ggml_backend_qnn_buffer_context * ctx = new ggml_backend_qnn_buffer_context; + + const size_t size_page = sysconf(_SC_PAGESIZE); + + size_t size_aligned = size; + if ((size_aligned % size_page) != 0) { + size_aligned += (size_page - (size_aligned % size_page)); + } + + //QNN_LOG_DEBUG("size %d, %d MB", size_aligned, size_aligned / (1 << 20)); + + //TODO:use pre-allocated buffer in internal memory pool + ctx->buffer = ggml_qnn_host_malloc(size_aligned); + ctx->buffer_size = size_aligned; + + //TODO: + QNN_LOG_DEBUG("device idx:%d", g_current_device); + ctx->backend_ctx = &g_qnn_mgr[g_current_device]; + + if (nullptr == ctx->buffer) { + QNN_LOG_WARN("%s: failed to allocate %.2f MiB\n", __func__, size / (1 << 20)); + LEAVE_FUNC(); + return nullptr; + } + //LEAVE_FUNC(); + + return ggml_backend_buffer_init(buft, ggml_backend_qnn_buffer_interface, ctx, size); +} + + +static size_t ggml_backend_qnn_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + return 32; +} + + +//TODO: this value is an experimental value +static size_t ggml_backend_qnn_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + //works fine with ggml-tiny.en-q8_0.bin for whisper.cpp + //return (38 * 1024 * 1024); + return (96 * 1024 * 1024); +} + + +static bool ggml_backend_qnn_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, + ggml_backend_t backend) { + ENTER_FUNC(); + GGML_UNUSED(buft); + LEAVE_FUNC(); + + return ggml_backend_is_qnn(backend) || ggml_backend_is_cpu(backend); +} + + +// attention here because Qualcomm's QNN SDK is a highly well-designed SDK +// +// refer to https://developer.qualcomm.com/sites/default/files/attachments/qnn_software_stack.png +// https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/overview.html +static bool ggml_backend_qnn_buffer_is_host(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + return true; +} + +static ggml_backend_buffer_type_i ggml_backend_qnn_buffer_type_interface = { + /* .get_name = */ ggml_backend_qnn_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_qnn_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_qnn_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_qnn_buffer_type_get_max_size, + /* .get_alloc_size = */ nullptr, + /* .supports_backend = */ ggml_backend_qnn_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_qnn_buffer_is_host +}; + + +static const char * ggml_backend_qnn_name(ggml_backend_t backend) { + return "QNN"; +} + + +static void ggml_backend_qnn_free(ggml_backend_t backend) { + QNN_LOG_DEBUG("enter %s\n", __func__ ); + ggml_backend_qnn_context * ctx = (ggml_backend_qnn_context *) backend->context; + QNN_LOG_DEBUG("idx %d, name:%s", ctx->device, g_qnn_mgr[ctx->device].name); + + qnn_instance * instance = (qnn_instance*)g_qnn_mgr[ctx->device].instance; + if (instance != nullptr) { + instance->qnn_finalize(); + delete instance; + g_qnn_mgr[ctx->device].instance = nullptr; + } + + qnn_buf_t * buffer_pool = (qnn_buf_t*)g_qnn_mgr[ctx->device].buffer_pool; + if (buffer_pool != nullptr) { + buffer_pool->destroy(buffer_pool); + g_qnn_mgr[ctx->device].buffer_pool = nullptr; + } + + if (g_qnn_mgr[ctx->device].backend != nullptr) { + delete backend; + g_qnn_backend = nullptr; + g_qnn_mgr[ctx->device].backend = nullptr; + } + QNN_LOG_DEBUG("leave %s\n", __func__ ); +} + + +static ggml_backend_buffer_type_t ggml_backend_qnn_get_default_buffer_type(ggml_backend_t backend) { + //ENTER_FUNC(); + ggml_backend_qnn_context * ctx = (ggml_backend_qnn_context *) backend->context; + //QNN_LOG_DEBUG("device %d,%s", ctx->device, ctx->name); + //LEAVE_FUNC(); + + return ggml_backend_qnn_buffer_type(ctx->device); +} + + +#if 0 +static bool ggml_backend_qnn_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + ENTER_FUNC(); + + GGML_UNUSED(backend); + + switch (op->op) { + case GGML_OP_UNARY: + switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_TANH: + return true; + default: + return false; + } + break; + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: { + struct ggml_tensor *a; + struct ggml_tensor *b; + if (op->op == GGML_OP_MUL_MAT) { + a = op->src[0]; + b = op->src[1]; + } else { + a = op->src[2]; + b = op->src[1]; + } + if (a->ne[3] != b->ne[3]) { + return false; + } + ggml_type a_type = a->type; + if (a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ2_S || + a_type == GGML_TYPE_IQ4_XS) { + return false; + } + return true; + } + break; + case GGML_OP_GET_ROWS: { + switch (op->src[0]->type) { + case GGML_TYPE_F16: + case GGML_TYPE_F32: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return true; + default: + return false; + } + } + break; + case GGML_OP_CPY: { + ggml_type src0_type = op->src[0]->type; + ggml_type src1_type = op->src[1]->type; + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) { + return true; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return true; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return true; + } + return false; + } + break; + case GGML_OP_CONCAT: { + ggml_type src0_type = op->src[0]->type; + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; + } + break; + case GGML_OP_DUP: + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_REPEAT: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NORM: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_RMS_NORM: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_CONT: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + case GGML_OP_ALIBI: + case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: + case GGML_OP_SUM_ROWS: + case GGML_OP_ARGSORT: + case GGML_OP_ACC: + case GGML_OP_GROUP_NORM: + case GGML_OP_UPSCALE: + case GGML_OP_PAD: + case GGML_OP_LEAKY_RELU: + return true; + default: + return false; + } + + LEAVE_FUNC(); +} +# else +static bool ggml_backend_qnn_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + GGML_UNUSED(backend); + + switch (op->op) { + case GGML_OP_MUL_MAT: + case GGML_OP_MUL: + case GGML_OP_ADD: + return true; + default: + return false; + } +} +#endif + + +//TODO: implement all supported GGML OP using QNN API +//PoC-S49: implementation of other GGML OP(non-mulmat) using QNN API +//https://github.com/zhouwg/kantv/issues/121 +static ggml_status ggml_backend_qnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + //ENTER_FUNC(); + enum ggml_status result = GGML_STATUS_SUCCESS; + int node_n = -1; + int task_phase = GGML_TASK_TYPE_FINALIZE; + ggml_backend_qnn_context * ctx = (ggml_backend_qnn_context *) backend->context; + + struct ggml_cplan plan = ggml_graph_plan(cgraph, 1); + + buf_element_t * qnn_buf = nullptr; + + if (plan.work_size > 0) { + //QNN_LOG_INFO("work size %d(%d MB)", plan.work_size, plan.work_size / (1 << 20)); + //plan.work_data = static_cast(malloc(plan.work_size)); + plan.work_data = static_cast(ctx->buffer_pool->buffer_pool_base); + if (plan.work_data == nullptr) { + QNN_LOG_ERROR("malloc failed"); + return GGML_STATUS_FAILED; + } + } + struct ggml_cplan * cplan = &plan; + GGML_ASSERT(cplan->n_threads > 0); + if (cplan->work_size > 0) { + GGML_ASSERT(cplan->work_data); + } + + while (true) { + if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { + result = GGML_STATUS_ABORTED; + break; + } + struct ggml_compute_params params = { + /*.type =*/ GGML_TASK_TYPE_FINALIZE, + /*.ith =*/ 0, + /*.nth =*/ 0, + /*.wsize =*/ cplan->work_size, + /*.wdata =*/ cplan->work_data, + }; + + if (node_n != -1) { + /* FINALIZE */ + struct ggml_tensor * node = cgraph->nodes[node_n]; + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.nth = 1; + ggml_qnn_compute_forward(¶ms, node); + } + } + + while (++node_n < cgraph->n_nodes) { + //QNN_LOG_DEBUG("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); + struct ggml_tensor * node = cgraph->nodes[node_n]; + params.nth = 1; + if (GGML_OP_HAS_INIT[node->op]) { + params.type = GGML_TASK_TYPE_INIT; + ggml_qnn_compute_forward(¶ms, node); + } + params.type = GGML_TASK_TYPE_COMPUTE; + ggml_qnn_compute_forward(¶ms, node); + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.type = GGML_TASK_TYPE_FINALIZE; + ggml_qnn_compute_forward(¶ms, node); + } + if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { + result = GGML_STATUS_ABORTED; + break; + } + } + task_phase = GGML_TASK_TYPE_INIT; + if (node_n >= cgraph->n_nodes) { + //QNN_LOG_INFO("node_n %d", node_n); + //QNN_LOG_INFO("cgraph->n_nodes %d", cgraph->n_nodes); + break; + } + } + + //free(plan.work_data); + //LEAVE_FUNC(); + + return result; +} + + +struct ggml_compute_state_shared { + const struct ggml_cgraph * cgraph; + const struct ggml_cplan * cplan; + + int64_t perf_node_start_cycles; + int64_t perf_node_start_time_us; + + const int n_threads; + + // synchronization primitives + atomic_int n_active; // num active threads + atomic_int node_n; // active graph node + atomic_int node_task; // active graph node task phase + + ggml_abort_callback abort_callback; // abort ggml_graph_compute when true + void * abort_callback_data; +}; + +struct ggml_compute_state { + pthread_t thrd; + int ith; + struct ggml_compute_state_shared * shared; + enum ggml_status ec; +}; + + +#ifdef GGML_PERF +#define ggml_perf_time_ms() ggml_time_ms() +#define ggml_perf_time_us() ggml_time_us() +#define ggml_perf_cycles() ggml_cycles() +#define ggml_perf_cycles_per_ms() ggml_cycles_per_ms() +#else +#define ggml_perf_time_ms() 0 +#define ggml_perf_time_us() 0 +#define ggml_perf_cycles() 0 +#define ggml_perf_cycles_per_ms() 0 +#endif +#undef MIN +#undef MAX + +#define MIN(a, b) ((a) < (b) ? (a) : (b)) +#define MAX(a, b) ((a) > (b) ? (a) : (b)) + + +static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { + int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; + int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; + + node->perf_runs++; + node->perf_cycles += cycles_cur; + node->perf_time_us += time_us_cur; +} + + +static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) { + // wait for other threads to finish + const int last_node_n = * node_n; + + while (true) { + if (do_yield) { + sched_yield(); + } + + * node_n = atomic_load(&state->shared->node_n); + if (* node_n != last_node_n) break; + } +} + + +static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) { + // wait for other threads to finish + const int last_task_phase = * task_phase; + + while (true) { + if (do_yield) { + sched_yield(); + } + + * task_phase = atomic_load(&state->shared->node_task); + if (* task_phase != last_task_phase) break; + } +} + + +static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) { + int n_tasks = 0; + + if (ggml_is_empty(node)) { + // no need to multi-thread a no-op + n_tasks = 1; + return n_tasks; + } + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_ACC: { + n_tasks = n_threads; + } + break; + case GGML_OP_SUB: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_LOG: + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + case GGML_OP_ARGMAX: + case GGML_OP_REPEAT: + case GGML_OP_REPEAT_BACK: + case GGML_OP_LEAKY_RELU: { + n_tasks = 1; + } + break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_HARDSIGMOID: { + n_tasks = 1; + } + break; + + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: { + n_tasks = n_threads; + } + break; + default: + GGML_ASSERT(false); + } + break; + case GGML_OP_SILU_BACK: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_RMS_NORM_BACK: + case GGML_OP_GROUP_NORM: + case GGML_OP_CONCAT: { + n_tasks = n_threads; + } + break; + case GGML_OP_MUL_MAT: { + n_tasks = n_threads; + } + break; + case GGML_OP_MUL_MAT_ID: { + n_tasks = n_threads; + } + break; + case GGML_OP_OUT_PROD: { + n_tasks = n_threads; + } + break; + case GGML_OP_GET_ROWS: { + n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1])); + } + break; + case GGML_OP_SCALE: + case GGML_OP_SET: + case GGML_OP_CONT: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_GET_ROWS_BACK: + case GGML_OP_DIAG: { + n_tasks = 1; + } + break; + case GGML_OP_DIAG_MASK_ZERO: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX_BACK: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + case GGML_OP_ADD_REL_POS: { + n_tasks = n_threads; + } + break; + case GGML_OP_ALIBI: { + n_tasks = 1; + } + break; + case GGML_OP_CLAMP: { + n_tasks = 1; + } + break; + case GGML_OP_SOFT_MAX: { + n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); + } + break; + case GGML_OP_CONV_TRANSPOSE_1D: { + n_tasks = n_threads; + } + break; + case GGML_OP_IM2COL: { + n_tasks = n_threads; + } + break; + case GGML_OP_CONV_TRANSPOSE_2D: { + n_tasks = n_threads; + } + break; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: { + n_tasks = 1; + } + break; + case GGML_OP_UPSCALE: { + n_tasks = n_threads; + } + break; + case GGML_OP_PAD: { + n_tasks = n_threads; + } + break; + case GGML_OP_ARANGE: { + n_tasks = n_threads; + } + break; + case GGML_OP_TIMESTEP_EMBEDDING: { + n_tasks = n_threads; + } + break; + case GGML_OP_ARGSORT: { + n_tasks = n_threads; + } + break; + case GGML_OP_FLASH_ATTN: { + n_tasks = n_threads; + } + break; + case GGML_OP_FLASH_FF: { + n_tasks = n_threads; + } + break; + case GGML_OP_FLASH_ATTN_BACK: { + n_tasks = n_threads; + } + break; + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: { + n_tasks = n_threads; + } + break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: + case GGML_OP_GET_REL_POS: + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: { + n_tasks = 1; + } + break; + case GGML_OP_MAP_CUSTOM1: { + QNN_LOG_ERROR("not support"); + } + break; + case GGML_OP_MAP_CUSTOM2: { + QNN_LOG_ERROR("not support"); + } + break; + case GGML_OP_MAP_CUSTOM3: { + QNN_LOG_ERROR("not support"); + } + break; + case GGML_OP_CROSS_ENTROPY_LOSS: { + n_tasks = n_threads; + } + break; + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { + n_tasks = n_threads; + } + break; + case GGML_OP_NONE: { + n_tasks = 1; + } + break; + case GGML_OP_COUNT: { + GGML_ASSERT(false); + } + break; + default: { + QNN_LOG_WARN("%s: op not implemented: ", __func__); + if (node->op < GGML_OP_COUNT) { + QNN_LOG_DEBUG("%s\n", ggml_op_name(node->op)); + } else { + QNN_LOG_DEBUG("%d\n", node->op); + } + GGML_ASSERT(false); + } + break; + } + + assert(n_tasks > 0); + + return n_tasks; +} + + +static void * ggml_graph_compute_thread(void * data) { + struct ggml_compute_state * state = (struct ggml_compute_state *) data; + + const struct ggml_cgraph * cgraph = state->shared->cgraph; + const struct ggml_cplan * cplan = state->shared->cplan; + + const int n_threads = state->shared->n_threads; + + int node_n = -1; + int task_phase = GGML_TASK_TYPE_FINALIZE; + + while (true) { + if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { + state->shared->node_n += 1; + state->ec = GGML_STATUS_ABORTED; + return 0; + } + + if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { + // all other threads are finished and spinning + // do finalize and init here so we don't have synchronize again + struct ggml_compute_params params = { + /*.type =*/ GGML_TASK_TYPE_FINALIZE, + /*.ith =*/ 0, + /*.nth =*/ 0, + /*.wsize =*/ cplan->work_size, + /*.wdata =*/ cplan->work_data, + }; + + if (node_n != -1) { + /* FINALIZE */ + struct ggml_tensor * node = cgraph->nodes[node_n]; + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); + ggml_qnn_compute_forward(¶ms, node); + } + ggml_graph_compute_perf_stats_node(node, state->shared); + } + + // distribute new work or execute it direct if 1T + while (++node_n < cgraph->n_nodes) { + //QNN_LOG_INFO("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); + struct ggml_tensor * node = cgraph->nodes[node_n]; + const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); + + state->shared->perf_node_start_cycles = ggml_perf_cycles(); + state->shared->perf_node_start_time_us = ggml_perf_time_us(); + + params.nth = n_tasks; + + if (n_tasks == 1) { + /* INIT */ + if (GGML_OP_HAS_INIT[node->op]) { + params.type = GGML_TASK_TYPE_INIT; + ggml_qnn_compute_forward(¶ms, node); + } + + // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, + // they do something more efficient than spinning (?) + params.type = GGML_TASK_TYPE_COMPUTE; + ggml_qnn_compute_forward(¶ms, node); + + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.type = GGML_TASK_TYPE_FINALIZE; + ggml_qnn_compute_forward(¶ms, node); + } + + ggml_graph_compute_perf_stats_node(node, state->shared); + } else { + break; + } + + if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { + break; + } + } + + task_phase = GGML_TASK_TYPE_INIT; + atomic_store(&state->shared->n_active, n_threads); + atomic_store(&state->shared->node_n, node_n); + atomic_store(&state->shared->node_task, task_phase); + } else { + ggml_graph_compute_thread_sync_node(&node_n, state, false); + ggml_graph_compute_thread_sync_task(&task_phase, state, false); + } + + // check if we should stop + if (node_n >= cgraph->n_nodes) break; + + /* INIT & COMPUTE */ + struct ggml_tensor * node = cgraph->nodes[node_n]; + const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); + + struct ggml_compute_params params = { + /*.type =*/ GGML_TASK_TYPE_INIT, + /*.ith =*/ state->ith, + /*.nth =*/ n_tasks, + /*.wsize =*/ cplan->work_size, + /*.wdata =*/ cplan->work_data, + }; + + if (state->ith < n_tasks) { + if (GGML_OP_HAS_INIT[node->op]) { + ggml_qnn_compute_forward(¶ms, node); + } + } + + if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { + task_phase = GGML_TASK_TYPE_COMPUTE; + atomic_store(&state->shared->n_active, n_threads); + atomic_store(&state->shared->node_task, task_phase); + } + else { + const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT; + ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield); + } + + if (state->ith < n_tasks) { + params.type = GGML_TASK_TYPE_COMPUTE; + ggml_qnn_compute_forward(¶ms, node); + } + + if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { + task_phase = GGML_TASK_TYPE_FINALIZE; + atomic_store(&state->shared->n_active, n_threads); + atomic_store(&state->shared->node_task, task_phase); + } + else { + ggml_graph_compute_thread_sync_task(&task_phase, state, false); + } + } + + return 0; +} + + +static ggml_status ggml_backend_qnn_graph_compute_multithread(ggml_backend_t backend, ggml_cgraph * cgraph) { + //ENTER_FUNC(); + ggml_backend_qnn_context * ctx = (ggml_backend_qnn_context *) backend->context; + //QNN_LOG_DEBUG("device %d, thread %d\n", ctx->device, ctx->threads); + + int num_threads = ctx->threads; + + if (QNN_GPU == ctx->device || QNN_HTP == ctx->device) { + //TODO:multithreading not supported using QNN GPU/HTP(aka DSP) backend + num_threads = 1; + } + struct ggml_cplan plan = ggml_graph_plan(cgraph, num_threads); + + + if (plan.work_size > 0) { + //QNN_LOG_INFO("work size %d(%d MB)", plan.work_size, plan.work_size / (1 << 20)); + plan.work_data = static_cast(malloc(plan.work_size)); + if (plan.work_data == nullptr) { + QNN_LOG_ERROR("malloc failed"); + return GGML_STATUS_FAILED; + } + } + + struct ggml_cplan * cplan = &plan; + GGML_ASSERT(cplan->n_threads > 0); + if (cplan->work_size > 0) { + GGML_ASSERT(cplan->work_data); + } + + //QNN_LOG_DEBUG("cgraph %p, cplan %p, work size %d, work data %p", cgraph, cplan, cplan->work_size, cplan->work_data); + const int n_threads = cplan->n_threads; + + struct ggml_compute_state_shared state_shared = { + /*.cgraph =*/ cgraph, + /*.cgraph_plan =*/ cplan, + /*.perf_node_start_cycles =*/ 0, + /*.perf_node_start_time_us =*/ 0, + /*.n_threads =*/ n_threads, + /*.n_active =*/ n_threads, + /*.node_n =*/ -1, + /*.node_task =*/ GGML_TASK_TYPE_FINALIZE, + /*.abort_callback =*/ nullptr, + /*.abort_callback_data =*/ nullptr, + }; + struct ggml_compute_state * workers = (struct ggml_compute_state*)alloca(sizeof(struct ggml_compute_state) * n_threads); + if (nullptr == workers) { + QNN_LOG_ERROR("malloc failed"); + if (plan.work_data != nullptr) { + free(plan.work_data); + } + return GGML_STATUS_FAILED; + } + + // create thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; ++j) { + workers[j] = (struct ggml_compute_state) { + .thrd = 0, + .ith = j, + .shared = &state_shared, + .ec = GGML_STATUS_SUCCESS, + }; + + const int rc = pthread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); + GGML_ASSERT(rc == 0); + } + } + + workers[0].ith = 0; + workers[0].shared = &state_shared; + workers[0].ec = GGML_STATUS_SUCCESS; + + // this is a work thread too + ggml_graph_compute_thread(&workers[0]); + enum ggml_status compute_status = workers[0].ec; + + // join or kill thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; j++) { + const int rc = pthread_join(workers[j].thrd, NULL); + GGML_ASSERT(rc == 0); + if (workers[j].ec != GGML_STATUS_SUCCESS) + compute_status = workers[j].ec; + } + } + + if (plan.work_data != nullptr) { + free(plan.work_data); + } + LEAVE_FUNC(); + return compute_status; +} + + +static bool ggml_backend_qnn_offload_op(ggml_backend_t backend, const ggml_tensor * op) { + ENTER_FUNC(); + GGML_UNUSED(backend); + + const int min_batch_size = 32; + + LEAVE_FUNC(); + + return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; + +} + + +static ggml_backend_i ggml_backend_qnn_interface = { + /* .get_name = */ ggml_backend_qnn_name, + /* .free = */ ggml_backend_qnn_free, + /* .get_default_buffer_type = */ ggml_backend_qnn_get_default_buffer_type, + /* .set_tensor_async = */ nullptr, + /* .get_tensor_async = */ nullptr, + /* .cpy_tensor_async = */ nullptr, + /* .synchronize = */ nullptr, + /* .graph_plan_create = */ nullptr, + /* .graph_plan_free = */ nullptr, + /* .graph_plan_compute = */ nullptr, + /* .graph_compute = */ ggml_backend_qnn_graph_compute_multithread, + /* .supports_op = */ ggml_backend_qnn_supports_op, + /* .offload_op = */ nullptr, + /* .event_new = */ nullptr, + /* .event_free = */ nullptr, + /* .event_record = */ nullptr, + /* .event_wait = */ nullptr, + /* .event_synchronize = */ nullptr, +}; + + +static ggml_guid_t ggml_backend_qnn_guid() { + //ENTER_FUNC(); + static ggml_guid guid = {0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81, 0x92, 0xa3, 0xb4, 0xc5, + 0xd6, 0xe7, 0xf8, 0x09}; + //LEAVE_FUNC(); + + return &guid; +} + + +static ggml_backend_t ggml_backend_qnn_reg_init(const char * params, void * user_data) { + ENTER_FUNC(); + /*if (nullptr == params) { + //this is data path of prebuit QNN libs provided by Qualcomm + //can be obtained through JNI from Java layer such as "/data/data/com.ggml.llamacpp/" + //or hardcoded to "/data/local/tmp/" which is an Android OS defined path + params = "/data/local/tmp/"; + }*/ + ggml_backend_t qnn_backend = ggml_backend_qnn_init((int) (intptr_t) user_data); + LEAVE_FUNC(); + + return qnn_backend; +} + + +bool ggml_backend_is_qnn(ggml_backend_t backend) { + return backend != nullptr && ggml_guid_matches(backend->guid, ggml_backend_qnn_guid()); +} + + +void ggml_backend_qnn_set_n_threads(ggml_backend_t backend, int n_threads) { + GGML_ASSERT(ggml_backend_is_qnn(backend)); + + struct ggml_backend_qnn_context * ctx = (struct ggml_backend_qnn_context *)backend->context; + ctx->threads = n_threads; +} + +const char * ggml_backend_qnn_get_name(ggml_backend_t backend) { + return backend->iface.get_name(backend); +} + +int ggml_backend_qnn_get_device_count() { + return GGML_QNN_MAX_DEVICES; +} + + +void ggml_backend_qnn_get_device_description(int device, char * description, size_t description_size) { + ENTER_FUNC(); + if (nullptr == description || 0 == description_size) { + QNN_LOG_WARN("invalid param"); + return; + } + + if (device >= GGML_QNN_MAX_DEVICES) { + QNN_LOG_WARN("invalid param"); + return; + } + + snprintf(description, description_size, "%s", g_qnn_mgr[device].name); + QNN_LOG_DEBUG("description:%s", description); + printf("[QNN] description:%s", description); + + LEAVE_FUNC(); +} + + +void ggml_backend_qnn_get_device_memory(int device, size_t * free, size_t * total) { + ENTER_FUNC(); + LEAVE_FUNC(); +} + + +ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t device_index) { + //ENTER_FUNC(); + + if (device_index >= GGML_QNN_MAX_DEVICES) { + QNN_LOG_DEBUG("ggml_backend_qnn_buffer_type error: device_index:%d is out of range [0, %d]\n", + device_index, GGML_QNN_MAX_DEVICES - 1); + return nullptr; + } + + static struct ggml_backend_buffer_type ggml_backend_buffer_type_qnn = { + /* .iface = */ { + /* .get_name = */ ggml_backend_qnn_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_qnn_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_qnn_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_qnn_buffer_type_get_max_size, + /* .get_alloc_size = */ nullptr,// defaults to ggml_nbytes + /* .supports_backend = */ ggml_backend_qnn_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_qnn_buffer_is_host + }, + /* .context = */ nullptr, + }; + //LEAVE_FUNC(); + + return &ggml_backend_buffer_type_qnn; +} + +// if build for windows, PATH_DELIMITER is '\' +#ifdef _WINDOWS_ + +#define PATH_DELIMITER '\\' +#define QNN_SYS_LIB_NAME "QnnSystem.dll" + +#else + +#define PATH_DELIMITER '/' +#define QNN_SYS_LIB_NAME "libQnnSystem.so" + +#endif + +/** + * + * @param device 0: QNN_CPU 1: QNN_GPU 2: QNN_HTP(aka DSP) + * @return + */ +ggml_backend_t ggml_backend_qnn_init(size_t device) { + ENTER_FUNC(); + int result = 0; + + QNN_LOG_DEBUG("device %d\n", device); + QNN_LOG_DEBUG("PATH: %s\n", getenv("PATH")); + if (device >= GGML_QNN_MAX_DEVICES) { + QNN_LOG_ERROR("invalid device %d", device); + return nullptr; + } + + if (nullptr != g_qnn_mgr[device].backend) { + QNN_LOG_ERROR("qnn backend %d(%s) already loaded, it should not happened, pls check why?", device, get_qnn_backend_name(device)); + if (device == g_current_device) { + g_qnn_backend = g_qnn_mgr[device].backend; + QNN_LOG_INFO("re-use cached backend %d(%s)", device, get_qnn_backend_name(device)); + return g_qnn_mgr[device].backend; + } else { + QNN_LOG_INFO("delete previous backend %d(%s)", device, get_qnn_backend_name(device)); + ggml_backend_qnn_free(g_qnn_backend); + } + } + + static bool is_first_call = true; + if (is_first_call) { + ggml_setup_op_has_task_pass(); + is_first_call = false; + } + + std::string qnn_lib_name; + std::string qnn_lib_path; + // split PATH by ":" + std::vector paths; + std::string path = getenv("PATH"); + std::string::size_type start = 0; + std::string::size_type end = path.find(':'); + while (end != std::string::npos) { + paths.push_back(path.substr(start, end - start)); + start = end + 1; + end = path.find(':', start); + } + paths.push_back(path.substr(start, end)); + + for (auto & p : paths) { + if (access((p + PATH_DELIMITER + QNN_SYS_LIB_NAME).c_str(), F_OK) == 0) { + qnn_lib_path = p + PATH_DELIMITER; + break; + } + } + + if (qnn_lib_path.empty()) { + QNN_LOG_ERROR("qnn lib not found in PATH\n"); + return nullptr; + } + +#ifdef __linux__ + QNN_LOG_INFO("qnn lib path: %s, qnn backend: %d\n", qnn_lib_path.c_str(), device); + // setup LD_LIBRARY_PATH + char* ld_library_path = getenv("LD_LIBRARY_PATH"); + int ret; + if (ld_library_path == NULL) { + ret = setenv("LD_LIBRARY_PATH", qnn_lib_path.c_str(), 1); + } else { + ret = setenv("LD_LIBRARY_PATH", (qnn_lib_path + ":" + ld_library_path).c_str(), 1); + } + if (setenv("LD_LIBRARY_PATH", qnn_lib_path.c_str(), 1) != 0) { + QNN_LOG_ERROR("setenv failed\n"); + return nullptr; + } +#endif + + QNN_LOG_INFO("qnn lib path: %s, qnn backend: %d\n", qnn_lib_path.c_str(), device); + qnn_instance * instance = nullptr; + instance = new qnn_instance(qnn_lib_path, g_qnn_mgr[device].lib, ""); + result = instance->qnn_init(nullptr); + if (0 != result) { + QNN_LOG_WARN("init qnn subsystem failed with qnn backend %s, pls check why\n", get_qnn_backend_name(device)); + delete instance; + return nullptr; + } + qnn_interface qnn_interface = instance->get_qnn_interface(); + if (!qnn_interface.is_loaded()) { + QNN_LOG_WARN("qnn subsystem failure\n"); + delete instance; + return nullptr; + } + + std::string device_name = GGML_QNN_NAME + std::string("_") + std::to_string(device) + std::string("_") + get_qnn_backend_name(device); + QNN_LOG_INFO("qnn device name %s", device_name.c_str()); + instance->init_qnn_graph(device_name.c_str(), false); + g_qnn_mgr[device].instance = instance; + g_qnn_mgr[device].raw_interface = instance->get_qnn_raw_interface(); + g_qnn_mgr[device].raw_system_interface = instance->get_qnn_raw_system_interface(); + //TODO:refine internal buffer management + g_qnn_mgr[device].buffer_pool = qnn_buf_new(get_qnn_backend_name(device), GGML_QNN_MAX_BUFFERS, (1 << 20)); + GGML_ASSERT(g_qnn_mgr[device].buffer_pool != nullptr); + + ggml_backend_t qnn_backend = new ggml_backend{ + /* .guid = */ ggml_backend_qnn_guid(), + /* .iface = */ ggml_backend_qnn_interface, + /* .context = */ &g_qnn_mgr[device] + }; + g_qnn_mgr[device].backend = qnn_backend; + g_qnn_backend = g_qnn_mgr[device].backend; + g_current_device = device; + + QNN_LOG_INFO("get_default_buffer_type %p", qnn_backend->iface.get_default_buffer_type);//TODO:why the pointer changed with QNN GPU backend? + QNN_LOG_INFO("qnn_backend %p", qnn_backend); + LEAVE_FUNC(); + + return qnn_backend; +} + + +extern "C" int ggml_backend_qnn_reg_devices(); + + +int ggml_backend_qnn_reg_devices() { + ENTER_FUNC(); + + printf("[QNN] ggml_backend_qnn_reg_devices"); + + for (size_t idx = 0; idx < GGML_QNN_MAX_DEVICES; idx++) { + printf("[QNN] ggml_backend_qnn_reg_devices idx: %ld", idx); + int id = g_qnn_mgr[idx].device; + char name[GGML_MAX_NAME]; + ggml_backend_qnn_get_device_description(idx, name, GGML_MAX_NAME); + ggml_backend_register(name, ggml_backend_qnn_reg_init, ggml_backend_qnn_buffer_type(idx), + (void *) (intptr_t)idx); + } + + LEAVE_FUNC(); + return GGML_QNN_MAX_DEVICES; +} diff --git a/src/ggml-qnn.h b/src/ggml-qnn.h new file mode 100644 index 0000000..b2fabaa --- /dev/null +++ b/src/ggml-qnn.h @@ -0,0 +1,54 @@ +/* + * MIT license + * Copyright (C) 2024 GGML Authors + * SPDX-License-Identifier: MIT + * + * this is implementation of ggml QNN(Qualcomm Nerual Network, aka AI Engine Direct) backend + */ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + +#define GGML_QNN_NAME "QNN" +#define GGML_QNN_MAX_DEVICES 3 + +//QNN cDSP and HTA backend would not be used currently, just focus on QNN CPU/GPU/HTP(aka DSP) backend currently +enum QNNBackend { + QNN_CPU, + QNN_GPU, + QNN_HTP, +}; + +GGML_API int ggml_backend_qnn_reg_devices(); + +/** + * + * @param device 0: QNN_CPU 1: QNN_GPU 2: QNN_HTP(aka DSP) + * @return + */ +GGML_API ggml_backend_t ggml_backend_qnn_init(size_t dev_num); + +GGML_API bool ggml_backend_is_qnn(ggml_backend_t backend); + +GGML_API void ggml_backend_qnn_set_n_threads(ggml_backend_t backend, int n_threads); + +GGML_API int ggml_backend_qnn_get_device_count(void); +GGML_API void ggml_backend_qnn_get_device_description(int device, char * description, size_t description_size); + + +GGML_API ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t dev_num); + + +//temporary API, should be removed in the future +GGML_API bool ggml_qnn_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); + + +#ifdef __cplusplus +} +#endif \ No newline at end of file