From d325088dbf8e86722a41b37ef44549b86211742d Mon Sep 17 00:00:00 2001 From: "zhou.weiguo" Date: Wed, 24 Apr 2024 16:28:18 +0800 Subject: [PATCH] ggml: add Qualcomm QNN(Qualcomm Neural Network,aka Qualcomm AI Engine Direct) backend --- ggml-qnn.cpp | 3590 ++++++++++++++++++++++++++++++ ggml-qnn.h | 43 + llama.cpp | 23 +- tests/ggml-qnn/CMakeLists.txt | 60 + tests/ggml-qnn/build-ggml-qnn.sh | 95 + tests/ggml-qnn/run-ggml-qnn.sh | 108 + tests/ggml-qnn/test-qnn-ops.cpp | 450 ++++ 7 files changed, 4368 insertions(+), 1 deletion(-) create mode 100644 ggml-qnn.cpp create mode 100644 ggml-qnn.h create mode 100644 tests/ggml-qnn/CMakeLists.txt create mode 100755 tests/ggml-qnn/build-ggml-qnn.sh create mode 100755 tests/ggml-qnn/run-ggml-qnn.sh create mode 100644 tests/ggml-qnn/test-qnn-ops.cpp diff --git a/ggml-qnn.cpp b/ggml-qnn.cpp new file mode 100644 index 000000000..9319db227 --- /dev/null +++ b/ggml-qnn.cpp @@ -0,0 +1,3590 @@ +#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 + +#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/helper declaration +// +// ================================================================================================= +class qnn_instance; + + +#if (defined __ANDROID__) || (defined ANDROID) +extern "C" int __android_log_print(int prio, const char * tag, const char * fmt, ...) +__attribute__((__format__(printf, 3, 4))); +#endif +static void ggml_qnn_log_internal(ggml_log_level level, const char * file, const char * func, int line, const char * format, ...); + + +// ================================================================================================= +// +// 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_DEBUG 1 //for troubleshooting QNN backend + +#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 + + +#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_op_config_version(op), err) + +#define QNN_VER_PTR(x) (&((x).v1)) +#define QNN_OP_CFG_VALID(op_config) ((op_config).version == QNN_OPCONFIG_VERSION_1) + +#define QNN_OP_CFG_GET_NAME(op_config) get_qnn_oponfig_name(op_config) +#define QNN_OP_CFG_GET_PACKAGE_NAME(op_config) get_qnn_op_config_packagename(op_config) +#define QNN_OP_CFG_GET_TYPE_NAME(op_config) get_qnn_op_config_typename(op_config) +#define QNN_OP_CFG_GET_NUM_PARAMS(op_config) get_qnn_op_config_numparams(op_config) +#define QNN_OP_CFG_GET_PARAMS(op_config) get_qnn_op_config_params(op_config) +#define QNN_OP_CFG_GET_NUM_INPUTS(op_config) get_qnn_op_config_numinputs(op_config) +#define QNN_OP_CFG_GET_INPUTS(op_config) get_qnn_op_config_inputs(op_config) +#define QNN_OP_CFG_GET_NUM_OUTPUTS(op_config) get_qnn_op_config_numoutputs(op_config) +#define QNN_OP_CFG_GET_OUTPUTS(op_config) get_qnn_op_config_outputs(op_config) + +#define QNN_OP_CFG_SET_NAME(op_config, value) set_qnn_op_config_name(op_config, value) +#define QNN_OP_CFG_SET_PACKAGE_NAME(op_config, value) set_qnn_op_config_packagename(op_config, value) +#define QNN_OP_CFG_SET_TYPE_NAME(op_config, value) set_qnn_op_config_typename(op_config, value) + +#define QNN_OP_CFG_SET_PARAMS(op_config, num_of_params, params) \ + set_qnn_op_config_params(op_config, num_of_params, params) + +#define QNN_OP_CFG_SET_INPUTS(op_config, num_of_inputs, inputTensors) \ + set_qnn_op_config_inputs(op_config, num_of_inputs, inputTensors) + +#define QNN_OP_CFG_SET_OUTPUTS(op_config, num_of_outputs, output_tensors) \ + set_qnn_op_config_outputs(op_config, num_of_outputs, output_tensors) + +#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 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 ggml_op, 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 ggml_backend_qnn_context { + int device; + int threads; + char name[GGML_MAX_NAME]; + char lib[GGML_MAX_NAME]; + qnn_instance * instance; + struct ggml_backend * backend; + QNN_INTERFACE_VER_TYPE raw_interface; + QNN_SYSTEM_INTERFACE_VER_TYPE raw_system_interface; +} ; + + +// ================================================================================================= +// +// static global variables +// +// ================================================================================================= +static ggml_backend_t g_qnn_backend = nullptr; + +static int g_current_device = QNN_BACKEND_GGML; + + +//QNN cDSP and HTA backend would not be used currently, just focus on QNN CPU/GPU/NPU(aka HTP/DSP) backend currently +static struct ggml_backend_qnn_context g_qnn_mgr[GGML_QNN_MAX_DEVICES] = { + [QNN_BACKEND_CPU] = {.device = 0, .threads = 1, .name = "qnn-cpu", .lib = "libQnnCpu.so", .instance = nullptr, .backend = nullptr, .raw_interface = {}, .raw_system_interface = {}}, + [QNN_BACKEND_GPU] = {.device = 1, .threads = 1, .name = "qnn-gpu", .lib = "libQnnGpu.so", .instance = nullptr, .backend = nullptr, .raw_interface = {}, .raw_system_interface = {}}, + [QNN_BACKEND_NPU] = {.device = 2, .threads = 1, .name = "qnn-npu", .lib = "libQnnHtp.so", .instance = nullptr, .backend = nullptr, .raw_interface = {}, .raw_system_interface = {}}, +}; + + +// ================================================================================================= +// +// QNN helper functions and other 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; +} + + +[[maybe_unused]] static inline int validate_op_config_version(Qnn_OpConfig_t op_config) { + if (op_config.version != QNN_OPCONFIG_VERSION_1) { + QNN_LOG_WARN("validate_op_config_version() op %s, got unsupported version %d\n", + op_config.v1.name, + op_config.version); + return 1; + } + return 0; +} + + +static inline const char * get_qnn_oponfig_name(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.name; + } + return nullptr; +} + + +[[maybe_unused]] static inline const char * get_qnn_oponfig_name(const Qnn_OpConfig_t * op_config) { + return get_qnn_oponfig_name(*op_config); +} + + +static inline const char * get_qnn_op_config_packagename(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.packageName; + } + return nullptr; +} + + +[[maybe_unused]] static inline const char * get_qnn_op_config_packagename(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_packagename(*op_config); +} + + +static inline const char * get_qnn_op_config_typename(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.typeName; + } + return nullptr; +} + + +[[maybe_unused]] static inline const char * get_qnn_op_config_typename(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_typename(*op_config); +} + + +static inline uint32_t get_qnn_op_config_numparams(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.numOfParams; + } + return 0u; +} + + +[[maybe_unused]] static inline uint32_t get_qnn_op_config_numparams(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_numparams(*op_config); +} + + +static inline const Qnn_Param_t * get_qnn_op_config_params(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.params; + } + return nullptr; +} + + +[[maybe_unused]] static inline const Qnn_Param_t * get_qnn_op_config_params(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_params(*op_config); +} + + +static inline uint32_t get_qnn_op_config_numinputs(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.numOfInputs; + } + return 0u; +} + + +[[maybe_unused]] static inline uint32_t get_qnn_op_config_numinputs(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_numinputs(*op_config); +} + + +static inline const Qnn_Tensor_t * get_qnn_op_config_inputs(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.inputTensors; + } + return nullptr; +} + + +[[maybe_unused]] static inline const Qnn_Tensor_t * get_qnn_op_config_inputs(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_inputs(*op_config); +} + + +static inline uint32_t get_qnn_op_config_numoutputs(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.numOfOutputs; + } + return 0u; +} + + +[[maybe_unused]] static inline uint32_t get_qnn_op_config_numoutputs(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_numoutputs(*op_config); +} + + +static inline const Qnn_Tensor_t * get_qnn_op_config_outputs(const Qnn_OpConfig_t & op_config) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + return op_config.v1.outputTensors; + } + return nullptr; +} + + +[[maybe_unused]] static inline const Qnn_Tensor_t * get_qnn_op_config_outputs(const Qnn_OpConfig_t * op_config) { + return get_qnn_op_config_outputs(*op_config); +} + + +static inline void set_qnn_op_config_name(Qnn_OpConfig_t & op_config, const char * name) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + op_config.v1.name = name; + } +} + + +[[maybe_unused]] static inline void set_qnn_op_config_name(Qnn_OpConfig_t * op_config, const char * name) { + set_qnn_op_config_name(*op_config, name); +} + + +static inline void set_qnn_op_config_packagename(Qnn_OpConfig_t & op_config, const char * package_name) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + op_config.v1.packageName = package_name; + } +} + + +[[maybe_unused]] static inline void set_qnn_op_config_packagename(Qnn_OpConfig_t * op_config, const char * package_name) { + set_qnn_op_config_packagename(*op_config, package_name); +} + + +static inline void set_qnn_op_config_typename(Qnn_OpConfig_t & op_config, const char * type_name) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + op_config.v1.typeName = type_name; + } +} + + +[[maybe_unused]] static inline void set_qnn_op_config_typename(Qnn_OpConfig_t * op_config, const char * type_name) { + set_qnn_op_config_typename(*op_config, type_name); +} + + +static inline void set_qnn_op_config_params(Qnn_OpConfig_t & op_config, + uint32_t num_of_params, + Qnn_Param_t * params) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + op_config.v1.numOfParams = num_of_params; + op_config.v1.params = params; + } +} + + +[[maybe_unused]] static inline void set_qnn_op_config_params(Qnn_OpConfig_t * op_config, + uint32_t num_of_params, + Qnn_Param_t * params) { + set_qnn_op_config_params(*op_config, num_of_params, params); +} + + +static inline void set_qnn_op_config_inputs(Qnn_OpConfig_t & op_config, + uint32_t num_of_inputs, + Qnn_Tensor_t * input_tensors) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + op_config.v1.numOfInputs = num_of_inputs; + op_config.v1.inputTensors = input_tensors; + } +} + + +[[maybe_unused]] static inline void set_qnn_op_config_inputs(Qnn_OpConfig_t * op_config, + uint32_t num_of_inputs, + Qnn_Tensor_t * input_tensors) { + set_qnn_op_config_inputs(*op_config, num_of_inputs, input_tensors); +} + + +static inline void set_qnn_op_config_outputs(Qnn_OpConfig_t & op_config, + uint32_t num_of_outputs, + Qnn_Tensor_t * output_tensors) { + if (op_config.version == QNN_OPCONFIG_VERSION_1) { + op_config.v1.numOfOutputs = num_of_outputs; + op_config.v1.outputTensors = output_tensors; + } +} + + +[[maybe_unused]] static inline void set_qnn_op_config_outputs(Qnn_OpConfig_t * op_config, + uint32_t num_of_outputs, + Qnn_Tensor_t * output_tensors) { + set_qnn_op_config_outputs(*op_config, num_of_outputs, output_tensors); +} + + +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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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; + } +} + + +[[maybe_unused]] 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 client_buf = {nullptr, 0}; + QNN_TENSOR_SET_CLIENT_BUF(dst, client_buf); + } else if (QNN_TENSOR_GET_MEM_TYPE(src) == QNN_TENSORMEMTYPE_MEMHANDLE) { + QNN_TENSOR_SET_MEM_HANDLE(dst, nullptr); + } else { + return 1; + } + + Qnn_QuantizeParams_t src_qparam = QNN_TENSOR_GET_QUANT_PARAMS(src); + Qnn_QuantizationEncoding_t encoding = src_qparam.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 src_qparam_cpy = src_qparam; + Qnn_AxisScaleOffset_t &axis_scale_offset = src_qparam_cpy.axisScaleOffsetEncoding; + Qnn_ScaleOffset_t **scaleOffset = &axis_scale_offset.scaleOffset; + size_t scaleOffsetSize = axis_scale_offset.numScaleOffsets * sizeof(Qnn_ScaleOffset_t); + *scaleOffset = (Qnn_ScaleOffset_t *)malloc(scaleOffsetSize); + memscpy(*scaleOffset, + scaleOffsetSize, + src_qparam.axisScaleOffsetEncoding.scaleOffset, + scaleOffsetSize); + QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam_cpy); + } 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 src_qparam_cpy = src_qparam; + Qnn_BwAxisScaleOffset_t &bwaxis_scale_offset = src_qparam_cpy.bwAxisScaleOffsetEncoding; + size_t scaleSize = bwaxis_scale_offset.numElements * sizeof(float); + float **scales = &bwaxis_scale_offset.scales; + int32_t **offsets = &bwaxis_scale_offset.offsets; + *scales = (float *)malloc(scaleSize); + memscpy(*scales, scaleSize, src_qparam.bwAxisScaleOffsetEncoding.scales, scaleSize); + + // only copy offsets if present, nullptr implies all offsets are 0 + if (bwaxis_scale_offset.offsets != nullptr) { + size_t offsetSize = bwaxis_scale_offset.numElements * sizeof(int32_t); + *offsets = (int32_t *)malloc(offsetSize); + memscpy(*offsets, offsetSize, src_qparam.bwAxisScaleOffsetEncoding.offsets, offsetSize); + } + QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam_cpy); + } else { + QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam); + } + + // allocate and copy memory for all the pointer members + uint32_t rank = QNN_TENSOR_GET_RANK(src); + 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; + } + 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) { + int err = 0; + VALIDATE_TENSOR_VERSION(tensor, err); + + free((void *) QNN_TENSOR_GET_NAME(tensor)); + free(QNN_TENSOR_GET_DIMENSIONS(tensor)); + + return err; +} + + +[[maybe_unused]] static int free_qnn_tensors(Qnn_Tensor_t *& tensors, uint32_t num_tensors) { + int err = 0; + + // free all pointer allocations in struct + for (size_t i = 0; i < num_tensors; i++) { + free_qnn_tensor(tensors[i]); + } + free(tensors); + + return err; +} + + +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: mapping more ggml data type to QNN data type +//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_F16: + return QNN_DATATYPE_FLOAT_16; + case GGML_TYPE_F32: + return QNN_DATATYPE_FLOAT_32; + default: + break; + + } + return QNN_DATATYPE_UNDEFINED; +} + + +//TODO: only support GGML_OP_ADD/GGML_OP_MUL/GGML_OP_MUL_MAT +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; + default: + break; + } + + 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 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-NPU"; + 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/NPU(aka HTP/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) + //for Android APK + __android_log_print(level, "ggml-qnn", "%s\n", s_ggml_qnn_log_internal_buf); +#endif + //for Android command line application or WoA + printf("%s\n", s_ggml_qnn_log_internal_buf); + } + 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: + 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; + + 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; + std::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 (!_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 //comment it for purpose of reduce size of APK + QnnSaver_Config_t outputdir_cfg; + outputdir_cfg.option = QNN_SAVER_CONFIG_OPTION_OUTPUT_DIRECTORY; + outputdir_cfg.outputDirectory = "/data/local/tmp/"; + + 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_WARN("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_initializeļ¼Œerror %d", QNN_GET_ERROR_CODE(error)); + return 7; + } + } else { + QNN_LOG_WARN("saver_initialize is null\n"); + } + + return 0; +} + + +int qnn_instance::unload_backend() { + 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(); + + return 0; +} + + +int qnn_instance::load_system() { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + + 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_INFO("initialize qnn system successfully\n"); + } + + return 0; +} + + +int qnn_instance::unload_system() { + 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; + + return result; +} + + +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 * log_level_desc = ""; + switch (level) { + case QNN_LOG_LEVEL_ERROR: + log_level_desc = " ERROR "; + break; + case QNN_LOG_LEVEL_WARN: + log_level_desc = "WARNING"; + break; + case QNN_LOG_LEVEL_INFO: + log_level_desc = " INFO "; + break; + case QNN_LOG_LEVEL_DEBUG: + log_level_desc = " DEBUG "; + break; + case QNN_LOG_LEVEL_VERBOSE: + log_level_desc = "VERBOSE"; + break; + case QNN_LOG_LEVEL_MAX: + log_level_desc = "UNKNOWN"; + break; + } + + double ms = (double) timestamp / 1000000.0; + + { + std::lock_guard lock(log_mutex); + + memset(s_ggml_qnn_logbuf, 0, GGML_QNN_LOGBUF_LEN); + vsnprintf(reinterpret_cast(s_ggml_qnn_logbuf), GGML_QNN_LOGBUF_LEN, fmt, argp); + QNN_LOG_DEBUG("%8.1fms [%-7s] %s\n", ms, log_level_desc, 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"); + } + + 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"); + } + } + } + + _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(); + + 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; +} + + +int qnn_instance::qnn_finalize() { + int ret_status = 0; + Qnn_ErrorHandle_t error = QNN_SUCCESS; + + if (nullptr != _pfn_rpc_mem_deinit) // make Qualcomm's SoC based low-end phone happy + _pfn_rpc_mem_deinit(); + + 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"); + } + + 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(); + + 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; + + 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); + } + + return 0; +} + + +int qnn_instance::finalize_qnn_graph() { + 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"); + } + + return 0; +} + + + +// ================================================================================================= +// +// implementation of GGML's QNN backend +// +// ================================================================================================= +static bool ggml_qnn_can_handle_op(const struct ggml_tensor * tensor, bool b_dump_tensor_info) { + if (nullptr == tensor) + return false; + if (b_dump_tensor_info) { + QNN_LOG_DEBUG("op name:%s, tensor type:%s", ggml_op_name(tensor->op), + ggml_type_name(tensor->type)); + } + //only support the following 3 OPs currently and ensure tensor->src[0] and tensor->src[1] is not nullptr + bool supported_op = ((tensor->op == GGML_OP_ADD) || (tensor->op == GGML_OP_MUL) || (tensor->op == GGML_OP_MUL_MAT)); + if (!supported_op) { + return false; + } + + const struct ggml_tensor * src0 = tensor->src[0]; + const struct ggml_tensor * src1 = tensor->src[1]; + + const int64_t ne00 = tensor->src[0]->ne[0]; + const int64_t ne01 = tensor->src[0]->ne[1]; + + const int64_t ne10 = tensor->src[1]->ne[0]; + const int64_t ne11 = tensor->src[1]->ne[1]; + + const int64_t ne0 = tensor->ne[0]; + const int64_t ne1 = tensor->ne[1]; + + GGML_UNUSED(ne0); + GGML_UNUSED(ne1); + + if (b_dump_tensor_info) { + QNN_LOG_DEBUG("src0 type:%s", ggml_type_name(tensor->src[0]->type)); + QNN_LOG_DEBUG("src1 type:%s", ggml_type_name(tensor->src[1]->type)); + + if (tensor->op == GGML_OP_MUL_MAT) { + QNN_LOG_DEBUG("GGML_OP_MUL_MAT"); + QNN_LOG_DEBUG( + "src0 %15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, + 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_DEBUG( + "src1 %15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, + 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_DEBUG( + " %15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + tensor->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]); + + } + } + + if (ggml_is_empty(tensor) || tensor->op == GGML_OP_RESHAPE || tensor->op == GGML_OP_TRANSPOSE || tensor->op == GGML_OP_VIEW || tensor->op == GGML_OP_PERMUTE || tensor->op == GGML_OP_NONE) { + return false; + } + + //make ggml_get_tensor_rank and QNN SDK happy + if ((ne00 <= 1 || ne01 <= 1 || ne10 <= 1 || ne11 <= 1)) { + return false; + } + + if (tensor->op == GGML_OP_ADD) { + //TODO: this is limitation + return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) + && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) + && (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_F16); + + } + + if (tensor->op == GGML_OP_MUL_MAT) { + //TODO: this is limitation + 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 == tensor->type); + + if (tensor->ne[1] < 32) { // GPU/NPU inference will slower then CPU inference when tensor->ne[1] < min batch size + return false; + } + + } + + //TODO: this is limitation + 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 == tensor->type); +} + + +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_duration = 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_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 1 + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, + 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; + + 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); + } + + uint32_t * tensor_0_dimensions = QNN_VER_PTR(*tensor_0)->dimensions; + uint32_t * tensor_1_dimensions = QNN_VER_PTR(*tensor_1)->dimensions; + uint32_t * tensor_2_dimensions = QNN_VER_PTR(*tensor_2)->dimensions; + + 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 op_config = { + (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, op_config); + 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); + } + } + QNN_VER_PTR(*tensor_0)->dimensions = tensor_0_dimensions; + QNN_VER_PTR(*tensor_1)->dimensions = tensor_1_dimensions; + QNN_VER_PTR(*tensor_2)->dimensions = tensor_2_dimensions; + n_end_time = ggml_time_us(); + n_duration = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ggml_qnn_add : %lld milliseconds\n", n_duration); +} + + + +/* + * 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_duration = 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_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 1 + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, + 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; + + 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); + } + + uint32_t * tensor_0_dimensions = QNN_VER_PTR(*tensor_0)->dimensions; + uint32_t * tensor_1_dimensions = QNN_VER_PTR(*tensor_1)->dimensions; + uint32_t * tensor_2_dimensions = QNN_VER_PTR(*tensor_2)->dimensions; + + 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 op_config = { + (Qnn_OpConfigVersion_t) 1, .v1 = { + "ggml_op_mul_mat", + QNN_OP_PACKAGE_NAME_QTI_AISW, + QNN_OP_MAT_MUL, + 0, + qnn_params, + 2, + tensor_inputs, + 1, + tensor_outputs + } + }; + error = qnn_raw_interface.graphAddNode(graph_handle, op_config); + 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); + } + } + QNN_VER_PTR(*tensor_0)->dimensions = tensor_0_dimensions; + QNN_VER_PTR(*tensor_1)->dimensions = tensor_1_dimensions; + QNN_VER_PTR(*tensor_2)->dimensions = tensor_2_dimensions; + n_end_time = ggml_time_us(); + n_duration = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ggml_qnn_mul_mat : %lld milliseconds\n", n_duration); + 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_duration = 0LL; + + qnn_instance * instance = nullptr; + struct ggml_backend_qnn_context * ctx = nullptr; + + std::string qnn_graph_name = "ggml_qnn_graph"; + std::string qnn_op_config_name = "ggml_qnn_op_config"; + 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_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(); +#if 1 + QNN_LOG_DEBUG("call %s\n", __func__); + QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, + 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; + + 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); + } + + uint32_t * tensor_0_dimensions = QNN_VER_PTR(*tensor_0)->dimensions; + uint32_t * tensor_1_dimensions = QNN_VER_PTR(*tensor_1)->dimensions; + uint32_t * tensor_2_dimensions = QNN_VER_PTR(*tensor_2)->dimensions; + + if (!graph_initialized) { + qnn_graph_name = qnn_graph_name + "_" + ggml_op_name(ggmlop) + std::to_string(ctx->threads) + src0->name + "_" + src1->name; + qnn_op_config_name = qnn_op_config_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 op_config name %s", qnn_op_config_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 op_config = { + (Qnn_OpConfigVersion_t) 1, .v1 = { + qnn_op_config_name.c_str(), + QNN_OP_PACKAGE_NAME_QTI_AISW, + qnn_op_name, + 0, + qnn_params, + 2, + tensor_inputs, + 1, + tensor_outputs + } + }; + error = qnn_raw_interface.graphAddNode(graph_handle, op_config); + 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); + } + } + QNN_VER_PTR(*tensor_0)->dimensions = tensor_0_dimensions; + QNN_VER_PTR(*tensor_1)->dimensions = tensor_1_dimensions; + QNN_VER_PTR(*tensor_2)->dimensions = tensor_2_dimensions; + n_end_time = ggml_time_us(); + n_duration = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ggml_qnn_%s : %lld milliseconds\n", ggml_op_name(ggmlop), n_duration); + 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_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) { + ggml_qnn_func_t func = nullptr; + ggml_qnn_func_common_t func_common = nullptr; + + switch (tensor->op) { + case GGML_OP_ADD: + func = ggml_qnn_add; + break; + + case GGML_OP_MUL: + func_common = ggml_qnn_hanlde_op; + break; + + case GGML_OP_MUL_MAT: + func = ggml_qnn_mul_mat; + 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_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); + + return true; +} + + +struct ggml_backend_qnn_buffer_context { + ~ggml_backend_qnn_buffer_context() { + 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); + } + + sub_buffers.clear(); + qnn_tensors.clear(); + } + 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"; +} + + +[[maybe_unused]] GGML_CALL static bool ggml_backend_buffer_is_qnn(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_qnn_buffer_get_name; +} + + +GGML_CALL static void ggml_backend_qnn_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *) buffer->context; + delete ctx; +} + + +GGML_CALL 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; +} + + +GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + Qnn_ErrorHandle_t error = QNN_SUCCESS; + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *)buffer->context; + + static int idx = 0; + char tensor_name[GGML_MAX_NAME] = { 0 }; + snprintf(tensor_name, GGML_MAX_NAME, "tensor_%2d", idx++); + + 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; + + if (tensor->flags & GGML_TENSOR_FLAG_INPUT) { + qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE; + } else if (tensor->flags & GGML_TENSOR_FLAG_OUTPUT) { + 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 *)calloc(1, sizeof(Qnn_Tensor_t)); + if (nullptr == p_qnn_tensor) { + QNN_LOG_WARN("calloc failed"); + return; + } + 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); +} + + +GGML_CALL 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) { + GGML_UNUSED(buffer); + + memcpy((char *)tensor->data + offset, data, size); +} + + +GGML_CALL 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) { + GGML_UNUSED(buffer); + memcpy(data, (const char *)tensor->data + offset, size); +} + + +GGML_CALL 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; +} + + +GGML_CALL static void ggml_backend_qnn_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_qnn_buffer_context * ctx = (ggml_backend_qnn_buffer_context *) buffer->context; + + memset(ctx->buffer, value, ctx->buffer_size); +} + + +[[maybe_unused]] GGML_CALL static void ggml_backend_qnn_buffer_reset(ggml_backend_buffer_t buffer) { + 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(); +} + + +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, +}; + + +GGML_CALL static const char * ggml_backend_qnn_buffer_type_name(ggml_backend_buffer_type_t buft) { + 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; +} + + +GGML_CALL static ggml_backend_buffer_t ggml_backend_qnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + 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)); + } + + //TODO:use pre-allocated buffer in internal memory pool + ctx->buffer = ggml_qnn_host_malloc(size_aligned); + ctx->buffer_size = size_aligned; + + 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)); + return nullptr; + } + + return ggml_backend_buffer_init(buft, ggml_backend_qnn_buffer_interface, ctx, size); +} + + +GGML_CALL 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, works fine with whisper/llm/minicpm-v inference on Android +GGML_CALL static size_t ggml_backend_qnn_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + + return (96 * 1024 * 1024); +} + + +GGML_CALL static bool ggml_backend_qnn_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, + ggml_backend_t backend) { + GGML_UNUSED(buft); + + return ggml_backend_is_qnn(backend) || ggml_backend_is_cpu(backend); +} + + +GGML_CALL static bool ggml_backend_qnn_buffer_is_host(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + return true; +} + + +GGML_CALL static const char * ggml_backend_qnn_name(ggml_backend_t backend) { + return "QNN"; +} + + +GGML_CALL static void ggml_backend_qnn_free(ggml_backend_t backend) { + QNN_LOG_INFO("enter %s", __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) { + std::map>::iterator graph_it; + for (graph_it = instance->_qnn_graph_map.begin(); graph_it != instance->_qnn_graph_map.end(); graph_it++) { + auto & graph_item = graph_it->second; + Qnn_GraphHandle_t & graph_handle = std::get<0>(graph_item); + GGML_UNUSED(graph_handle); + QNN_LOG_DEBUG("graph type:%s", graph_it->first.c_str()); + } + instance->_qnn_graph_map.clear(); + + instance->qnn_finalize(); + delete instance; + g_qnn_mgr[ctx->device].instance = nullptr; + } + + if (g_qnn_mgr[ctx->device].backend != nullptr) { + delete backend; + g_qnn_backend = nullptr; + g_qnn_mgr[ctx->device].backend = nullptr; + } + QNN_LOG_INFO("leave %s", __func__ ); +} + + +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_qnn_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_qnn_context * ctx = (ggml_backend_qnn_context *) backend->context; + + return ggml_backend_qnn_buffer_type(ctx->device); +} + + +GGML_CALL static ggml_status ggml_backend_qnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + enum ggml_status result = GGML_STATUS_SUCCESS; + ggml_backend_qnn_context * ctx = (ggml_backend_qnn_context *) backend->context; + GGML_UNUSED(ctx); + + ggml_compute_params params = {}; + params.type = GGML_TASK_TYPE_COMPUTE; + params.ith = 0; + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_tensor * node = cgraph->nodes[i]; + if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + continue; + } + bool ok = ggml_qnn_compute_forward(¶ms, node); + if (!ok) { + QNN_LOG_DEBUG("%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + } + } + + return result; +} + + +GGML_CALL static bool ggml_backend_qnn_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + GGML_UNUSED(backend); + + return (ggml_qnn_can_handle_op(op, true)); +} + + +//note: this function be used with proposal/refined ggml backend subsystem in this PR: +// https://github.com/ggerganov/llama.cpp/pull/7641 +// new ggml backend(only using system memory: ggml_backend_xxx_buffer_is_host return true) +// can following this style for mixed inference between CPU&GPU / CPU&NPU very easily +GGML_CALL static bool ggml_backend_qnn_offload_op(ggml_backend_t backend, const ggml_tensor * tensor) { + GGML_UNUSED(backend); + + return ggml_qnn_compute_forward(nullptr, (ggml_tensor*)tensor); +} + + +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, + /* .supports_op = */ ggml_backend_qnn_supports_op, + /* .offload_op = */ ggml_backend_qnn_offload_op, + /* .event_new = */ nullptr, + /* .event_free = */ nullptr, + /* .event_record = */ nullptr, + /* .event_wait = */ nullptr, + /* .event_synchronize = */ nullptr, +}; + + +static ggml_guid_t ggml_backend_qnn_guid() { + static ggml_guid guid = {0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81, 0x92, 0xa3, 0xb4, 0xc5, + 0xd6, 0xe7, 0xf8, 0x09}; + return &guid; +} + + +static ggml_backend_t ggml_backend_qnn_reg_init(const char * params, void * user_data) { + if (nullptr == params) { + //QNN library path + //can be hardcoded to "/data/local/tmp/" for Android command line application + //or specified in JNI layer for Android APK + params = "/data/local/tmp/"; + } + ggml_backend_t qnn_backend = ggml_backend_qnn_init((int) (intptr_t) user_data, params); + + 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(size_t dev_num, char * description, size_t description_size) { + if (nullptr == description || 0 == description_size) { + QNN_LOG_WARN("invalid param"); + return; + } + + if (dev_num >= GGML_QNN_MAX_DEVICES) { + QNN_LOG_WARN("invalid param"); + return; + } + + snprintf(description, description_size, "%s", g_qnn_mgr[dev_num].name); + QNN_LOG_DEBUG("description:%s", description); +} + + +ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t device_index) { + 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, + }; + + return &ggml_backend_buffer_type_qnn; +} + + +/** + * + * @param device 0: QNN_BACKEND_CPU 1: QNN_BACKEND_GPU 2: QNN_BACKEND_NPU(aka HTP/DSP) + * @param qnn_lib_path qnn library path, such as "/data/local/tmp/" on Android or specified in JNI layer + * @return + */ +ggml_backend_t ggml_backend_qnn_init(size_t device, const char * qnn_lib_path) { + int result = 0; + + if (nullptr == qnn_lib_path) + return nullptr; + + QNN_LOG_DEBUG("device %d", device); + QNN_LOG_DEBUG("qnn_lib_path %s", qnn_lib_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", 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); + } + } + + std::string path = qnn_lib_path; + if (QNN_BACKEND_NPU == device) { + if (0 == setenv("LD_LIBRARY_PATH", + (path + + ":/vendor/dsp/cdsp:/vendor/lib64:/vendor/dsp/dsp:/vendor/dsp/images").c_str(), + 1)) { + QNN_LOG_INFO("QNN DSP backend setenv successfully"); + } else { + QNN_LOG_ERROR("QNN DSP backend setenv failure"); + } + if (0 == setenv("ADSP_LIBRARY_PATH", + (path + + ";/vendor/dsp/cdsp;/vendor/lib/rfsa/adsp;/system/lib/rfsa/adsp;/vendor/dsp/dsp;/vendor/dsp/images;/dsp").c_str(), + 1)) { + QNN_LOG_INFO("QNN DSP backend setenv successfully"); + } else { + QNN_LOG_ERROR("QNN DSP backend setenv failure"); + } + } else { + if (0 == setenv("LD_LIBRARY_PATH", + (path + + ":/vendor/dsp/cdsp:/vendor/lib64:/vendor/dsp/dsp:/vendor/dsp/images").c_str(), + 1)) { + QNN_LOG_INFO("%s backend setenv successfully\n", get_qnn_backend_name(device)); + } else { + QNN_LOG_ERROR("%s backend setenv failure\n", get_qnn_backend_name(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 = 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(); + + 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; + + return qnn_backend; +} + + +extern "C" GGML_CALL int ggml_backend_qnn_reg_devices(void); + +GGML_CALL int ggml_backend_qnn_reg_devices() { + for (size_t idx = 0; idx < GGML_QNN_MAX_DEVICES; idx++) { + 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); + } + + return GGML_QNN_MAX_DEVICES; +} diff --git a/ggml-qnn.h b/ggml-qnn.h new file mode 100644 index 000000000..c61ebd25d --- /dev/null +++ b/ggml-qnn.h @@ -0,0 +1,43 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + +#define GGML_QNN_MAX_DEVICES 3 + +//QNN cDSP and HTA backend would not be used currently, just focus on QNN CPU/GPU/NPU(aka HTP/DSP) backend currently +enum QNNBackend { + QNN_BACKEND_CPU, + QNN_BACKEND_GPU, + QNN_BACKEND_NPU, + QNN_BACKEND_GGML, //"fake" QNN backend just for compare performance between QNN and original GGML +}; + +GGML_API int ggml_backend_qnn_reg_devices(void); + +/** + * + * @param device 0: QNN_BACKEND_CPU 1: QNN_BACKEND_GPU 2: QNN_BACKEND_NPU(aka HTP/DSP) + * @param qnn_lib_path qnn library path, such as "/data/local/tmp/" on Android or specified in JNI layer + * @return + */ +GGML_API ggml_backend_t ggml_backend_qnn_init(size_t dev_num, const char * qnn_lib_path); + +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 thread_counts); + +GGML_API int ggml_backend_qnn_get_device_count(void); + +GGML_API void ggml_backend_qnn_get_device_description(size_t dev_num, char * description, size_t description_size); + +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t dev_num); + +#ifdef __cplusplus +} +#endif diff --git a/llama.cpp b/llama.cpp index 06889126e..42a9cb2a4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -19,6 +19,8 @@ # 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 @@ -2377,6 +2379,8 @@ static size_t llama_get_device_count(const llama_model & model) { count = ggml_backend_sycl_get_device_count(); #elif defined(GGML_USE_VULKAN) count = ggml_backend_vk_get_device_count(); +#elif defined(GGML_USE_QNN) + count = ggml_backend_qnn_get_device_count(); #endif #if defined(GGML_USE_RPC) count += model.rpc_servers.size(); @@ -2409,6 +2413,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_ if (buft == nullptr) { LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); } +#elif defined(GGML_USE_QNN) + buft = ggml_backend_qnn_buffer_type(gpu); #endif if (buft == nullptr) { @@ -15899,6 +15905,8 @@ size_t llama_max_devices(void) { return GGML_SYCL_MAX_DEVICES; #elif defined(GGML_USE_VULKAN) return GGML_VK_MAX_DEVICES; +#elif defined(GGML_USE_QNN) + return GGML_QNN_MAX_DEVICES; #else return 1; #endif @@ -15914,7 +15922,7 @@ bool llama_supports_mlock(void) { bool llama_supports_gpu_offload(void) { #if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC) + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC) || defined(GGML_USE_QNN) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; #else @@ -16225,6 +16233,19 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(backend); } +#elif defined(GGML_USE_QNN) + if (model->n_gpu_layers > 0) { + //the second param is data path of prebuit QNN libs provided by Qualcomm + //can be hardcoded to "/data/local/tmp/" for Android command line application + //or specified in JNI layer for Android APK application + ggml_backend_t backend = ggml_backend_qnn_init(model->main_gpu, "/data/local/tmp/"); + if (nullptr == backend) { + LLAMA_LOG_ERROR("%s: failed to initialize QNN backend\n", __func__); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } #endif #if defined(GGML_USE_RPC) if (model->n_gpu_layers > 0) { diff --git a/tests/ggml-qnn/CMakeLists.txt b/tests/ggml-qnn/CMakeLists.txt new file mode 100644 index 000000000..15ad7be6f --- /dev/null +++ b/tests/ggml-qnn/CMakeLists.txt @@ -0,0 +1,60 @@ +cmake_minimum_required(VERSION 3.22.1) +project(ggml-qnn-test) + +set(CMAKE_VERBOSE_MAKEFILE on) +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_POSITION_INDEPENDENT_CODE ON) + +#set to ON if target Android phone is based on Qualcomm Snapdragon 8 Gen 3 +set(TARGET_SNAPDRAGON_8_GEN3 OFF) + +set(QNN_INC_PATH ${QNN_SDK_PATH}/include/QNN) +set(QNN_LIB_PATH ${QNN_SDK_PATH}/lib/aarch64-android) + +include_directories(${QNN_INC_PATH}) +include_directories(../../) # ggml.h + +set(SOURCE_FILES + ../../ggml.c + ../../ggml-alloc.c + ../../ggml-backend.c + ../../ggml-quants.c + ../../ggml-qnn.cpp + test-qnn-ops.cpp +) + + +message("QNN_SDK_PATH : ${QNN_SDK_PATH}") +message("QNN_INC_PATH : ${QNN_INC_PATH}") +message("QNN_LIB_PATH : ${QNN_LIB_PATH}") + +add_definitions(-D__ARM_NEON) +add_definitions(-DGGML_USE_QNN) + +if(CMAKE_BUILD_TYPE STREQUAL "Release") +add_definitions(-DNDEBUG) +add_definitions(-O3) +endif() + +if (TARGET_SNAPDRAGON_8_GEN3) +# the below build optimization only verified and works well on Qualcomm SM8650-AB Snapdragon 8 Gen 3 +add_definitions(-march=armv8.7-a) +add_definitions(-mcpu=cortex-x1) +add_definitions(-mtune=cortex-x1) + +else() +# the below build optimization might be works well on ALL mainstream Android phone based on Qualcomm mobile SoC +add_definitions(-mcpu=cortex-a72) + +endif() + +add_compile_options("-Wall" "-Wno-sign-compare") + +find_library(LOG_LIB log) + +link_libraries(${LOG_LIB} android) + +add_executable(${TARGET_NAME} + ${SOURCE_FILES} +) diff --git a/tests/ggml-qnn/build-ggml-qnn.sh b/tests/ggml-qnn/build-ggml-qnn.sh new file mode 100755 index 000000000..baca02f91 --- /dev/null +++ b/tests/ggml-qnn/build-ggml-qnn.sh @@ -0,0 +1,95 @@ +#!/bin/bash + +set -e + +#https://qpm.qualcomm.com/#/main/tools/details/qualcomm_ai_engine_direct +#https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools +QNN_SDK_PATH=/opt/qcom/aistack/qnn/2.20.0.240223/ + +ANDROID_NDK=`pwd`/android-ndk-r26c +ANDROID_PLATFORM=android-34 +TARGET=ggml-qnn-test + + +function dump_vars() +{ + echo -e "ANDROID_NDK: ${ANDROID_NDK}" + echo -e "QNN_SDK_PATH: ${QNN_SDK_PATH}" +} + + +function show_pwd() +{ + echo -e "current working path:$(pwd)\n" +} + + +function check_qnn_sdk() +{ + if [ ! -d ${QNN_SDK_PATH} ]; then + echo -e "QNN_SDK_PATH ${QNN_SDK_PATH} not exist, pls check...\n" + exit 1 + fi +} + + +function check_and_download_ndk() +{ + is_android_ndk_exist=1 + + if [ ! -d ${ANDROID_NDK} ]; then + is_android_ndk_exist=0 + fi + + if [ ! -f ${ANDROID_NDK}/build/cmake/android.toolchain.cmake ]; then + is_android_ndk_exist=0 + fi + + if [ ${is_android_ndk_exist} -eq 0 ]; then + + if [ ! -f android-ndk-r26c-linux.zip ]; then + wget --no-config --quiet --show-progress -O android-ndk-r26c-linux.zip https://dl.google.com/android/repository/android-ndk-r26c-linux.zip + fi + + unzip android-ndk-r26c-linux.zip + + if [ $? -ne 0 ]; then + printf "failed to download android ndk to %s \n" "${ANDROID_NDK}" + exit 1 + fi + + printf "android ndk saved to ${ANDROID_NDK} \n\n" + else + printf "android ndk already exist:${ANDROID_NDK} \n\n" + fi +} + + +function build_arm64 +{ + cmake -H. -B./out/arm64-v8a -DTARGET_NAME=${TARGET} -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=${ANDROID_PLATFORM} -DANDROID_NDK=${ANDROID_NDK} -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK}/build/cmake/android.toolchain.cmake -DQNN_SDK_PATH=${QNN_SDK_PATH} + + cd ./out/arm64-v8a + make + + ls -lah ${TARGET} + /bin/cp ${TARGET} ../../ + cd - +} + + +function remove_temp_dir() +{ + if [ -d out ]; then + echo "remove out directory in `pwd`" + rm -rf out + fi +} + + +show_pwd +check_and_download_ndk +check_qnn_sdk +dump_vars +remove_temp_dir +build_arm64 diff --git a/tests/ggml-qnn/run-ggml-qnn.sh b/tests/ggml-qnn/run-ggml-qnn.sh new file mode 100755 index 000000000..a4c1f22ad --- /dev/null +++ b/tests/ggml-qnn/run-ggml-qnn.sh @@ -0,0 +1,108 @@ +#!/bin/bash + +#https://qpm.qualcomm.com/#/main/tools/details/qualcomm_ai_engine_direct +#https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools +QNN_SDK_PATH=/opt/qcom/aistack/qnn/2.20.0.240223/ +GGML_QNN_TEST=ggml-qnn-test +REMOTE_PATH=/data/local/tmp/ + + +function check_qnn_sdk() +{ + if [ ! -d ${QNN_SDK_PATH} ]; then + echo -e "QNN_SDK_PATH ${QNN_SDK_PATH} not exist, pls check or download it from https://qpm.qualcomm.com/#/main/tools/details/qualcomm_ai_engine_direct...\n" + exit 1 + fi +} + + +function check_qnn_libs() +{ + #reuse the cached qnn libs in Android phone + adb shell ls ${REMOTE_PATH}/libQnnCpu.so + if [ $? -eq 0 ]; then + printf "QNN libs already exist on Android phone\n" + else + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnSystem.so ${REMOTE_PATH}/ + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnCpu.so ${REMOTE_PATH}/ + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnGpu.so ${REMOTE_PATH}/ + + #the QNN NPU(aka HTP/DSP) backend only verified on Xiaomi14(Qualcomm SM8650-AB Snapdragon 8 Gen 3) successfully + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnHtp.so ${REMOTE_PATH}/ + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnHtpNetRunExtensions.so ${REMOTE_PATH}/ + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnHtpPrepare.so ${REMOTE_PATH}/ + adb push ${QNN_SDK_PATH}/lib/aarch64-android/libQnnHtpV75Stub.so ${REMOTE_PATH}/ + adb push ${QNN_SDK_PATH}/lib/hexagon-v75/unsigned/libQnnHtpV75Skel.so ${REMOTE_PATH}/ + fi +} + + +function show_usage() +{ + echo "Usage:" + echo " $0 GGML_OP_ADD 0/1/2" + echo " $0 GGML_OP_MUL 0/1/2" + echo " $0 GGML_OP_MUL_MAT 0/1/2" + echo -e "\n\n\n" +} + + +function main() +{ + check_qnn_libs + + #upload the latest ggml_qnn_test + adb push ${GGML_QNN_TEST} ${REMOTE_PATH} + adb shell chmod +x ${REMOTE_PATH}/${GGML_QNN_TEST} + + case "$ggmlop" in + GGML_OP_ADD) + echo "adb shell ${REMOTE_PATH}/${GGML_QNN_TEST} -t GGML_OP_ADD -b $qnnbackend" + adb shell ${REMOTE_PATH}/${GGML_QNN_TEST} -t GGML_OP_ADD -b $qnnbackend + ;; + + GGML_OP_MUL) + adb shell ${REMOTE_PATH}/${GGML_QNN_TEST} -t GGML_OP_MUL -b $qnnbackend + ;; + + GGML_OP_MUL_MAT) + adb shell ${REMOTE_PATH}/${GGML_QNN_TEST} -t GGML_OP_MUL_MAT -b $qnnbackend + ;; + + *) + printf " \n$arg not supported currently\n" + show_usage + exit 1 + ;; + esac +} + + +check_qnn_sdk + +unset ggmlop +unset qnnbackend +if [ $# == 0 ]; then + show_usage + exit 1 +elif [ $# == 1 ]; then + if [ "$1" == "-h" ]; then + #avoid upload command line program to Android phone in this scenario + show_usage + exit 1 + elif [ "$1" == "help" ]; then + #avoid upload command line program to Android phone in this scenario + show_usage + exit 1 + else + ggmlop=$1 + qnnbackend=0 + fi +elif [ $# == 2 ]; then + ggmlop=$1 + qnnbackend=$2 +else + show_usage + exit 1 +fi +main $arg diff --git a/tests/ggml-qnn/test-qnn-ops.cpp b/tests/ggml-qnn/test-qnn-ops.cpp new file mode 100644 index 000000000..27967270b --- /dev/null +++ b/tests/ggml-qnn/test-qnn-ops.cpp @@ -0,0 +1,450 @@ +#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 +#include +#include +#include +#include +#include + +#include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-backend.h" +#include "ggml-qnn.h" + +#define GGML_QNN_DEBUG 1 +#define GGML_QNN_LOGBUF_LEN 4096 + +#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 + +static void tensor_dump(const ggml_tensor * tensor, const char * name); + +#define TENSOR_DUMP(tensor) tensor_dump(tensor, #tensor) + +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)) { + //for Android command line application or WoA + printf("%s\n", s_ggml_qnn_log_internal_buf); + } + va_end(args); + } +} + + +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-NPU(HTP/DSP)"; + case 3: + return "ggml"; + default: + return "unknown"; + } +} + + +static bool ggml_graph_compute_helper( + struct ggml_backend * backend, + struct ggml_cgraph * graph, + std::vector & buf, + int n_threads, + ggml_abort_callback abort_callback, + void * abort_callback_data) { + struct ggml_cplan plan = ggml_graph_plan(graph, n_threads); + + plan.abort_callback = abort_callback; + plan.abort_callback_data = abort_callback_data; + + if (plan.work_size > 0) { + buf.resize(plan.work_size); + plan.work_data = buf.data(); + } + + if (ggml_backend_is_cpu(backend)) { + ggml_backend_cpu_set_n_threads(backend, n_threads); + } + +#ifdef GGML_USE_QNN + if (ggml_backend_is_qnn(backend)) { + ggml_backend_qnn_set_n_threads(backend, n_threads); + } +#endif + + //a new approch of mixed inference + if (nullptr != backend) + return ggml_backend_graph_compute(backend, graph) == GGML_STATUS_SUCCESS; + else + return ggml_graph_compute(graph, &plan); +} + + +static void tensor_dump_elements(const ggml_tensor * tensor) { + 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]; + tmposs << std::setw(8) << std::fixed << std::setprecision(2) << value + << " "; + } + if (strlen(tmposs.str().c_str()) <= (GGML_QNN_LOGBUF_LEN - 96)) { + QNN_LOG_DEBUG("%s", tmposs.str().c_str()); + } + tmposs.clear(); + tmposs.str(""); + //QNN_LOG_DEBUG("\n"); + } + } + } + } + + //QNN_LOG_DEBUG("\n"); +} + + +static void tensor_dump(const ggml_tensor * tensor, const char * name) { + QNN_LOG_DEBUG("dump ggml tensor %s(%s)", name, tensor->name); + QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)", + 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]); + tensor_dump_elements(tensor); + + QNN_LOG_DEBUG("\n"); +} + + +static uint32_t 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; +} + + +static uint32_t get_tensor_data_size(const ggml_tensor * tensor) { + size_t data_size = ggml_row_size(tensor->type, tensor->ne[0]); + size_t n_dims = get_tensor_rank(tensor); + for (int i = 1; i < n_dims; i++) { + data_size *= tensor->ne[i]; + } + + QNN_LOG_DEBUG("get_tensor_data_size %d", data_size); + QNN_LOG_DEBUG("ggml_nbytes(tensor) %d", ggml_nbytes(tensor)); + + return ggml_nbytes(tensor); +} + + +//ref: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-backend-ops.cpp#L20 +static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { + // static RNG initialization (revisit if n_threads stops being constant) + static const size_t n_threads = std::thread::hardware_concurrency(); + static std::vector generators = []() { + std::random_device rd; + std::vector vec; + vec.reserve(n_threads); + //for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed + for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); } + return vec; + }(); + + size_t size = ggml_nelements(tensor); + std::vector data(size); + + auto init_thread = [&](size_t ith, size_t start, size_t end) { + std::uniform_real_distribution distribution(min, max); + for (size_t i = start; i < end; i++) { + data[i] = distribution(generators[ith]); + } + }; + + std::vector threads; + threads.reserve(n_threads); + for (size_t i = 0; i < n_threads; i++) { + size_t start = i*size/n_threads; + size_t end = (i+1)*size/n_threads; + threads.emplace_back(init_thread, i, start, end); + } + for (auto & t : threads) { + t.join(); + } + if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) { + ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float)); + } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) { + GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0); + std::vector dataq(ggml_row_size(tensor->type, size)); + std::vector imatrix(tensor->ne[0], 1.0f); // dummy importance matrix + const float * im = imatrix.data(); + if (!ggml_quantize_requires_imatrix(tensor->type)) { + // when the imatrix is optional, we want to test both quantization with and without imatrix + // use one of the random numbers to decide + if (data[0] > 0.5f*(min + max)) { + im = nullptr; + } + } + ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im); + GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size())); + ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); + } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { + // This is going to create some weird integers though. + ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor)); + } else { + GGML_ASSERT(false); + } +} + + +//ref: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-backend-ops.cpp#L310 +static void initialize_tensors(ggml_context * ctx) { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + init_tensor_uniform(t); + } +} + + +static void show_usage() { + printf(" " \ + "\nUsage: test_qnn_ops [options]\n" \ + "\n" \ + "Options:\n" \ + " -t GGML_OP_ADD / GGML_OP_MUL / GGML_OP_MULMAT\n" \ + " -b 0(QNN_CPU) 1(QNN_GPU) 2(QNN_NPU)\n" \ + " ?/h print usage infomation\n\n" + ); +} + + +int main(int argc, char * argv[]) { + int64_t n_begin_time = 0LL; + int64_t n_end_time = 0LL; + int64_t n_duration = 0LL; + size_t ctx_size = 0; + int sizey = 4; + int sizex = 4; + int num_threads = 4; + int n_backend_type = QNN_BACKEND_CPU; + int n_ggml_op_type = GGML_OP_ADD; + + struct ggml_context * ctx = nullptr; + struct ggml_cgraph * gf = nullptr; + struct ggml_tensor * src0 = nullptr; + struct ggml_tensor * src1 = nullptr; + struct ggml_tensor * dst = nullptr; + ggml_backend_t backend = nullptr; + ggml_backend_buffer_t buffer= nullptr; + ggml_type qtype = GGML_TYPE_F32; + std::vector work_buffer; + + for (int i = 1; i < argc; i++) { + if (0 == strcmp(argv[i], "-t")) { + if (i + 1 < argc) { + if (0 == memcmp(argv[i + 1], "GGML_OP_ADD", 11)) { + n_ggml_op_type = GGML_OP_ADD; + } else if (0 == memcmp(argv[i + 1], "GGML_OP_MUL_MAT", 15)) { + n_ggml_op_type = GGML_OP_MUL_MAT; + } else if (0 == memcmp(argv[i + 1], "GGML_OP_MUL", 11)) { + n_ggml_op_type = GGML_OP_MUL; + } else { + show_usage(); + return 1; + } + i++; + } + } else if (0 == strcmp(argv[i], "-b")) { + if (i + 1 < argc) { + int backend = atoi(argv[i + 1]); + if (backend <= QNN_BACKEND_NPU) + n_backend_type = backend; + else { + show_usage(); + return 1; + } + i++; + } + } else { + show_usage(); + return 1; + } + } + + QNN_LOG_DEBUG("enter qnn_ggml_op\n"); + QNN_LOG_DEBUG("ggml op:%d(%s)", n_ggml_op_type, ggml_op_name((enum ggml_op) n_ggml_op_type)); + + n_begin_time = ggml_time_us(); + srand(time(NULL)); + + ctx_size += 1024 * 1024 * 32; + QNN_LOG_DEBUG("Allocating Memory of size %zi bytes, %zi MB\n", ctx_size, + (ctx_size / 1024 / 1024)); + + struct ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /* no_alloc =*/ 0 + }; + + if (n_backend_type != QNN_BACKEND_GGML) { + params.no_alloc = true; + backend = ggml_backend_qnn_init(n_backend_type, "/data/local/tmp/"); + if (nullptr == backend) { + QNN_LOG_ERROR("create qnn backend %d(%s) failed", n_backend_type, get_qnn_backend_name(n_backend_type)); + return 1; + } + } + + ctx = ggml_init(params); + if (!ctx) { + QNN_LOG_ERROR("%s: ggml_init() failed\n"); + return 2; + } + + QNN_LOG_DEBUG("creating new tensors\n"); + QNN_LOG_DEBUG("ggml_blck_size(%s) %d", ggml_type_name(qtype), ggml_blck_size(qtype)); + QNN_LOG_DEBUG("ggml_type_size(%s) %d", ggml_type_name(qtype), ggml_type_size(qtype)); + if (qtype != GGML_TYPE_F32) { + sizex = ggml_blck_size(qtype); + } + + src0 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); + ggml_set_input(src0); + src1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey); + ggml_set_input(src1); + + switch (n_ggml_op_type) { + case GGML_OP_ADD: + dst = ggml_add(ctx, src0, src1); + break; + case GGML_OP_MUL: + dst = ggml_mul(ctx, src0, src1); + break; + case GGML_OP_MUL_MAT: + dst = ggml_mul_mat(ctx, src0, src1); + break; + default: + QNN_LOG_WARN("ggml op %d(%s) not supported", n_ggml_op_type, + ggml_op_name((enum ggml_op) n_ggml_op_type)); + ggml_free(ctx); + ggml_backend_free(backend); + return 3; + } + + ggml_set_output(dst); +#ifdef GGML_USE_QNN + if (n_backend_type != QNN_BACKEND_GGML) { + buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); + if (!buffer) { + QNN_LOG_ERROR("%s: failed to allocate backend buffer\n", __func__); + ggml_free(ctx); + ggml_backend_free(backend); + return 4; + } + } +#endif + + QNN_LOG_DEBUG("creating compute graph\n"); + gf = ggml_new_graph(ctx); + ggml_build_forward_expand(gf, dst); + +#if 0 + ggml_set_f32(src0, (rand() % 100 + 1)); + ggml_set_f32(src1, (rand() % 100 + 1)); + ggml_set_f32(dst, 0.0f); +#else + if (n_backend_type != QNN_BACKEND_GGML) { + initialize_tensors(ctx); + } +#endif + + ggml_graph_compute_helper(backend, gf, work_buffer, num_threads, nullptr, nullptr); + if (get_tensor_data_size(dst) < (32 * 32)) { + QNN_LOG_DEBUG("dump tensors:\n"); + TENSOR_DUMP(src0); + TENSOR_DUMP(src1); + TENSOR_DUMP(dst); + } else { + QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src0->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + src1->name, + 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_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n", + dst->name, + 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_free(ctx); + ggml_backend_buffer_free(buffer); + ggml_backend_free(backend); + + n_end_time = ggml_time_us(); + n_duration = (n_end_time - n_begin_time) / 1000; + QNN_LOG_DEBUG("duration of ut GGML_OP_%s using QNN backend %s: %lld milliseconds\n", ggml_op_name((enum ggml_op)n_ggml_op_type), get_qnn_backend_name(n_backend_type), n_duration); + + return 0; +}