309 lines
11 KiB
C++
309 lines
11 KiB
C++
#pragma once
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#include "QnnTypes.h"
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#include "ggml.h"
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#include "qnn-types.hpp"
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namespace qnn {
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// TODO: mapping more ggml data type to QNN data type
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// ref:explanation of k-quants, https://github.com/ggerganov/llama.cpp/pull/1684
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Qnn_DataType_t datatype_from_ggml_datatype(enum ggml_type ggmltype) {
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switch (ggmltype) {
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case GGML_TYPE_F16:
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return QNN_DATATYPE_FLOAT_16;
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case GGML_TYPE_F32:
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return QNN_DATATYPE_FLOAT_32;
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case GGML_TYPE_I8:
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return QNN_DATATYPE_INT_8;
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case GGML_TYPE_Q8_0:
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return QNN_DATATYPE_SFIXED_POINT_8;
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case GGML_TYPE_Q4_0:
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return QNN_DATATYPE_SFIXED_POINT_4;
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default:
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break;
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}
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return QNN_DATATYPE_UNDEFINED;
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}
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uint32_t get_ggml_tensor_rank(const ggml_tensor* tensor) {
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uint32_t rank = 0;
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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if ((0 != tensor->ne[i]) && (1 != tensor->ne[i])) {
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rank++;
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}
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}
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return rank;
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}
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const char* get_backend_name(int n_backend_type) {
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switch (n_backend_type) {
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case QNN_BACKEND_CPU:
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return "QNN-CPU";
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case QNN_BACKEND_GPU:
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return "QNN-GPU";
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case QNN_BACKEND_NPU:
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return "QNN-NPU";
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case QNN_BACKEND_GGML:
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return "ggml"; //"fake" QNN backend, used for compare performance between QNN backend and original GGML
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default:
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return "unknown";
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}
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}
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const char* get_chipset_desc(uint32_t chipset_id) {
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switch (chipset_id) {
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case SM8450:
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return "SM8450";
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case SM8475:
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return "SM8475";
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case SM8550:
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return "SM8550";
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case SM8650:
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return "SM8650";
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default:
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return "unknown";
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}
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}
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const char* get_htparch_desc(size_t htp_arch) {
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switch (htp_arch) {
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case V68:
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return "QCOM_HTP_V68";
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case V69:
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return "QCOM_HTP_V69";
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case V73:
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return "QCOM_HTP_V73";
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case V75:
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return "QCOM_HTP_V75";
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default:
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return "unknown";
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}
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}
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template <typename Fn> Fn load_qnn_functionpointers(void* handle, const char* function_name) {
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return reinterpret_cast<Fn>(dlsym(handle, function_name));
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}
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intptr_t align_to(size_t alignment, intptr_t offset) {
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return offset % alignment == 0
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? offset
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: offset + (static_cast<intptr_t>(alignment) -
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offset % static_cast<intptr_t>(alignment));
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}
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uint32_t get_ggml_tensor_data_size(const ggml_tensor* tensor) {
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/*
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size_t data_size = ggml_row_size(tensor->type, tensor->ne[0]);
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size_t n_dims = qnn_get_ggml_tensor_rank(tensor);
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for (int i = 1; i < n_dims; i++) {
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data_size *= tensor->ne[i];
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}
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return data_size;
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*/
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return ggml_nbytes(tensor);
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}
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// =================================================================================================
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//
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// QNN backend internal helper functions
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//
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// =================================================================================================
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// TODO: only support GGML_OP_ADD/GGML_OP_MUL/GGML_OP_MUL_MAT
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const char* opname_from_ggmlop(enum ggml_op ggmlop) {
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switch (ggmlop) {
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case GGML_OP_ADD:
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return QNN_OP_ELEMENT_WISE_ADD;
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case GGML_OP_MUL:
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return QNN_OP_ELEMENT_WISE_MULTIPLY;
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case GGML_OP_MUL_MAT:
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return QNN_OP_MAT_MUL;
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default:
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break;
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}
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return nullptr;
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}
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inline int validate_tensor_version(Qnn_Tensor_t tensor) {
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if (tensor.version != QNN_TENSOR_VERSION_1) {
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QNN_LOG_WARN(
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"validate_tensor_version() tensor %s, got unsupported version %d\n",
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tensor.v1.name, tensor.version);
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return 1;
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}
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return 0;
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}
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inline uint32_t get_qnn_tensorid(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.id;
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}
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return 0u;
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}
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inline const char* get_qnn_tensorname(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.name;
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}
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return nullptr;
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}
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inline Qnn_TensorType_t get_qnn_tensortype(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.type;
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}
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return QNN_TENSOR_TYPE_UNDEFINED;
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}
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inline Qnn_TensorDataFormat_t
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get_qnn_tensor_dataformat(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.dataFormat;
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}
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return QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
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}
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inline Qnn_DataType_t
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get_qnn_tensor_datatype(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.dataType;
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}
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return QNN_DATATYPE_UNDEFINED;
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}
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inline Qnn_QuantizeParams_t
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get_qnn_tensor_quantparams(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.quantizeParams;
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}
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return QNN_QUANTIZE_PARAMS_INIT;
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}
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inline uint32_t get_qnn_tensor_rank(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.rank;
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}
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return 0u;
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}
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inline uint32_t* get_qnn_tensor_dimensions(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.dimensions;
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}
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return nullptr;
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}
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inline Qnn_TensorMemType_t get_qnn_tensor_memtype(const Qnn_Tensor_t& tensor) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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return tensor.v1.memType;
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}
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return QNN_TENSORMEMTYPE_UNDEFINED;
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}
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inline void set_qnn_tensor_id(Qnn_Tensor_t& tensor, uint32_t id) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.id = id;
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}
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}
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inline void set_qnn_tensor_name(Qnn_Tensor_t& tensor, const char* name) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.name = name;
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}
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}
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inline void set_qnn_tensor_type(Qnn_Tensor_t& tensor, Qnn_TensorType_t type) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.type = type;
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}
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}
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inline void set_qnn_tensor_dataformat(Qnn_Tensor_t& tensor, Qnn_TensorDataFormat_t format) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.dataFormat = format;
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}
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}
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inline void set_qnn_tensor_datatype(Qnn_Tensor_t& tensor, Qnn_DataType_t dataType) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.dataType = dataType;
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}
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}
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inline void set_qnn_tensor_quantparams(Qnn_Tensor_t& tensor, Qnn_QuantizeParams_t params) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.quantizeParams = params;
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}
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}
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inline void set_qnn_tensor_rank(Qnn_Tensor_t& tensor, uint32_t rank) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.rank = rank;
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}
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}
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inline void set_qnn_tensor_dimensions(Qnn_Tensor_t& tensor, uint32_t* dims) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.dimensions = dims;
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}
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}
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inline void set_qnn_tensor_memtype(Qnn_Tensor_t& tensor, Qnn_TensorMemType_t mem_type) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.memType = mem_type;
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}
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}
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inline void set_qnn_tensor_clientbuf(Qnn_Tensor_t& tensor, Qnn_ClientBuffer_t client_buf) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.clientBuf = client_buf;
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}
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}
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inline void set_qnn_tensor_memhandle(Qnn_Tensor_t& tensor, Qnn_MemHandle_t handle) {
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if (tensor.version == QNN_TENSOR_VERSION_1) {
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tensor.v1.memHandle = handle;
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}
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}
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}
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#define VALIDATE(value, status) \
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do { \
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status = value; \
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if (status != QNN_SUCCESS) { \
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QNN_LOG_WARN("%s expected QNN_SUCCESS\n", #value); \
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return status; \
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} \
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} while (0)
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#define QNN_TENSOR_GET_ID(tensor) qnn::get_qnn_tensorid(tensor)
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#define QNN_TENSOR_GET_NAME(tensor) qnn::get_qnn_tensorname(tensor)
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#define QNN_TENSOR_GET_TYPE(tensor) qnn::get_qnn_tensortype(tensor)
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#define QNN_TENSOR_GET_DATA_FORMAT(tensor) qnn::get_qnn_tensor_dataformat(tensor)
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#define QNN_TENSOR_GET_DATA_TYPE(tensor) qnn::get_qnn_tensor_datatype(tensor)
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#define QNN_TENSOR_GET_QUANT_PARAMS(tensor) qnn::get_qnn_tensor_quantparams(tensor)
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#define QNN_TENSOR_GET_RANK(tensor) qnn::get_qnn_tensor_rank(tensor)
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#define QNN_TENSOR_GET_DIMENSIONS(tensor) qnn::get_qnn_tensor_dimensions(tensor)
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#define QNN_TENSOR_GET_MEM_TYPE(tensor) qnn::get_qnn_tensor_memtype(tensor)
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#define QNN_TENSOR_SET_ID(tensor, value) qnn::set_qnn_tensor_id(tensor, value)
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#define QNN_TENSOR_SET_NAME(tensor, value) qnn::set_qnn_tensor_name(tensor, value)
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#define QNN_TENSOR_SET_TYPE(tensor, value) qnn::set_qnn_tensor_type(tensor, value)
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#define QNN_TENSOR_SET_DATA_FORMAT(tensor, value) qnn::set_qnn_tensor_dataformat(tensor, value)
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#define QNN_TENSOR_SET_DATA_TYPE(tensor, value) qnn::set_qnn_tensor_datatype(tensor, value)
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#define QNN_TENSOR_SET_QUANT_PARAMS(tensor, value) qnn::set_qnn_tensor_quantparams(tensor, value)
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#define QNN_TENSOR_SET_RANK(tensor, value) qnn::set_qnn_tensor_rank(tensor, value)
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#define QNN_TENSOR_SET_DIMENSIONS(tensor, value) qnn::set_qnn_tensor_dimensions(tensor, value)
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#define QNN_TENSOR_SET_MEM_TYPE(tensor, value) qnn::set_qnn_tensor_memtype(tensor, value)
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#define QNN_TENSOR_SET_CLIENT_BUF(tensor, value) qnn::set_qnn_tensor_clientbuf(tensor, value)
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#define QNN_TENSOR_SET_MEM_HANDLE(tensor, value) qnn::set_qnn_tensor_memhandle(tensor, value)
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#define VALIDATE_TENSOR_VERSION(tensor, err) VALIDATE(qnn::validate_tensor_version(tensor), err)
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