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//=============================================================================
//
// Copyright (c) Qualcomm Technologies, Inc. and/or its subsidiaries.
// All Rights Reserved.
// Confidential and Proprietary - Qualcomm Technologies, Inc.
//
//=============================================================================
#include "QnnModel.hpp"
#include "QnnOpDef.h"
// Flag to determine if Backend should do node validation for each opNode added
#define DO_GRAPH_NODE_VALIDATIONS 1
#ifdef _MSC_VER
#define MODEL_LIB_EXPORT __declspec(dllexport)
#else
#define MODEL_LIB_EXPORT __attribute__((visibility("default")))
#endif
using namespace qnn_wrapper_api;
extern "C" {
MODEL_LIB_EXPORT ModelError_t QnnModel_GenAI_composeGraphs(Qnn_BackendHandle_t backendHandle,
QNN_INTERFACE_VER_TYPE interface,
Qnn_ContextHandle_t contextHandle,
const GraphConfigInfo_t** graphsConfigInfo,
const uint32_t numGraphsConfigInfo,
uint32_t* inputDim,
uint32_t inputRank,
uint32_t* outputDim,
uint32_t outputRank,
uint32_t* kvDim,
uint32_t kvRank,
Qnn_Param_t* params,
uint32_t numParams,
GraphInfoPtr_t** graphsInfo,
uint32_t* numGraphsInfo,
bool debug,
QnnLog_Callback_t logCallback,
QnnLog_Level_t maxLogLevel) {
(void) logCallback;
(void) maxLogLevel;
ModelError_t err = MODEL_NO_ERROR;
/* model/graph for qnn_model*/
QnnModel qnn_model;
const QnnGraph_Config_t** graphConfigs = nullptr;
VALIDATE(
getQnnGraphConfigFromInfo("qnn_model", graphsConfigInfo, numGraphsConfigInfo, graphConfigs),
err);
VALIDATE(qnn_model.initialize(backendHandle,
interface,
contextHandle,
"qnn_model",
debug,
DO_GRAPH_NODE_VALIDATIONS,
graphConfigs),
err);
Qnn_Tensor_t tin;
tin.version = QNN_TENSOR_VERSION_1;
tin.v1.id = 0;
tin.v1.name = "x0";
tin.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin.v1.dataType = QNN_DATATYPE_UINT_32;
tin.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin.v1.rank = inputRank;
tin.v1.dimensions = inputDim;
tin.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x0", // Node Name
(Qnn_Tensor_t)tin),
err);
uint32_t input1Dim[1] = {1};
Qnn_Tensor_t tin2;
tin2.version = QNN_TENSOR_VERSION_1;
tin2.v1.id = 0;
tin2.v1.name = "x1";
tin2.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin2.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin2.v1.dataType = QNN_DATATYPE_UINT_32;
tin2.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin2.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin2.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin2.v1.rank = 1;
tin2.v1.dimensions = input1Dim;
tin2.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin2.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x1", // Node Name
(Qnn_Tensor_t)tin2),
err);
uint32_t input2Dim[1] = {1};
Qnn_Tensor_t tin3;
tin3.version = QNN_TENSOR_VERSION_1;
tin3.v1.id = 0;
tin3.v1.name = "x2";
tin3.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin3.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin3.v1.dataType = QNN_DATATYPE_UINT_32;
tin3.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin3.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin3.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin3.v1.rank = 1;
tin3.v1.dimensions = input2Dim;
tin3.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin3.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x2", // Node Name
(Qnn_Tensor_t)tin3),
err);
Qnn_Tensor_t tin4;
tin4.version = QNN_TENSOR_VERSION_1;
tin4.v1.id = 0;
tin4.v1.name = "x3";
tin4.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin4.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin4.v1.dataType = QNN_DATATYPE_UINT_32;
tin4.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin4.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin4.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin4.v1.rank = kvRank;
tin4.v1.dimensions = kvDim;
tin4.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin4.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x3", // Node Name
(Qnn_Tensor_t)tin4),
err);
Qnn_Tensor_t tin5;
tin5.version = QNN_TENSOR_VERSION_1;
tin5.v1.id = 0;
tin5.v1.name = "x4";
tin5.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin5.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin5.v1.dataType = QNN_DATATYPE_UINT_32;
tin5.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin5.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin5.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin5.v1.rank = kvRank;
tin5.v1.dimensions = kvDim;
tin5.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin5.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x4", // Node Name
(Qnn_Tensor_t)tin5),
err);
uint32_t input5Dim[1] = {1};
Qnn_Tensor_t tin6;
tin6.version = QNN_TENSOR_VERSION_1;
tin6.v1.id = 0;
tin6.v1.name = "x5";
tin6.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin6.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin6.v1.dataType = QNN_DATATYPE_UINT_32;
tin6.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin6.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin6.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin6.v1.rank = 1;
tin6.v1.dimensions = input5Dim;
tin6.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin6.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x5", // Node Name
(Qnn_Tensor_t)tin6),
err);
uint32_t input6Dim[1] = {1};
Qnn_Tensor_t tin7;
tin7.version = QNN_TENSOR_VERSION_1;
tin7.v1.id = 0;
tin7.v1.name = "x6";
tin7.v1.type = QNN_TENSOR_TYPE_APP_WRITE;
tin7.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tin7.v1.dataType = QNN_DATATYPE_FLOAT_32;
tin7.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tin7.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tin7.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tin7.v1.rank = 1;
tin7.v1.dimensions = input6Dim;
tin7.v1.memType = QNN_TENSORMEMTYPE_RAW;
tin7.v1.clientBuf = {.data = nullptr, .dataSize = 0};
VALIDATE(qnn_model.addTensor(
"x6", // Node Name
(Qnn_Tensor_t)tin7),
err);
/* ADDING NODE FOR genAI */
const char* inputs_genAI[] = {"x0", "x1", "x2", "x3", "x4", "x5", "x6"};
Qnn_Tensor_t tout;
tout.version = QNN_TENSOR_VERSION_1;
tout.v1.id = 0;
tout.v1.name = "output_genAI";
tout.v1.type = QNN_TENSOR_TYPE_APP_READ;
tout.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tout.v1.dataType = QNN_DATATYPE_FLOAT_32;
tout.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tout.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tout.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tout.v1.rank = outputRank;
tout.v1.dimensions = outputDim;
tout.v1.memType = QNN_TENSORMEMTYPE_RAW;
tout.v1.clientBuf = {.data = nullptr, .dataSize = 0};
uint32_t output1Dim[1] = {1};
Qnn_Tensor_t tout1;
tout1.version = QNN_TENSOR_VERSION_1;
tout1.v1.id = 0;
tout1.v1.name = "output_npast";
tout1.v1.type = QNN_TENSOR_TYPE_APP_READ;
tout1.v1.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER;
tout1.v1.dataType = QNN_DATATYPE_UINT_32;
tout1.v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
tout1.v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
tout1.v1.quantizeParams.scaleOffsetEncoding = {.scale = 0.0000000000000000f,
.offset = 0};
tout1.v1.rank = 1;
tout1.v1.dimensions = output1Dim;
tout1.v1.memType = QNN_TENSORMEMTYPE_RAW;
tout1.v1.clientBuf = {.data = nullptr, .dataSize = 0};
Qnn_Tensor_t outputs_genAI[] = {(Qnn_Tensor_t)tout, (Qnn_Tensor_t)tout1};
VALIDATE(qnn_model.addNode(QNN_OPCONFIG_VERSION_1, // Op_Config_t Version
"LLM", // Node Name
"llm_engine.oppackage", // Package Name
"LLM", // Qnn Node Type
params, // Node Params
numParams, // Num Node Params
inputs_genAI, // Input Tensor Names
7, // Num Input Tensor Names
outputs_genAI, // Output Tensors
2 // Num Output Tensors
),
err);
// Add all models to array to get graphsInfo
QnnModel* models[] = {&qnn_model};
uint32_t numModels = 1;
// Populate the constructed graphs in provided output variables
VALIDATE(getGraphInfoFromModels(*models, numModels, graphsInfo), err);
*numGraphsInfo = numModels;
return err;
} // PREPARE_GRAPHS
MODEL_LIB_EXPORT ModelError_t QnnModel_freeGraphsInfo(GraphInfoPtr_t** graphsInfo, uint32_t numGraphsInfo) {
return qnn_wrapper_api::freeGraphsInfo(graphsInfo, numGraphsInfo);
} // FREEGRAPHINFO
}
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