[GNA] Support export model with multiple inputs/outputs and Permute layer

inference-engine/src/gna_plugin/backend/dnn_types.h

@@ -80,7 +80,7 @@ static const char *intel_dnn_softmax_name[kSoftmaxNumType] = {
 };
 
 typedef enum {
- kDnnUnknownOrientation,
+ kDnnUnknownOrientation = 100,
  kDnnInterleavedOrientation,
  kDnnNonInterleavedOrientation,
  kDnnNumOrientation

inference-engine/src/gna_plugin/gna_device.cpp

@@ -25,7 +25,7 @@
 #include "gna_plugin_log.hpp"
 
 uint8_t* GNADeviceHelper::alloc(uint32_t size_requested, uint32_t *size_granted) {
- void * memPtr;
+ void * memPtr = nullptr;
 #if GNA_LIB_VER == 1
  memPtr = GNAAlloc(nGNAHandle, size_requested, size_granted);
 #else

inference-engine/src/gna_plugin/gna_model_serial.cpp

@@ -8,6 +8,9 @@
 #include <ios>
 #include <iomanip>
 #include <map>
+#include <ie_algorithm.hpp>
+#include <ie_common.h>
+#include <ie_precision.hpp>
 
 #if defined __INTEL_COMPILER || defined _MSC_VER
 #include <malloc.h>
@@ -119,11 +122,21 @@ const std::map<Gna2OperationType, std::vector<uint32_t>> GnaParamSize{
  sizeof(Gna2Shape),
  sizeof(Gna2Shape)}},
  {Gna2OperationTypeCopy, {sizeof(Gna2Shape)}},
+ {Gna2OperationTypeTransposition, {sizeof(Gna2Shape)}},
 };
 
-void GNAModelSerial::Import(void *basePointer, size_t gnaGraphSize, std::istream & is) {
+void GNAModelSerial::Import(void *basePointer,
+ size_t gnaGraphSize,
+ std::istream & is,
+ std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
+ std::vector<GNAPluginNS::OutputDesc> &desc,
+ InferenceEngine::InputsDataMap& inputsDataMap,
+ InferenceEngine::OutputsDataMap& outputsDataMap) {
  is.exceptions(std::istream::failbit);
 
+ ImportInputs(is, basePointer, inputsDesc, inputsDataMap);
+ ImportOutputs(is, basePointer, desc, outputsDataMap);
+
  for (auto operation = gna2Model->Operations; operation != gna2Model->Operations + gna2Model->NumberOfOperations; ++operation) {
  readNBits<32>(operation->Type, is);
  readBits(operation->NumberOfOperands, is);
@@ -146,11 +159,10 @@ void GNAModelSerial::Import(void *basePointer, size_t gnaGraphSize, std::istream
  case Gna2OperationTypeFullyConnectedAffine:
  case Gna2OperationTypeConvolution:
  case Gna2OperationTypeCopy:
+ case Gna2OperationTypeTransposition:
  break;
  case Gna2OperationTypeRecurrent:
  THROW_GNA_EXCEPTION << "Importing of recurrent operation not supported";
- case Gna2OperationTypeTransposition:
- THROW_GNA_EXCEPTION << "Importing of transposition operation not supported";
  default:
  THROW_GNA_EXCEPTION << "Importing of unknown GNA operation type(" << operation->Type << ") not supported";
  }
@@ -237,11 +249,12 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
  };
 
  auto convert_to_serial = [getOffsetFromBase](const GNAModelSerial::RuntimeEndPoint& ep) {
- ModelHeader::EndPoint out;
+ RuntimeEndPoint out;
  out.elements_count = ep.elements_count;
  out.descriptor_offset = offsetFromBase(ep.descriptor_ptr);
  out.scaleFactor = ep.scaleFactor;
  out.element_size = ep.element_size;
+ out.orientation = ep.orientation;
  return out;
  };
  /**
@@ -258,15 +271,21 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
  header.gnaMemSize = gnaGraphSize;
  header.layersCount = layers.size();
  header.nGroup = guessGrouping(*gna2Model);
- header.input = convert_to_serial(input);
- header.output = convert_to_serial(output);
-
+ header.nInputs = inputs.size();
+ header.nOutputs = outputs.size();
  header.nRotateRows = nRotateRows;
  header.nRotateColumns = nRotateColumns;
 
 
  writeBits(header, os);
 
+ for (const auto &input : inputs) {
+ writeBits(convert_to_serial(input), os);
+ }
+ for (const auto &output : outputs) {
+ writeBits(convert_to_serial(output), os);
+ }
+
  for (const auto & layer : layers) {
  writeBits(static_cast<uint32_t>(layer.Type), os);
  writeBits(layer.NumberOfOperands, os);
@@ -286,11 +305,10 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
  case Gna2OperationTypeFullyConnectedAffine:
  case Gna2OperationTypeConvolution:
  case Gna2OperationTypeCopy:
+ case Gna2OperationTypeTransposition:
  break;
  case Gna2OperationTypeRecurrent:
  THROW_GNA_EXCEPTION << "Exporting of recurrent operation not supported";
- case Gna2OperationTypeTransposition:
- THROW_GNA_EXCEPTION << "Exporting of interleave operation not supported";
  default:
  THROW_GNA_EXCEPTION << "Exporting of unknown GNA operation type(" << layer.Type << ") not supported";
  }
@@ -316,9 +334,18 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
 }
 #else
 
-void GNAModelSerial::Import(void *basePointer, size_t gnaGraphSize, std::istream & is) {
+void GNAModelSerial::Import(void *basePointer,
+ size_t gnaGraphSize,
+ std::istream & is,
+ std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
+ std::vector<GNAPluginNS::OutputDesc> &desc,
+ InferenceEngine::InputsDataMap& inputsDataMap,
+ InferenceEngine::OutputsDataMap& outputsDataMap) {
  is.exceptions(std::istream::failbit);
 
+ ImportInputs(is, basePointer, inputsDesc, inputsDataMap);
+ ImportOutputs(is, basePointer, desc, outputsDataMap);
+
  auto readPwl = [&is, basePointer](intel_pwl_func_t & value) {
  readBits(value.nSegments, is);
  if (value.nSegments != 0) {
@@ -468,11 +495,12 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
  };
 
  auto convert_to_serial = [getOffsetFromBase](const GNAModelSerial::RuntimeEndPoint& ep){
- ModelHeader::EndPoint out;
+ RuntimeEndPoint out;
  out.elements_count = ep.elements_count;
  out.element_size = ep.element_size;
  out.descriptor_offset = offsetFromBase(ep.descriptor_ptr);
  out.scaleFactor = ep.scaleFactor;
+ out.orientation = ep.orientation;
  return out;
  };
  /**
@@ -488,14 +516,16 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
  header.gnaMemSize = gnaGraphSize;
  header.layersCount = layers.size();
  header.nGroup = ptr_nnet->nGroup;
- header.input = convert_to_serial(input);
- header.output = convert_to_serial(output);
+ header.nInputs = 1;
+ header.nOutputs = 1;
  header.headerSize = sizeof(ModelHeader);
  header.nRotateRows = nRotateRows;
  header.nRotateColumns = nRotateColumns;
 
 
  writeBits(header, os);
+ writeBits(convert_to_serial(inputs[0]), os);
+ writeBits(convert_to_serial(outputs[0]), os);
 
  for (auto & layer : layers) {
  writeBits(layer.nInputColumns, os);
@@ -574,3 +604,108 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
 }
 
 #endif
+
+std::vector<GNAModelSerial::RuntimeEndPoint> GNAModelSerial::serializeOutputs(const InferenceEngine::OutputsDataMap& outputsDataMap,
+ const std::vector<GNAPluginNS::OutputDesc>& outputsDesc) {
+ std::vector<GNAModelSerial::RuntimeEndPoint> endPoints;
+ std::size_t outputIndex = 0;
+ for (auto const &output : outputsDataMap) {
+ auto outputName = output.first;
+ auto inputDims = output.second->getTensorDesc().getDims();
+ uint32_t elementsCount = static_cast<uint32_t>(InferenceEngine::details::product(inputDims.begin(), inputDims.end()));
+
+ GNAModelSerial::RuntimeEndPoint endPoint(outputsDesc[outputIndex].scale_factor,
+ outputsDesc[outputIndex].ptrs[0],
+ outputsDesc[outputIndex].num_bytes_per_element,
+ elementsCount,
+ outputsDesc[outputIndex].orientation);
+ endPoints.push_back(endPoint);
+ outputIndex++;
+ }
+ return endPoints;
+}
+
+std::vector<GNAModelSerial::RuntimeEndPoint> GNAModelSerial::serializeInputs(const InferenceEngine::InputsDataMap& inputsDataMap,
+ std::shared_ptr<GNAPluginNS::InputDesc> inputDesc) {
+ std::vector<GNAModelSerial::RuntimeEndPoint> endPoints;
+
+ std::size_t inputIndex = 0;
+ for (auto const& input : inputsDataMap) {
+ auto inputName = input.first;
+ auto inputDims = input.second->getTensorDesc().getDims();
+
+ double scaleFactor = inputDesc->getScaleFactor(inputIndex);
+ std::vector<void *> descriptor_ptr = inputDesc->getPtrInputsGlobal(inputName);
+ IE_ASSERT(descriptor_ptr.size() > 0);
+ uint32_t element_size = 2u;
+ uint32_t elementsCount = static_cast<uint32_t>(InferenceEngine::details::product(inputDims.begin(), inputDims.end()));
+ intel_dnn_orientation_t orientation = inputDesc->getOrientation(inputName);
+
+ GNAModelSerial::RuntimeEndPoint endPoint(scaleFactor,
+ descriptor_ptr[0],
+ element_size,
+ elementsCount,
+ orientation);
+ endPoints.push_back(endPoint);
+ inputIndex++;
+ }
+ return endPoints;
+}
+
+void GNAModelSerial::ImportInputs(std::istream &is,
+ void* basePtr,
+ std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
+ InferenceEngine::InputsDataMap& dataMap) {
+ dataMap.clear();
+
+ for (auto inputIndex = 0; inputIndex < modelHeader.nInputs; inputIndex++) {
+ std::string name = "input" + std::to_string(inputIndex);
+ RuntimeEndPoint input;
+ is.read(reinterpret_cast<char *>(&input), sizeof(input));
+ inputsDesc->getPtrInputsGlobal(name).push_back(reinterpret_cast<float*>(reinterpret_cast<uint8_t *> (basePtr) + input.descriptor_offset));
+ inputsDesc->orientation_in[name] = input.orientation;
+
+ auto inputDims = InferenceEngine::SizeVector({modelHeader.nGroup, input.elements_count / modelHeader.nGroup});
+
+ dataMap[name] = std::make_shared<InferenceEngine::InputInfo>();
+ dataMap[name]->setInputData(std::make_shared<InferenceEngine::Data>(name,
+ InferenceEngine::TensorDesc(
+ InferenceEngine::Precision::FP32,
+ inputDims,
+ InferenceEngine::Layout::NC)));
+ inputsDesc->inputScaleFactors.push_back(input.scaleFactor);
+ }
+}
+
+void GNAModelSerial::ImportOutputs(std::istream &is,
+ void* basePtr,
+ std::vector<GNAPluginNS::OutputDesc> &desc,
+ InferenceEngine::OutputsDataMap& dataMap) {
+ desc.clear();
+ dataMap.clear();
+ desc.resize(modelHeader.nOutputs);
+
+ for (auto outputIndex = 0; outputIndex < modelHeader.nOutputs; outputIndex++) {
+ std::string name = "output" + std::to_string(outputIndex);
+ RuntimeEndPoint output;
+ is.read(reinterpret_cast<char *>(&output), sizeof(output));
+ GNAPluginNS::OutputDesc description;
+ description.ptrs.push_back(reinterpret_cast<float*>(reinterpret_cast<uint8_t *> (basePtr) + output.descriptor_offset));
+ description.orientation = kDnnInterleavedOrientation;
+ description.orientation = output.orientation;
+ description.num_bytes_per_element = output.element_size;
+ description.scale_factor = output.scaleFactor;
+
+ auto outputDims = InferenceEngine::SizeVector({modelHeader.nGroup, output.elements_count / modelHeader.nGroup});
+ dataMap[name] = std::make_shared<InferenceEngine::Data>(name,
+ InferenceEngine::TensorDesc(
+ InferenceEngine::Precision::FP32,
+ outputDims,
+ InferenceEngine::Layout::NC));
+ desc.at(outputIndex) = description;
+ }
+}
+
+void GNAModelSerial::setHeader(ModelHeader header) {
+ modelHeader = header;
+}