diff options
Diffstat (limited to 'src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp')
| -rw-r--r-- | src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp | 87 |
1 files changed, 63 insertions, 24 deletions
diff --git a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp index c65f1891da..6d1699d87b 100644 --- a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp +++ b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp @@ -4,6 +4,8 @@ // #include "Conv2dOperator.hpp" +#include "TosaRescaleOperatorUtils.hpp" +#include <ResolveType.hpp> TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, const std::vector<const TensorInfo*>& inputs, @@ -14,14 +16,17 @@ TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, std::string outputName = std::string("output0_"); std::string blockName = std::string("Op_CONV2D_block_") + GetUniqueTosaMappingID(); + DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); + DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); + // Set input names for validation purposes only. if(layer == nullptr) { - inputNames.emplace_back("input0_"); - inputNames.emplace_back("input1_"); + inputNames.emplace_back("input_0"); + inputNames.emplace_back("input_1"); if(conv2dDescriptor->m_BiasEnabled) { - inputNames.emplace_back("input2_"); + inputNames.emplace_back("input_2"); } } // If a layer is present then the block will be used for execution, so input and output names need to be @@ -32,14 +37,12 @@ TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, // Get the layer connected to the input slot and determine unique tensor names. for (uint32_t i = 0; i < inputs.size(); ++i) { - Layer& connectedLayer = layer->GetInputSlot(i).GetConnectedOutputSlot()->GetOwningLayer(); - - std::string inputName = GenerateUniqueName(connectedLayer, i); + std::string inputName = GenerateUniqueInputName(layer->GetInputSlot(i)); inputNames.push_back(inputName); } // Determine unique output tensor name. - outputName = GenerateUniqueOutputName(*layer, 0); + outputName = GenerateUniqueOutputName(*layer); } std::vector<TosaSerializationTensor*> tensors; @@ -49,10 +52,9 @@ TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, // Only add tensor if connected layer is an input layer. // As intermediate or constant tensors will be created separately. // There also can't be duplicate tensors. - if(inputNames[0].find("input0_") != std::string::npos) + if(inputNames[0].find("input_") != std::string::npos) { std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); - DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); tensors.push_back(new TosaSerializationTensor(inputNames[0], inputShape0, inputDType0, {})); } @@ -87,23 +89,32 @@ TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, // The size of the bias must match the channels dimension, so get the correct index. unsigned int index = (conv2dDescriptor->m_DataLayout == DataLayout::NHWC) ? 3 : 1; - std::vector<uint8_t> uint8Data; - std::vector<float> data(outputs[0]->GetShape()[index], 0.0f); + const DType dType = (inputDType0 == DType_INT8) ? DType_INT32 : outputDType0; + std::vector<float> data(outputs[0]->GetShape()[index], 0); + std::vector<uint8_t> uint8Data; TosaSerializationHandler::ConvertF32toU8(data, uint8Data); tensors.push_back(new TosaSerializationTensor(constantName, {static_cast<int32_t>(outputs[0]->GetShape()[index])}, - DType_FP32, + dType, uint8Data)); inputNames.emplace_back(constantName); } // Setup Output Tensor - std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); - DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); - - tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); + std::vector<int32_t> outputShape0 = {GetTosaTensorShape(outputs[0]->GetShape())}; + std::string outputConv2dName; + bool isInputInt8 = (inputDType0 == DType_INT8); + if (isInputInt8) + { + outputConv2dName = std::string("intermediate0_") + GetUniqueTosaMappingID(); + tensors.push_back(new TosaSerializationTensor(outputConv2dName, outputShape0, DType_INT32, {})); + } + else + { + tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); + } // Set up CONV2D operator std::vector<int> pad = {static_cast<int>(conv2dDescriptor->m_PadTop), @@ -114,14 +125,42 @@ TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, static_cast<int>(conv2dDescriptor->m_StrideX)}; std::vector<int> dilation = {static_cast<int>(conv2dDescriptor->m_DilationY), static_cast<int>(conv2dDescriptor->m_DilationX)}; - TosaConvAttribute attribute(pad, stride, dilation, 0, 0, false); // input_zp, weight_zp, local_bound - - auto* op = new TosaSerializationOperator(Op_CONV2D, - Attribute_ConvAttribute, - &attribute, - inputNames, - {outputName}); - operators.push_back(op); + TosaConvAttribute attribute(pad, stride, dilation, + inputs[0]->GetQuantizationOffset(), // input_zp + inputs[1]->GetQuantizationOffset(), // weight_zp + false); // local_bound + + std::string& convOutStr = isInputInt8 ? outputConv2dName : outputName; + auto* conv2d_op = new TosaSerializationOperator(Op_CONV2D, + Attribute_ConvAttribute, + &attribute, + inputNames, + {convOutStr}); + operators.push_back(conv2d_op); + + if (isInputInt8) + { + int32_t output_zp = outputs[0]->GetQuantizationOffset(); + double output_scale = outputs[0]->GetQuantizationScales()[0]; + double input_scale = inputs[0]->GetQuantizationScales()[0]; + const std::vector<float>& weight_scales = inputs[1]->GetQuantizationScales(); + + TosaSerializationOperator* rescaleOp = nullptr; + CreateRescaleTosaOperatorPerChannel(outputConv2dName, + outputName, + 0, + output_zp, + true, + true, + input_scale, + output_scale, + weight_scales, + &rescaleOp); + operators.push_back(rescaleOp); + tensors.push_back(new TosaSerializationTensor(outputName, + outputShape0, + DType_INT8, {})); + } // operatorInputNames/operatorOutputNames ends up being the same as // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings |