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Diffstat (limited to 'delegate/opaque/src/Convolution.hpp')
| -rw-r--r-- | delegate/opaque/src/Convolution.hpp | 499 |
1 files changed, 499 insertions, 0 deletions
diff --git a/delegate/opaque/src/Convolution.hpp b/delegate/opaque/src/Convolution.hpp index e16969768e..163290b542 100644 --- a/delegate/opaque/src/Convolution.hpp +++ b/delegate/opaque/src/Convolution.hpp @@ -2,3 +2,502 @@ // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // + +#include <OpaqueDelegateUtils.hpp> +#include <SharedFunctions.hpp> + +#include <tensorflow/lite/builtin_ops.h> +#include <tensorflow/lite/c/builtin_op_data.h> +#include <tensorflow/lite/c/common.h> +#include <tensorflow/lite/minimal_logging.h> + +namespace armnnOpaqueDelegate +{ + +TfLiteStatus VisitConv2dOperator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); + if (numInputs < 2) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + // Gather input indices and use to get input tensor. + const int* inputTensors; + if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + // Use input indices to get filter tensor. + const TfLiteOpaqueTensor* tfLiteFilterTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); + if(!IsValid(tfLiteFilterTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Invalid filter tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteFilterTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic filter tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + + // Gather output indices and use to get output tensors. + int numOutputs = 0; + const int* outputTensors; + if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); + const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteFilterTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConvParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); + TfLiteFusedActivation activationType = kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, + tfLiteContext, + outputTensorInfo, + outputTensorInfo, + activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + } + + armnn::TensorInfo biasTensorInfo; + const TfLiteOpaqueTensor* tfLiteBiasTensor = nullptr; + + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + if(biasEnabled) + { + // Use input indices to get bias tensor. + tfLiteBiasTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[2]); + if(!IsValid(tfLiteBiasTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Invalid bias tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteBiasTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic bias tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); + + armnn::Convolution2dDescriptor descriptor; + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_StrideX = NonNegative(tfLiteNodeParameters->stride_width, nodeIndex); + descriptor.m_StrideY = NonNegative(tfLiteNodeParameters->stride_height, nodeIndex); + descriptor.m_DataLayout = armnn::DataLayout::NHWC; + descriptor.m_DilationX = NonNegative(tfLiteNodeParameters->dilation_width_factor, nodeIndex); + descriptor.m_DilationY = NonNegative(tfLiteNodeParameters->dilation_height_factor, nodeIndex); + + // TfLite uses NHWC tensors + const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; + const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; + + const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; + const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; + + // Calculate padding + CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, + descriptor.m_PadTop, descriptor.m_PadBottom, tfLiteNodeParameters->padding); + CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, + descriptor.m_PadLeft, descriptor.m_PadRight, tfLiteNodeParameters->padding); + + armnn::BackendId setBackend; + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("CONV2D", + tfLiteContext, + IsConvolution2dSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + optionalBiasInfo); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + // Set up filter and biases + armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution2dLayer(descriptor); + layer->SetBackendId(setBackend); + + if(filterTensorInfo.IsConstant()) + { + auto filter = CreateConstTensor(tfLiteFilterTensor, filterTensorInfo); + + armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); + weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); + weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); + } + + if (biasEnabled) + { + if(biasTensorInfo.IsConstant()) + { + auto biasTensor = CreateConstTensor(tfLiteBiasTensor, biasTensorInfo); + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); + ARMNN_ASSERT(biasLayer != nullptr); + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + // The data input can also be constant, so we must check that this is also allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = CreateConstTensor(tfLiteInputTensor, inputTensorInfo); + + armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + if(Connect(layer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + + // Check and Create activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); +} + +TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); + if (numInputs < 2) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + // Gather input indices and use to get input tensor. + const int* inputTensors; + if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + // Use input indices to get filter tensor. + const TfLiteOpaqueTensor* tfLiteFilterTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); + if(!IsValid(tfLiteFilterTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Invalid filter tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteFilterTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic filter tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + + // Gather output indices and use to get output tensors. + int numOutputs = 0; + const int* outputTensors; + if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); + const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteFilterTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + + auto* tfLiteNodeParameters = + reinterpret_cast<TfLiteDepthwiseConvParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); + + TfLiteFusedActivation activationType = kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, + tfLiteContext, + outputTensorInfo, + outputTensorInfo, + activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + } + + armnn::TensorInfo biasTensorInfo; + const TfLiteOpaqueTensor* tfLiteBiasTensor = nullptr; + + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + if(biasEnabled) + { + // Use input indices to get bias tensor. + tfLiteBiasTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[2]); + if(!IsValid(tfLiteBiasTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Invalid bias tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteBiasTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic bias tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::DepthwiseConvolution2dDescriptor descriptor; + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_StrideX = NonNegative(tfLiteNodeParameters->stride_width, nodeIndex); + descriptor.m_StrideY = NonNegative(tfLiteNodeParameters->stride_height, nodeIndex); + descriptor.m_DataLayout = armnn::DataLayout::NHWC; + descriptor.m_DilationX = NonNegative(tfLiteNodeParameters->dilation_width_factor, nodeIndex); + descriptor.m_DilationY = NonNegative(tfLiteNodeParameters->dilation_height_factor, nodeIndex); + + // Assuming input is NHWC + unsigned int inputHeight = inputTensorInfo.GetShape()[1]; + unsigned int inputWidth = inputTensorInfo.GetShape()[2]; + + // TensorflowLite weights come in the format [1, H, W, I * M] + unsigned int filterHeight = filterTensorInfo.GetShape()[1]; + unsigned int filterWidth = filterTensorInfo.GetShape()[2]; + + // Calculate padding + CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, + descriptor.m_PadTop, descriptor.m_PadBottom, tfLiteNodeParameters->padding); + CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, + descriptor.m_PadLeft, descriptor.m_PadRight, tfLiteNodeParameters->padding); + + armnn::BackendId setBackend; + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("DEPTHWISE_CONV2D", + tfLiteContext, + IsDepthwiseConvolutionSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + armnn::Optional<armnn::TensorInfo>(biasTensorInfo)); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor); + layer->SetBackendId(setBackend); + + if(filterTensorInfo.IsConstant()) + { + // For depthwise the weights layout is the same as for tflite [1, H, W, I*M]. No permutation required. + auto filter = CreateConstTensor(tfLiteFilterTensor, filterTensorInfo); + + armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); + weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); + weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); + } + + if (biasEnabled) + { + if(biasTensorInfo.IsConstant()) + { + auto biasTensor = CreateConstTensor(tfLiteBiasTensor, biasTensorInfo); + + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); + ARMNN_ASSERT(biasLayer != nullptr); + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + // The data input can also be constant, so we must check that this is also allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = CreateConstTensor(tfLiteInputTensor, inputTensorInfo); + + armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + if(Connect(layer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + // Check and create activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); +} + +TfLiteStatus VisitConvolutionOperator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + switch(operatorCode) + { + case kTfLiteBuiltinConv2d: + return VisitConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); + case kTfLiteBuiltinDepthwiseConv2d: + return VisitDepthwiseConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); + default: + return kTfLiteError; + } +} + +}
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