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//
// 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;
}
// Use input indices to get filter tensor.
const TfLiteOpaqueTensor* tfLiteFilterTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, 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;
}
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;
}
// Use input indices to get filter tensor.
const TfLiteOpaqueTensor* tfLiteFilterTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, 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;
}
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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