diff options
Diffstat (limited to 'delegate/src/Lstm.hpp')
| -rw-r--r-- | delegate/src/Lstm.hpp | 262 |
1 files changed, 255 insertions, 7 deletions
diff --git a/delegate/src/Lstm.hpp b/delegate/src/Lstm.hpp index b81b2565b1..829e3bf9c6 100644 --- a/delegate/src/Lstm.hpp +++ b/delegate/src/Lstm.hpp @@ -5,6 +5,10 @@ #pragma once +#include "DelegateUtils.hpp" + +#include <armnn/LstmParams.hpp> +#include <armnn/Tensor.hpp> #include <armnn/utility/IgnoreUnused.hpp> #include <tensorflow/lite/builtin_ops.h> @@ -15,19 +19,263 @@ namespace armnnDelegate { +bool IsOptional(TfLiteNode* tfLiteNode, const int index) +{ + if (tfLiteNode->inputs->data[index] < 0) { + return true; + } + return false; + +} + +armnn::ConstTensor* CreateConstTensor(const TfLiteTensor* tfLiteTensors, TfLiteNode* tfLiteNode, int index) +{ + const TfLiteTensor &tfLiteTensor = tfLiteTensors[tfLiteNode->inputs->data[index]]; + armnn::TensorInfo tensorInfo = GetTensorInfoForTfLiteTensor(tfLiteTensor); + return new armnn::ConstTensor(tensorInfo, tfLiteTensor.data.data); +} + TfLiteStatus VisitLstmOperator(DelegateData& delegateData, TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode, int nodeIndex, int32_t operatorCode) { - armnn::IgnoreUnused(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - operatorCode); + auto numInputs = tfLiteNode->inputs->size; + if (numInputs < 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + + const auto nodeParams = reinterpret_cast<TfLiteLSTMParams*>(tfLiteNode->builtin_data); + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + // Set the params structure for the AddLstmLayer call + armnn::LstmInputParams params; + + if (!IsOptional(tfLiteNode, 1)) + { + params.m_InputToInputWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 1); + } + + params.m_InputToForgetWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 2); + params.m_InputToCellWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 3); + params.m_InputToOutputWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 4); + + // Recurrent weight tensors of size {n_cell, n_output} + if (!IsOptional(tfLiteNode, 5)) + { + params.m_RecurrentToInputWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 5); + } + + params.m_RecurrentToForgetWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 6); + params.m_RecurrentToCellWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 7); + params.m_RecurrentToOutputWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 8); + + // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. + if (!IsOptional(tfLiteNode, 9)) + { + params.m_CellToInputWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 9); + } + + if (!IsOptional(tfLiteNode, 10)) + { + params.m_CellToForgetWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 10); + } + + if (!IsOptional(tfLiteNode, 11)) + { + params.m_CellToOutputWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 11); + } + + // Gates bias tensors of size {n_cell} + if (!IsOptional(tfLiteNode, 12)) + { + params.m_InputGateBias = CreateConstTensor(tfLiteTensors, tfLiteNode, 12); + } + + params.m_ForgetGateBias = CreateConstTensor(tfLiteTensors, tfLiteNode, 13); + params.m_CellBias = CreateConstTensor(tfLiteTensors, tfLiteNode, 14); + params.m_OutputGateBias = CreateConstTensor(tfLiteTensors, tfLiteNode, 15); + + // Projection weight tensor of size {n_output, n_cell} + if (!IsOptional(tfLiteNode, 16)) + { + params.m_ProjectionWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 16); + } + // Projection bias tensor of size {n_output} + if (!IsOptional(tfLiteNode, 17)) + { + params.m_ProjectionBias = CreateConstTensor(tfLiteTensors, tfLiteNode, 17); + } + + // These state tensors are defined as variable tensors, and will be modified by this op. + armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[18]]); + armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[19]]); + + // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix. + if (tfLiteNode->inputs->size >= 21 && !IsOptional(tfLiteNode, 20)) + { + params.m_InputLayerNormWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 20); + } + + if (tfLiteNode->inputs->size >= 22 && !IsOptional(tfLiteNode, 21)) + { + params.m_ForgetLayerNormWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 21); + } + + if (tfLiteNode->inputs->size >= 23 && !IsOptional(tfLiteNode, 22)) + { + params.m_CellLayerNormWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 22); + } + + if (tfLiteNode->inputs->size >= 24 && !IsOptional(tfLiteNode, 23)) + { + params.m_OutputLayerNormWeights = CreateConstTensor(tfLiteTensors, tfLiteNode, 23); + } + + // set the layer descriptor + armnn::LstmDescriptor desc; + desc.m_ActivationFunc = NonNegative(nodeParams->activation, nodeIndex); + desc.m_ClippingThresCell = nodeParams->cell_clip; + desc.m_ClippingThresProj = nodeParams->proj_clip; + desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr + || params.m_RecurrentToInputWeights == nullptr + || params.m_InputGateBias == nullptr); + desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || params.m_CellToOutputWeights != nullptr); + desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); + desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr + || params.m_ForgetLayerNormWeights != nullptr + || params.m_CellLayerNormWeights != nullptr + || params.m_OutputLayerNormWeights != nullptr); + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + unsigned int batchSize = inputTensorInfo.GetShape()[0]; + unsigned int outputSize = outputTensorInfo.GetShape()[1]; + unsigned int numUnits = cellStateInInfo.GetShape()[1]; + + armnn::DataType dataType = inputTensorInfo.GetDataType(); + float qScale = inputTensorInfo.GetQuantizationScale(); + float qOffset = inputTensorInfo.GetQuantizationOffset(); + + armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, dataType, qScale, qOffset); + if (!desc.m_CifgEnabled) + { + scratchBufferTensorInfo = armnn::TensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset); + } + armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, dataType, qScale, qOffset); + armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset); + + armnn::LstmInputParamsInfo paramsInfo; + paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); + paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); + paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); + paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); + paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); + paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); + paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); + paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); + paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); + + if (!desc.m_CifgEnabled) + { + paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); + paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); + if (params.m_CellToInputWeights != nullptr) + { + paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); + } + paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); + } + + if (desc.m_ProjectionEnabled) + { + paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); + if (params.m_ProjectionBias != nullptr) + { + paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); + } + } + + if (desc.m_PeepholeEnabled) + { + paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); + paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); + } + + if (desc.m_LayerNormEnabled) + { + if(!desc.m_CifgEnabled) + { + paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); + } + paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); + paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); + paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); + } + + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsLstmSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputStateInInfo, + cellStateInInfo, + scratchBufferTensorInfo, + outputStateOutTensorInfo, + cellStateOutTensorInfo, + outputInfo, + desc, + paramsInfo); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddLstmLayer(desc, params); + ARMNN_ASSERT(layer != nullptr); + + layer->GetOutputSlot(0).SetTensorInfo(scratchBufferTensorInfo); + layer->GetOutputSlot(1).SetTensorInfo(outputStateOutTensorInfo); + layer->GetOutputSlot(2).SetTensorInfo(cellStateOutTensorInfo); + layer->GetOutputSlot(3).SetTensorInfo(outputTensorInfo); + + // Connect the inputs + // input_layer + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(layer->GetInputSlot(0)); + // cellStateIn + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[18]]->Connect(layer->GetInputSlot(1)); + //outputStateIn + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[19]]->Connect(layer->GetInputSlot(2)); - return kTfLiteError; + // In the test_model there is only 1 Output + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(1); + delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[0])] = &outputSlot; + return kTfLiteOk; } -} // namespace armnnDelegate +} // namespace armnnDelegate
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