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Diffstat (limited to 'delegate/src/Lstm.hpp')
| -rw-r--r-- | delegate/src/Lstm.hpp | 268 |
1 files changed, 0 insertions, 268 deletions
diff --git a/delegate/src/Lstm.hpp b/delegate/src/Lstm.hpp deleted file mode 100644 index 8c1f877ec9..0000000000 --- a/delegate/src/Lstm.hpp +++ /dev/null @@ -1,268 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" - -#include <armnn/LstmParams.hpp> -#include <armnn/Tensor.hpp> -#include <armnn/utility/IgnoreUnused.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 armnnDelegate -{ - -TfLiteStatus VisitLstmOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t 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 (IsOptionalOperandPresent(tfLiteNode, 1)) - { - params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 1); - } - - params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 2); - params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 3); - params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 4); - - // Recurrent weight tensors of size {n_cell, n_output} - if (IsOptionalOperandPresent(tfLiteNode, 5)) - { - params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 5); - } - - params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 6); - params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 7); - params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 8); - - // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. - if (IsOptionalOperandPresent(tfLiteNode, 9)) - { - params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 9); - } - - if (IsOptionalOperandPresent(tfLiteNode, 10)) - { - params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 10); - } - - if (IsOptionalOperandPresent(tfLiteNode, 11)) - { - params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 11); - } - - // Gates bias tensors of size {n_cell} - if (IsOptionalOperandPresent(tfLiteNode, 12)) - { - params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 12); - } - - params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 13); - params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 14); - params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 15); - - // Projection weight tensor of size {n_output, n_cell} - if (IsOptionalOperandPresent(tfLiteNode, 16)) - { - params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 16); - } - // Projection bias tensor of size {n_output} - if (IsOptionalOperandPresent(tfLiteNode, 17)) - { - params.m_ProjectionBias = GetConstTensorForTfLiteTensor(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 (IsOptionalOperandPresent(tfLiteNode, 20)) - { - params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 20); - } - - if (IsOptionalOperandPresent(tfLiteNode, 21)) - { - params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 21); - } - - if (IsOptionalOperandPresent(tfLiteNode, 22)) - { - params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 22); - } - - if (IsOptionalOperandPresent(tfLiteNode, 23)) - { - params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(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, true); - - 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; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("LSTM", - tfLiteContext, - IsLstmSupported, - delegateData.m_Backends, - isSupported, - setBackend, - 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); - layer->SetBackendId(setBackend); - 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)); - - // 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
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