IVGCVSW-7618 Implement UnidirectionalSequenceLstm operator for Opaque Delegate
* Intermediate tensors aren't accessible through the new
Opaque interface yet, so we have to cast to TfLiteNode for now.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: Ifd91131e5d5ff6cc057b80729fea9afa68ed240b
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index ef04913..f0b0e97 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -330,6 +330,8 @@
test/TransposeConvolution2dTest.cpp
test/TransposeTest.cpp
test/TransposeTestHelper.hpp
+ test/UnidirectionalSequenceLstmTest.cpp
+ test/UnidirectionalSequenceLstmTestHelper.hpp
test/UnpackTest.cpp
test/UnpackTestHelper.hpp)
diff --git a/delegate/opaque/CMakeLists.txt b/delegate/opaque/CMakeLists.txt
index a82b75ae..d7aed37 100644
--- a/delegate/opaque/CMakeLists.txt
+++ b/delegate/opaque/CMakeLists.txt
@@ -40,6 +40,7 @@
src/Split.hpp
src/StridedSlice.hpp
src/Transpose.hpp
+ src/UnidirectionalSequenceLstm.hpp
src/Unpack.hpp)
add_library(armnnOpaqueDelegateObject OBJECT ${armnnOpaqueDelegateObject_sources})
diff --git a/delegate/opaque/src/UnidirectionalSequenceLstm.hpp b/delegate/opaque/src/UnidirectionalSequenceLstm.hpp
index e169697..790f287 100644
--- a/delegate/opaque/src/UnidirectionalSequenceLstm.hpp
+++ b/delegate/opaque/src/UnidirectionalSequenceLstm.hpp
@@ -2,3 +2,344 @@
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
+
+#pragma once
+
+#include <OpaqueDelegateUtils.hpp>
+
+namespace armnnOpaqueDelegate
+{
+
+TfLiteStatus VisitUnidirectionalSequenceLstmOperator(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;
+ }
+
+ // 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;
+ }
+
+ // 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;
+ }
+
+ // Set the params structure for the AddUnidirectionalSequenceLstmLayer call
+ // Please refer to each operand at
+ // https://www.tensorflow.org/mlir/tfl_ops#tflunidirectional_sequence_lstm_tflunidirectionalsequencelstmop
+ armnn::LstmInputParams params;
+
+ if (IsOptionalOperandPresent(tfLiteNode, 1))
+ {
+ params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 1);
+ }
+
+ params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 2);
+ params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 3);
+ params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 4);
+
+ // Recurrent weight tensors of size {n_cell, n_output}
+ if (IsOptionalOperandPresent(tfLiteNode, 5))
+ {
+ params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 5);
+ }
+
+ params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 6);
+ params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 7);
+ params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 8);
+
+ // Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
+ if (IsOptionalOperandPresent(tfLiteNode, 9))
+ {
+ params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 9);
+ }
+
+ if (IsOptionalOperandPresent(tfLiteNode, 10))
+ {
+ params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 10);
+ }
+
+ if (IsOptionalOperandPresent(tfLiteNode, 11))
+ {
+ params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 11);
+ }
+
+ // Gates bias tensors of size {n_cell}
+ if (IsOptionalOperandPresent(tfLiteNode, 12))
+ {
+ params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 12);
+ }
+
+ params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 13);
+ params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 14);
+ params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 15);
+
+ // Projection weight tensor of size {n_output, n_cell}
+ if (IsOptionalOperandPresent(tfLiteNode, 16))
+ {
+ params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 16);
+ }
+ // Projection bias tensor of size {n_output}
+ if (IsOptionalOperandPresent(tfLiteNode, 17))
+ {
+ params.m_ProjectionBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 17);
+ }
+
+ // These state tensors are defined as variable tensors, and will be modified by this op.
+ const TfLiteOpaqueTensor* tfLiteOutputStateIn = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[18]);
+ if (!IsValid(tfLiteContext, tfLiteOutputStateIn, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+ const TfLiteOpaqueTensor* cellStateIn = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[19]);
+ if (!IsValid(tfLiteContext, cellStateIn, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+ armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputStateIn);
+ armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteOpaqueTensor(cellStateIn);
+
+ // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix.
+ if (IsOptionalOperandPresent(tfLiteNode, 20))
+ {
+ params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 20);
+ }
+
+ if (IsOptionalOperandPresent(tfLiteNode, 21))
+ {
+ params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 21);
+ }
+
+ if (IsOptionalOperandPresent(tfLiteNode, 22))
+ {
+ params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 22);
+ }
+
+ if (IsOptionalOperandPresent(tfLiteNode, 23))
+ {
+ params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 23);
+ }
+
+ const auto nodeParams =
+ reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
+
+ // set the layer descriptor
+ armnn::UnidirectionalSequenceLstmDescriptor 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);
+ desc.m_TimeMajor = nodeParams->time_major;
+
+ // Intermediates tensors aren't accessible through the new Opaque Interface yet, so we have to cast it for now.
+ // This should be changed to use the accessor functions once added.
+ auto* classicTfliteNode = reinterpret_cast<const TfLiteNode*>(tfLiteNode);
+
+ if (classicTfliteNode->intermediates->size > 3 && desc.m_LayerNormEnabled)
+ {
+ auto inputIntermediateTensorInfo =
+ GetTensorInfoForTfLiteOpaqueTensor(
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[0]));
+ auto forgetIntermediateTensorInfo =
+ GetTensorInfoForTfLiteOpaqueTensor(
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[1]));
+ auto cellIntermediateTensorInfo =
+ GetTensorInfoForTfLiteOpaqueTensor(
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[2]));
+ auto outputIntermediateTensorInfo =
+ GetTensorInfoForTfLiteOpaqueTensor(
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[3]));
+
+ desc.m_InputIntermediateScale = inputIntermediateTensorInfo.GetQuantizationScale();
+ desc.m_ForgetIntermediateScale = forgetIntermediateTensorInfo.GetQuantizationScale();
+ desc.m_CellIntermediateScale = cellIntermediateTensorInfo.GetQuantizationScale();
+ desc.m_OutputIntermediateScale = outputIntermediateTensorInfo.GetQuantizationScale();
+ }
+ else
+ {
+ float defaultIntermediate = std::pow(2, -12);
+ desc.m_InputIntermediateScale = defaultIntermediate;
+ desc.m_ForgetIntermediateScale = defaultIntermediate;
+ desc.m_CellIntermediateScale = defaultIntermediate;
+ desc.m_OutputIntermediateScale = defaultIntermediate;
+ }
+ if (classicTfliteNode->intermediates->size > 4)
+ {
+ auto hiddenTensorInfo =
+ GetTensorInfoForTfLiteOpaqueTensor(
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[4]));
+ desc.m_HiddenStateScale = hiddenTensorInfo.GetQuantizationScale();
+ desc.m_HiddenStateZeroPoint = hiddenTensorInfo.GetQuantizationOffset();
+ }
+
+ float defaultIntermediate = std::pow(2, -12);
+ desc.m_InputIntermediateScale = defaultIntermediate;
+ desc.m_ForgetIntermediateScale = defaultIntermediate;
+ desc.m_CellIntermediateScale = defaultIntermediate;
+ desc.m_OutputIntermediateScale = defaultIntermediate;
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
+
+ unsigned int batchSize = inputTensorInfo.GetShape()[0];
+ unsigned int outputSize = outputTensorInfo.GetShape()[2];
+ 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},
+ cellStateInInfo.GetDataType(),
+ cellStateInInfo.GetQuantizationScale(),
+ cellStateInInfo.GetQuantizationOffset());
+
+ 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_OPAQUE_SUPPORT_FUNC("UNIDIRECTIONAL_SEQUENCE_LSTM",
+ tfLiteContext,
+ IsUnidirectionalSequenceLstmSupported,
+ delegateData.m_Backends,
+ isSupported,
+ setBackend,
+ inputTensorInfo,
+ outputStateInInfo,
+ cellStateInInfo,
+ outputStateOutTensorInfo,
+ cellStateOutTensorInfo,
+ outputInfo,
+ desc,
+ paramsInfo);
+ };
+
+ if (!delegateData.m_Network)
+ {
+ validateFunc(outputTensorInfo, isSupported);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddUnidirectionalSequenceLstmLayer(desc, params);
+ layer->SetBackendId(setBackend);
+ ARMNN_ASSERT(layer != nullptr);
+
+ layer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
+ layer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
+ layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);
+
+ // Connect the inputs
+ // input_layer
+ delegateData.m_OutputSlotForNode[inputTensors[0]]->Connect(layer->GetInputSlot(0));
+ // cellStateIn
+ delegateData.m_OutputSlotForNode[inputTensors[18]]->Connect(layer->GetInputSlot(1));
+ //outputStateIn
+ delegateData.m_OutputSlotForNode[inputTensors[19]]->Connect(layer->GetInputSlot(2));
+
+ armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(2);
+ delegateData.m_OutputSlotForNode[static_cast<unsigned long>(outputTensors[0])] = &outputSlot;
+
+ return kTfLiteOk;
+}
+
+} // namespace armnnOpaqueDelegate
\ No newline at end of file
diff --git a/delegate/opaque/src/armnn_delegate.cpp b/delegate/opaque/src/armnn_delegate.cpp
index cae1ea5..fa64679 100644
--- a/delegate/opaque/src/armnn_delegate.cpp
+++ b/delegate/opaque/src/armnn_delegate.cpp
@@ -1131,6 +1131,12 @@
tfLiteNode,
nodeIndex,
kTfLiteBuiltinTransposeConv);
+ case kTfLiteBuiltinUnidirectionalSequenceLstm:
+ return VisitUnidirectionalSequenceLstmOperator(delegateData,
+ tfLiteContext,
+ tfLiteNode,
+ nodeIndex,
+ kTfLiteBuiltinUnidirectionalSequenceLstm);
case kTfLiteBuiltinUnpack:
return VisitUnpackOperator(delegateData,
tfLiteContext,