From b2397fd9d68f12792b04dd90ae72dfb8cb9f4a60 Mon Sep 17 00:00:00 2001 From: Ferran Balaguer Date: Thu, 25 Jul 2019 12:12:39 +0100 Subject: IVGCVSW-3398 Add LSTM normalization parameters to HAL 1.2 * Adding LSTM processing function in HAL 1.2 with normalization parameters * Refactoring LSTM tests !armnn:1608 Signed-off-by: Ferran Balaguer Change-Id: I0e00f14ef078a333e9f2f23d6278a5d92a3001d6 --- 1.2/ArmnnDriverImpl.cpp | 2 +- 1.2/HalPolicy.cpp | 429 ++++++++- 1.2/HalPolicy.hpp | 2 + test/1.0/Lstm.cpp | 34 + test/1.1/Lstm.cpp | 34 + test/1.2/Lstm.cpp | 44 + test/Android.mk | 9 +- test/DriverTestHelpers.cpp | 46 + test/DriverTestHelpers.hpp | 48 + test/Lstm.cpp | 1555 -------------------------------- test/Lstm.hpp | 2099 ++++++++++++++++++++++++++++++++++++++++++++ 11 files changed, 2742 insertions(+), 1560 deletions(-) create mode 100644 test/1.0/Lstm.cpp create mode 100644 test/1.1/Lstm.cpp create mode 100644 test/1.2/Lstm.cpp delete mode 100644 test/Lstm.cpp create mode 100644 test/Lstm.hpp diff --git a/1.2/ArmnnDriverImpl.cpp b/1.2/ArmnnDriverImpl.cpp index 82eacde8..8a444e5d 100644 --- a/1.2/ArmnnDriverImpl.cpp +++ b/1.2/ArmnnDriverImpl.cpp @@ -49,7 +49,7 @@ void NotifyCallbackAndCheck(const sp& callback, ErrorStatus errorStatus, const sp& preparedModelPtr) { - Return returned = callback->notify(errorStatus, preparedModelPtr); + Return returned = callback->notify_1_2(errorStatus, preparedModelPtr); // This check is required, if the callback fails and it isn't checked it will bring down the service if (!returned.isOk()) { diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp index 9cad29fa..8dbfd897 100644 --- a/1.2/HalPolicy.cpp +++ b/1.2/HalPolicy.cpp @@ -36,7 +36,6 @@ bool HandledByV1_0(V1_2::OperationType operationType) case V1_0::OperationType::LOCAL_RESPONSE_NORMALIZATION: case V1_0::OperationType::LOGISTIC: case V1_0::OperationType::LSH_PROJECTION: - case V1_0::OperationType::LSTM: case V1_0::OperationType::MUL: case V1_0::OperationType::RESHAPE: case V1_0::OperationType::RNN: @@ -164,6 +163,8 @@ bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, return ConvertSpaceToDepth(operation, model, data); case V1_2::OperationType::TANH: return ConvertTanH(operation, model, data); + case V1_2::OperationType::LSTM: + return ConvertLstm(operation, model, data); default: return Fail("%s: Operation type %s not supported in ArmnnDriver", __func__, toString(operation.type).c_str()); @@ -1008,5 +1009,431 @@ bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, Conv return ::ConvertTanH(operation, model, data); } +bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data) +{ + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); + if (!input.IsValid()) + { + return Fail("%s: Could not read input 0: input", __func__); + } + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + LayerInputHandle outputStateIn = ConvertToLayerInputHandle(operation, 18, model, data); + if (!outputStateIn.IsValid()) + { + return Fail("%s: Could not read input 18: outputStateIn", __func__); + } + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + LayerInputHandle cellStateIn = ConvertToLayerInputHandle(operation, 19, model, data); + if (!cellStateIn.IsValid()) + { + return Fail("%s: Could not read input 19: cellStateIn", __func__); + } + + // Get the mandatory input tensors: + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + const ConstTensorPin inputToForgetWeightsPin = + ConvertOperationInputToConstTensorPin(operation, 2, model, data); + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + const ConstTensorPin inputToCellWeightsPin = + ConvertOperationInputToConstTensorPin(operation, 3, model, data); + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + const ConstTensorPin inputToOutputWeightsPin = + ConvertOperationInputToConstTensorPin(operation, 4, model, data); + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + const ConstTensorPin recurrentToForgetWeightsPin = + ConvertOperationInputToConstTensorPin(operation, 6, model, data); + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + const ConstTensorPin recurrentToCellWeightsPin = + ConvertOperationInputToConstTensorPin(operation, 7, model, data); + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + const ConstTensorPin recurrentToOutputWeightsPin = + ConvertOperationInputToConstTensorPin(operation, 8, model, data); + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin forgetGateBiasPin = + ConvertOperationInputToConstTensorPin(operation, 13, model, data); + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin cellBiasPin = + ConvertOperationInputToConstTensorPin(operation, 14, model, data); + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin outputGateBiasPin = + ConvertOperationInputToConstTensorPin(operation, 15, model, data); + + if (!inputToForgetWeightsPin.IsValid() || + !inputToCellWeightsPin.IsValid() || + !inputToOutputWeightsPin.IsValid() || + !recurrentToForgetWeightsPin.IsValid() || + !recurrentToCellWeightsPin.IsValid() || + !recurrentToOutputWeightsPin.IsValid() || + !forgetGateBiasPin.IsValid() || + !cellBiasPin.IsValid() || + !outputGateBiasPin.IsValid()) + { + return Fail("%s: Operation has invalid tensor inputs", __func__); + } + + // Get the optional input tensors: + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + const ConstTensorPin inputToInputWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 1, + model, + data, + g_DontPermute, + nullptr, + true); + + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + const ConstTensorPin recurrentToInputWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 5, + model, + data, + g_DontPermute, + nullptr, + true); + + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin cellToInputWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 9, + model, + data, + g_DontPermute, + nullptr, + true); + + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin cellToForgetWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 10, + model, + data, + g_DontPermute, + nullptr, + true); + + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin cellToOutputWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 11, + model, + data, + g_DontPermute, + nullptr, + true); + + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + const ConstTensorPin inputGateBiasPin = + ConvertOperationInputToConstTensorPin(operation, + 12, + model, + data, + g_DontPermute, + nullptr, + true); + + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + const ConstTensorPin projectionWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 16, + model, + data, + g_DontPermute, + nullptr, + true); + + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + const ConstTensorPin projectionBiasPin = + ConvertOperationInputToConstTensorPin(operation, + 17, + model, + data, + g_DontPermute, + nullptr, + true); + + if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) || + (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) || + (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) || + (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) || + (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) || + (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) || + (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) || + (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional())) + { + return Fail("%s: Operation has invalid tensor inputs", __func__); + } + + // Get the mandatory input scalars (actually 1-D tensors of size 1): + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + ActivationFn activation; + float cellClip; + float projClip; + if (!GetInputActivationFunctionFromTensor(operation, 20, activation, model, data) || + !GetInputScalar(operation, 21, OperandType::FLOAT32, cellClip, model, data) || + !GetInputScalar(operation, 22, OperandType::FLOAT32, projClip, model, data)) + { + return Fail("%s: Operation has invalid scalar inputs", __func__); + } + + // Get the normalization tensors + // 23: The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + const ConstTensorPin inputLayerNormWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 23, + model, + data, + g_DontPermute, + nullptr, + true); + + // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + const ConstTensorPin forgetLayerNormWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 24, + model, + data, + g_DontPermute, + nullptr, + true); + + // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + const ConstTensorPin cellLayerNormWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 25, + model, + data, + g_DontPermute, + nullptr, + true); + + // 26: The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + const ConstTensorPin outputLayerNormWeightsPin = + ConvertOperationInputToConstTensorPin(operation, + 26, + model, + data, + g_DontPermute, + nullptr, + true); + + // Outputs: + // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] + // with CIFG, or [batch_size, num_units * 3] without CIFG. + const Operand* scratchBuffer = GetOutputOperand(operation, 0, model); + if (!scratchBuffer) + { + return Fail("%s: Could not read output 0: scratchBuffer", __func__); + } + // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + const Operand* outputStateOut = GetOutputOperand(operation, 1, model); + if (!outputStateOut) + { + return Fail("%s: Could not read output 1: outputStateOut", __func__); + } + // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + const Operand* cellStateOut = GetOutputOperand(operation, 2, model); + if (!cellStateOut) + { + return Fail("%s: Could not read output 2: cellStateOut", __func__); + } + // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + const Operand* output = GetOutputOperand(operation, 3, model); + if (!output) + { + return Fail("%s: Could not read output 3: output", __func__); + } + + // set the params structure for the AddLstmLayer call + armnn::LstmInputParams params; + params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr(); + params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr(); + params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr(); + params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr(); + params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr(); + params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr(); + params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr(); + params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr(); + params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr(); + params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr(); + params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr(); + params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr(); + params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr(); + params.m_CellBias = cellBiasPin.GetConstTensorPtr(); + params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr(); + params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr(); + params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr(); + params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr(); + params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr(); + params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr(); + params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr(); + + // set the layer descriptor + armnn::LstmDescriptor desc; + desc.m_ActivationFunc = activation; + desc.m_ClippingThresCell = cellClip; + desc.m_ClippingThresProj = projClip; + 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); + + // validate the optional input groups + if (desc.m_CifgEnabled && + (params.m_InputToInputWeights != nullptr || + params.m_RecurrentToInputWeights != nullptr || + params.m_InputGateBias != nullptr)) + { + return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights," + " and input gate bias must be provided", __func__); + } + + if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr) + { + return Fail("%s: projection bias should not be provided without projection weights", __func__); + } + + if (desc.m_PeepholeEnabled && + (params.m_CellToForgetWeights == nullptr || + params.m_CellToOutputWeights == nullptr || + (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr))) + { + return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided" + " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__); + } + + if (desc.m_LayerNormEnabled && + (params.m_ForgetLayerNormWeights == nullptr || + params.m_CellLayerNormWeights == nullptr || + params.m_OutputLayerNormWeights == nullptr || + (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr))) + { + return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be" + " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__); + } + + // Check if the layer is supported + // Inputs + const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); + const armnn::TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo(); + const armnn::TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo(); + + // Outputs + const armnn::TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer); + const armnn::TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut); + const armnn::TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); + const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); + + // Basic parameters + 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()); + + // Optional parameters + 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; + FORWARD_LAYER_SUPPORT_FUNC(__func__, + IsLstmSupported, + data.m_Backends, + isSupported, + inputInfo, + outputStateInInfo, + cellStateInInfo, + scratchBufferInfo, + outputStateOutInfo, + cellStateOutInfo, + outputInfo, + desc, + paramsInfo); + if (!isSupported) + { + return false; + } + + // Add the layer + armnn::IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm"); + + input.Connect(layer->GetInputSlot(0)); + outputStateIn.Connect(layer->GetInputSlot(1)); + cellStateIn.Connect(layer->GetInputSlot(2)); + + return (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) && + SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) && + SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data) && + SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3, model, data)); +} + } // namespace hal_1_2 } // namespace armnn_driver diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp index f689613f..74683136 100644 --- a/1.2/HalPolicy.hpp +++ b/1.2/HalPolicy.hpp @@ -67,6 +67,8 @@ private: static bool ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data); static bool ConvertTanH(const Operation& operation, const Model& model, ConversionData& data); + + static bool ConvertLstm(const Operation& operation, const Model& model, ConversionData& data); }; } // namespace hal_1_2 diff --git a/test/1.0/Lstm.cpp b/test/1.0/Lstm.cpp new file mode 100644 index 00000000..5f0a209d --- /dev/null +++ b/test/1.0/Lstm.cpp @@ -0,0 +1,34 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "../Lstm.hpp" + +#include + +BOOST_AUTO_TEST_SUITE(LstmTests) + +using namespace armnn_driver; + +BOOST_DATA_TEST_CASE(LstmNoCifgNoPeepholeNoProjectionTest, COMPUTE_DEVICES) +{ + LstmNoCifgNoPeepholeNoProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionTest, COMPUTE_DEVICES) +{ + LstmCifgPeepholeNoProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmNoCifgPeepholeProjectionTest, COMPUTE_DEVICES) +{ + LstmNoCifgPeepholeProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionBatch2Test, COMPUTE_DEVICES) +{ + LstmCifgPeepholeNoProjectionBatch2(sample); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/test/1.1/Lstm.cpp b/test/1.1/Lstm.cpp new file mode 100644 index 00000000..703597e5 --- /dev/null +++ b/test/1.1/Lstm.cpp @@ -0,0 +1,34 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "../Lstm.hpp" + +#include + +BOOST_AUTO_TEST_SUITE(LstmTests) + +using namespace armnn_driver; + +BOOST_DATA_TEST_CASE(LstmNoCifgNoPeepholeNoProjectionTest, COMPUTE_DEVICES) +{ + LstmNoCifgNoPeepholeNoProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionTest, COMPUTE_DEVICES) +{ + LstmCifgPeepholeNoProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmNoCifgPeepholeProjectionTest, COMPUTE_DEVICES) +{ + LstmNoCifgPeepholeProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionBatch2Test, COMPUTE_DEVICES) +{ + LstmCifgPeepholeNoProjectionBatch2(sample); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/test/1.2/Lstm.cpp b/test/1.2/Lstm.cpp new file mode 100644 index 00000000..a76b7610 --- /dev/null +++ b/test/1.2/Lstm.cpp @@ -0,0 +1,44 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "../Lstm.hpp" + +#include + +BOOST_AUTO_TEST_SUITE(LstmTests) + +using namespace armnn_driver; + +BOOST_DATA_TEST_CASE(LstmNoCifgNoPeepholeNoProjectionTest, COMPUTE_DEVICES) +{ + LstmNoCifgNoPeepholeNoProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionTest, COMPUTE_DEVICES) +{ + LstmCifgPeepholeNoProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmNoCifgPeepholeProjectionTest, COMPUTE_DEVICES) +{ + LstmNoCifgPeepholeProjection(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionBatch2Test, COMPUTE_DEVICES) +{ + LstmCifgPeepholeNoProjectionBatch2(sample); +} + +BOOST_DATA_TEST_CASE(LstmNoCifgPeepholeProjectionNoClippingLayerNormTest, COMPUTE_DEVICES) +{ + LstmNoCifgPeepholeProjectionNoClippingLayerNorm(sample); +} + +BOOST_DATA_TEST_CASE(LstmCifgPeepholeProjectionNoClippingLayerNormTest, COMPUTE_DEVICES) +{ + LstmCifgPeepholeProjectionNoClippingLayerNorm(sample); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/test/Android.mk b/test/Android.mk index a078e0a3..2f0e03ec 100644 --- a/test/Android.mk +++ b/test/Android.mk @@ -57,6 +57,7 @@ endif # PLATFORM_VERSION == Q or later LOCAL_SRC_FILES := \ 1.0/Convolution2D.cpp \ 1.0/FullyConnectedReshape.cpp \ + 1.0/Lstm.cpp \ Tests.cpp \ UtilsTests.cpp \ Concurrent.cpp \ @@ -64,7 +65,6 @@ LOCAL_SRC_FILES := \ GenericLayerTests.cpp \ DriverTestHelpers.cpp \ SystemProperties.cpp \ - Lstm.cpp \ Concat.cpp \ TestTensor.cpp @@ -160,6 +160,8 @@ LOCAL_SRC_FILES := \ 1.1/Convolution2D.cpp \ 1.1/Mean.cpp \ 1.1/Transpose.cpp \ + 1.0/Lstm.cpp \ + 1.1/Lstm.cpp \ Tests.cpp \ UtilsTests.cpp \ Concurrent.cpp \ @@ -167,7 +169,6 @@ LOCAL_SRC_FILES := \ GenericLayerTests.cpp \ DriverTestHelpers.cpp \ SystemProperties.cpp \ - Lstm.cpp \ Concat.cpp \ TestTensor.cpp @@ -257,6 +258,9 @@ LOCAL_SRC_FILES := \ 1.1/Transpose.cpp \ 1.2/Dilation.cpp \ 1.2/Capabilities.cpp \ + 1.0/Lstm.cpp \ + 1.1/Lstm.cpp \ + 1.2/Lstm.cpp \ Tests.cpp \ UtilsTests.cpp \ Concurrent.cpp \ @@ -264,7 +268,6 @@ LOCAL_SRC_FILES := \ GenericLayerTests.cpp \ DriverTestHelpers.cpp \ SystemProperties.cpp \ - Lstm.cpp \ Concat.cpp \ TestTensor.cpp diff --git a/test/DriverTestHelpers.cpp b/test/DriverTestHelpers.cpp index 675757f0..4c26174b 100644 --- a/test/DriverTestHelpers.cpp +++ b/test/DriverTestHelpers.cpp @@ -61,6 +61,26 @@ Return PreparedModelCallback::notify(ErrorStatus status, return Void(); } +#ifdef ARMNN_ANDROID_NN_V1_2 + +Return PreparedModelCallback_1_2::notify(ErrorStatus status, + const android::sp& preparedModel) +{ + m_ErrorStatus = status; + m_PreparedModel = preparedModel; + return Void(); +} + +Return PreparedModelCallback_1_2::notify_1_2(ErrorStatus status, + const android::sp& preparedModel) +{ + m_ErrorStatus = status; + m_PreparedModel_1_2 = preparedModel; + return Void(); +} + +#endif + // lifted from common/Utils.cpp hidl_memory allocateSharedMemory(int64_t size) { @@ -147,6 +167,32 @@ android::sp PrepareModelWithStatus(const V1_1::Model& mode #endif +#ifdef ARMNN_ANDROID_NN_V1_2 + +android::sp PrepareModelWithStatus_1_2(const armnn_driver::hal_1_2::HalPolicy::Model& model, + armnn_driver::ArmnnDriver& driver, + ErrorStatus& prepareStatus, + ErrorStatus expectedStatus) +{ + android::sp cb(new PreparedModelCallback_1_2()); + + android::hardware::hidl_vec emptyHandle1; + android::hardware::hidl_vec emptyHandle2; + armnn_driver::ArmnnDriver::HidlToken emptyToken; + + driver.prepareModel_1_2(model, V1_1::ExecutionPreference::LOW_POWER, emptyHandle1, emptyHandle2, emptyToken, cb); + + prepareStatus = cb->GetErrorStatus(); + BOOST_TEST(prepareStatus == expectedStatus); + if (expectedStatus == ErrorStatus::NONE) + { + BOOST_TEST((cb->GetPreparedModel_1_2() != nullptr)); + } + return cb->GetPreparedModel_1_2(); +} + +#endif + ErrorStatus Execute(android::sp preparedModel, const Request& request, ErrorStatus expectedStatus) diff --git a/test/DriverTestHelpers.hpp b/test/DriverTestHelpers.hpp index 980b3a72..c6f3f1fe 100644 --- a/test/DriverTestHelpers.hpp +++ b/test/DriverTestHelpers.hpp @@ -67,6 +67,36 @@ private: android::sp m_PreparedModel; }; +#ifdef ARMNN_ANDROID_NN_V1_2 + +class PreparedModelCallback_1_2 : public V1_2::IPreparedModelCallback +{ +public: + PreparedModelCallback_1_2() + : m_ErrorStatus(ErrorStatus::NONE) + , m_PreparedModel() + , m_PreparedModel_1_2() + { } + ~PreparedModelCallback_1_2() override { } + + Return notify(ErrorStatus status, const android::sp& preparedModel) override; + + Return notify_1_2(ErrorStatus status, const android::sp& preparedModel) override; + + ErrorStatus GetErrorStatus() { return m_ErrorStatus; } + + android::sp GetPreparedModel() { return m_PreparedModel; } + + android::sp GetPreparedModel_1_2() { return m_PreparedModel_1_2; } + +private: + ErrorStatus m_ErrorStatus; + android::sp m_PreparedModel; + android::sp m_PreparedModel_1_2; +}; + +#endif + hidl_memory allocateSharedMemory(int64_t size); android::sp AddPoolAndGetData(uint32_t size, Request& request); @@ -259,6 +289,24 @@ android::sp PrepareModel(const HalModel& model, return PrepareModelWithStatus(model, driver, prepareStatus); } +#ifdef ARMNN_ANDROID_NN_V1_2 + +android::sp PrepareModelWithStatus_1_2(const armnn_driver::hal_1_2::HalPolicy::Model& model, + armnn_driver::ArmnnDriver& driver, + ErrorStatus& prepareStatus, + ErrorStatus expectedStatus = ErrorStatus::NONE); + +template +android::sp PrepareModel_1_2(const HalModel& model, + armnn_driver::ArmnnDriver& driver) +{ + ErrorStatus prepareStatus = ErrorStatus::NONE; + return PrepareModelWithStatus_1_2(model, driver, prepareStatus); +} + +#endif + + ErrorStatus Execute(android::sp preparedModel, const Request& request, ErrorStatus expectedStatus = ErrorStatus::NONE); diff --git a/test/Lstm.cpp b/test/Lstm.cpp deleted file mode 100644 index 579524ca..00000000 --- a/test/Lstm.cpp +++ /dev/null @@ -1,1555 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "DriverTestHelpers.hpp" -#include "OperationsUtils.h" - -#include "../1.0/HalPolicy.hpp" - -#include -#include -#include -#include -#include - -#include - -BOOST_AUTO_TEST_SUITE(LstmTests) - -using ArmnnDriver = armnn_driver::ArmnnDriver; -using DriverOptions = armnn_driver::DriverOptions; -using HalPolicy = armnn_driver::hal_1_0::HalPolicy; - -using namespace driverTestHelpers; -using namespace android::hardware; - -namespace -{ - -template -RequestArgument CreateRequestArgument(const std::vector& value, unsigned int poolIndex) -{ - DataLocation inputInloc = {}; - inputInloc.poolIndex = poolIndex; - inputInloc.offset = 0; - inputInloc.length = value.size() * sizeof(T); - RequestArgument inputRequestArgument = {}; - inputRequestArgument.location = inputInloc; - inputRequestArgument.dimensions = hidl_vec{}; - return inputRequestArgument; -} - -// Returns true if the relative difference between two float values is less than the tolerance value given. -// This is used because the floating point comparison tolerance (set on each BOOST_AUTO_TEST_CASE) does not work! -bool TolerantCompareEqual(float a, float b, float tolerance = 0.00001f) -{ - float rd; - if (a == 0.0f) - { - rd = fabs(b); - } - else if (b == 0.0f) - { - rd = fabs(a); - } - else - { - rd = boost::math::relative_difference(a, b); - } - return rd < tolerance; -} - -// Helper function to create an OperandLifeTime::NO_VALUE for testing. -// To be used on optional input operands that have no values - these are valid and should be tested. -OperandLifeTime CreateNoValueLifeTime(const hidl_vec& dimensions) -{ - // Only create a NO_VALUE for optional operands that have no elements - if (dimensions.size() == 0 || dimensions[0] == 0) - { - return OperandLifeTime::NO_VALUE; - } - return OperandLifeTime::CONSTANT_COPY; -} -} // anonymous namespace - -// Add our own tests here since we fail the lstm tests which Google supplies (because of non-const weights) - -void LstmTestImpl(const hidl_vec& inputDimensions, - const std::vector& inputValue, - const hidl_vec& inputToInputWeightsDimensions, - const std::vector& inputToInputWeightsValue, - const hidl_vec& inputToForgetWeightsDimensions, - const std::vector& inputToForgetWeightsValue, - const hidl_vec& inputToCellWeightsDimensions, - const std::vector& inputToCellWeightsValue, - const hidl_vec& inputToOutputWeightsDimensions, - const std::vector& inputToOutputWeightsValue, - const hidl_vec& recurrentToInputWeightsDimensions, - const std::vector& recurrentToInputWeightsValue, - const hidl_vec& recurrentToForgetWeightsDimensions, - const std::vector& recurrentToForgetWeightsValue, - const hidl_vec& recurrentToCellWeightsDimensions, - const std::vector& recurrentToCellWeightsValue, - const hidl_vec& recurrentToOutputWeightsDimensions, - const std::vector& recurrentToOutputWeightsValue, - const hidl_vec& cellToInputWeightsDimensions, - const std::vector& cellToInputWeightsValue, - const hidl_vec& cellToForgetWeightsDimensions, - const std::vector& cellToForgetWeightsValue, - const hidl_vec& cellToOutputWeightsDimensions, - const std::vector& cellToOutputWeightsValue, - const hidl_vec& inputGateBiasDimensions, - const std::vector& inputGateBiasValue, - const hidl_vec& forgetGateBiasDimensions, - const std::vector& forgetGateBiasValue, - const hidl_vec& cellBiasDimensions, - const std::vector& cellBiasValue, - const hidl_vec& outputGateBiasDimensions, - const std::vector& outputGateBiasValue, - const hidl_vec& projectionWeightsDimensions, - const std::vector& projectionWeightsValue, - const hidl_vec& projectionBiasDimensions, - const std::vector& projectionBiasValue, - const hidl_vec& outputStateInDimensions, - const std::vector& outputStateInValue, - const hidl_vec& cellStateInDimensions, - const std::vector& cellStateInValue, - const hidl_vec& activationFunctionDimensions, - const std::vector& activationFunctionValue, - const hidl_vec& cellClippingThresholdDimensions, - const std::vector& cellClippingThresholdValue, - const hidl_vec& projectionClippingThresholdDimensions, - const std::vector& projectionClippingThresholdValue, - const hidl_vec& scratchBufferDimensions, - const std::vector& scratchBufferValue, - const hidl_vec& outputStateOutDimensions, - const std::vector& outputStateOutValue, - const hidl_vec& cellStateOutDimensions, - const std::vector& cellStateOutValue, - const hidl_vec& outputDimensions, - const std::vector& outputValue, - armnn::Compute compute) -{ - auto driver = std::make_unique(DriverOptions(compute)); - HalPolicy::Model model = {}; - - // Inputs: - // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where - // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - AddInputOperand(model, inputDimensions); - - // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size], where “num_units” corresponds to the number of cell units. - AddTensorOperand(model, - inputToInputWeightsDimensions, - inputToInputWeightsValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(inputToInputWeightsDimensions)); - // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - AddTensorOperand(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - AddTensorOperand(model, inputToCellWeightsDimensions, inputToCellWeightsValue); - // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - AddTensorOperand(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); - // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., - // “num_units”), or the second dimension of the “projection_weights”, if defined. - AddTensorOperand(model, - recurrentToInputWeightsDimensions, - recurrentToInputWeightsValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); - // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - AddTensorOperand(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); - // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - AddTensorOperand(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); - // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - AddTensorOperand(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); - // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, - cellToInputWeightsDimensions, - cellToInputWeightsValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(cellToInputWeightsDimensions)); - // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, - cellToForgetWeightsDimensions, - cellToForgetWeightsValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(cellToForgetWeightsDimensions)); - // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, - cellToOutputWeightsDimensions, - cellToOutputWeightsValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(cellToOutputWeightsDimensions)); - // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, - inputGateBiasDimensions, - inputGateBiasValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(inputGateBiasDimensions)); - // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, forgetGateBiasDimensions, forgetGateBiasValue); - // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, cellBiasDimensions, cellBiasValue); - // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, outputGateBiasDimensions, outputGateBiasValue); - // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [output_size, num_units]. - AddTensorOperand(model, - projectionWeightsDimensions, - projectionWeightsValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(projectionWeightsDimensions)); - // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - AddTensorOperand(model, - projectionBiasDimensions, - projectionBiasValue, - HalPolicy::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(projectionBiasDimensions)); - - // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - AddInputOperand(model, outputStateInDimensions); - // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - AddInputOperand(model, cellStateInDimensions); - - // Constant scalar values (the VTS test adds these as tensors of dim {}) - // 20: The activation function: A value indicating the activation function: - // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - AddTensorOperand(model, - activationFunctionDimensions, - activationFunctionValue, - HalPolicy::OperandType::INT32); - // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. - // If set to 0.0 then clipping is disabled. - AddTensorOperand(model, - cellClippingThresholdDimensions, - cellClippingThresholdValue, - HalPolicy::OperandType::FLOAT32); - // 22: The clipping threshold: for the output from the projection layer, such that values are bound within - // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - AddTensorOperand(model, - projectionClippingThresholdDimensions, - projectionClippingThresholdValue, - HalPolicy::OperandType::FLOAT32); - - // Outputs: - // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with - // CIFG, or [batch_size, num_units * 3] without CIFG. - AddOutputOperand(model, scratchBufferDimensions); - // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - AddOutputOperand(model, outputStateOutDimensions); - // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - AddOutputOperand(model, cellStateOutDimensions); - // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is - // effectively the same as the current “output state (out)” value. - AddOutputOperand(model, outputDimensions); - - // make the lstm operation - model.operations.resize(1); - model.operations[0].type = HalPolicy::OperationType::LSTM; - model.operations[0].inputs = - hidl_vec {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22}; - model.operations[0].outputs = hidl_vec {23, 24, 25, 26}; - - // define the input values - hidl_vec inputArguments; - inputArguments.resize(3); - - inputArguments[0] = CreateRequestArgument(inputValue, 0); - inputArguments[1] = CreateRequestArgument(outputStateInValue, 1); - inputArguments[2] = CreateRequestArgument(cellStateInValue, 2); - - // define the expected output values - hidl_vec outputArguments; - outputArguments.resize(4); - - outputArguments[0] = CreateRequestArgument(scratchBufferValue, 3); - outputArguments[1] = CreateRequestArgument(outputStateOutValue, 4); - outputArguments[2] = CreateRequestArgument(cellStateOutValue, 5); - outputArguments[3] = CreateRequestArgument(outputValue, 6); - - Request request = {}; - request.inputs = inputArguments; - request.outputs = outputArguments; - - // set the input data - AddPoolAndSetData(inputValue.size(), request, inputValue.data()); - AddPoolAndSetData(outputStateInValue.size(), request, outputStateInValue.data()); - AddPoolAndSetData(cellStateInValue.size(), request, cellStateInValue.data()); - - // add memory for the outputs - AddPoolAndGetData(scratchBufferValue.size(), request); - android::sp outputStateOutMemory = AddPoolAndGetData(outputStateOutValue.size(), request); - float* outputStateOutData = static_cast(static_cast(outputStateOutMemory->getPointer())); - android::sp cellStateOutMemory = AddPoolAndGetData(cellStateOutValue.size(), request); - float* cellStateOutData = static_cast(static_cast(cellStateOutMemory->getPointer())); - android::sp outputMemory = AddPoolAndGetData(outputValue.size(), request); - float* outputData = static_cast(static_cast(outputMemory->getPointer())); - - // make the prepared model and run the execution - android::sp preparedModel = PrepareModel(model, *driver); - if (preparedModel.get() != nullptr) - { - Execute(preparedModel, request); - } - - // check the results - for (size_t i = 0; i < outputStateOutValue.size(); ++i) - { - BOOST_TEST(TolerantCompareEqual(outputStateOutValue[i], outputStateOutData[i]), - "outputStateOut[" << i << "]: " << outputStateOutValue[i] << " != " << outputStateOutData[i]); - } - for (size_t i = 0; i < cellStateOutValue.size(); ++i) - { - BOOST_TEST(TolerantCompareEqual(cellStateOutValue[i], cellStateOutData[i]), - "cellStateOut[" << i << "]: " << cellStateOutValue[i] << " != " << cellStateOutData[i]); - } - for (size_t i = 0; i < outputValue.size(); ++i) - { - BOOST_TEST(TolerantCompareEqual(outputValue[i], outputData[i]), - "output[" << i << "]: " << outputValue[i] << " != " << outputData[i]); - } -} - -void LstmNoCifgNoPeepholeNoProjection(armnn::Compute compute) -{ - // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm.model.cpp - // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm.example.cpp - // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). - - uint32_t batchSize = 1; - uint32_t inputSize = 2; - uint32_t numUnits = 4; - uint32_t outputSize = numUnits; - - // Inputs: - // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where - // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - hidl_vec inputDimensions{batchSize, inputSize}; - std::vector inputValue{2.0f, 3.0f}; - - // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size], where “num_units” corresponds to the number of cell units. - hidl_vec inputToInputWeightsDimensions{numUnits, inputSize}; - std::vector inputToInputWeightsValue{-0.45018822f, -0.02338299f, - -0.08705890f, -0.34550029f, - 0.04266912f, -0.15680569f, - -0.34856534f, 0.43890524f}; - // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; - std::vector inputToForgetWeightsValue{ 0.09701663f, 0.20334584f, - -0.50592935f, -0.31343272f, - -0.40032279f, 0.44781327f, - 0.01387155f, -0.35593212f}; - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. - hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; - std::vector inputToCellWeightsValue{-0.50013041f, 0.13702840f, - 0.11810488f, 0.20131630f, - -0.20583314f, 0.44344562f, - 0.22077113f, -0.29909778f}; - // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; - std::vector inputToOutputWeightsValue{-0.25065863f, -0.28290087f, - 0.04613829f, 0.40525138f, - 0.44272184f, 0.03897077f, - -0.15568960f, 0.19487578f}; - // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., - // “num_units”), or the second dimension of the “projection_weights”, if defined. - hidl_vec recurrentToInputWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToInputWeightsValue{-0.00635350f, -0.20423880f, 0.31454784f, -0.35746509f, - 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, - -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, - 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}; - // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToForgetWeightsValue{-0.48684245f, -0.06655136f, 0.42224967f, 0.21126390f, - 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, - 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, - 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}; - // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToCellWeightsValue{-0.34074140f, 0.24443203f, -0.20785320f, 0.26320225f, - 0.05695659f, -0.00123841f, -0.47447860f, -0.35869038f, - -0.06418842f, -0.13502428f, -0.50176400f, 0.22830659f, - -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}; - // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToOutputWeightsValue{ 0.43385774f, -0.17194885f, 0.27182370f, 0.09215671f, - 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, - 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, - -0.51818722f, -0.15390486f, 0.04681480f, 0.39922136f}; - // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToInputWeightsDimensions{0}; - std::vector cellToInputWeightsValue; - // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToForgetWeightsDimensions{0}; - std::vector cellToForgetWeightsValue; - // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToOutputWeightsDimensions{0}; - std::vector cellToOutputWeightsValue; - // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec inputGateBiasDimensions{numUnits}; - std::vector inputGateBiasValue(numUnits, 0.0f); - // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec forgetGateBiasDimensions{numUnits}; - std::vector forgetGateBiasValue(numUnits, 1.0f); - // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellBiasDimensions{numUnits}; - std::vector cellBiasValue(numUnits, 0.0f); - // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec outputGateBiasDimensions{numUnits}; - std::vector outputGateBiasValue(numUnits, 0.0f); - // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [output_size, num_units]. - hidl_vec projectionWeightsDimensions{0}; - std::vector projectionWeightsValue; - // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - hidl_vec projectionBiasDimensions{0}; - std::vector projectionBiasValue; - - // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateInDimensions{batchSize, outputSize}; - std::vector outputStateInValue(batchSize * outputSize, 0.0f); - // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateInDimensions{batchSize, numUnits}; - std::vector cellStateInValue(batchSize * numUnits, 0.0f); - - // Constant scalar values (the VTS test adds these as tensors of dim {}) - // 20: The activation function: A value indicating the activation function: - // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - hidl_vec activationFunctionDimensions{}; - std::vector activationFunctionValue{4}; - // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. - // If set to 0.0 then clipping is disabled. - hidl_vec cellClippingThresholdDimensions{}; - std::vector cellClippingThresholdValue{0.0f}; - // 22: The clipping threshold: for the output from the projection layer, such that values are bound within - // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - hidl_vec projectionClippingThresholdDimensions{}; - std::vector projectionClippingThresholdValue{0.0f}; - - // Outputs: - // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with - // CIFG, or [batch_size, num_units * 3] without CIFG. - // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits - // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 - // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 - // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 - hidl_vec scratchBufferDimensions{batchSize, numUnits * 4}; - std::vector scratchBufferValue(batchSize * numUnits * 4, 0.0f); - // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateOutDimensions{batchSize, outputSize}; - std::vector outputStateOutValue {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}; - // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateOutDimensions{batchSize, numUnits}; - std::vector cellStateOutValue {-0.145439f, 0.157475f, 0.293663f, -0.277353f}; - // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is - // effectively the same as the current “output state (out)” value. - hidl_vec outputDimensions{batchSize, outputSize}; - std::vector outputValue {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}; - - LstmTestImpl(inputDimensions, inputValue, - inputToInputWeightsDimensions, inputToInputWeightsValue, - inputToForgetWeightsDimensions, inputToForgetWeightsValue, - inputToCellWeightsDimensions, inputToCellWeightsValue, - inputToOutputWeightsDimensions, inputToOutputWeightsValue, - recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, - recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, - recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, - recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, - cellToInputWeightsDimensions, cellToInputWeightsValue, - cellToForgetWeightsDimensions, cellToForgetWeightsValue, - cellToOutputWeightsDimensions, cellToOutputWeightsValue, - inputGateBiasDimensions, inputGateBiasValue, - forgetGateBiasDimensions, forgetGateBiasValue, - cellBiasDimensions, cellBiasValue, - outputGateBiasDimensions, outputGateBiasValue, - projectionWeightsDimensions, projectionWeightsValue, - projectionBiasDimensions, projectionBiasValue, - outputStateInDimensions, outputStateInValue, - cellStateInDimensions, cellStateInValue, - activationFunctionDimensions, activationFunctionValue, - cellClippingThresholdDimensions, cellClippingThresholdValue, - projectionClippingThresholdDimensions, projectionClippingThresholdValue, - scratchBufferDimensions, scratchBufferValue, - outputStateOutDimensions, outputStateOutValue, - cellStateOutDimensions, cellStateOutValue, - outputDimensions, outputValue, - compute); -} - -void LstmCifgPeepholeNoProjection(armnn::Compute compute) -{ - // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm2.model.cpp - // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm2.example.cpp - // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). - - uint32_t batchSize = 1; - uint32_t inputSize = 2; - uint32_t numUnits = 4; - uint32_t outputSize = numUnits; - - // Inputs: - // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where - // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - hidl_vec inputDimensions{batchSize, inputSize}; - std::vector inputValue{2.0f, 3.0f}; - - // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size], where “num_units” corresponds to the number of cell units. - hidl_vec inputToInputWeightsDimensions{0}; - std::vector inputToInputWeightsValue; - // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; - std::vector inputToForgetWeightsValue{-0.55291498f, -0.42866567f, - 0.13056988f, -0.36333650f, - -0.22755712f, 0.28253698f, - 0.24407166f, 0.33826375f}; - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. - hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; - std::vector inputToCellWeightsValue{-0.49770179f, -0.27711356f, - -0.09624726f, 0.05100781f, - 0.04717243f, 0.48944736f, - -0.38535351f, -0.17212132f}; - // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; - std::vector inputToOutputWeightsValue{ 0.10725588f, -0.02335852f, - -0.55932593f, -0.09426838f, - -0.44257352f, 0.54939759f, - 0.01533556f, 0.42751634f}; - // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., - // “num_units”), or the second dimension of the “projection_weights”, if defined. - hidl_vec recurrentToInputWeightsDimensions{0}; // VTS was {4, 4} -> {0} ? - std::vector recurrentToInputWeightsValue; - // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToForgetWeightsValue{-0.13832897f, -0.05151010f, -0.23590070f, -0.16661474f, - -0.14340827f, 0.36986142f, 0.23414481f, 0.55899000f, - 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, - -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}; - // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToCellWeightsValue{ 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, - 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, - -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, - -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}; - // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToOutputWeightsValue{0.41613156f, 0.42610586f, -0.16495961f, -0.56638730f, - 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, - 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, - 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}; - // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToInputWeightsDimensions{0}; - std::vector cellToInputWeightsValue; - // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToForgetWeightsDimensions{4}; - std::vector cellToForgetWeightsValue{0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; - // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToOutputWeightsDimensions{4}; - std::vector cellToOutputWeightsValue{-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; - // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec inputGateBiasDimensions{0}; // VTS was {4} -> {0} ? - std::vector inputGateBiasValue; - // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec forgetGateBiasDimensions{numUnits}; - std::vector forgetGateBiasValue(numUnits, 1.0f); - // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellBiasDimensions{numUnits}; - std::vector cellBiasValue(numUnits, 0.0f); - // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec outputGateBiasDimensions{numUnits}; - std::vector outputGateBiasValue(numUnits, 0.0f); - // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [output_size, num_units]. - hidl_vec projectionWeightsDimensions{0}; - std::vector projectionWeightsValue; - // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - hidl_vec projectionBiasDimensions{0}; - std::vector projectionBiasValue; - - // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateInDimensions{batchSize, outputSize}; - std::vector outputStateInValue(batchSize * outputSize, 0.0f); - // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateInDimensions{batchSize, numUnits}; - std::vector cellStateInValue(batchSize * numUnits, 0.0f); - - // Constant scalar values (the VTS test adds these as tensors of dim {}) - // 20: The activation function: A value indicating the activation function: - // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - hidl_vec activationFunctionDimensions{}; - std::vector activationFunctionValue{4}; - // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. - // If set to 0.0 then clipping is disabled. - hidl_vec cellClippingThresholdDimensions{}; - std::vector cellClippingThresholdValue{0.0f}; - // 22: The clipping threshold: for the output from the projection layer, such that values are bound within - // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - hidl_vec projectionClippingThresholdDimensions{}; - std::vector projectionClippingThresholdValue{0.0f}; - - // Outputs: - // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with - // CIFG, or [batch_size, num_units * 3] without CIFG. - // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits - // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 - // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 - // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 - hidl_vec scratchBufferDimensions{batchSize, numUnits * 3}; - std::vector scratchBufferValue(batchSize * numUnits * 3, 0.0f); - // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateOutDimensions{batchSize, outputSize}; - std::vector outputStateOutValue{-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}; - // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateOutDimensions{batchSize, numUnits}; - std::vector cellStateOutValue{-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}; - // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is - // effectively the same as the current “output state (out)” value. - hidl_vec outputDimensions{batchSize, outputSize}; - std::vector outputValue{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}; - - LstmTestImpl(inputDimensions, inputValue, - inputToInputWeightsDimensions, inputToInputWeightsValue, - inputToForgetWeightsDimensions, inputToForgetWeightsValue, - inputToCellWeightsDimensions, inputToCellWeightsValue, - inputToOutputWeightsDimensions, inputToOutputWeightsValue, - recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, - recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, - recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, - recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, - cellToInputWeightsDimensions, cellToInputWeightsValue, - cellToForgetWeightsDimensions, cellToForgetWeightsValue, - cellToOutputWeightsDimensions, cellToOutputWeightsValue, - inputGateBiasDimensions, inputGateBiasValue, - forgetGateBiasDimensions, forgetGateBiasValue, - cellBiasDimensions, cellBiasValue, - outputGateBiasDimensions, outputGateBiasValue, - projectionWeightsDimensions, projectionWeightsValue, - projectionBiasDimensions, projectionBiasValue, - outputStateInDimensions, outputStateInValue, - cellStateInDimensions, cellStateInValue, - activationFunctionDimensions, activationFunctionValue, - cellClippingThresholdDimensions, cellClippingThresholdValue, - projectionClippingThresholdDimensions, projectionClippingThresholdValue, - scratchBufferDimensions, scratchBufferValue, - outputStateOutDimensions, outputStateOutValue, - cellStateOutDimensions, cellStateOutValue, - outputDimensions, outputValue, - compute); -} - -void LstmNoCifgPeepholeProjection(armnn::Compute compute) -{ - // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm3.model.cpp - // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm3.example.cpp - // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). - - uint32_t batchSize = 2; - uint32_t inputSize = 5; - uint32_t numUnits = 20; - uint32_t outputSize = 16; - - // Inputs: - // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where - // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - hidl_vec inputDimensions{batchSize, inputSize}; - std::vector inputValue{0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, - 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}; - - // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size], where “num_units” corresponds to the number of cell units. - hidl_vec inputToInputWeightsDimensions{numUnits, inputSize}; - std::vector inputToInputWeightsValue - { - 0.0213936830f, 0.0612455100f, 0.0469051670f, -0.0146576770f, -0.0314946300f, - 0.0917180300f, 0.1464780100f, 0.1079719300f, -0.0057968358f, 0.0019193048f, - -0.2726754000f, 0.1015402900f, -0.0185398850f, 0.0803498850f, -0.1026238500f, - -0.0225997870f, -0.0912115500f, -0.0086759670f, -0.0452061030f, -0.0821282000f, - -0.0080459520f, 0.0154780810f, 0.0552172470f, 0.0387195870f, 0.0441536270f, - -0.0645324300f, 0.0503182500f, -0.0469351080f, -0.0081644309f, 0.0145742260f, - -0.1671009000f, -0.1551955200f, -0.1681979700f, -0.1397126900f, -0.1195305900f, - 0.2500548700f, -0.2279098300f, 0.0098550870f, -0.0281409580f, -0.1120069800f, - 0.1129540800f, -0.0035217577f, 0.0544850750f, 0.0518469500f, 0.0647112060f, - 0.1098919300f, 0.1167478600f, 0.0349060700f, 0.0772735700f, 0.1139058500f, - -0.1863375000f, -0.1034451000f, -0.1394518900f, -0.0494012270f, -0.1876706300f, - 0.0424839030f, 0.1423355200f, 0.1383258100f, 0.1835016500f, 0.1454560300f, - -0.0285457040f, 0.0249395310f, 0.0509297180f, 0.0076203286f, -0.0029723682f, - -0.0424842240f, -0.1182759600f, -0.0917110400f, -0.1080862800f, -0.1632798800f, - -0.2273378000f, -0.0993647000f, -0.0171551070f, 0.0023917493f, 0.0492727640f, - 0.0038534778f, 0.0547645050f, 0.0897537840f, 0.0694723400f, 0.0801447600f, - -0.0454423400f, -0.0497073000f, -0.0713563100f, -0.0489291060f, -0.0040420120f, - -0.0092840260f, 0.0180420540f, 0.0036860977f, -0.0742730200f, -0.1143460400f, - -0.0189954560f, 0.0314875430f, 0.0128349080f, 0.0199777540f, 0.0442566540f, - -0.3929261300f, -0.1851933400f, -0.1165128100f, -0.0680989200f, 0.0113736770f - }; - // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; - std::vector inputToForgetWeightsValue - { - -0.0018401089f, -0.0048522370f, 0.0369842400f, 0.0141817040f, 0.0282732360f, - -0.0167261940f, -0.0524975900f, -0.1020426100f, 0.0086106600f, -0.0409795050f, - -0.0098991870f, 0.0192389200f, -0.0281772690f, -0.0853510300f, -0.1458549500f, - 0.1066256700f, -0.0190973100f, -0.0178835340f, -0.0047269356f, -0.0451033230f, - 0.0030784295f, 0.0767847750f, 0.0746369600f, 0.0945313950f, 0.0814421000f, - -0.1225789900f, -0.0339457580f, -0.0313034650f, 0.0456306260f, 0.0684388700f, - -0.1349294500f, -0.0124800070f, -0.0811829000f, -0.0722449900f, -0.0962879100f, - 0.0451009460f, 0.0012300825f, 0.0139646620f, 0.0993723940f, 0.0254305900f, - 0.0695832400f, 0.0342572960f, 0.0482646000f, 0.0626799700f, 0.0526250680f, - 0.1278466600f, 0.0707789700f, 0.0257259350f, 0.0416500900f, 0.0724190500f, - 0.0186686440f, -0.0373772940f, -0.0627778300f, -0.0883363600f, -0.0401206050f, - -0.0114055860f, -0.0078083350f, -0.0103013860f, -0.0051021670f, 0.0277174640f, - 0.0548342300f, 0.1144911100f, 0.1128965200f, 0.1093983900f, 0.1339650600f, - -0.0840216600f, -0.0190146200f, -0.0446783040f, -0.0772056500f, 0.0143500630f, - -0.1175795800f, -0.0652038000f, -0.0818573300f, -0.0767543240f, -0.0926143750f, - 0.1040549100f, 0.0529603360f, 0.0357558950f, 0.0358393860f, -0.0125405530f, - 0.0368812980f, 0.0291337600f, 0.0342015900f, 0.0544844700f, -0.0545233530f, - 0.0258271500f, 0.0232735500f, -0.0118571790f, -0.0011980024f, -0.0346417170f, - -0.0261250940f, -0.1758261500f, -0.1592365700f, -0.2748677400f, -0.0006143371f, - 0.0001771948f, -8.470171e-05f, 0.0265180700f, 0.0457907650f, 0.069564960f - }; - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. - hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; - std::vector inputToCellWeightsValue - { - -0.0458028300f, -0.0954946200f, -0.0324189850f, -0.0645463300f, -0.0435284530f, - 0.0430185870f, -0.0491523440f, -0.1241814400f, -0.0789854750f, -0.0759688900f, - 0.0194843620f, -0.1143496200f, -0.0074034138f, -0.0631484400f, -0.0929814950f, - 0.0062155537f, -0.0250343380f, -0.0028890965f, 0.0489295270f, 0.0623507500f, - 0.1066591800f, -0.0320367920f, -0.0850591600f, -0.1084335800f, -0.1300243300f, - -0.0368164370f, -0.0213013400f, -0.0165182390f, 0.0047691227f, -0.0025825808f, - 0.0660178660f, 0.0299915340f, -0.1065283600f, -0.1037554000f, -0.1305607100f, - -0.0326664300f, -0.0337024140f, -0.0064734240f, -0.0461169200f, 0.0144193390f, - -0.0251743230f, 0.0396852000f, 0.0817775060f, 0.0615746800f, 0.1021009500f, - -0.0096581940f, 0.0465117170f, 0.0360390600f, 0.0069369148f, 0.0159600950f, - -0.0650766600f, 0.0955159800f, 0.0535688360f, 0.0640871400f, 0.1283566700f, - -0.0087143290f, -0.2021196600f, -0.1209367400f, 0.0294504720f, 0.2849013000f, - -0.0292279010f, 0.1164364000f, -0.0856026300f, 0.0994178600f, -0.0369995650f, - -0.0288426260f, -0.0033637602f, -0.0170129020f, -0.0972086500f, -0.1119335100f, - -0.0291551170f, -0.0179360340f, -0.0097689360f, -0.0422332400f, -0.0361596350f, - 0.0650511200f, -0.0217428920f, -0.0233772120f, -0.0722136400f, -0.0643055200f, - 0.0545386500f, 0.0911498140f, 0.0638733100f, 0.0075183930f, 0.0559609530f, - 0.0697793440f, 0.0464111680f, 0.1050991100f, 0.0746389400f, 0.0075130584f, - 0.0128509820f, 0.0455543100f, 0.0569556880f, 0.0655528500f, 0.0508014560f, - -0.0098626830f, 0.0082677200f, -0.0265556090f, -0.0073611983f, -0.0014897042f - }; - // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; - std::vector inputToOutputWeightsValue - { - -0.0998932000f, -0.0720195600f, -0.0528037730f, -0.1562959300f, -0.1500191800f, - -0.0765075100f, 0.0235985500f, -0.0751553550f, -0.0803770900f, -0.1509353400f, - 0.0295175520f, -0.0475139300f, 0.0103505310f, -0.0266485100f, -0.0168397220f, - -0.0231211630f, 0.0077019283f, 0.0128512570f, -0.0504064900f, -0.0129761000f, - -0.0217377470f, -0.0383057930f, -0.0687058600f, -0.0148124700f, -0.0012853940f, - 0.1012423600f, 0.0831228350f, 0.0533130060f, -0.0622356460f, -0.0756371540f, - -0.0278339030f, 0.0297749710f, 0.1130802000f, 0.0921890600f, 0.0950613500f, - -0.0866657640f, -0.0371627060f, -0.0388809140f, -0.0358328450f, -0.0144815640f, - -0.0982500300f, -0.1204856900f, -0.0976655860f, -0.0528763300f, -0.0964047000f, - -0.1136642900f, 0.0357775050f, 0.1356881900f, 0.0524513830f, 0.0506493040f, - 0.0579895100f, -0.0218523350f, -0.0998488440f, 0.0147404750f, -0.0788979460f, - 0.0497469900f, 0.0141604730f, 0.0697393200f, 0.0496494200f, 0.0333646460f, - 0.0819012400f, 0.0255353670f, 0.0508931650f, 0.0485142540f, 0.0694581300f, - -0.0789075640f, -0.0670761600f, -0.1184450800f, -0.0998668800f, -0.0750940300f, - 0.0626322600f, 0.1492558700f, 0.2018843600f, 0.1209845100f, 0.1463941500f, - 0.0015017595f, -0.0142673820f, -0.0341725700f, 0.0127114680f, 0.0028300495f, - -0.0247584820f, -0.0509854800f, -0.0821182000f, 0.0142256720f, 0.0215441580f, - 0.0894972500f, 0.0750526800f, -0.0020780868f, 0.0490825800f, 0.0647629500f, - -0.0229070630f, 0.0275624560f, 0.0401857350f, 0.0195675770f, -0.0155987390f, - -0.0490973030f, -0.0171218660f, -0.0833682340f, -0.0233200200f, -0.084095600f - }; - // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., - // “num_units”), or the second dimension of the “projection_weights”, if defined. - hidl_vec recurrentToInputWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToInputWeightsValue - { - -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, // 00 - -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, - -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, - -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, - 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, // 01 - 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, - -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, - 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, - -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, // 02 - 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, - -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, - 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, - -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, // 03 - 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, - -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, - -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, - -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, // 04 - -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, - -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, - 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, - 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, // 05 - 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, - 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, - 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, - -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, // 06 - -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, - 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, - -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, - -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, // 07 - -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, - -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, - -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, - -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, // 08 - 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, - -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, - 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, - -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, // 09 - -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, - 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, - -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, - -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, // 10 - 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, - -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, - 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, - 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, // 11 - 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, - 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, - 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, - 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, // 12 - -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, - -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, - -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, - -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, // 13 - -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, - 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, - 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, - -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f, // 14 - 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f, - 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f, - 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f, - -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f, // 15 - -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f, - 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f, - -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f, - -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f, // 16 - 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f, - 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f, - -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f, - -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f, // 17 - 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f, - 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f, - 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f, - 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f, // 18 - -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f, - -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f, - -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f, - -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f, // 19 - 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f, - -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f, - -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f - }; - // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToForgetWeightsValue - { - -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, // 00 - 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, - -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, - 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, - 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, // 01 - -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, - -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, - 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f, - -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f, // 02 - -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f, - 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f, - -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f, - -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f, // 03 - -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f, - 0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f, - 0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f, - -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f, // 04 - 0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f, - 0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f, - -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f, - -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f, // 05 - 0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f, - -0.027794661f, -0.08077226f, -0.016179763f, 0.1169753f, - 0.17213494f, -0.0056326236f, -0.053934924f, -0.0124349f, - -0.11520337f, 0.05409887f, 0.088759385f, 0.0019655675f, // 06 - 0.0042065294f, 0.03881498f, 0.019844765f, 0.041858196f, - -0.05695512f, 0.047233116f, 0.038937137f, -0.06542224f, - 0.014429736f, -0.09719407f, 0.13908425f, -0.05379757f, - 0.012321099f, 0.082840554f, -0.029899208f, 0.044217527f, // 07 - 0.059855383f, 0.07711018f, -0.045319796f, 0.0948846f, - -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f, - -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f, - -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f, // 08 - 0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f, - 0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f, - 0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f, - 0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f, // 09 - -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f, - 0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f, - 0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f, - -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f, // 10 - -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f, - -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f, - 0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f, - 0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f, // 11 - 0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f, - 0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f, - -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f, - -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f, // 12 - 0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f, - -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f, - -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f, - -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f, // 13 - -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f, - 0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f, - -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f, - -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f, // 14 - 0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f, - -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f, - 0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f, - 0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f, // 15 - 0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f, - 0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f, - 0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f, - 0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f, // 16 - -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f, - 0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f, - -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f, - -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f, // 17 - 0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f, - -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f, - -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f, - 0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f, // 18 - 0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f, - 0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f, - 0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f, - -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f, // 19 - -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f, - 0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f, - -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f - }; - // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToCellWeightsValue - { - -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, - 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, - 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, - -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, - 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, - 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, - -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, - -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f, - -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f, - -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f, - 0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f, - -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f, - -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f, - 0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f, - 0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f, - 0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f, - -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f, - 0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f, - 0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f, - -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f, - 0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f, - 0.015983658f, 0.03406988f, -0.06939408f, 0.040699873f, - 0.02111075f, 0.09669095f, 0.041345075f, -0.08316494f, - -0.07684199f, -0.045768797f, 0.032298047f, -0.041805092f, - 0.0119405f, 0.0061010392f, 0.12652606f, 0.0064572375f, - -0.024950314f, 0.11574242f, 0.04508852f, -0.04335324f, - 0.06760663f, -0.027437469f, 0.07216407f, 0.06977076f, - -0.05438599f, 0.034033038f, -0.028602652f, 0.05346137f, - 0.043184172f, -0.037189785f, 0.10420091f, 0.00882477f, - -0.054019816f, -0.074273005f, -0.030617684f, -0.0028467078f, - 0.024302477f, -0.0038869337f, 0.005332455f, 0.0013399826f, - 0.04361412f, -0.007001822f, 0.09631092f, -0.06702025f, - -0.042049985f, -0.035070654f, -0.04103342f, -0.10273396f, - 0.0544271f, 0.037184782f, -0.13150354f, -0.0058036847f, - -0.008264958f, 0.042035464f, 0.05891794f, 0.029673764f, - 0.0063542654f, 0.044788733f, 0.054816857f, 0.062257513f, - -0.00093483756f, 0.048938446f, -0.004952862f, -0.007730018f, - -0.04043371f, -0.017094059f, 0.07229206f, -0.023670016f, - -0.052195564f, -0.025616996f, -0.01520939f, 0.045104615f, - -0.007376126f, 0.003533447f, 0.006570588f, 0.056037236f, - 0.12436656f, 0.051817212f, 0.028532185f, -0.08686856f, - 0.11868599f, 0.07663395f, -0.07323171f, 0.03463402f, - -0.050708205f, -0.04458982f, -0.11590894f, 0.021273347f, - 0.1251325f, -0.15313013f, -0.12224372f, 0.17228661f, - 0.023029093f, 0.086124025f, 0.006445803f, -0.03496501f, - 0.028332196f, 0.04449512f, -0.042436164f, -0.026587414f, - -0.006041347f, -0.09292539f, -0.05678812f, 0.03897832f, - 0.09465633f, 0.008115513f, -0.02171956f, 0.08304309f, - 0.071401566f, 0.019622514f, 0.032163795f, -0.004167056f, - 0.02295182f, 0.030739572f, 0.056506045f, 0.004612461f, - 0.06524936f, 0.059999723f, 0.046395954f, -0.0045512207f, - -0.1335546f, -0.030136576f, 0.11584653f, -0.014678886f, - 0.0020118146f, -0.09688814f, -0.0790206f, 0.039770417f, - -0.0329582f, 0.07922767f, 0.029322514f, 0.026405897f, - 0.04207835f, -0.07073373f, 0.063781224f, 0.0859677f, - -0.10925287f, -0.07011058f, 0.048005477f, 0.03438226f, - -0.09606514f, -0.006669445f, -0.043381985f, 0.04240257f, - -0.06955775f, -0.06769346f, 0.043903265f, -0.026784198f, - -0.017840602f, 0.024307009f, -0.040079936f, -0.019946516f, - 0.045318738f, -0.12233574f, 0.026170589f, 0.0074471775f, - 0.15978073f, 0.10185836f, 0.10298046f, -0.015476589f, - -0.039390966f, -0.072174534f, 0.0739445f, -0.1211869f, - -0.0347889f, -0.07943156f, 0.014809798f, -0.12412325f, - -0.0030663363f, 0.039695457f, 0.0647603f, -0.08291318f, - -0.018529687f, -0.004423833f, 0.0037507233f, 0.084633216f, - -0.01514876f, -0.056505352f, -0.012800942f, -0.06994386f, - 0.012962922f, -0.031234352f, 0.07029052f, 0.016418684f, - 0.03618972f, 0.055686004f, -0.08663945f, -0.017404709f, - -0.054761406f, 0.029065743f, 0.052404847f, 0.020238016f, - 0.0048197987f, -0.0214882f, 0.07078733f, 0.013016777f, - 0.06262858f, 0.009184685f, 0.020785125f, -0.043904778f, - -0.0270329f, -0.03299152f, -0.060088247f, -0.015162964f, - -0.001828936f, 0.12642565f, -0.056757294f, 0.013586685f, - 0.09232601f, -0.035886683f, 0.06000002f, 0.05229691f, - -0.052580316f, -0.082029596f, -0.010794592f, 0.012947712f, - -0.036429964f, -0.085508935f, -0.13127148f, -0.017744139f, - 0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f, - -0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f, - -0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f, - -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f - }; - // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToOutputWeightsValue - { - 0.025825322f, -0.05813119f, 0.09495884f, -0.045984812f, - -0.01255415f, -0.0026479573f, -0.08196161f, -0.054914974f, - -0.0046604523f, -0.029587349f, -0.044576716f, -0.07480124f, - -0.082868785f, 0.023254942f, 0.027502948f, -0.0039728214f, - -0.08683098f, -0.08116779f, -0.014675607f, -0.037924774f, - -0.023314456f, -0.007401714f, -0.09255757f, 0.029460307f, - -0.08829125f, -0.005139627f, -0.08989442f, -0.0555066f, - 0.13596267f, -0.025062224f, -0.048351806f, -0.03850004f, - 0.07266485f, -0.022414139f, 0.05940088f, 0.075114764f, - 0.09597592f, -0.010211725f, -0.0049794707f, -0.011523867f, - -0.025980417f, 0.072999895f, 0.11091378f, -0.081685916f, - 0.014416728f, 0.043229222f, 0.034178585f, -0.07530371f, - 0.035837382f, -0.085607f, -0.007721233f, -0.03287832f, - -0.043848954f, -0.06404588f, -0.06632928f, -0.073643476f, - 0.008214239f, -0.045984086f, 0.039764922f, 0.03474462f, - 0.060612556f, -0.080590084f, 0.049127717f, 0.04151091f, - -0.030063879f, 0.008801774f, -0.023021035f, -0.019558564f, - 0.05158114f, -0.010947698f, -0.011825728f, 0.0075720972f, - 0.0699727f, -0.0039981045f, 0.069350146f, 0.08799282f, - 0.016156472f, 0.035502106f, 0.11695009f, 0.006217345f, - 0.13392477f, -0.037875112f, 0.025745004f, 0.08940699f, - -0.00924166f, 0.0046702605f, -0.036598757f, -0.08811812f, - 0.10522024f, -0.032441203f, 0.008176899f, -0.04454919f, - 0.07058152f, 0.0067963637f, 0.039206743f, 0.03259838f, - 0.03725492f, -0.09515802f, 0.013326398f, -0.052055415f, - -0.025676316f, 0.03198509f, -0.015951829f, -0.058556724f, - 0.036879618f, 0.043357447f, 0.028362012f, -0.05908629f, - 0.0059240665f, -0.04995891f, -0.019187413f, 0.0276265f, - -0.01628143f, 0.0025863599f, 0.08800015f, 0.035250366f, - -0.022165963f, -0.07328642f, -0.009415526f, -0.07455109f, - 0.11690406f, 0.0363299f, 0.07411125f, 0.042103454f, - -0.009660886f, 0.019076364f, 0.018299393f, -0.046004917f, - 0.08891175f, 0.0431396f, -0.026327137f, -0.051502608f, - 0.08979574f, -0.051670972f, 0.04940282f, -0.07491107f, - -0.021240504f, 0.022596184f, -0.034280192f, 0.060163025f, - -0.058211457f, -0.051837247f, -0.01349775f, -0.04639988f, - -0.035936575f, -0.011681591f, 0.064818054f, 0.0073146066f, - -0.021745546f, -0.043124277f, -0.06471268f, -0.07053354f, - -0.029321948f, -0.05330136f, 0.016933719f, -0.053782392f, - 0.13747959f, -0.1361751f, -0.11569455f, 0.0033329215f, - 0.05693899f, -0.053219706f, 0.063698f, 0.07977434f, - -0.07924483f, 0.06936997f, 0.0034815092f, -0.007305279f, - -0.037325785f, -0.07251102f, -0.033633437f, -0.08677009f, - 0.091591336f, -0.14165086f, 0.021752775f, 0.019683983f, - 0.0011612234f, -0.058154266f, 0.049996935f, 0.0288841f, - -0.0024567875f, -0.14345716f, 0.010955264f, -0.10234828f, - 0.1183656f, -0.0010731248f, -0.023590032f, -0.072285876f, - -0.0724771f, -0.026382286f, -0.0014920527f, 0.042667855f, - 0.0018776858f, 0.02986552f, 0.009814309f, 0.0733756f, - 0.12289186f, 0.018043943f, -0.0458958f, 0.049412545f, - 0.033632483f, 0.05495232f, 0.036686596f, -0.013781798f, - -0.010036754f, 0.02576849f, -0.08307328f, 0.010112348f, - 0.042521734f, -0.05869831f, -0.071689695f, 0.03876447f, - -0.13275425f, -0.0352966f, -0.023077697f, 0.10285965f, - 0.084736146f, 0.15568255f, -0.00040734606f, 0.027835453f, - -0.10292561f, -0.032401145f, 0.10053256f, -0.026142767f, - -0.08271222f, -0.0030240538f, -0.016368777f, 0.1070414f, - 0.042672627f, 0.013456989f, -0.0437609f, -0.022309763f, - 0.11576483f, 0.04108048f, 0.061026827f, -0.0190714f, - -0.0869359f, 0.037901703f, 0.0610107f, 0.07202949f, - 0.01675338f, 0.086139716f, -0.08795751f, -0.014898893f, - -0.023771819f, -0.01965048f, 0.007955471f, -0.043740474f, - 0.03346837f, -0.10549954f, 0.090567775f, 0.042013682f, - -0.03176985f, 0.12569028f, -0.02421228f, -0.029526481f, - 0.023851605f, 0.031539805f, 0.05292009f, -0.02344001f, - -0.07811758f, -0.08834428f, 0.10094801f, 0.16594367f, - -0.06861939f, -0.021256343f, -0.041093912f, -0.06669611f, - 0.035498552f, 0.021757556f, -0.09302526f, -0.015403468f, - -0.06614931f, -0.051798206f, -0.013874718f, 0.03630673f, - 0.010412845f, -0.08077351f, 0.046185967f, 0.0035662893f, - 0.03541868f, -0.094149634f, -0.034814864f, 0.003128424f, - -0.020674974f, -0.03944324f, -0.008110165f, -0.11113267f, - 0.08484226f, 0.043586485f, 0.040582247f, 0.0968012f, - -0.065249965f, -0.028036479f, 0.0050708856f, 0.0017462453f, - 0.0326779f, 0.041296225f, 0.09164146f, -0.047743853f, - -0.015952192f, -0.034451712f, 0.084197424f, -0.05347844f, - -0.11768019f, 0.085926116f, -0.08251791f, -0.045081906f, - 0.0948852f, 0.068401024f, 0.024856757f, 0.06978981f, - -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, - -0.041040014f, -0.024264973f, 0.063464895f, 0.05431621f - }; - // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToInputWeightsDimensions{numUnits}; - std::vector cellToInputWeightsValue - { - 0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, - -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, - -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f, -0.052169047f, - 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f - }; - // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToForgetWeightsDimensions{numUnits}; - std::vector cellToForgetWeightsValue - { - -0.01998659f, -0.15568835f, -0.24248174f, -0.012770197f, 0.041331276f, - -0.072311886f, -0.052123554f, -0.0066330447f, -0.043891653f, 0.036225766f, - -0.047248036f, 0.021479502f, 0.033189066f, 0.11952997f, -0.020432774f, - 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f - }; - // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToOutputWeightsDimensions{numUnits}; - std::vector cellToOutputWeightsValue - { - 0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, - -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, - -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, - 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f - }; - // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec inputGateBiasDimensions{numUnits}; - std::vector inputGateBiasValue - { - 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, - -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, - -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, - 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f - }; - // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec forgetGateBiasDimensions{numUnits}; - std::vector forgetGateBiasValue - { - 0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, - 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, - 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, - 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f - }; - // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellBiasDimensions{numUnits}; - std::vector cellBiasValue - { - -0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, - -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, - -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, - 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f - }; - // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec outputGateBiasDimensions{numUnits}; - std::vector outputGateBiasValue - { - 0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, - 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, - 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, - -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f - }; - // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [output_size, num_units]. - hidl_vec projectionWeightsDimensions{outputSize, numUnits}; - std::vector projectionWeightsValue - { - -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, - 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, - -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, - -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, - 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, - 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, - 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, - 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, - -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, - -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, - -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, - 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, - 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, - 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, - 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, - 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, - -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, - 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, - -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, - 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, - -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, - -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, - 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, - -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, - 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, - -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, - -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, - 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, - -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, - -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, - -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, - 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, - 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, - -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, - 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, - 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, - 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, - 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, - 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, - -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, - -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, - 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, - -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, - -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, - 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, - 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, - 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, - -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, - -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, - -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, - 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, - -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, - 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, - 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, - -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, - -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, - -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, - 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, - -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, - -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, - -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, - 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, - 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, - 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f - }; - // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - hidl_vec projectionBiasDimensions{outputSize}; - std::vector projectionBiasValue(outputSize, 0.0f); - - // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateInDimensions{batchSize, outputSize}; - std::vector outputStateInValue(batchSize * outputSize, 0.0f); - // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateInDimensions{batchSize, numUnits}; - std::vector cellStateInValue(batchSize * numUnits, 0.0f); - - // Constant scalar values (the VTS test adds these as tensors of dim {}) - // 20: The activation function: A value indicating the activation function: - // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - hidl_vec activationFunctionDimensions{}; - std::vector activationFunctionValue{4}; - // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. - // If set to 0.0 then clipping is disabled. - hidl_vec cellClippingThresholdDimensions{}; - std::vector cellClippingThresholdValue{0.0f}; - // 22: The clipping threshold: for the output from the projection layer, such that values are bound within - // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - hidl_vec projectionClippingThresholdDimensions{}; - std::vector projectionClippingThresholdValue{0.0f}; - - // Outputs: - // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with - // CIFG, or [batch_size, num_units * 3] without CIFG. - // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits - // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 - // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 - // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 - hidl_vec scratchBufferDimensions{batchSize, numUnits * 4}; - std::vector scratchBufferValue(batchSize * numUnits * 4, 0.0f); - // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateOutDimensions{batchSize, outputSize}; - std::vector outputStateOutValue - { - -0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, - 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, - -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, - 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f - }; - // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateOutDimensions{batchSize, numUnits}; - std::vector cellStateOutValue - { - -0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, - -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, - -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, - 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, - -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, - -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, - 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, - -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f - }; - // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is - // effectively the same as the current “output state (out)” value. - hidl_vec outputDimensions{batchSize, outputSize}; - std::vector outputValue - { - -0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, - 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, - -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, - 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f - }; - - LstmTestImpl(inputDimensions, inputValue, - inputToInputWeightsDimensions, inputToInputWeightsValue, - inputToForgetWeightsDimensions, inputToForgetWeightsValue, - inputToCellWeightsDimensions, inputToCellWeightsValue, - inputToOutputWeightsDimensions, inputToOutputWeightsValue, - recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, - recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, - recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, - recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, - cellToInputWeightsDimensions, cellToInputWeightsValue, - cellToForgetWeightsDimensions, cellToForgetWeightsValue, - cellToOutputWeightsDimensions, cellToOutputWeightsValue, - inputGateBiasDimensions, inputGateBiasValue, - forgetGateBiasDimensions, forgetGateBiasValue, - cellBiasDimensions, cellBiasValue, - outputGateBiasDimensions, outputGateBiasValue, - projectionWeightsDimensions, projectionWeightsValue, - projectionBiasDimensions, projectionBiasValue, - outputStateInDimensions, outputStateInValue, - cellStateInDimensions, cellStateInValue, - activationFunctionDimensions, activationFunctionValue, - cellClippingThresholdDimensions, cellClippingThresholdValue, - projectionClippingThresholdDimensions, projectionClippingThresholdValue, - scratchBufferDimensions, scratchBufferValue, - outputStateOutDimensions, outputStateOutValue, - cellStateOutDimensions, cellStateOutValue, - outputDimensions, outputValue, - compute); -} - -void LstmCifgPeepholeNoProjectionBatch2(armnn::Compute compute) -{ - // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm2.model.cpp - // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm2.example.cpp - // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). - // The batch size has been increased to 2 (it was 1 in the VTS test) with appropriate input and output values added. - - uint32_t batchSize = 2; - uint32_t inputSize = 2; - uint32_t numUnits = 4; - uint32_t outputSize = numUnits; - - // Inputs: - // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where - // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - hidl_vec inputDimensions{batchSize, inputSize}; - std::vector inputValue{2.0f, 3.0f, 3.0f, 4.0f}; - - // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size], where “num_units” corresponds to the number of cell units. - hidl_vec inputToInputWeightsDimensions{0}; - std::vector inputToInputWeightsValue; - // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; - std::vector inputToForgetWeightsValue{-0.55291498f, -0.42866567f, - 0.13056988f, -0.36333650f, - -0.22755712f, 0.28253698f, - 0.24407166f, 0.33826375f}; - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. - hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; - std::vector inputToCellWeightsValue{-0.49770179f, -0.27711356f, - -0.09624726f, 0.05100781f, - 0.04717243f, 0.48944736f, - -0.38535351f, -0.17212132f}; - // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; - std::vector inputToOutputWeightsValue{ 0.10725588f, -0.02335852f, - -0.55932593f, -0.09426838f, - -0.44257352f, 0.54939759f, - 0.01533556f, 0.42751634f}; - // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., - // “num_units”), or the second dimension of the “projection_weights”, if defined. - hidl_vec recurrentToInputWeightsDimensions{0}; // VTS was {4, 4} -> {0} ? - std::vector recurrentToInputWeightsValue; - // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToForgetWeightsValue{-0.13832897f, -0.05151010f, -0.23590070f, -0.16661474f, - -0.14340827f, 0.36986142f, 0.23414481f, 0.55899000f, - 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, - -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}; - // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToCellWeightsValue{ 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, - 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, - -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, - -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}; - // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; - std::vector recurrentToOutputWeightsValue{0.41613156f, 0.42610586f, -0.16495961f, -0.56638730f, - 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, - 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, - 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}; - // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToInputWeightsDimensions{0}; - std::vector cellToInputWeightsValue; - // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToForgetWeightsDimensions{numUnits}; - std::vector cellToForgetWeightsValue{0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; - // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellToOutputWeightsDimensions{numUnits}; - std::vector cellToOutputWeightsValue{-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; - // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec inputGateBiasDimensions{0}; // VTS was {4} -> {0} ? - std::vector inputGateBiasValue; - // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec forgetGateBiasDimensions{numUnits}; - std::vector forgetGateBiasValue{1.0f, 1.0f, 1.0f, 1.0f}; - // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec cellBiasDimensions{numUnits}; - std::vector cellBiasValue(numUnits, 0.0f); - // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - hidl_vec outputGateBiasDimensions{numUnits}; - std::vector outputGateBiasValue(numUnits, 0.0f); - // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [output_size, num_units]. - hidl_vec projectionWeightsDimensions{0}; - std::vector projectionWeightsValue; - // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - hidl_vec projectionBiasDimensions{0}; - std::vector projectionBiasValue; - - // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateInDimensions{batchSize, outputSize}; - std::vector outputStateInValue(batchSize * outputSize, 0.0f); - // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateInDimensions{batchSize, numUnits}; - std::vector cellStateInValue(batchSize * numUnits, 0.0f); - - // Constant scalar values (the VTS test adds these as tensors of dim {}) - // 20: The activation function: A value indicating the activation function: - // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - hidl_vec activationFunctionDimensions{}; - std::vector activationFunctionValue{4}; - // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. - // If set to 0.0 then clipping is disabled. - hidl_vec cellClippingThresholdDimensions{}; - std::vector cellClippingThresholdValue{0.0f}; - // 22: The clipping threshold: for the output from the projection layer, such that values are bound within - // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - hidl_vec projectionClippingThresholdDimensions{}; - std::vector projectionClippingThresholdValue{0.0f}; - - // Outputs: - // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with - // CIFG, or [batch_size, num_units * 3] without CIFG. - // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits - // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 - // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 - // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 - hidl_vec scratchBufferDimensions{batchSize, numUnits * 3}; - std::vector scratchBufferValue(batchSize * numUnits * 3, 0.0f); - // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - hidl_vec outputStateOutDimensions{batchSize, outputSize}; - std::vector outputStateOutValue{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, - -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; - // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - hidl_vec cellStateOutDimensions{batchSize, numUnits}; - std::vector cellStateOutValue{-0.76044439f, -0.01804161f, 0.18226376f, -0.06493707f, - -0.90477051f, -0.04355603f, 0.18475688f, -0.04158677f}; - // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is - // effectively the same as the current “output state (out)” value. - hidl_vec outputDimensions{batchSize, outputSize}; - std::vector outputValue{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, - -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; - - LstmTestImpl(inputDimensions, inputValue, - inputToInputWeightsDimensions, inputToInputWeightsValue, - inputToForgetWeightsDimensions, inputToForgetWeightsValue, - inputToCellWeightsDimensions, inputToCellWeightsValue, - inputToOutputWeightsDimensions, inputToOutputWeightsValue, - recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, - recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, - recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, - recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, - cellToInputWeightsDimensions, cellToInputWeightsValue, - cellToForgetWeightsDimensions, cellToForgetWeightsValue, - cellToOutputWeightsDimensions, cellToOutputWeightsValue, - inputGateBiasDimensions, inputGateBiasValue, - forgetGateBiasDimensions, forgetGateBiasValue, - cellBiasDimensions, cellBiasValue, - outputGateBiasDimensions, outputGateBiasValue, - projectionWeightsDimensions, projectionWeightsValue, - projectionBiasDimensions, projectionBiasValue, - outputStateInDimensions, outputStateInValue, - cellStateInDimensions, cellStateInValue, - activationFunctionDimensions, activationFunctionValue, - cellClippingThresholdDimensions, cellClippingThresholdValue, - projectionClippingThresholdDimensions, projectionClippingThresholdValue, - scratchBufferDimensions, scratchBufferValue, - outputStateOutDimensions, outputStateOutValue, - cellStateOutDimensions, cellStateOutValue, - outputDimensions, outputValue, - compute); -} -#ifndef ARMCOMPUTECL_ENABLED - static const boost::array COMPUTE_DEVICES = {{ armnn::Compute::CpuRef }}; -#else - static const boost::array COMPUTE_DEVICES = {{ armnn::Compute::CpuRef, armnn::Compute::GpuAcc }}; -#endif - -BOOST_DATA_TEST_CASE(LstmNoCifgNoPeepholeNoProjectionTest, COMPUTE_DEVICES) -{ - LstmNoCifgNoPeepholeNoProjection(sample); -} - -BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionTest, COMPUTE_DEVICES) -{ - LstmCifgPeepholeNoProjection(sample); -} - -BOOST_DATA_TEST_CASE(LstmNoCifgPeepholeProjectionTest, COMPUTE_DEVICES) -{ - LstmNoCifgPeepholeProjection(sample); -} - -BOOST_DATA_TEST_CASE(LstmCifgPeepholeNoProjectionBatch2Test, COMPUTE_DEVICES) -{ - LstmCifgPeepholeNoProjectionBatch2(sample); -} - -BOOST_AUTO_TEST_SUITE_END() diff --git a/test/Lstm.hpp b/test/Lstm.hpp new file mode 100644 index 00000000..3d9bf77f --- /dev/null +++ b/test/Lstm.hpp @@ -0,0 +1,2099 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "DriverTestHelpers.hpp" + +#include +#include + +using ArmnnDriver = armnn_driver::ArmnnDriver; +using DriverOptions = armnn_driver::DriverOptions; + +using namespace driverTestHelpers; +using namespace android::hardware; + +namespace +{ + +template +RequestArgument CreateRequestArgument(const std::vector& value, unsigned int poolIndex) +{ + DataLocation inputInloc = {}; + inputInloc.poolIndex = poolIndex; + inputInloc.offset = 0; + inputInloc.length = value.size() * sizeof(T); + RequestArgument inputRequestArgument = {}; + inputRequestArgument.location = inputInloc; + inputRequestArgument.dimensions = hidl_vec{}; + return inputRequestArgument; +} + +// Returns true if the relative difference between two float values is less than the tolerance value given. +// This is used because the floating point comparison tolerance (set on each BOOST_AUTO_TEST_CASE) does not work! +bool TolerantCompareEqual(float a, float b, float tolerance = 0.00001f) +{ + float rd; + if (a == 0.0f) + { + rd = fabs(b); + } + else if (b == 0.0f) + { + rd = fabs(a); + } + else + { + rd = boost::math::relative_difference(a, b); + } + return rd < tolerance; +} + +// Helper function to create an OperandLifeTime::NO_VALUE for testing. +// To be used on optional input operands that have no values - these are valid and should be tested. +OperandLifeTime CreateNoValueLifeTime(const hidl_vec& dimensions) +{ + // Only create a NO_VALUE for optional operands that have no elements + if (dimensions.size() == 0 || dimensions[0] == 0) + { + return OperandLifeTime::NO_VALUE; + } + return OperandLifeTime::CONSTANT_COPY; +} + +template +void ExecuteModel(const HalModel& model, armnn_driver::ArmnnDriver& driver, const Request& request) +{ + android::sp preparedModel = PrepareModel(model, driver); + if (preparedModel.get() != nullptr) + { + Execute(preparedModel, request); + } +} + +#ifdef ARMNN_ANDROID_NN_V1_2 + +template<> +void ExecuteModel(const armnn_driver::hal_1_2::HalPolicy::Model& model, + armnn_driver::ArmnnDriver& driver, + const Request& request) +{ + android::sp preparedModel = PrepareModel_1_2(model, driver); + if (preparedModel.get() != nullptr) + { + Execute(preparedModel, request); + } +} + +#endif + +} // anonymous namespace + +#ifndef ARMCOMPUTECL_ENABLED +static const boost::array COMPUTE_DEVICES = {{ armnn::Compute::CpuRef }}; +#else +static const boost::array COMPUTE_DEVICES = {{ armnn::Compute::CpuRef, armnn::Compute::GpuAcc }}; +#endif + +// Add our own tests here since we fail the lstm tests which Google supplies (because of non-const weights) +template +void LstmTestImpl(const hidl_vec& inputDimensions, + const std::vector& inputValue, + const hidl_vec& inputToInputWeightsDimensions, + const std::vector& inputToInputWeightsValue, + const hidl_vec& inputToForgetWeightsDimensions, + const std::vector& inputToForgetWeightsValue, + const hidl_vec& inputToCellWeightsDimensions, + const std::vector& inputToCellWeightsValue, + const hidl_vec& inputToOutputWeightsDimensions, + const std::vector& inputToOutputWeightsValue, + const hidl_vec& recurrentToInputWeightsDimensions, + const std::vector& recurrentToInputWeightsValue, + const hidl_vec& recurrentToForgetWeightsDimensions, + const std::vector& recurrentToForgetWeightsValue, + const hidl_vec& recurrentToCellWeightsDimensions, + const std::vector& recurrentToCellWeightsValue, + const hidl_vec& recurrentToOutputWeightsDimensions, + const std::vector& recurrentToOutputWeightsValue, + const hidl_vec& cellToInputWeightsDimensions, + const std::vector& cellToInputWeightsValue, + const hidl_vec& cellToForgetWeightsDimensions, + const std::vector& cellToForgetWeightsValue, + const hidl_vec& cellToOutputWeightsDimensions, + const std::vector& cellToOutputWeightsValue, + const hidl_vec& inputGateBiasDimensions, + const std::vector& inputGateBiasValue, + const hidl_vec& forgetGateBiasDimensions, + const std::vector& forgetGateBiasValue, + const hidl_vec& cellBiasDimensions, + const std::vector& cellBiasValue, + const hidl_vec& outputGateBiasDimensions, + const std::vector& outputGateBiasValue, + const hidl_vec& projectionWeightsDimensions, + const std::vector& projectionWeightsValue, + const hidl_vec& projectionBiasDimensions, + const std::vector& projectionBiasValue, + const hidl_vec& outputStateInDimensions, + const std::vector& outputStateInValue, + const hidl_vec& cellStateInDimensions, + const std::vector& cellStateInValue, + const hidl_vec& activationFunctionDimensions, + const std::vector& activationFunctionValue, + const hidl_vec& cellClippingThresholdDimensions, + const std::vector& cellClippingThresholdValue, + const hidl_vec& projectionClippingThresholdDimensions, + const std::vector& projectionClippingThresholdValue, + const hidl_vec& inputLayerNormWeightsDimensions, + const std::vector& inputLayerNormWeightsValue, + const hidl_vec& forgetLayerNormWeightsDimensions, + const std::vector& forgetLayerNormWeightsValue, + const hidl_vec& cellLayerNormWeightsDimensions, + const std::vector& cellLayerNormWeightsValue, + const hidl_vec& outputLayerNormWeightsDimensions, + const std::vector& outputLayerNormWeightsValue, + const hidl_vec& scratchBufferDimensions, + const std::vector& scratchBufferValue, + const hidl_vec& outputStateOutDimensions, + const std::vector& outputStateOutValue, + const hidl_vec& cellStateOutDimensions, + const std::vector& cellStateOutValue, + const hidl_vec& outputDimensions, + const std::vector& outputValue, + armnn::Compute compute) +{ + auto driver = std::make_unique(DriverOptions(compute)); + using Model = typename HalPolicy::Model; + Model model = {}; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + AddInputOperand(model, inputDimensions); + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + AddTensorOperand(model, + inputToInputWeightsDimensions, + inputToInputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(inputToInputWeightsDimensions)); + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + AddTensorOperand(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + AddTensorOperand(model, inputToCellWeightsDimensions, inputToCellWeightsValue); + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + AddTensorOperand(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + AddTensorOperand(model, + recurrentToInputWeightsDimensions, + recurrentToInputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + AddTensorOperand(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + AddTensorOperand(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + AddTensorOperand(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, + cellToInputWeightsDimensions, + cellToInputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellToInputWeightsDimensions)); + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, + cellToForgetWeightsDimensions, + cellToForgetWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellToForgetWeightsDimensions)); + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, + cellToOutputWeightsDimensions, + cellToOutputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellToOutputWeightsDimensions)); + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, + inputGateBiasDimensions, + inputGateBiasValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(inputGateBiasDimensions)); + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, forgetGateBiasDimensions, forgetGateBiasValue); + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, cellBiasDimensions, cellBiasValue); + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, outputGateBiasDimensions, outputGateBiasValue); + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + AddTensorOperand(model, + projectionWeightsDimensions, + projectionWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(projectionWeightsDimensions)); + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + AddTensorOperand(model, + projectionBiasDimensions, + projectionBiasValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(projectionBiasDimensions)); + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + AddInputOperand(model, outputStateInDimensions); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + AddInputOperand(model, cellStateInDimensions); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + AddTensorOperand(model, + activationFunctionDimensions, + activationFunctionValue, + HalPolicy::OperandType::INT32); + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + AddTensorOperand(model, + cellClippingThresholdDimensions, + cellClippingThresholdValue, + HalPolicy::OperandType::FLOAT32); + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + AddTensorOperand(model, + projectionClippingThresholdDimensions, + projectionClippingThresholdValue, + HalPolicy::OperandType::FLOAT32); + + bool normalizationEnabled = false; + + // If any of the tensors have a value all normalization tensors are set + if (!inputLayerNormWeightsValue.empty() || + !forgetLayerNormWeightsValue.empty() || + !cellLayerNormWeightsValue.empty() || + !outputLayerNormWeightsValue.empty()) + { + // Normalization: + // 23:The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + AddTensorOperand(model, + inputLayerNormWeightsDimensions, + inputLayerNormWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(inputLayerNormWeightsDimensions)); + // 24:The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + AddTensorOperand(model, + forgetLayerNormWeightsDimensions, + forgetLayerNormWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(forgetLayerNormWeightsDimensions)); + // 25:The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + AddTensorOperand(model, + cellLayerNormWeightsDimensions, + cellLayerNormWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellLayerNormWeightsDimensions)); + // 26:The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + AddTensorOperand(model, + outputLayerNormWeightsDimensions, + outputLayerNormWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(outputLayerNormWeightsDimensions)); + + normalizationEnabled = true; + } + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + AddOutputOperand(model, scratchBufferDimensions); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + AddOutputOperand(model, outputStateOutDimensions); + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + AddOutputOperand(model, cellStateOutDimensions); + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + AddOutputOperand(model, outputDimensions); + + // make the lstm operation + model.operations.resize(1); + model.operations[0].type = HalPolicy::OperationType::LSTM; + + if (normalizationEnabled) + { + model.operations[0].inputs = hidl_vec { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, + 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26}; + model.operations[0].outputs = hidl_vec {27, 28, 29, 30}; + } + else + { + model.operations[0].inputs = hidl_vec { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, + 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22}; + model.operations[0].outputs = hidl_vec {23, 24, 25, 26}; + } + + // define the input values + hidl_vec inputArguments; + inputArguments.resize(3); + + inputArguments[0] = CreateRequestArgument(inputValue, 0); + inputArguments[1] = CreateRequestArgument(outputStateInValue, 1); + inputArguments[2] = CreateRequestArgument(cellStateInValue, 2); + + // define the expected output values + hidl_vec outputArguments; + outputArguments.resize(4); + + outputArguments[0] = CreateRequestArgument(scratchBufferValue, 3); + outputArguments[1] = CreateRequestArgument(outputStateOutValue, 4); + outputArguments[2] = CreateRequestArgument(cellStateOutValue, 5); + outputArguments[3] = CreateRequestArgument(outputValue, 6); + + Request request = {}; + request.inputs = inputArguments; + request.outputs = outputArguments; + + // set the input data + AddPoolAndSetData(inputValue.size(), request, inputValue.data()); + AddPoolAndSetData(outputStateInValue.size(), request, outputStateInValue.data()); + AddPoolAndSetData(cellStateInValue.size(), request, cellStateInValue.data()); + + // add memory for the outputs + AddPoolAndGetData(scratchBufferValue.size(), request); + android::sp outputStateOutMemory = AddPoolAndGetData(outputStateOutValue.size(), request); + float* outputStateOutData = static_cast(static_cast(outputStateOutMemory->getPointer())); + android::sp cellStateOutMemory = AddPoolAndGetData(cellStateOutValue.size(), request); + float* cellStateOutData = static_cast(static_cast(cellStateOutMemory->getPointer())); + android::sp outputMemory = AddPoolAndGetData(outputValue.size(), request); + float* outputData = static_cast(static_cast(outputMemory->getPointer())); + + // make the prepared model and run the execution + ExecuteModel(model, *driver, request); + + // check the results + for (size_t i = 0; i < outputStateOutValue.size(); ++i) + { + BOOST_TEST(TolerantCompareEqual(outputStateOutValue[i], outputStateOutData[i]), + "outputStateOut[" << i << "]: " << outputStateOutValue[i] << " != " << outputStateOutData[i]); + } + for (size_t i = 0; i < cellStateOutValue.size(); ++i) + { + BOOST_TEST(TolerantCompareEqual(cellStateOutValue[i], cellStateOutData[i]), + "cellStateOut[" << i << "]: " << cellStateOutValue[i] << " != " << cellStateOutData[i]); + } + for (size_t i = 0; i < outputValue.size(); ++i) + { + BOOST_TEST(TolerantCompareEqual(outputValue[i], outputData[i]), + "output[" << i << "]: " << outputValue[i] << " != " << outputData[i]); + } +} + +template +void LstmNoCifgNoPeepholeNoProjection(armnn::Compute compute) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + uint32_t batchSize = 1; + uint32_t inputSize = 2; + uint32_t numUnits = 4; + uint32_t outputSize = numUnits; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions{batchSize, inputSize}; + std::vector inputValue{2.0f, 3.0f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions{numUnits, inputSize}; + std::vector inputToInputWeightsValue{-0.45018822f, -0.02338299f, + -0.08705890f, -0.34550029f, + 0.04266912f, -0.15680569f, + -0.34856534f, 0.43890524f}; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; + std::vector inputToForgetWeightsValue{ 0.09701663f, 0.20334584f, + -0.50592935f, -0.31343272f, + -0.40032279f, 0.44781327f, + 0.01387155f, -0.35593212f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; + std::vector inputToCellWeightsValue{-0.50013041f, 0.13702840f, + 0.11810488f, 0.20131630f, + -0.20583314f, 0.44344562f, + 0.22077113f, -0.29909778f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; + std::vector inputToOutputWeightsValue{-0.25065863f, -0.28290087f, + 0.04613829f, 0.40525138f, + 0.44272184f, 0.03897077f, + -0.15568960f, 0.19487578f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToInputWeightsValue{-0.00635350f, -0.20423880f, 0.31454784f, -0.35746509f, + 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, + -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, + 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToForgetWeightsValue{-0.48684245f, -0.06655136f, 0.42224967f, 0.21126390f, + 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, + 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, + 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToCellWeightsValue{-0.34074140f, 0.24443203f, -0.20785320f, 0.26320225f, + 0.05695659f, -0.00123841f, -0.47447860f, -0.35869038f, + -0.06418842f, -0.13502428f, -0.50176400f, 0.22830659f, + -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToOutputWeightsValue{ 0.43385774f, -0.17194885f, 0.27182370f, 0.09215671f, + 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, + 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, + -0.51818722f, -0.15390486f, 0.04681480f, 0.39922136f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions{0}; + std::vector cellToInputWeightsValue; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions{0}; + std::vector cellToForgetWeightsValue; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions{0}; + std::vector cellToOutputWeightsValue; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions{numUnits}; + std::vector inputGateBiasValue(numUnits, 0.0f); + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions{numUnits}; + std::vector forgetGateBiasValue(numUnits, 1.0f); + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions{numUnits}; + std::vector cellBiasValue(numUnits, 0.0f); + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions{numUnits}; + std::vector outputGateBiasValue(numUnits, 0.0f); + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions{0}; + std::vector projectionWeightsValue; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions{0}; + std::vector projectionBiasValue; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions{batchSize, outputSize}; + std::vector outputStateInValue(batchSize * outputSize, 0.0f); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions{batchSize, numUnits}; + std::vector cellStateInValue(batchSize * numUnits, 0.0f); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions{}; + std::vector activationFunctionValue{4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions{}; + std::vector cellClippingThresholdValue{0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions{}; + std::vector projectionClippingThresholdValue{0.0f}; + + // Normalization: + // 23:The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + hidl_vec inputLayerNormWeightsDimensions{0}; + std::vector inputLayerNormWeightsValue; + // 24:The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + hidl_vec forgetLayerNormWeightsDimensions{0}; + std::vector forgetLayerNormWeightsValue; + // 25:The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + hidl_vec cellLayerNormWeightsDimensions{0}; + std::vector cellLayerNormWeightsValue; + // 26:The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + hidl_vec outputLayerNormWeightsDimensions{0}; + std::vector outputLayerNormWeightsValue; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits + // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 + // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 + // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 + hidl_vec scratchBufferDimensions{batchSize, numUnits * 4}; + std::vector scratchBufferValue(batchSize * numUnits * 4, 0.0f); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions{batchSize, outputSize}; + std::vector outputStateOutValue {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions{batchSize, numUnits}; + std::vector cellStateOutValue {-0.145439f, 0.157475f, 0.293663f, -0.277353f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions{batchSize, outputSize}; + std::vector outputValue {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, + forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, + cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, + outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue, + compute); +} + +template +void LstmCifgPeepholeNoProjection(armnn::Compute compute) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm2.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm2.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + uint32_t batchSize = 1; + uint32_t inputSize = 2; + uint32_t numUnits = 4; + uint32_t outputSize = numUnits; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions{batchSize, inputSize}; + std::vector inputValue{2.0f, 3.0f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions{0}; + std::vector inputToInputWeightsValue; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; + std::vector inputToForgetWeightsValue{-0.55291498f, -0.42866567f, + 0.13056988f, -0.36333650f, + -0.22755712f, 0.28253698f, + 0.24407166f, 0.33826375f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; + std::vector inputToCellWeightsValue{-0.49770179f, -0.27711356f, + -0.09624726f, 0.05100781f, + 0.04717243f, 0.48944736f, + -0.38535351f, -0.17212132f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; + std::vector inputToOutputWeightsValue{ 0.10725588f, -0.02335852f, + -0.55932593f, -0.09426838f, + -0.44257352f, 0.54939759f, + 0.01533556f, 0.42751634f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions{0}; // VTS was {4, 4} -> {0} ? + std::vector recurrentToInputWeightsValue; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToForgetWeightsValue{-0.13832897f, -0.05151010f, -0.23590070f, -0.16661474f, + -0.14340827f, 0.36986142f, 0.23414481f, 0.55899000f, + 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, + -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToCellWeightsValue{ 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, + 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, + -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, + -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToOutputWeightsValue{0.41613156f, 0.42610586f, -0.16495961f, -0.56638730f, + 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, + 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, + 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions{0}; + std::vector cellToInputWeightsValue; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions{4}; + std::vector cellToForgetWeightsValue{0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions{4}; + std::vector cellToOutputWeightsValue{-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions{0}; // VTS was {4} -> {0} ? + std::vector inputGateBiasValue; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions{numUnits}; + std::vector forgetGateBiasValue(numUnits, 1.0f); + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions{numUnits}; + std::vector cellBiasValue(numUnits, 0.0f); + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions{numUnits}; + std::vector outputGateBiasValue(numUnits, 0.0f); + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions{0}; + std::vector projectionWeightsValue; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions{0}; + std::vector projectionBiasValue; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions{batchSize, outputSize}; + std::vector outputStateInValue(batchSize * outputSize, 0.0f); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions{batchSize, numUnits}; + std::vector cellStateInValue(batchSize * numUnits, 0.0f); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions{}; + std::vector activationFunctionValue{4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions{}; + std::vector cellClippingThresholdValue{0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions{}; + std::vector projectionClippingThresholdValue{0.0f}; + + // Normalization: + // 23:The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + hidl_vec inputLayerNormWeightsDimensions{0}; + std::vector inputLayerNormWeightsValue; + // 24:The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + hidl_vec forgetLayerNormWeightsDimensions{0}; + std::vector forgetLayerNormWeightsValue; + // 25:The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + hidl_vec cellLayerNormWeightsDimensions{0}; + std::vector cellLayerNormWeightsValue; + // 26:The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + hidl_vec outputLayerNormWeightsDimensions{0}; + std::vector outputLayerNormWeightsValue; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits + // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 + // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 + // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 + hidl_vec scratchBufferDimensions{batchSize, numUnits * 3}; + std::vector scratchBufferValue(batchSize * numUnits * 3, 0.0f); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions{batchSize, outputSize}; + std::vector outputStateOutValue{-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions{batchSize, numUnits}; + std::vector cellStateOutValue{-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions{batchSize, outputSize}; + std::vector outputValue{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, + forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, + cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, + outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue, + compute); +} + +template +void LstmNoCifgPeepholeProjection(armnn::Compute compute) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm3.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm3.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + uint32_t batchSize = 2; + uint32_t inputSize = 5; + uint32_t numUnits = 20; + uint32_t outputSize = 16; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions{batchSize, inputSize}; + std::vector inputValue{0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, + 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions{numUnits, inputSize}; + std::vector inputToInputWeightsValue + { + 0.0213936830f, 0.0612455100f, 0.0469051670f, -0.0146576770f, -0.0314946300f, + 0.0917180300f, 0.1464780100f, 0.1079719300f, -0.0057968358f, 0.0019193048f, + -0.2726754000f, 0.1015402900f, -0.0185398850f, 0.0803498850f, -0.1026238500f, + -0.0225997870f, -0.0912115500f, -0.0086759670f, -0.0452061030f, -0.0821282000f, + -0.0080459520f, 0.0154780810f, 0.0552172470f, 0.0387195870f, 0.0441536270f, + -0.0645324300f, 0.0503182500f, -0.0469351080f, -0.0081644309f, 0.0145742260f, + -0.1671009000f, -0.1551955200f, -0.1681979700f, -0.1397126900f, -0.1195305900f, + 0.2500548700f, -0.2279098300f, 0.0098550870f, -0.0281409580f, -0.1120069800f, + 0.1129540800f, -0.0035217577f, 0.0544850750f, 0.0518469500f, 0.0647112060f, + 0.1098919300f, 0.1167478600f, 0.0349060700f, 0.0772735700f, 0.1139058500f, + -0.1863375000f, -0.1034451000f, -0.1394518900f, -0.0494012270f, -0.1876706300f, + 0.0424839030f, 0.1423355200f, 0.1383258100f, 0.1835016500f, 0.1454560300f, + -0.0285457040f, 0.0249395310f, 0.0509297180f, 0.0076203286f, -0.0029723682f, + -0.0424842240f, -0.1182759600f, -0.0917110400f, -0.1080862800f, -0.1632798800f, + -0.2273378000f, -0.0993647000f, -0.0171551070f, 0.0023917493f, 0.0492727640f, + 0.0038534778f, 0.0547645050f, 0.0897537840f, 0.0694723400f, 0.0801447600f, + -0.0454423400f, -0.0497073000f, -0.0713563100f, -0.0489291060f, -0.0040420120f, + -0.0092840260f, 0.0180420540f, 0.0036860977f, -0.0742730200f, -0.1143460400f, + -0.0189954560f, 0.0314875430f, 0.0128349080f, 0.0199777540f, 0.0442566540f, + -0.3929261300f, -0.1851933400f, -0.1165128100f, -0.0680989200f, 0.0113736770f + }; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; + std::vector inputToForgetWeightsValue + { + -0.0018401089f, -0.0048522370f, 0.0369842400f, 0.0141817040f, 0.0282732360f, + -0.0167261940f, -0.0524975900f, -0.1020426100f, 0.0086106600f, -0.0409795050f, + -0.0098991870f, 0.0192389200f, -0.0281772690f, -0.0853510300f, -0.1458549500f, + 0.1066256700f, -0.0190973100f, -0.0178835340f, -0.0047269356f, -0.0451033230f, + 0.0030784295f, 0.0767847750f, 0.0746369600f, 0.0945313950f, 0.0814421000f, + -0.1225789900f, -0.0339457580f, -0.0313034650f, 0.0456306260f, 0.0684388700f, + -0.1349294500f, -0.0124800070f, -0.0811829000f, -0.0722449900f, -0.0962879100f, + 0.0451009460f, 0.0012300825f, 0.0139646620f, 0.0993723940f, 0.0254305900f, + 0.0695832400f, 0.0342572960f, 0.0482646000f, 0.0626799700f, 0.0526250680f, + 0.1278466600f, 0.0707789700f, 0.0257259350f, 0.0416500900f, 0.0724190500f, + 0.0186686440f, -0.0373772940f, -0.0627778300f, -0.0883363600f, -0.0401206050f, + -0.0114055860f, -0.0078083350f, -0.0103013860f, -0.0051021670f, 0.0277174640f, + 0.0548342300f, 0.1144911100f, 0.1128965200f, 0.1093983900f, 0.1339650600f, + -0.0840216600f, -0.0190146200f, -0.0446783040f, -0.0772056500f, 0.0143500630f, + -0.1175795800f, -0.0652038000f, -0.0818573300f, -0.0767543240f, -0.0926143750f, + 0.1040549100f, 0.0529603360f, 0.0357558950f, 0.0358393860f, -0.0125405530f, + 0.0368812980f, 0.0291337600f, 0.0342015900f, 0.0544844700f, -0.0545233530f, + 0.0258271500f, 0.0232735500f, -0.0118571790f, -0.0011980024f, -0.0346417170f, + -0.0261250940f, -0.1758261500f, -0.1592365700f, -0.2748677400f, -0.0006143371f, + 0.0001771948f, -8.470171e-05f, 0.0265180700f, 0.0457907650f, 0.069564960f + }; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; + std::vector inputToCellWeightsValue + { + -0.0458028300f, -0.0954946200f, -0.0324189850f, -0.0645463300f, -0.0435284530f, + 0.0430185870f, -0.0491523440f, -0.1241814400f, -0.0789854750f, -0.0759688900f, + 0.0194843620f, -0.1143496200f, -0.0074034138f, -0.0631484400f, -0.0929814950f, + 0.0062155537f, -0.0250343380f, -0.0028890965f, 0.0489295270f, 0.0623507500f, + 0.1066591800f, -0.0320367920f, -0.0850591600f, -0.1084335800f, -0.1300243300f, + -0.0368164370f, -0.0213013400f, -0.0165182390f, 0.0047691227f, -0.0025825808f, + 0.0660178660f, 0.0299915340f, -0.1065283600f, -0.1037554000f, -0.1305607100f, + -0.0326664300f, -0.0337024140f, -0.0064734240f, -0.0461169200f, 0.0144193390f, + -0.0251743230f, 0.0396852000f, 0.0817775060f, 0.0615746800f, 0.1021009500f, + -0.0096581940f, 0.0465117170f, 0.0360390600f, 0.0069369148f, 0.0159600950f, + -0.0650766600f, 0.0955159800f, 0.0535688360f, 0.0640871400f, 0.1283566700f, + -0.0087143290f, -0.2021196600f, -0.1209367400f, 0.0294504720f, 0.2849013000f, + -0.0292279010f, 0.1164364000f, -0.0856026300f, 0.0994178600f, -0.0369995650f, + -0.0288426260f, -0.0033637602f, -0.0170129020f, -0.0972086500f, -0.1119335100f, + -0.0291551170f, -0.0179360340f, -0.0097689360f, -0.0422332400f, -0.0361596350f, + 0.0650511200f, -0.0217428920f, -0.0233772120f, -0.0722136400f, -0.0643055200f, + 0.0545386500f, 0.0911498140f, 0.0638733100f, 0.0075183930f, 0.0559609530f, + 0.0697793440f, 0.0464111680f, 0.1050991100f, 0.0746389400f, 0.0075130584f, + 0.0128509820f, 0.0455543100f, 0.0569556880f, 0.0655528500f, 0.0508014560f, + -0.0098626830f, 0.0082677200f, -0.0265556090f, -0.0073611983f, -0.0014897042f + }; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; + std::vector inputToOutputWeightsValue + { + -0.0998932000f, -0.0720195600f, -0.0528037730f, -0.1562959300f, -0.1500191800f, + -0.0765075100f, 0.0235985500f, -0.0751553550f, -0.0803770900f, -0.1509353400f, + 0.0295175520f, -0.0475139300f, 0.0103505310f, -0.0266485100f, -0.0168397220f, + -0.0231211630f, 0.0077019283f, 0.0128512570f, -0.0504064900f, -0.0129761000f, + -0.0217377470f, -0.0383057930f, -0.0687058600f, -0.0148124700f, -0.0012853940f, + 0.1012423600f, 0.0831228350f, 0.0533130060f, -0.0622356460f, -0.0756371540f, + -0.0278339030f, 0.0297749710f, 0.1130802000f, 0.0921890600f, 0.0950613500f, + -0.0866657640f, -0.0371627060f, -0.0388809140f, -0.0358328450f, -0.0144815640f, + -0.0982500300f, -0.1204856900f, -0.0976655860f, -0.0528763300f, -0.0964047000f, + -0.1136642900f, 0.0357775050f, 0.1356881900f, 0.0524513830f, 0.0506493040f, + 0.0579895100f, -0.0218523350f, -0.0998488440f, 0.0147404750f, -0.0788979460f, + 0.0497469900f, 0.0141604730f, 0.0697393200f, 0.0496494200f, 0.0333646460f, + 0.0819012400f, 0.0255353670f, 0.0508931650f, 0.0485142540f, 0.0694581300f, + -0.0789075640f, -0.0670761600f, -0.1184450800f, -0.0998668800f, -0.0750940300f, + 0.0626322600f, 0.1492558700f, 0.2018843600f, 0.1209845100f, 0.1463941500f, + 0.0015017595f, -0.0142673820f, -0.0341725700f, 0.0127114680f, 0.0028300495f, + -0.0247584820f, -0.0509854800f, -0.0821182000f, 0.0142256720f, 0.0215441580f, + 0.0894972500f, 0.0750526800f, -0.0020780868f, 0.0490825800f, 0.0647629500f, + -0.0229070630f, 0.0275624560f, 0.0401857350f, 0.0195675770f, -0.0155987390f, + -0.0490973030f, -0.0171218660f, -0.0833682340f, -0.0233200200f, -0.084095600f + }; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToInputWeightsValue + { + -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, // 00 + -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, + -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, + -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, + 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, // 01 + 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, + -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, + 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, + -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, // 02 + 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, + -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, + 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, + -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, // 03 + 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, + -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, + -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, + -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, // 04 + -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, + -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, + 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, + 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, // 05 + 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, + 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, + 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, + -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, // 06 + -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, + 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, + -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, + -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, // 07 + -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, + -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, + -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, + -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, // 08 + 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, + -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, + 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, + -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, // 09 + -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, + 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, + -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, + -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, // 10 + 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, + -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, + 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, + 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, // 11 + 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, + 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, + 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, + 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, // 12 + -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, + -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, + -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, + -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, // 13 + -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, + 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, + 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, + -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f, // 14 + 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f, + 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f, + 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f, + -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f, // 15 + -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f, + 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f, + -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f, + -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f, // 16 + 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f, + 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f, + -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f, + -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f, // 17 + 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f, + 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f, + 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f, + 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f, // 18 + -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f, + -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f, + -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f, + -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f, // 19 + 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f, + -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f, + -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f + }; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToForgetWeightsValue + { + -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, // 00 + 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, + -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, + 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, + 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, // 01 + -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, + -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, + 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f, + -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f, // 02 + -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f, + 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f, + -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f, + -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f, // 03 + -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f, + 0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f, + 0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f, + -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f, // 04 + 0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f, + 0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f, + -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f, + -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f, // 05 + 0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f, + -0.027794661f, -0.08077226f, -0.016179763f, 0.1169753f, + 0.17213494f, -0.0056326236f, -0.053934924f, -0.0124349f, + -0.11520337f, 0.05409887f, 0.088759385f, 0.0019655675f, // 06 + 0.0042065294f, 0.03881498f, 0.019844765f, 0.041858196f, + -0.05695512f, 0.047233116f, 0.038937137f, -0.06542224f, + 0.014429736f, -0.09719407f, 0.13908425f, -0.05379757f, + 0.012321099f, 0.082840554f, -0.029899208f, 0.044217527f, // 07 + 0.059855383f, 0.07711018f, -0.045319796f, 0.0948846f, + -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f, + -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f, + -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f, // 08 + 0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f, + 0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f, + 0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f, + 0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f, // 09 + -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f, + 0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f, + 0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f, + -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f, // 10 + -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f, + -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f, + 0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f, + 0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f, // 11 + 0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f, + 0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f, + -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f, + -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f, // 12 + 0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f, + -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f, + -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f, + -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f, // 13 + -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f, + 0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f, + -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f, + -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f, // 14 + 0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f, + -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f, + 0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f, + 0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f, // 15 + 0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f, + 0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f, + 0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f, + 0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f, // 16 + -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f, + 0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f, + -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f, + -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f, // 17 + 0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f, + -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f, + -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f, + 0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f, // 18 + 0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f, + 0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f, + 0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f, + -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f, // 19 + -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f, + 0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f, + -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f + }; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToCellWeightsValue + { + -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, + 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, + 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, + -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, + 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, + 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, + -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, + -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f, + -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f, + -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f, + 0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f, + -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f, + -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f, + 0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f, + 0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f, + 0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f, + -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f, + 0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f, + 0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f, + -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f, + 0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f, + 0.015983658f, 0.03406988f, -0.06939408f, 0.040699873f, + 0.02111075f, 0.09669095f, 0.041345075f, -0.08316494f, + -0.07684199f, -0.045768797f, 0.032298047f, -0.041805092f, + 0.0119405f, 0.0061010392f, 0.12652606f, 0.0064572375f, + -0.024950314f, 0.11574242f, 0.04508852f, -0.04335324f, + 0.06760663f, -0.027437469f, 0.07216407f, 0.06977076f, + -0.05438599f, 0.034033038f, -0.028602652f, 0.05346137f, + 0.043184172f, -0.037189785f, 0.10420091f, 0.00882477f, + -0.054019816f, -0.074273005f, -0.030617684f, -0.0028467078f, + 0.024302477f, -0.0038869337f, 0.005332455f, 0.0013399826f, + 0.04361412f, -0.007001822f, 0.09631092f, -0.06702025f, + -0.042049985f, -0.035070654f, -0.04103342f, -0.10273396f, + 0.0544271f, 0.037184782f, -0.13150354f, -0.0058036847f, + -0.008264958f, 0.042035464f, 0.05891794f, 0.029673764f, + 0.0063542654f, 0.044788733f, 0.054816857f, 0.062257513f, + -0.00093483756f, 0.048938446f, -0.004952862f, -0.007730018f, + -0.04043371f, -0.017094059f, 0.07229206f, -0.023670016f, + -0.052195564f, -0.025616996f, -0.01520939f, 0.045104615f, + -0.007376126f, 0.003533447f, 0.006570588f, 0.056037236f, + 0.12436656f, 0.051817212f, 0.028532185f, -0.08686856f, + 0.11868599f, 0.07663395f, -0.07323171f, 0.03463402f, + -0.050708205f, -0.04458982f, -0.11590894f, 0.021273347f, + 0.1251325f, -0.15313013f, -0.12224372f, 0.17228661f, + 0.023029093f, 0.086124025f, 0.006445803f, -0.03496501f, + 0.028332196f, 0.04449512f, -0.042436164f, -0.026587414f, + -0.006041347f, -0.09292539f, -0.05678812f, 0.03897832f, + 0.09465633f, 0.008115513f, -0.02171956f, 0.08304309f, + 0.071401566f, 0.019622514f, 0.032163795f, -0.004167056f, + 0.02295182f, 0.030739572f, 0.056506045f, 0.004612461f, + 0.06524936f, 0.059999723f, 0.046395954f, -0.0045512207f, + -0.1335546f, -0.030136576f, 0.11584653f, -0.014678886f, + 0.0020118146f, -0.09688814f, -0.0790206f, 0.039770417f, + -0.0329582f, 0.07922767f, 0.029322514f, 0.026405897f, + 0.04207835f, -0.07073373f, 0.063781224f, 0.0859677f, + -0.10925287f, -0.07011058f, 0.048005477f, 0.03438226f, + -0.09606514f, -0.006669445f, -0.043381985f, 0.04240257f, + -0.06955775f, -0.06769346f, 0.043903265f, -0.026784198f, + -0.017840602f, 0.024307009f, -0.040079936f, -0.019946516f, + 0.045318738f, -0.12233574f, 0.026170589f, 0.0074471775f, + 0.15978073f, 0.10185836f, 0.10298046f, -0.015476589f, + -0.039390966f, -0.072174534f, 0.0739445f, -0.1211869f, + -0.0347889f, -0.07943156f, 0.014809798f, -0.12412325f, + -0.0030663363f, 0.039695457f, 0.0647603f, -0.08291318f, + -0.018529687f, -0.004423833f, 0.0037507233f, 0.084633216f, + -0.01514876f, -0.056505352f, -0.012800942f, -0.06994386f, + 0.012962922f, -0.031234352f, 0.07029052f, 0.016418684f, + 0.03618972f, 0.055686004f, -0.08663945f, -0.017404709f, + -0.054761406f, 0.029065743f, 0.052404847f, 0.020238016f, + 0.0048197987f, -0.0214882f, 0.07078733f, 0.013016777f, + 0.06262858f, 0.009184685f, 0.020785125f, -0.043904778f, + -0.0270329f, -0.03299152f, -0.060088247f, -0.015162964f, + -0.001828936f, 0.12642565f, -0.056757294f, 0.013586685f, + 0.09232601f, -0.035886683f, 0.06000002f, 0.05229691f, + -0.052580316f, -0.082029596f, -0.010794592f, 0.012947712f, + -0.036429964f, -0.085508935f, -0.13127148f, -0.017744139f, + 0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f, + -0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f, + -0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f, + -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f + }; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToOutputWeightsValue + { + 0.025825322f, -0.05813119f, 0.09495884f, -0.045984812f, + -0.01255415f, -0.0026479573f, -0.08196161f, -0.054914974f, + -0.0046604523f, -0.029587349f, -0.044576716f, -0.07480124f, + -0.082868785f, 0.023254942f, 0.027502948f, -0.0039728214f, + -0.08683098f, -0.08116779f, -0.014675607f, -0.037924774f, + -0.023314456f, -0.007401714f, -0.09255757f, 0.029460307f, + -0.08829125f, -0.005139627f, -0.08989442f, -0.0555066f, + 0.13596267f, -0.025062224f, -0.048351806f, -0.03850004f, + 0.07266485f, -0.022414139f, 0.05940088f, 0.075114764f, + 0.09597592f, -0.010211725f, -0.0049794707f, -0.011523867f, + -0.025980417f, 0.072999895f, 0.11091378f, -0.081685916f, + 0.014416728f, 0.043229222f, 0.034178585f, -0.07530371f, + 0.035837382f, -0.085607f, -0.007721233f, -0.03287832f, + -0.043848954f, -0.06404588f, -0.06632928f, -0.073643476f, + 0.008214239f, -0.045984086f, 0.039764922f, 0.03474462f, + 0.060612556f, -0.080590084f, 0.049127717f, 0.04151091f, + -0.030063879f, 0.008801774f, -0.023021035f, -0.019558564f, + 0.05158114f, -0.010947698f, -0.011825728f, 0.0075720972f, + 0.0699727f, -0.0039981045f, 0.069350146f, 0.08799282f, + 0.016156472f, 0.035502106f, 0.11695009f, 0.006217345f, + 0.13392477f, -0.037875112f, 0.025745004f, 0.08940699f, + -0.00924166f, 0.0046702605f, -0.036598757f, -0.08811812f, + 0.10522024f, -0.032441203f, 0.008176899f, -0.04454919f, + 0.07058152f, 0.0067963637f, 0.039206743f, 0.03259838f, + 0.03725492f, -0.09515802f, 0.013326398f, -0.052055415f, + -0.025676316f, 0.03198509f, -0.015951829f, -0.058556724f, + 0.036879618f, 0.043357447f, 0.028362012f, -0.05908629f, + 0.0059240665f, -0.04995891f, -0.019187413f, 0.0276265f, + -0.01628143f, 0.0025863599f, 0.08800015f, 0.035250366f, + -0.022165963f, -0.07328642f, -0.009415526f, -0.07455109f, + 0.11690406f, 0.0363299f, 0.07411125f, 0.042103454f, + -0.009660886f, 0.019076364f, 0.018299393f, -0.046004917f, + 0.08891175f, 0.0431396f, -0.026327137f, -0.051502608f, + 0.08979574f, -0.051670972f, 0.04940282f, -0.07491107f, + -0.021240504f, 0.022596184f, -0.034280192f, 0.060163025f, + -0.058211457f, -0.051837247f, -0.01349775f, -0.04639988f, + -0.035936575f, -0.011681591f, 0.064818054f, 0.0073146066f, + -0.021745546f, -0.043124277f, -0.06471268f, -0.07053354f, + -0.029321948f, -0.05330136f, 0.016933719f, -0.053782392f, + 0.13747959f, -0.1361751f, -0.11569455f, 0.0033329215f, + 0.05693899f, -0.053219706f, 0.063698f, 0.07977434f, + -0.07924483f, 0.06936997f, 0.0034815092f, -0.007305279f, + -0.037325785f, -0.07251102f, -0.033633437f, -0.08677009f, + 0.091591336f, -0.14165086f, 0.021752775f, 0.019683983f, + 0.0011612234f, -0.058154266f, 0.049996935f, 0.0288841f, + -0.0024567875f, -0.14345716f, 0.010955264f, -0.10234828f, + 0.1183656f, -0.0010731248f, -0.023590032f, -0.072285876f, + -0.0724771f, -0.026382286f, -0.0014920527f, 0.042667855f, + 0.0018776858f, 0.02986552f, 0.009814309f, 0.0733756f, + 0.12289186f, 0.018043943f, -0.0458958f, 0.049412545f, + 0.033632483f, 0.05495232f, 0.036686596f, -0.013781798f, + -0.010036754f, 0.02576849f, -0.08307328f, 0.010112348f, + 0.042521734f, -0.05869831f, -0.071689695f, 0.03876447f, + -0.13275425f, -0.0352966f, -0.023077697f, 0.10285965f, + 0.084736146f, 0.15568255f, -0.00040734606f, 0.027835453f, + -0.10292561f, -0.032401145f, 0.10053256f, -0.026142767f, + -0.08271222f, -0.0030240538f, -0.016368777f, 0.1070414f, + 0.042672627f, 0.013456989f, -0.0437609f, -0.022309763f, + 0.11576483f, 0.04108048f, 0.061026827f, -0.0190714f, + -0.0869359f, 0.037901703f, 0.0610107f, 0.07202949f, + 0.01675338f, 0.086139716f, -0.08795751f, -0.014898893f, + -0.023771819f, -0.01965048f, 0.007955471f, -0.043740474f, + 0.03346837f, -0.10549954f, 0.090567775f, 0.042013682f, + -0.03176985f, 0.12569028f, -0.02421228f, -0.029526481f, + 0.023851605f, 0.031539805f, 0.05292009f, -0.02344001f, + -0.07811758f, -0.08834428f, 0.10094801f, 0.16594367f, + -0.06861939f, -0.021256343f, -0.041093912f, -0.06669611f, + 0.035498552f, 0.021757556f, -0.09302526f, -0.015403468f, + -0.06614931f, -0.051798206f, -0.013874718f, 0.03630673f, + 0.010412845f, -0.08077351f, 0.046185967f, 0.0035662893f, + 0.03541868f, -0.094149634f, -0.034814864f, 0.003128424f, + -0.020674974f, -0.03944324f, -0.008110165f, -0.11113267f, + 0.08484226f, 0.043586485f, 0.040582247f, 0.0968012f, + -0.065249965f, -0.028036479f, 0.0050708856f, 0.0017462453f, + 0.0326779f, 0.041296225f, 0.09164146f, -0.047743853f, + -0.015952192f, -0.034451712f, 0.084197424f, -0.05347844f, + -0.11768019f, 0.085926116f, -0.08251791f, -0.045081906f, + 0.0948852f, 0.068401024f, 0.024856757f, 0.06978981f, + -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, + -0.041040014f, -0.024264973f, 0.063464895f, 0.05431621f + }; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions{numUnits}; + std::vector cellToInputWeightsValue + { + 0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, + -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, + -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f, -0.052169047f, + 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f + }; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions{numUnits}; + std::vector cellToForgetWeightsValue + { + -0.01998659f, -0.15568835f, -0.24248174f, -0.012770197f, 0.041331276f, + -0.072311886f, -0.052123554f, -0.0066330447f, -0.043891653f, 0.036225766f, + -0.047248036f, 0.021479502f, 0.033189066f, 0.11952997f, -0.020432774f, + 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f + }; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions{numUnits}; + std::vector cellToOutputWeightsValue + { + 0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, + -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, + -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, + 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f + }; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions{numUnits}; + std::vector inputGateBiasValue + { + 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, + -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, + -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, + 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f + }; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions{numUnits}; + std::vector forgetGateBiasValue + { + 0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, + 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, + 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, + 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f + }; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions{numUnits}; + std::vector cellBiasValue + { + -0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, + -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, + -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, + 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f + }; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions{numUnits}; + std::vector outputGateBiasValue + { + 0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, + 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, + 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, + -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f + }; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions{outputSize, numUnits}; + std::vector projectionWeightsValue + { + -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, + 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, + -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, + -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, + 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, + 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, + 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, + 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, + -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, + -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, + -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, + 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, + 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, + 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, + 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, + 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, + -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, + 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, + -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, + 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, + -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, + -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, + 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, + -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, + 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, + -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, + -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, + 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, + -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, + -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, + -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, + 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, + 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, + -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, + 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, + 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, + 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, + 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, + 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, + -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, + -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, + 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, + -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, + -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, + 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, + 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, + 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, + -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, + -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, + -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, + 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, + -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, + 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, + 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, + -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, + -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, + -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, + 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, + -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, + -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, + -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, + 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, + 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, + 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f + }; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions{outputSize}; + std::vector projectionBiasValue(outputSize, 0.0f); + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions{batchSize, outputSize}; + std::vector outputStateInValue(batchSize * outputSize, 0.0f); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions{batchSize, numUnits}; + std::vector cellStateInValue(batchSize * numUnits, 0.0f); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions{}; + std::vector activationFunctionValue{4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions{}; + std::vector cellClippingThresholdValue{0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions{}; + std::vector projectionClippingThresholdValue{0.0f}; + + // Normalization: + // 23:The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + hidl_vec inputLayerNormWeightsDimensions{0}; + std::vector inputLayerNormWeightsValue; + // 24:The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + hidl_vec forgetLayerNormWeightsDimensions{0}; + std::vector forgetLayerNormWeightsValue; + // 25:The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + hidl_vec cellLayerNormWeightsDimensions{0}; + std::vector cellLayerNormWeightsValue; + // 26:The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + hidl_vec outputLayerNormWeightsDimensions{0}; + std::vector outputLayerNormWeightsValue; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits + // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 + // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 + // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 + hidl_vec scratchBufferDimensions{batchSize, numUnits * 4}; + std::vector scratchBufferValue(batchSize * numUnits * 4, 0.0f); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions{batchSize, outputSize}; + std::vector outputStateOutValue + { + -0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, + 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, + -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, + 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f + }; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions{batchSize, numUnits}; + std::vector cellStateOutValue + { + -0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, + -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, + -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, + 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, + -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, + -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, + 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, + -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f + }; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions{batchSize, outputSize}; + std::vector outputValue + { + -0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, + 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, + -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, + 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f + }; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, + forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, + cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, + outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue, + compute); +} + +template +void LstmCifgPeepholeNoProjectionBatch2(armnn::Compute compute) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm2.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm2.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + // The batch size has been increased to 2 (it was 1 in the VTS test) with appropriate input and output values added. + + uint32_t batchSize = 2; + uint32_t inputSize = 2; + uint32_t numUnits = 4; + uint32_t outputSize = numUnits; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions{batchSize, inputSize}; + std::vector inputValue{2.0f, 3.0f, 3.0f, 4.0f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions{0}; + std::vector inputToInputWeightsValue; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; + std::vector inputToForgetWeightsValue{-0.55291498f, -0.42866567f, + 0.13056988f, -0.36333650f, + -0.22755712f, 0.28253698f, + 0.24407166f, 0.33826375f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; + std::vector inputToCellWeightsValue{-0.49770179f, -0.27711356f, + -0.09624726f, 0.05100781f, + 0.04717243f, 0.48944736f, + -0.38535351f, -0.17212132f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; + std::vector inputToOutputWeightsValue{ 0.10725588f, -0.02335852f, + -0.55932593f, -0.09426838f, + -0.44257352f, 0.54939759f, + 0.01533556f, 0.42751634f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions{0}; // VTS was {4, 4} -> {0} ? + std::vector recurrentToInputWeightsValue; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToForgetWeightsValue{-0.13832897f, -0.05151010f, -0.23590070f, -0.16661474f, + -0.14340827f, 0.36986142f, 0.23414481f, 0.55899000f, + 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, + -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToCellWeightsValue{ 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, + 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, + -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, + -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToOutputWeightsValue{0.41613156f, 0.42610586f, -0.16495961f, -0.56638730f, + 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, + 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, + 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions{0}; + std::vector cellToInputWeightsValue; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions{numUnits}; + std::vector cellToForgetWeightsValue{0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions{numUnits}; + std::vector cellToOutputWeightsValue{-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions{0}; // VTS was {4} -> {0} ? + std::vector inputGateBiasValue; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions{numUnits}; + std::vector forgetGateBiasValue{1.0f, 1.0f, 1.0f, 1.0f}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions{numUnits}; + std::vector cellBiasValue(numUnits, 0.0f); + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions{numUnits}; + std::vector outputGateBiasValue(numUnits, 0.0f); + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions{0}; + std::vector projectionWeightsValue; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions{0}; + std::vector projectionBiasValue; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions{batchSize, outputSize}; + std::vector outputStateInValue(batchSize * outputSize, 0.0f); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions{batchSize, numUnits}; + std::vector cellStateInValue(batchSize * numUnits, 0.0f); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions{}; + std::vector activationFunctionValue{4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions{}; + std::vector cellClippingThresholdValue{0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions{}; + std::vector projectionClippingThresholdValue{0.0f}; + + // Normalization: + // 23:The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + hidl_vec inputLayerNormWeightsDimensions{0}; + std::vector inputLayerNormWeightsValue; + // 24:The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + hidl_vec forgetLayerNormWeightsDimensions{0}; + std::vector forgetLayerNormWeightsValue; + // 25:The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + hidl_vec cellLayerNormWeightsDimensions{0}; + std::vector cellLayerNormWeightsValue; + // 26:The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + hidl_vec outputLayerNormWeightsDimensions{0}; + std::vector outputLayerNormWeightsValue; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits + // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 + // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 + // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 + hidl_vec scratchBufferDimensions{batchSize, numUnits * 3}; + std::vector scratchBufferValue(batchSize * numUnits * 3, 0.0f); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions{batchSize, outputSize}; + std::vector outputStateOutValue{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions{batchSize, numUnits}; + std::vector cellStateOutValue{-0.76044439f, -0.01804161f, 0.18226376f, -0.06493707f, + -0.90477051f, -0.04355603f, 0.18475688f, -0.04158677f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions{batchSize, outputSize}; + std::vector outputValue{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, + forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, + cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, + outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue, + compute); +} + +template +void LstmNoCifgPeepholeProjectionNoClippingLayerNorm(armnn::Compute compute) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/layer_norm_lstm.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/layer_norm_lstm.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + uint32_t batchSize = 2; + uint32_t inputSize = 5; + uint32_t numUnits = 4; + uint32_t outputSize = 3; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions{batchSize, inputSize}; + std::vector inputValue{ 0.7f, 0.8f, 0.1f, 0.2f, 0.3f, // batch 0 + 0.3f, 0.2f, 0.9f, 0.8f, 0.1f}; // batch 1 + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions{numUnits, inputSize}; + std::vector inputToInputWeightsValue{ 0.5, 0.6, 0.7, -0.8, -0.9, + 0.1, 0.2, 0.3, -0.4, 0.5, + -0.8, 0.7, -0.6, 0.5, -0.4, + -0.5, -0.4, -0.3, -0.2, -0.1}; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; + std::vector inputToForgetWeightsValue{-0.6, -0.1, 0.3, 0.2, 0.9, + -0.5, -0.2, -0.4, 0.3, -0.8, + -0.4, 0.3, -0.5, -0.4, -0.6, + 0.3, -0.4, -0.6, -0.5, -0.5}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; + std::vector inputToCellWeightsValue{-0.4, -0.3, -0.2, -0.1, -0.5, + 0.5, -0.2, -0.3, -0.2, -0.6, + 0.6, -0.1, -0.4, -0.3, -0.7, + 0.7, -0.9, -0.5, 0.8, 0.6}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; + std::vector inputToOutputWeightsValue{-0.8, -0.4, -0.2, -0.9, -0.1, + -0.7, 0.3, -0.3, -0.8, -0.2, + 0.6, -0.2, 0.4, -0.7, -0.3, + -0.5, 0.1, 0.5, -0.6, -0.4}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToInputWeightsValue{-0.2, -0.3, 0.4, + 0.1, -0.5, 0.9, + -0.2, -0.3, -0.7, + 0.05, -0.2, -0.6}; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToForgetWeightsValue{-0.5, -0.3, -0.5, + -0.2, 0.6, 0.4, + 0.9, 0.3, -0.1, + 0.2, 0.5, 0.2}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToCellWeightsValue{-0.3, 0.2, 0.1, + -0.3, 0.8,-0.08, + -0.2, 0.3, 0.8, + -0.6, -0.1, 0.2}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToOutputWeightsValue{ 0.3, -0.1, 0.1, + -0.2, -0.5, -0.7, + -0.2, -0.6, -0.1, + -0.4, -0.7, -0.2}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions{numUnits}; + std::vector cellToInputWeightsValue{0.05, 0.1, 0.25, 0.15}; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions{numUnits}; + std::vector cellToForgetWeightsValue{-0.02, -0.15, -0.25, -0.03}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions{numUnits}; + std::vector cellToOutputWeightsValue{0.1, -0.1, -0.5, 0.05}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions{numUnits}; + std::vector inputGateBiasValue{0.03, 0.15, 0.22, 0.38}; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions{numUnits}; + std::vector forgetGateBiasValue{0.1, -0.3, -0.2, 0.1}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions{numUnits}; + std::vector cellBiasValue{-0.05, 0.72, 0.25, 0.08}; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions{numUnits}; + std::vector outputGateBiasValue{0.05, -0.01, 0.2, 0.1}; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions{numUnits, outputSize}; + std::vector projectionWeightsValue{-0.1, 0.2, 0.01, + -0.2, 0.1, 0.5, + 0.3, 0.08, 0.07, + 0.2, -0.4, 0.2}; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions{outputSize}; + std::vector projectionBiasValue(outputSize, 0.0f); + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions{batchSize, outputSize}; + std::vector outputStateInValue(batchSize * outputSize, 0.0f); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions{batchSize, numUnits}; + std::vector cellStateInValue(batchSize * numUnits, 0.0f); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions{}; + std::vector activationFunctionValue{4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions{}; + std::vector cellClippingThresholdValue{0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions{}; + std::vector projectionClippingThresholdValue{0.0f}; + + // Normalization: + // 23: The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + hidl_vec inputLayerNormWeightsDimensions{numUnits}; + std::vector inputLayerNormWeightsValue{0.1, 0.2, 0.3, 0.5}; + // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + hidl_vec forgetLayerNormWeightsDimensions{numUnits}; + std::vector forgetLayerNormWeightsValue{0.2, 0.2, 0.4, 0.3}; + // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + hidl_vec cellLayerNormWeightsDimensions{numUnits}; + std::vector cellLayerNormWeightsValue{0.7, 0.2, 0.3, 0.8}; + // 26: The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + hidl_vec outputLayerNormWeightsDimensions{numUnits}; + std::vector outputLayerNormWeightsValue{0.6, 0.2, 0.2, 0.5}; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits + // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 + // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 + // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 + hidl_vec scratchBufferDimensions{batchSize, numUnits * 4}; + std::vector scratchBufferValue(batchSize * numUnits * 4, 0.0f); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions{batchSize, outputSize}; + std::vector outputStateOutValue { 0.02440767f, 0.12802738f, -0.00170918f, + -0.00692428f, 0.08487406f, 0.06344498f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions{batchSize, numUnits}; + std::vector cellStateOutValue {-0.45177122f, 0.37691566f, 0.22542511f, 0.23240635f, + -0.25258583f, 0.33042118f, 0.01730525f, 0.36660123f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions{batchSize, outputSize}; + std::vector outputValue{ 0.02440767f, 0.12802738f, -0.00170918f, + -0.00692428f, 0.08487406f, 0.06344498f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, + forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, + cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, + outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue, + compute); +} + +template +void LstmCifgPeepholeProjectionNoClippingLayerNorm(armnn::Compute compute) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/layer_norm_lstm.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/layer_norm_lstm.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + uint32_t batchSize = 2; + uint32_t inputSize = 5; + uint32_t numUnits = 4; + uint32_t outputSize = 3; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions{batchSize, inputSize}; + std::vector inputValue{ 0.7f, 0.8f, 0.1f, 0.2f, 0.3f, // batch 0 + 0.3f, 0.2f, 0.9f, 0.8f, 0.1f}; // batch 1 + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions{0}; + std::vector inputToInputWeightsValue; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions{numUnits, inputSize}; + std::vector inputToForgetWeightsValue{-0.6, -0.1, 0.3, 0.2, 0.9, + -0.5, -0.2, -0.4, 0.3, -0.8, + -0.4, 0.3, -0.5, -0.4, -0.6, + 0.3, -0.4, -0.6, -0.5, -0.5}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions{numUnits, inputSize}; + std::vector inputToCellWeightsValue{-0.4, -0.3, -0.2, -0.1, -0.5, + 0.5, -0.2, -0.3, -0.2, -0.6, + 0.6, -0.1, -0.4, -0.3, -0.7, + 0.7, -0.9, -0.5, 0.8, 0.6}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions{numUnits, inputSize}; + std::vector inputToOutputWeightsValue{-0.8, -0.4, -0.2, -0.9, -0.1, + -0.7, 0.3, -0.3, -0.8, -0.2, + 0.6, -0.2, 0.4, -0.7, -0.3, + -0.5, 0.1, 0.5, -0.6, -0.4}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions{0}; + std::vector recurrentToInputWeightsValue; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToForgetWeightsValue{-0.5, -0.3, -0.5, + -0.2, 0.6, 0.4, + 0.9, 0.3, -0.1, + 0.2, 0.5, 0.2}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToCellWeightsValue{-0.3, 0.2, 0.1, + -0.3, 0.8,-0.08, + -0.2, 0.3, 0.8, + -0.6, -0.1, 0.2}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions{numUnits, outputSize}; + std::vector recurrentToOutputWeightsValue{ 0.3, -0.1, 0.1, + -0.2, -0.5, -0.7, + -0.2, -0.6, -0.1, + -0.4, -0.7, -0.2}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions{0}; + std::vector cellToInputWeightsValue; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions{numUnits}; + std::vector cellToForgetWeightsValue{-0.02, -0.15, -0.25, -0.03}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions{numUnits}; + std::vector cellToOutputWeightsValue{0.1, -0.1, -0.5, 0.05}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions{0}; + std::vector inputGateBiasValue; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions{numUnits}; + std::vector forgetGateBiasValue{0.1, -0.3, -0.2, 0.1}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions{numUnits}; + std::vector cellBiasValue{-0.05, 0.72, 0.25, 0.08}; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions{numUnits}; + std::vector outputGateBiasValue{0.05, -0.01, 0.2, 0.1}; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions{numUnits, outputSize}; + std::vector projectionWeightsValue{-0.1, 0.2, 0.01, + -0.2, 0.1, 0.5, + 0.3, 0.08, 0.07, + 0.2, -0.4, 0.2}; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions{outputSize}; + std::vector projectionBiasValue(outputSize, 0.0f); + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions{batchSize, outputSize}; + std::vector outputStateInValue(batchSize * outputSize, 0.0f); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions{batchSize, numUnits}; + std::vector cellStateInValue(batchSize * numUnits, 0.0f); + + // Constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions{}; + std::vector activationFunctionValue{4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions{}; + std::vector cellClippingThresholdValue{0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions{}; + std::vector projectionClippingThresholdValue{0.0f}; + + // Normalization: + // 23: The input layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at input gate. + hidl_vec inputLayerNormWeightsDimensions{numUnits}; + std::vector inputLayerNormWeightsValue{0.1, 0.2, 0.3, 0.5}; + // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at forget gate. + hidl_vec forgetLayerNormWeightsDimensions{numUnits}; + std::vector forgetLayerNormWeightsValue{0.2, 0.2, 0.4, 0.3}; + // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at cell gate. + hidl_vec cellLayerNormWeightsDimensions{numUnits}; + std::vector cellLayerNormWeightsValue{0.7, 0.2, 0.3, 0.8}; + // 26: The output layer normalization weights. A 1-D tensor of shape [num_units]. + // Used to rescale normalized inputs to activation at output gate. + hidl_vec outputLayerNormWeightsDimensions{numUnits}; + std::vector outputLayerNormWeightsValue{0.6, 0.2, 0.2, 0.5}; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + // HOWEVER, by looking at the code, seems that it's the opposite: (cifg ? 3 : 4) * numUnits + // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp:319 + // android/frameworks/ml/nn/common/operations/LSTMTest.cpp:114 + // tensorflow/tensorflow/contrib/lite/kernels/lstm.cc:332 + hidl_vec scratchBufferDimensions{batchSize, numUnits * 3}; + std::vector scratchBufferValue(batchSize * numUnits * 3, 0.0f); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions{batchSize, outputSize}; + std::vector outputStateOutValue { 0.02129706f, 0.14081624f, 0.01127331f, + -0.02263505f, 0.09169482f, 0.07691758f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions{batchSize, numUnits}; + std::vector cellStateOutValue{-0.35102980f, 0.42610350f, 0.21463650f, 0.27716520f, + -0.18855170f, 0.32522000f, 0.02036650f, 0.48967660f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions{batchSize, outputSize}; + std::vector outputValue{ 0.02129706f, 0.14081624f, 0.01127331f, + -0.02263505f, 0.09169482f, 0.07691758f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, + forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, + cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, + outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue, + compute); +} -- cgit v1.2.1