| // |
| // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "LstmTestHelper.hpp" |
| |
| #include <armnn_delegate.hpp> |
| |
| #include <flatbuffers/flatbuffers.h> |
| #include <tensorflow/lite/schema/schema_generated.h> |
| #include <doctest/doctest.h> |
| |
| namespace armnnDelegate |
| { |
| |
| void LstmTest(std::vector<armnn::BackendId>& backends) |
| { |
| int32_t batchSize = 2; |
| int32_t inputSize = 2; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| std::vector<int32_t> inputShape {batchSize , inputSize}; |
| std::vector<int32_t> cellStateInTensorInfo {batchSize , numUnits}; |
| std::vector<int32_t> outputStateInTensorInfo {batchSize , outputSize}; |
| |
| std::vector<int32_t> scratchBufferTensorInfo {batchSize, numUnits * 4}; |
| std::vector<int32_t> cellStateOutTensorInfo {batchSize, numUnits}; |
| std::vector<int32_t> outputStateOutTensorInfo {batchSize, outputSize}; |
| std::vector<int32_t> outputTensorInfo {batchSize, outputSize}; |
| |
| std::vector<int32_t> tensorInfo4 {numUnits}; |
| std::vector<int32_t> tensorInfo8 {numUnits, 2}; |
| std::vector<int32_t> tensorInfo16 {numUnits, 4}; |
| |
| //tensorInfo8, |
| bool hasInputToInputWeights = true; |
| std::vector<float> inputToInputWeights {-0.45018822f, -0.02338299f, -0.0870589f, |
| -0.34550029f, 0.04266912f, -0.15680569f, |
| -0.34856534f, 0.43890524f}; |
| |
| std::vector<float> inputToForgetWeights {0.09701663f, 0.20334584f, -0.50592935f, |
| -0.31343272f, -0.40032279f, 0.44781327f, |
| 0.01387155f, -0.35593212f}; |
| |
| std::vector<float> inputToCellWeights {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, |
| -0.20583314f, 0.44344562f, 0.22077113f, |
| -0.29909778f}; |
| |
| std::vector<float> inputToOutputWeights {-0.25065863f, -0.28290087f, 0.04613829f, |
| 0.40525138f, 0.44272184f, 0.03897077f, |
| -0.1556896f, 0.19487578f}; |
| |
| //tensorInfo16, |
| bool hasRecurrentToInputWeights = true; |
| std::vector<float> recurrentToInputWeights {-0.0063535f, -0.2042388f, 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}; |
| |
| std::vector<float> recurrentToForgetWeights {-0.48684245f, -0.06655136f, 0.42224967f, |
| 0.2112639f, 0.27654213f, 0.20864892f, |
| -0.07646349f, 0.45877004f, 0.00141793f, |
| -0.14609534f, 0.36447752f, 0.09196436f, |
| 0.28053468f, 0.01560611f, -0.20127171f, |
| -0.01140004f}; |
| |
| std::vector<float> recurrentToCellWeights {-0.3407414f, 0.24443203f, -0.2078532f, |
| 0.26320225f, 0.05695659f, -0.00123841f, |
| -0.4744786f, -0.35869038f, -0.06418842f, |
| -0.13502428f, -0.501764f, 0.22830659f, |
| -0.46367589f, 0.26016325f, -0.03894562f, |
| -0.16368064f}; |
| |
| std::vector<float> recurrentToOutputWeights {0.43385774f, -0.17194885f, 0.2718237f, |
| 0.09215671f, 0.24107647f, -0.39835793f, |
| 0.18212086f, 0.01301402f, 0.48572797f, |
| -0.50656658f, 0.20047462f, -0.20607421f, |
| -0.51818722f, -0.15390486f, 0.0468148f, |
| 0.39922136f}; |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<float> cellToInputWeights {}; |
| bool hasCellToForgetWeights = false; |
| std::vector<float> cellToForgetWeights {}; |
| bool hasCellToOutputWeights = false; |
| std::vector<float> cellToOutputWeights {}; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias {0., 0., 0., 0.}; |
| std::vector<float> forgetGateBias {1., 1., 1., 1.}; |
| std::vector<float> cellBias {0., 0., 0., 0.}; |
| std::vector<float> outputGateBias {0., 0., 0., 0.}; |
| |
| bool hasProjectionWeights = false; |
| std::vector<float> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues {2., 3., 3., 4.}; |
| std::vector<float> expectedOutputValues {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, |
| -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 0.f; |
| float clippingThresProj = 0.f; |
| |
| LstmTestImpl<float>(backends, |
| ::tflite::TensorType_FLOAT32, |
| batchSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj); |
| } |
| |
| TEST_SUITE("LstmTest_CpuRefTests") |
| { |
| |
| TEST_CASE ("LstmTest_CpuRef_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| LstmTest(backends); |
| } |
| |
| } //End of TEST_SUITE("Convolution2dTest_CpuRef") |
| |
| TEST_SUITE("LstmTest_CpuAccTests") |
| { |
| |
| TEST_CASE ("LstmTest_CpuAcc_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; |
| LstmTest(backends); |
| } |
| |
| } //End of TEST_SUITE("Convolution2dTest_CpuAcc") |
| |
| } // namespace armnnDelegate |