diff options
Diffstat (limited to 'src/backends/backendsCommon/test/LstmTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/LstmTestImpl.hpp | 386 |
1 files changed, 385 insertions, 1 deletions
diff --git a/src/backends/backendsCommon/test/LstmTestImpl.hpp b/src/backends/backendsCommon/test/LstmTestImpl.hpp index dae9c8a3f1..2ed0a974fc 100644 --- a/src/backends/backendsCommon/test/LstmTestImpl.hpp +++ b/src/backends/backendsCommon/test/LstmTestImpl.hpp @@ -16,6 +16,119 @@ #include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/WorkloadFactory.hpp> +#include "reference/workloads/LstmUtils.hpp" + +//LstmUtils Tests +// TODO: Add tests for the remaining functions in LstmUtils.hpp + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +void LstmUtilsVectorBatchVectorAddTestImpl( + boost::multi_array<float, 1>& vec, + boost::multi_array<float, 2>& batchVec, + uint32_t vSize, + uint32_t nBatch, + boost::multi_array<float, 2>& expectedOutput ) +{ + float qScale = 0.0f; + int32_t qOffset = 0; + armnn::TensorInfo tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset ); + + // Make encoder and decoder + std::unique_ptr<armnn::Decoder<float>> vecDecoder = armnn::MakeDecoder<float>(tensorInfo, vec.data()); + std::unique_ptr<armnn::Decoder<float>> batchVecDecoder = armnn::MakeDecoder<float>(tensorInfo, batchVec.data()); + std::unique_ptr<armnn::Encoder<float>> batchVecEncoder = armnn::MakeEncoder<float>(tensorInfo, batchVec.data()); + + VectorBatchVectorAdd(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder); + + // check shape and compare values + BOOST_TEST(CompareTensors(batchVec, expectedOutput)); + + // check if iterator is back at start position + batchVecEncoder->Set(1.0f); + BOOST_TEST(batchVec[0][0] == 1.0f); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +void LstmUtilsZeroVectorTestImpl( + boost::multi_array<float, 1>& input, + uint32_t vSize, + boost::multi_array<float, 1>& expectedOutput) { + + float qScale = 0.0f; + int32_t qOffset = 0; + + armnn::TensorInfo tensorInfo({vSize}, ArmnnType, qScale, qOffset ); + + // Make encoder for input + std::unique_ptr<armnn::Encoder<float>> outputEncoder = armnn::MakeEncoder<float>(tensorInfo, input.data()); + + // call ZeroVector + ZeroVector(*outputEncoder, vSize); + + // check shape and compare values + BOOST_TEST(CompareTensors(input, expectedOutput)); + + // check if iterator is back at start position + outputEncoder->Set(1.0f); + BOOST_TEST(input[0] == 1.0f); + +} + + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +void LstmUtilsMeanStddevNormalizationTestImpl( + boost::multi_array<float, 2>& input, + uint32_t vSize, + uint32_t nBatch, + boost::multi_array<float, 2>& expectedOutput) +{ + float qScale = 0.0f; + int32_t qOffset = 0; + armnn::TensorInfo tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset ); + + // Make encoder and decoder for input + std::unique_ptr<armnn::Decoder<float>> inputDecoder = armnn::MakeDecoder<float>(tensorInfo, input.data()); + std::unique_ptr<armnn::Encoder<float>> outputEncoder = armnn::MakeEncoder<float>(tensorInfo, input.data()); + + MeanStddevNormalization(*inputDecoder, *outputEncoder, vSize, nBatch, 1e-8f); + + // check shape and compare values + BOOST_TEST(CompareTensors(input, expectedOutput)); + + // check if iterator is back at start position + outputEncoder->Set(1.0f); + BOOST_TEST(input[0][0] == 1.0f); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +void LstmUtilsVectorBatchVectorCwiseProductTestImpl( + boost::multi_array<float, 1>& vec, + boost::multi_array<float, 2>& batchVec, + uint32_t vSize, + uint32_t nBatch, + boost::multi_array<float, 2>& expectedOutput) +{ + float qScale = 0.0f; + int32_t qOffset = 0; + armnn::TensorInfo tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset ); + + // Make encoder and decoder + std::unique_ptr<armnn::Decoder<float>> vecDecoder = armnn::MakeDecoder<float>(tensorInfo, vec.data()); + std::unique_ptr<armnn::Decoder<float>> batchVecDecoder = armnn::MakeDecoder<float>(tensorInfo, batchVec.data()); + std::unique_ptr<armnn::Encoder<float>> batchVecEncoder = armnn::MakeEncoder<float>(tensorInfo, batchVec.data()); + + VectorBatchVectorCwiseProduct(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder); + + // check shape and compare values + BOOST_TEST(CompareTensors(batchVec, expectedOutput)); + + // check if iterator is back at start position + batchVecEncoder->Set(1.0f); + BOOST_TEST(batchVec[0][0] == 1.0f); +} + +// Lstm Layer tests: + template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl( @@ -187,7 +300,6 @@ LstmNoCifgNoPeepholeNoProjectionTestImpl( data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; - data.m_CellToInputWeights = &cellToInputWeightsTensor; data.m_InputGateBias = &inputGateBiasTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_CellBias = &cellBiasTensor; @@ -1157,3 +1269,275 @@ LayerTestResult<T, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl( return ret3; } + + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 2> +LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl(armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const boost::multi_array<T, 2>& input, + const boost::multi_array<T, 2>& outputExpected, + float qScale = 0.0f, + int32_t qOffset = 0, + armnn::DataType constantDataType = armnn::DataType::Float32) +{ + unsigned int batchSize = 2; + unsigned int outputSize = 3; + unsigned int inputSize = 5; + unsigned numUnits = 4; + + armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); + armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset); + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset); + + // Scratch buffer size without CIFG [batchSize, numUnits * 4] + armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset); + armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); + armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); + armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); + + LayerTestResult<T, 2> ret(outputTensorInfo); + + std::vector<float> inputVector; + inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); + auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector); + + std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); + auto cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector); + + std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); + auto outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector); + + std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f); + auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector); + + std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f); + auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector); + + std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f); + auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector); + + std::vector<float> outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); + ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + workloadFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + workloadFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle = + workloadFactory.CreateTensorHandle(outputStateOutTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle = + workloadFactory.CreateTensorHandle(cellStateOutTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::LstmQueueDescriptor data; + armnn::WorkloadInfo info; + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); + AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); + + AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); + AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); + AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo3({outputSize}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo4x5({numUnits, inputSize}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo4x3({numUnits, outputSize}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo3x4({outputSize, numUnits}, constantDataType, qScale, qOffset); + + auto inputToInputWeights = + MakeTensor<float, 2>(tensorInfo4x5, { 0.5f, 0.6f, 0.7f, -0.8f, -0.9f, + 0.1f, 0.2f, 0.3f, -0.4f, 0.5f, + -0.8f, 0.7f, -0.6f, 0.5f, -0.4f, + -0.5f, -0.4f, -0.3f, -0.2f, -0.1f}); //{numUnits, inputSize} + + auto inputToForgetWeights = + MakeTensor<float, 2>(tensorInfo4x5, {-0.6f, -0.1f, 0.3f, 0.2f, 0.9f, + -0.5f, -0.2f, -0.4f, 0.3f, -0.8f, + -0.4f, 0.3f, -0.5f, -0.4f, -0.6f, + 0.3f, -0.4f, -0.6f, -0.5f, -0.5f}); //{numUnits, inputSize} + + auto inputToCellWeights = + MakeTensor<float, 2>(tensorInfo4x5, {-0.4f, -0.3f, -0.2f, -0.1f, -0.5f, + 0.5f, -0.2f, -0.3f, -0.2f, -0.6f, + 0.6f, -0.1f, -0.4f, -0.3f, -0.7f, + 0.7f, -0.9f, -0.5f, 0.8f, 0.6f}); //{numUnits, inputSize} + + auto inputToOutputWeights = + MakeTensor<float, 2>(tensorInfo4x5, {-0.8f, -0.4f, -0.2f, -0.9f, -0.1f, + -0.7f, 0.3f, -0.3f, -0.8f, -0.2f, + 0.6f, -0.2f, 0.4f, -0.7f, -0.3f, + -0.5f, 0.1f, 0.5f, -0.6f, -0.4f}); //{numUnits, inputSize} + + auto inputGateBias = + MakeTensor<float, 1>(tensorInfo4, {0.03f, 0.15f, 0.22f, 0.38f}); //{numUnits} + + auto forgetGateBias = + MakeTensor<float, 1>(tensorInfo4, {0.1f, -0.3f, -0.2f, 0.1f}); //{numUnits} + + auto cellBias = + MakeTensor<float, 1>(tensorInfo4, {-0.05f, 0.72f, 0.25f, 0.08f}); //{numUnits} + + auto outputGateBias = + MakeTensor<float, 1>(tensorInfo4, {0.05f, -0.01f, 0.2f, 0.1f}); //{numUnits} + + auto recurrentToInputWeights = + MakeTensor<float, 2>(tensorInfo4x3, {-0.2f, -0.3f, 0.4f, + 0.1f, -0.5f, 0.9f, + -0.2f, -0.3f, -0.7f, + 0.05f, -0.2f, -0.6f}); //{numUnits, outputSize} + + auto recurrentToCellWeights = + MakeTensor<float, 2>(tensorInfo4x3, {-0.3f, 0.2f, 0.1f, + -0.3f, 0.8f, -0.08f, + -0.2f, 0.3f, 0.8f, + -0.6f, -0.1f, 0.2f}); //{numUnits, outputSize} + + auto recurrentToForgetWeights = + MakeTensor<float, 2>(tensorInfo4x3, {-0.5f, -0.3f, -0.5f, + -0.2f, 0.6f, 0.4f, + 0.9f, 0.3f, -0.1f, + 0.2f, 0.5f, 0.2f}); //{numUnits, outputSize} + + auto recurrentToOutputWeights = + MakeTensor<float, 2>(tensorInfo4x3, { 0.3f, -0.1f, 0.1f, + -0.2f, -0.5f, -0.7f, + -0.2f, -0.6f, -0.1f, + -0.4f, -0.7f, -0.2f}); //{numUnits, outputSize} + + auto cellToInputWeights = + MakeTensor<float, 1>(tensorInfo4, {0.05f, 0.1f, 0.25f, 0.15f}); //{numUnits} + + auto cellToForgetWeights = + MakeTensor<float, 1>(tensorInfo4, {-0.02f, -0.15f, -0.25f, -0.03f}); //{numUnits} + + auto cellToOutputWeights = + MakeTensor<float, 1>(tensorInfo4, {0.1f, -0.1f, -0.5f, 0.05f}); //{numUnits} + + auto projectionWeights = + MakeTensor<float, 2>(tensorInfo3x4, + {-0.1f, 0.2f, 0.01f, -0.2f, + 0.1f, 0.5f, 0.3f, 0.08f, + 0.07f, 0.2f, -0.4f, 0.2f}); //{outputSize, numUnits} + + std::vector<float> projectionBiasVector(outputSize, 0.f); + auto projectionBias = MakeTensor<float,1>(tensorInfo3, projectionBiasVector); //{outputSize} + + auto inputLayerNormWeights = + MakeTensor<float, 1>(tensorInfo4, {0.1f, 0.2f, 0.3f, 0.5f}); //{numUnits} + + auto forgetLayerNormWeights = + MakeTensor<float, 1>(tensorInfo4, {0.2f, 0.2f, 0.4f, 0.3f}); //{numUnits} + + auto cellLayerNormWeights = + MakeTensor<float, 1>(tensorInfo4, {0.7f, 0.2f, 0.3f, 0.8f}); //{numUnits} + + auto outputLayerNormWeights = + MakeTensor<float, 1>(tensorInfo4, {0.6f, 0.2f, 0.2f, 0.5f}); //{numUnits} + + + armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo4x5); + armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo4x5); + armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo4x5); + armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo4x5); + armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo4x3); + armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo4x3); + armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo4x3); + armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo4x3); + armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle projectionWeightsTensor(tensorInfo3x4); + armnn::ScopedCpuTensorHandle projectionBiasTensor(tensorInfo3); + + armnn::ScopedCpuTensorHandle inputLayerNormWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle forgetLayerNormWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle cellLayerNormWeightsTensor(tensorInfo4); + armnn::ScopedCpuTensorHandle outputLayerNormWeightsTensor(tensorInfo4); + + AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]); + AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]); + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); + AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]); + AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]); + AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]); + + AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, &inputLayerNormWeights[0]); + AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]); + AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]); + AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]); + + data.m_InputToInputWeights = &inputToInputWeightsTensor; + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + data.m_CellToInputWeights = &cellToInputWeightsTensor; + data.m_InputGateBias = &inputGateBiasTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + data.m_CellToForgetWeights = &cellToForgetWeightsTensor; + data.m_CellToOutputWeights = &cellToOutputWeightsTensor; + data.m_ProjectionWeights = &projectionWeightsTensor; + data.m_ProjectionBias = &projectionBiasTensor; + + data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor; + data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; + data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; + data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; + + // Flags to set test configuration + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = false; + data.m_Parameters.m_PeepholeEnabled = true; + data.m_Parameters.m_ProjectionEnabled = true; + data.m_Parameters.m_LayerNormEnabled = true; + + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->Allocate(); + + scratchHandle->Allocate(); + outputStateOutHandle->Allocate(); + cellStateOutHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); + CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]); + CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]); + + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); + + return ret; + +}
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