diff options
Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp | 253 |
1 files changed, 253 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp index 50ef5c9758..0ae55e4c60 100644 --- a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp @@ -1733,6 +1733,243 @@ LayerTestResult<uint8_t, 2> QuantizedLstmTestImpl( return ret; } +// QLSTM +LayerTestResult<int8_t, 2> QLstmTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const boost::multi_array<int8_t, 2>& input, + const boost::multi_array<int8_t, 2>& outputExpected) +{ + IgnoreUnused(memoryManager); + unsigned int numBatches = 2; + unsigned int inputSize = 5; + unsigned int outputSize = 4; + unsigned int numUnits = 4; + + bool cifgEnabled = true; + bool peepholeEnabled = false; + bool projectionEnabled = false; + bool layerNormEnabled = true; + + // Scale/Offset quantization info + float inputScale = 0.0078125f; + int32_t inputOffset = 0; + + int32_t hiddenStateZeroPoint = 0; + float hiddenStateScale = 0.007f; + + // if (!projectionEnabled) outputScale == hiddenStateScale + float outputScale = hiddenStateScale; + int32_t outputOffset = hiddenStateZeroPoint; + + float cellStateScale = 3.05176e-05f; + int32_t cellStateOffset = 0; + + float weightsScale = 0.00784314f; + int32_t weightsOffset = 0; + + float layerNormScale = 3.05182e-05f; + int32_t layerNormOffset = 0; + + float biasScale = layerNormScale / 1024; + int32_t biasOffset = 0; + + float inputIntermediateScale = 0.007059f; + float forgetIntermediateScale = 0.007812f; + float cellIntermediateScale = inputIntermediateScale; + float outputIntermediateScale = forgetIntermediateScale; + + float cellClip = 0.0f; + float projectionClip = 0.0f; + + // Input/Output tensor info + armnn::TensorInfo inputInfo({numBatches , inputSize}, + armnn::DataType::QAsymmS8, + inputScale, + inputOffset); + + armnn::TensorInfo cellStateInfo({numBatches , numUnits}, + armnn::DataType::QSymmS16, + cellStateScale, + cellStateOffset); + + armnn::TensorInfo outputStateInfo({numBatches , outputSize}, + armnn::DataType::QAsymmS8, + outputScale, + outputOffset); + + LayerTestResult<int8_t, 2> ret(outputStateInfo); + + // Input tensors + std::vector<int8_t> inputVector; + inputVector.assign(input.data(), input.data() + (numBatches * inputSize)); + auto inputTensor = MakeTensor<int8_t, 2>(inputInfo, inputVector); + + std::vector<int16_t> cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; + auto cellStateInTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateInVector); + + std::vector<int8_t> outputStateInVector = {0, 0, 0, 0, 0, 0, 0, 02}; + auto outputStateInTensor = MakeTensor<int8_t, 2>(outputStateInfo, outputStateInVector); + + // Output tensors + std::vector<int16_t> cellStateOutVector = {-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149}; + auto cellStateOutTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateOutVector); + + std::vector<int8_t> outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize)); + ret.outputExpected = MakeTensor<int8_t, 2>(outputStateInfo, outputVector); + + // Create tensor handles + std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + workloadFactory.CreateTensorHandle(cellStateInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + workloadFactory.CreateTensorHandle(outputStateInfo); + + std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle = workloadFactory.CreateTensorHandle(outputStateInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle = + workloadFactory.CreateTensorHandle(cellStateInfo); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputStateInfo); + + armnn::QLstmQueueDescriptor data; + armnn::WorkloadInfo info; + + // Add inputs and outputs to workload + AddInputToWorkload(data, info, inputInfo, inputHandle.get()); + AddInputToWorkload(data, info, outputStateInfo, outputStateInHandle.get()); + AddInputToWorkload(data, info, cellStateInfo, cellStateInHandle.get()); + + AddOutputToWorkload(data, info, outputStateInfo, outputStateOutHandle.get()); + AddOutputToWorkload(data, info, cellStateInfo, cellStateOutHandle.get()); + AddOutputToWorkload(data, info, outputStateInfo, outputHandle.get()); + + // Weights and bias tensor and quantization info + armnn::TensorInfo inputWeightsInfo({outputSize, inputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset); + + armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset); + + // Weights and bias tensor data + auto inputToForgetWeights = MakeTensor<int8_t, 2>(inputWeightsInfo, + {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64}); + auto inputToCellWeights = MakeTensor<int8_t, 2>(inputWeightsInfo, + {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77}); + auto inputToOutputWeights = MakeTensor<int8_t, 2>(inputWeightsInfo, + {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51}); + + auto recurrentToForgetWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo, + {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51}); + auto recurrentToCellWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo, + {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64}); + auto recurrentToOutputWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo, + {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38}); + + auto forgetGateBias = MakeTensor<int32_t, 1>(biasInfo, {2147484, -6442451, -4294968, 2147484}); + auto cellBias = MakeTensor<int32_t, 1>(biasInfo, {-1073742, 15461883, 5368709, 1717987}); + auto outputGateBias = MakeTensor<int32_t, 1>(biasInfo, {1073742, -214748, 4294968, 2147484}); + + auto forgetLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {6553, 6553, 13107, 9830}); + auto cellLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {22937, 6553, 9830, 26214}); + auto outputLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {19660, 6553, 6553, 16384}); + + // ScopedCpuTensorHandles + armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(inputWeightsInfo); + armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(inputWeightsInfo); + armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(inputWeightsInfo); + + armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(recurrentWeightsInfo); + armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(recurrentWeightsInfo); + armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(recurrentWeightsInfo); + + armnn::ScopedCpuTensorHandle forgetGateBiasTensor(biasInfo); + armnn::ScopedCpuTensorHandle cellBiasTensor(biasInfo); + armnn::ScopedCpuTensorHandle outputGateBiasTensor(biasInfo); + + armnn::ScopedCpuTensorHandle forgetLayerNormWeightsTensor(layerNormWeightsInfo); + armnn::ScopedCpuTensorHandle cellLayerNormWeightsTensor(layerNormWeightsInfo); + armnn::ScopedCpuTensorHandle outputLayerNormWeightsTensor(layerNormWeightsInfo); + + // Allocate and copy data + AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]); + + AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]); + AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]); + + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]); + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]); + + AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]); + AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]); + AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]); + + // Setup queue descriptor + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + + data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; + data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; + data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; + + data.m_Parameters.m_CifgEnabled = cifgEnabled; + data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; + data.m_Parameters.m_ProjectionEnabled = projectionEnabled; + data.m_Parameters.m_LayerNormEnabled = layerNormEnabled; + + data.m_Parameters.m_InputIntermediateScale = inputIntermediateScale; + data.m_Parameters.m_ForgetIntermediateScale = forgetIntermediateScale; + data.m_Parameters.m_CellIntermediateScale = cellIntermediateScale; + data.m_Parameters.m_OutputIntermediateScale = outputIntermediateScale; + + data.m_Parameters.m_HiddenStateZeroPoint = hiddenStateZeroPoint; + data.m_Parameters.m_HiddenStateScale = hiddenStateScale; + + data.m_Parameters.m_CellClip = cellClip; + data.m_Parameters.m_ProjectionClip = projectionClip; + + // Create workload and allocate tensor handles + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateQLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->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; +} + + } // anonymous namespace #if defined(ARMNNREF_ENABLED) @@ -2107,3 +2344,19 @@ LayerTestResult<uint8_t, 2> QuantizedLstmTest( return QuantizedLstmTestImpl(workloadFactory, memoryManager, input, expectedOutput); } + +// QLSTM +LayerTestResult<int8_t, 2> QLstmTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8); + boost::multi_array<int8_t, 2> input = MakeTensor<int8_t, 2>(inputDesc, std::vector<int8_t>( + {90, 102, 13, 26, 38, 102, 13, 26, 51, 64})); + + armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmS8); + boost::multi_array<int8_t, 2> expectedOutput = MakeTensor<int8_t, 2>(outputDesc, std::vector<int8_t>( + {-15, 21, 14, 20, -15, 15, 5, 27})); + + return QLstmTestImpl(workloadFactory, memoryManager, input, expectedOutput); +} |