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-rw-r--r--src/backends/backendsCommon/test/LstmTestImpl.hpp114
1 files changed, 60 insertions, 54 deletions
diff --git a/src/backends/backendsCommon/test/LstmTestImpl.hpp b/src/backends/backendsCommon/test/LstmTestImpl.hpp
index e300a529ce..dae9c8a3f1 100644
--- a/src/backends/backendsCommon/test/LstmTestImpl.hpp
+++ b/src/backends/backendsCommon/test/LstmTestImpl.hpp
@@ -16,11 +16,16 @@
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
-LayerTestResult<float, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl(
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2>
+LstmNoCifgNoPeepholeNoProjectionTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const boost::multi_array<float, 2>& input,
- const boost::multi_array<float, 2>& outputExpected)
+ 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 = boost::numeric_cast<unsigned int>(input.shape()[0]);
unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
@@ -28,19 +33,16 @@ LayerTestResult<float, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl(
// cellSize and outputSize have the same size when there is no projection.
unsigned numUnits = outputSize;
+ armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset );
+ armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);
- armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::DataType::Float32);
- armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ 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);
-
- armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, armnn::DataType::Float32);
- armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
-
-
- LayerTestResult<float, 2> ret(outputTensorInfo);
+ LayerTestResult<T, 2> ret(outputTensorInfo);
std::vector<float> inputVector;
inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
@@ -91,9 +93,9 @@ LayerTestResult<float, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl(
AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
- armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfo8({numUnits, 2}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset);
+ armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
auto inputToInputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f,
-0.34550029f, 0.04266912f, -0.15680569f,
@@ -191,14 +193,12 @@ LayerTestResult<float, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl(
data.m_CellBias = &cellBiasTensor;
data.m_OutputGateBias = &outputGateBiasTensor;
-
// Flags to set test configuration
data.m_Parameters.m_ActivationFunc = 4;
data.m_Parameters.m_CifgEnabled = false;
data.m_Parameters.m_PeepholeEnabled = false;
data.m_Parameters.m_ProjectionEnabled = false;
-
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
inputHandle->Allocate();
outputStateInHandle->Allocate();
@@ -220,29 +220,32 @@ LayerTestResult<float, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl(
return ret;
}
-
-LayerTestResult<float, 2>
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2>
LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const boost::multi_array<float, 2>& input,
- const boost::multi_array<float, 2>& outputExpected)
+ 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 = 16;
unsigned int inputSize = 5;
unsigned numUnits = 20;
- armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::DataType::Float32);
- armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ 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}, armnn::DataType::Float32);
- armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
+ 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<float, 2> ret(outputTensorInfo);
+ LayerTestResult<T, 2> ret(outputTensorInfo);
std::vector<float> inputVector;
inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
@@ -292,11 +295,11 @@ LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workl
AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
- armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo16({outputSize}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo20({numUnits}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, constantDataType, qScale, qOffset);
auto inputToInputWeights =
MakeTensor<float, 2>(tensorInfo20x5, {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f,
@@ -931,12 +934,15 @@ LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workl
}
-
-LayerTestResult<float, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const boost::multi_array<float, 2>& input,
- const boost::multi_array<float, 2>& outputExpected)
+ 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)
{
bool cifgEnabled = true;
bool peepholeEnabled = true;
@@ -950,15 +956,15 @@ LayerTestResult<float, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(
const unsigned int cellSize = outputSize;
// Decide the shape of all input tensors
- armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, armnn::DataType::Float32);
+ armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); // change to ArmnnType
+ armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset);
unsigned int scratchBufferSize = cifgEnabled ? cellSize * 3 : cellSize * 4;
- armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, armnn::DataType::Float32);
- armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
// List of inputs
std::vector<float> inputData;
@@ -974,9 +980,9 @@ LayerTestResult<float, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(
// Prepare all the weights in the descriptor for LSTM
armnn::LstmQueueDescriptor data;
- armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, armnn::DataType::Float32);
- armnn::TensorInfo tensorInfoNumUnits({cellSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfoNumUnits({cellSize}, constantDataType, qScale, qOffset);
auto inputToCellWeights = MakeTensor<float, 2>(tensorInfoInput,
{-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f,
@@ -1074,24 +1080,24 @@ LayerTestResult<float, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(
// List of outputs
std::vector<float> scratchBufferVector(batchSize * scratchBufferSize, 0.f);
auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
- LayerTestResult<float, 2> ret0(scratchBufferTensorInfo);
+ LayerTestResult<T, 2> ret0(scratchBufferTensorInfo);
// Output state for a certain time step
std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
- LayerTestResult<float, 2> ret1(outputStateOutTensorInfo);
+ LayerTestResult<T, 2> ret1(outputStateOutTensorInfo);
// Cell state for a certain time step
std::vector<float> cellStateOutVector(batchSize * cellSize, 0.f);
auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
- LayerTestResult<float, 2> ret2(cellStateOutTensorInfo);
+ LayerTestResult<T, 2> ret2(cellStateOutTensorInfo);
// Output for a certain time step
std::vector<float> outputVector(batchSize * outputSize, 0.f);
auto outputTensor = MakeTensor<float, 2>(outputTensorInfo, outputVector);
std::vector<float> outputData;
outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize);
- LayerTestResult<float, 2> ret3(outputTensorInfo);
+ LayerTestResult<T, 2> ret3(outputTensorInfo);
ret3.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputData);
// Prepare the inputs and outputs for the workload
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-17 09:10:48 +0000