IVGCVSW-4549 Add front end for new QLSTM layer

* Added new layer QLstm (Android R HAL 1.3)
* Made necessary updates to APIs
* Added unit tests
* This layer is functionally equivalent to the
  original unquantized LSTM layer with some
  additonal quantization features added. Due
  to this, original LstmParams are used for
  this layer.

Signed-off-by: James Conroy <james.conroy@arm.com>
Change-Id: I5b7f2d2fb6e17e81573b41a31bc55f49ae79608f

1 files changed, 78 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
index d646847cbc..7534c8a7c9 100644
--- a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
+++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
@@ -308,6 +308,82 @@ struct DummyLayer<armnn::LstmLayer>
 {
 };
 
+template <typename QLstmLayerType>
+struct DummyQLstmLayer
+{
+    DummyQLstmLayer()
+    {
+        typename QLstmLayerType::DescriptorType desc;
+        desc.m_CifgEnabled = false;
+        desc.m_PeepholeEnabled = true;
+        desc.m_ProjectionEnabled = true;
+        desc.m_LayerNormEnabled = true;
+
+        m_Layer = dummyGraph.AddLayer<QLstmLayerType>(armnn::QLstmDescriptor(), "qLstm");
+
+        // Basic params
+        m_Layer->m_BasicParameters.m_InputToForgetWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_BasicParameters.m_InputToCellWeights       = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_BasicParameters.m_InputToOutputWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+
+        m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_BasicParameters.m_RecurrentToCellWeights   = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+
+        m_Layer->m_BasicParameters.m_ForgetGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
+        m_Layer->m_BasicParameters.m_CellBias                 = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
+        m_Layer->m_BasicParameters.m_OutputGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
+
+        // CIFG optional params
+        m_Layer->m_CifgParameters.m_InputToInputWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_CifgParameters.m_InputGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
+
+        // Projection optional params
+        m_Layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
+        m_Layer->m_ProjectionParameters.m_ProjectionBias    = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
+
+        // Peephole optional params
+        m_Layer->m_PeepholeParameters.m_CellToInputWeights  = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+        m_Layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+        m_Layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+
+        // Layer normalization optional params
+        m_Layer->m_LayerNormParameters.m_InputLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+        m_Layer->m_LayerNormParameters.m_ForgetLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+        m_Layer->m_LayerNormParameters.m_CellLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+        m_Layer->m_LayerNormParameters.m_OutputLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
+    }
+
+    ~DummyQLstmLayer()
+    {
+        dummyGraph.EraseLayer(m_Layer);
+    }
+
+    armnn::QLstmLayer* m_Layer;
+};
+
 template<>
 struct DummyLayer<armnn::QuantizedLstmLayer, void>
 {
@@ -513,6 +589,8 @@ DECLARE_LAYER_POLICY_2_PARAM(PreCompiled)
 
 DECLARE_LAYER_POLICY_1_PARAM(Prelu)
 
+DECLARE_LAYER_POLICY_2_PARAM(QLstm)
+
 DECLARE_LAYER_POLICY_1_PARAM(QuantizedLstm)
 
 DECLARE_LAYER_POLICY_1_PARAM(Division)