IVGCVSW-6967 Add Optimizer Test for FullyConnected in Fp32ToBf16
* Test already existed but bias was not enabled so yielded false positive
* Updated Conv2d and FC to have const layers as inputs
Signed-off-by: Keith Davis <keith.davis@arm.com>
Change-Id: Id4193adef2ac67b3a4681345e4dc01414cbbbad7
diff --git a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp
index 37d7701..d3a6932 100644
--- a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp
+++ b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp
@@ -71,34 +71,50 @@
input->GetOutputSlot().SetTensorInfo(infoFP32);
armnn::Convolution2dDescriptor descriptor;
-
+ descriptor.m_BiasEnabled = true;
auto conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d");
- conv->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights);
- conv->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(bias);
conv->GetOutputSlot().SetTensorInfo(infoFP32);
+ auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("Weights");
+ weightsLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(weights);
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo());
+
+ auto biasLayer = graph.AddLayer<armnn::ConstantLayer>("Bias");
+ biasLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(bias);
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo());
+
auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
// Connect up the layers
input->GetOutputSlot().Connect(conv->GetInputSlot(0));
+ weightsLayer->GetOutputSlot(0).Connect(conv->GetInputSlot(1));
+ biasLayer->GetOutputSlot(0).Connect(conv->GetInputSlot(2));
conv->GetOutputSlot().Connect(output->GetInputSlot(0));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::Convolution2dLayer>, &IsLayerOfType<armnn::OutputLayer>));
+ &IsLayerOfType<armnn::Convolution2dLayer>, &IsLayerOfType<armnn::ConstantLayer>,
+ &IsLayerOfType<armnn::ConstantLayer>, &IsLayerOfType<armnn::OutputLayer>));
// Run the optimizer
- armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter()));
+ armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs(),
+ Fp32NetworkToBf16Converter()));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::Convolution2dLayer>,
- &IsLayerOfType<armnn::OutputLayer>));
+ &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>,
+ &IsLayerOfType<armnn::ConstantLayer>, &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>,
+ &IsLayerOfType<armnn::ConstantLayer>, &IsLayerOfType<armnn::Convolution2dLayer>,
+ &IsLayerOfType<armnn::OutputLayer>));
armnn::TensorInfo inputTensor = conv->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo();
+ armnn::TensorInfo weightTensor = conv->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo();
+ armnn::TensorInfo biasTensor = conv->GetInputSlot(2).GetConnectedOutputSlot()->GetTensorInfo();
armnn::TensorInfo outputTensor = conv->GetOutputSlot(0).GetTensorInfo();
CHECK((conv->GetDataType() == armnn::DataType::BFloat16));
CHECK((conv->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16));
CHECK((conv->m_Bias->GetTensorInfo().GetDataType() == armnn::DataType::Float32));
CHECK((inputTensor.GetDataType() == armnn::DataType::BFloat16));
+ CHECK((weightTensor.GetDataType() == armnn::DataType::BFloat16));
+ CHECK((biasTensor.GetDataType() == armnn::DataType::Float32));
CHECK((outputTensor.GetDataType() == armnn::DataType::Float32));
// Check whether data matches expected Bf16 data
@@ -141,34 +157,50 @@
input->GetOutputSlot().SetTensorInfo(infoFP32);
armnn::FullyConnectedDescriptor descriptor;
+ descriptor.m_BiasEnabled = true;
auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(descriptor, "fully");
- fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights);
- fc->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(bias);
fc->GetOutputSlot().SetTensorInfo(infoFP32);
+ auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("Weights");
+ weightsLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(weights);
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo());
+
+ auto biasLayer = graph.AddLayer<armnn::ConstantLayer>("Bias");
+ biasLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(bias);
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo());
+
auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
// Connect up the layers
input->GetOutputSlot().Connect(fc->GetInputSlot(0));
+ weightsLayer->GetOutputSlot(0).Connect(fc->GetInputSlot(1));
+ biasLayer->GetOutputSlot(0).Connect(fc->GetInputSlot(2));
fc->GetOutputSlot().Connect(output->GetInputSlot(0));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::FullyConnectedLayer>, &IsLayerOfType<armnn::OutputLayer>));
+ &IsLayerOfType<armnn::FullyConnectedLayer>, &IsLayerOfType<armnn::ConstantLayer>,
+ &IsLayerOfType<armnn::ConstantLayer>, &IsLayerOfType<armnn::OutputLayer>));
// Run the optimizer
- armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter()));
+ armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs(),
+ Fp32NetworkToBf16Converter()));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::FullyConnectedLayer>,
- &IsLayerOfType<armnn::OutputLayer>));
+ &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::ConstantLayer>,
+ &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::ConstantLayer>,
+ &IsLayerOfType<armnn::FullyConnectedLayer>, &IsLayerOfType<armnn::OutputLayer>));
armnn::TensorInfo inputTensor = fc->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo();
+ armnn::TensorInfo weightTensor = fc->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo();
+ armnn::TensorInfo biasTensor = fc->GetInputSlot(2).GetConnectedOutputSlot()->GetTensorInfo();
armnn::TensorInfo outputTensor = fc->GetOutputSlot(0).GetTensorInfo();
CHECK((fc->GetDataType() == armnn::DataType::BFloat16));
CHECK((fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16));
CHECK((fc->m_Bias->GetTensorInfo().GetDataType() == armnn::DataType::Float32));
CHECK((inputTensor.GetDataType() == armnn::DataType::BFloat16));
+ CHECK((weightTensor.GetDataType() == armnn::DataType::BFloat16));
+ CHECK((biasTensor.GetDataType() == armnn::DataType::Float32));
CHECK((outputTensor.GetDataType() == armnn::DataType::Float32));
// Check whether data matches expected Bf16 data