| // |
| // Copyright © 2020 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "../TestUtils.hpp" |
| |
| #include <Optimizer.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| BOOST_AUTO_TEST_SUITE(Optimizer) |
| using namespace armnn::optimizations; |
| |
| BOOST_AUTO_TEST_CASE(Fp32NetworkToBf16OptimizationNoConversionTest) |
| { |
| armnn::Graph graph; |
| |
| const armnn::TensorInfo infoFP32({ 2, 2, 1, 3 }, armnn::DataType::Float32); |
| |
| // Create the simple test network without Conv2D/FullyConnected. |
| auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| input->GetOutputSlot().SetTensorInfo(infoFP32); |
| |
| auto floor = graph.AddLayer<armnn::FloorLayer>("floor"); |
| floor->GetOutputSlot().SetTensorInfo(infoFP32); |
| |
| auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); |
| |
| // Connect up the layers |
| input->GetOutputSlot().Connect(floor->GetInputSlot(0)); |
| floor->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| |
| BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::OutputLayer>)); |
| |
| // Run the optimizer |
| armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); |
| |
| BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<armnn::FloorLayer>, |
| &IsLayerOfType<armnn::OutputLayer>)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(Fp32NetworkToBf16OptimizationConv2DTest) |
| { |
| armnn::Graph graph; |
| |
| const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32); |
| |
| // Create const tensor fp32 data |
| unsigned int dims[] = { 4, 2, 1, 1 }; |
| std::vector<float> floatWeights{ 0.0f, -1.0f, |
| 3.8f, // 0x40733333 Round down |
| 3.1055E+29f, // 0x707ADC3C Round up |
| 9.149516E-10f, // 0x307B7FFF Round down |
| -3.8f, // 0xC0733333 Round down |
| -3.1055E+29f, // 0xF07ADC3C Round up |
| -9.149516E-10f // 0xB07B7FFF Round down |
| }; |
| armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32), floatWeights); |
| |
| // Create const bias fp32 data |
| unsigned int biasDims[] {4}; |
| std::vector<float> floatBias{ 1.0f, 2.0f, 3.0f, 4.0f }; |
| armnn::ConstTensor bias(armnn::TensorInfo(1, biasDims, armnn::DataType::Float32), floatBias); |
| |
| // A network with Convolution2d layer |
| auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| input->GetOutputSlot().SetTensorInfo(infoFP32); |
| |
| armnn::Convolution2dDescriptor descriptor; |
| |
| auto conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d"); |
| conv->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); |
| conv->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(bias); |
| conv->GetOutputSlot().SetTensorInfo(infoFP32); |
| |
| auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); |
| |
| // Connect up the layers |
| input->GetOutputSlot().Connect(conv->GetInputSlot(0)); |
| conv->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| |
| BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<armnn::Convolution2dLayer>, &IsLayerOfType<armnn::OutputLayer>)); |
| |
| // Run the optimizer |
| armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); |
| |
| BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::Convolution2dLayer>, |
| &IsLayerOfType<armnn::OutputLayer>)); |
| |
| armnn::TensorInfo inputTensor = conv->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); |
| armnn::TensorInfo outputTensor = conv->GetOutputSlot(0).GetTensorInfo(); |
| BOOST_TEST((conv->GetDataType() == armnn::DataType::BFloat16)); |
| BOOST_TEST((conv->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16)); |
| BOOST_TEST((conv->m_Bias->GetTensorInfo().GetDataType() == armnn::DataType::Float32)); |
| BOOST_TEST((inputTensor.GetDataType() == armnn::DataType::BFloat16)); |
| BOOST_TEST((outputTensor.GetDataType() == armnn::DataType::Float32)); |
| |
| // Check whether data matches expected Bf16 data |
| armnn::BFloat16* data = conv->m_Weight->GetTensor<armnn::BFloat16>(); |
| BOOST_CHECK(data[0] == armnn::BFloat16(0.0f)); |
| BOOST_CHECK(data[1] == armnn::BFloat16(-1.0f)); |
| BOOST_CHECK(data[2] == armnn::BFloat16(3.796875f)); // 0x4073 |
| BOOST_CHECK(data[3] == armnn::BFloat16(3.1072295E29f)); // 0x707B |
| BOOST_CHECK(data[4] == armnn::BFloat16(9.131327E-10f)); // 0x307B |
| BOOST_CHECK(data[5] == armnn::BFloat16(-3.796875f)); // 0xC073 |
| BOOST_CHECK(data[6] == armnn::BFloat16(-3.1072295E29f)); // 0xF07B |
| BOOST_CHECK(data[7] == armnn::BFloat16(-9.131327E-10f)); // 0xB07B |
| } |
| |
| BOOST_AUTO_TEST_CASE(Fp32NetworkToBf16OptimizationFullyConnectedTest) |
| { |
| armnn::Graph graph; |
| |
| const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32); |
| |
| // Create const tensor fp32 data |
| unsigned int dims[] = { 4, 2, 1, 1 }; |
| std::vector<float> floatWeights{ 0.0f, -1.0f, |
| 3.8f, // 0x40733333 Round down |
| 3.1055E+29f, // 0x707ADC3C Round up |
| 9.149516E-10f, // 0x307B7FFF Round down |
| -3.8f, // 0xC0733333 Round down |
| -3.1055E+29f, // 0xF07ADC3C Round up |
| -9.149516E-10f // 0xB07B7FFF Round down |
| }; |
| armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32), floatWeights); |
| |
| // Create const bias fp32 data |
| unsigned int biasDims[] {4}; |
| std::vector<float> floatBias{ 1.0f, 2.0f, 3.0f, 4.0f }; |
| armnn::ConstTensor bias(armnn::TensorInfo(1, biasDims, armnn::DataType::Float32), floatBias); |
| |
| // A network with FullyConnected layer |
| auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| input->GetOutputSlot().SetTensorInfo(infoFP32); |
| |
| armnn::FullyConnectedDescriptor descriptor; |
| |
| auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(descriptor, "fully"); |
| fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); |
| fc->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(bias); |
| fc->GetOutputSlot().SetTensorInfo(infoFP32); |
| |
| auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); |
| |
| // Connect up the layers |
| input->GetOutputSlot().Connect(fc->GetInputSlot(0)); |
| fc->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| |
| BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<armnn::FullyConnectedLayer>, &IsLayerOfType<armnn::OutputLayer>)); |
| |
| // Run the optimizer |
| armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); |
| |
| BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::FullyConnectedLayer>, |
| &IsLayerOfType<armnn::OutputLayer>)); |
| |
| armnn::TensorInfo inputTensor = fc->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); |
| armnn::TensorInfo outputTensor = fc->GetOutputSlot(0).GetTensorInfo(); |
| BOOST_TEST((fc->GetDataType() == armnn::DataType::BFloat16)); |
| BOOST_TEST((fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16)); |
| BOOST_TEST((fc->m_Bias->GetTensorInfo().GetDataType() == armnn::DataType::Float32)); |
| BOOST_TEST((inputTensor.GetDataType() == armnn::DataType::BFloat16)); |
| BOOST_TEST((outputTensor.GetDataType() == armnn::DataType::Float32)); |
| |
| // Check whether data matches expected Bf16 data |
| armnn::BFloat16* data = fc->m_Weight->GetTensor<armnn::BFloat16>(); |
| BOOST_CHECK(data[0] == armnn::BFloat16(0.0f)); |
| BOOST_CHECK(data[1] == armnn::BFloat16(-1.0f)); |
| BOOST_CHECK(data[2] == armnn::BFloat16(3.796875f)); // 0x4073 |
| BOOST_CHECK(data[3] == armnn::BFloat16(3.1072295E29f)); // 0x707B |
| BOOST_CHECK(data[4] == armnn::BFloat16(9.131327E-10f)); // 0x307B |
| BOOST_CHECK(data[5] == armnn::BFloat16(-3.796875f)); // 0xC073 |
| BOOST_CHECK(data[6] == armnn::BFloat16(-3.1072295E29f)); // 0xF07B |
| BOOST_CHECK(data[7] == armnn::BFloat16(-9.131327E-10f)); // 0xB07B |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |