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//
// Copyright © 2020 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../TestUtils.hpp"
#include <Optimizer.hpp>
#include <doctest/doctest.h>
TEST_SUITE("Optimizer")
{
using namespace armnn::optimizations;
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));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::OutputLayer>));
// Run the optimizer
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter()));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::FloorLayer>,
&IsLayerOfType<armnn::OutputLayer>));
}
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::ScopedTensorHandle>(weights);
conv->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(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));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::Convolution2dLayer>, &IsLayerOfType<armnn::OutputLayer>));
// Run the optimizer
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter()));
CHECK(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();
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((outputTensor.GetDataType() == armnn::DataType::Float32));
// Check whether data matches expected Bf16 data
const armnn::BFloat16* data = conv->m_Weight->GetConstTensor<armnn::BFloat16>();
CHECK(data[0] == armnn::BFloat16(0.0f));
CHECK(data[1] == armnn::BFloat16(-1.0f));
CHECK(data[2] == armnn::BFloat16(3.796875f)); // 0x4073
CHECK(data[3] == armnn::BFloat16(3.1072295E29f)); // 0x707B
CHECK(data[4] == armnn::BFloat16(9.131327E-10f)); // 0x307B
CHECK(data[5] == armnn::BFloat16(-3.796875f)); // 0xC073
CHECK(data[6] == armnn::BFloat16(-3.1072295E29f)); // 0xF07B
CHECK(data[7] == armnn::BFloat16(-9.131327E-10f)); // 0xB07B
}
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::ScopedTensorHandle>(weights);
fc->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(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));
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::FullyConnectedLayer>, &IsLayerOfType<armnn::OutputLayer>));
// Run the optimizer
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter()));
CHECK(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();
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((outputTensor.GetDataType() == armnn::DataType::Float32));
// Check whether data matches expected Bf16 data
const armnn::BFloat16* data = fc->m_Weight->GetConstTensor<armnn::BFloat16>();
CHECK(data[0] == armnn::BFloat16(0.0f));
CHECK(data[1] == armnn::BFloat16(-1.0f));
CHECK(data[2] == armnn::BFloat16(3.796875f)); // 0x4073
CHECK(data[3] == armnn::BFloat16(3.1072295E29f)); // 0x707B
CHECK(data[4] == armnn::BFloat16(9.131327E-10f)); // 0x307B
CHECK(data[5] == armnn::BFloat16(-3.796875f)); // 0xC073
CHECK(data[6] == armnn::BFloat16(-3.1072295E29f)); // 0xF07B
CHECK(data[7] == armnn::BFloat16(-9.131327E-10f)); // 0xB07B
}
}
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