IVGCVSW-4520 Implement BFloat16 Optimizer

 * Add ReduceFp32ToBf16 to OptimizerOptions
 * Add ConvertFp32NetworkToBf16
 * Add utility functions to insert conversion layers
 * Add constant conversion BF16 <-> FP32
 * Unit tests

Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: Iaff77e20c721400b052cb37eb9ef6fe16d7abaff

1 files changed, 127 insertions, 0 deletions

diff --git a/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp b/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp
new file mode 100644
index 0000000000..5cb89daafd
--- /dev/null
+++ b/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp
@@ -0,0 +1,127 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../TestUtils.hpp"
+
+#include <BFloat16.hpp>
+#include <Optimizer.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+using namespace armnn;
+
+BOOST_AUTO_TEST_SUITE(Optimizer)
+using namespace armnn::optimizations;
+
+BOOST_AUTO_TEST_CASE(ConvertConstantsFloatToBFloatTest)
+{
+    armnn::Graph graph;
+
+    const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::BFloat16);
+
+    // Create const tensor from 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 simple test network
+    auto input = graph.AddLayer<armnn::InputLayer>(0, "input");
+    input->GetOutputSlot().SetTensorInfo(info);
+
+    auto fc      = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc");
+    fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);
+    fc->GetOutputSlot().SetTensorInfo(info);
+
+    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 tensor data type before conversion
+    BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
+
+    // Run the optimizer
+    armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToBFloat()));
+
+    // Check tensor data type after conversion
+    BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16);
+
+    // Check whether data matches expected Bf16 data
+    BFloat16* data = fc->m_Weight->GetTensor<BFloat16>();
+    BOOST_CHECK(data[0] == BFloat16(0.0f));
+    BOOST_CHECK(data[1] == BFloat16(-1.0f));
+    BOOST_CHECK(data[2] == BFloat16(3.796875f)); // 0x4073
+    BOOST_CHECK(data[3] == BFloat16(3.1072295E29f)); // 0x707B
+    BOOST_CHECK(data[4] == BFloat16(9.131327E-10f)); // 0x307B
+    BOOST_CHECK(data[5] == BFloat16(-3.796875f)); // 0xC073
+    BOOST_CHECK(data[6] == BFloat16(-3.1072295E29f)); // 0xF07B
+    BOOST_CHECK(data[7] == BFloat16(-9.131327E-10f)); // 0xB07B
+}
+
+BOOST_AUTO_TEST_CASE(ConvertConstantsBFloatToFloatTest)
+{
+    armnn::Graph graph;
+
+    const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::Float32);
+
+    // Create the BFloat16 precision input data
+    unsigned int dims[] = { 4, 2, 1, 1 };
+    std::vector<float> convWeightsData{ 0.f, -1.f,
+                                        3.796875f, // 0x4073
+                                        3.1072295E29f, // 0x707B
+                                        9.131327E-10f, // 0x307B
+                                       -3.796875f, // 0xC073
+                                       -3.1072295E29f, // 0xF07B
+                                       -9.131327E-10f // 0xB07B
+                                       };
+    std::vector<uint16_t> bfWeights(8);
+    armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(convWeightsData.data(), convWeightsData.size(),
+                                                                 bfWeights.data());
+    armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::BFloat16), bfWeights);
+
+    //Create the simple test network
+    auto input = graph.AddLayer<armnn::InputLayer>(0, "input");
+    input->GetOutputSlot().SetTensorInfo(info);
+
+    auto fc      = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc");
+    fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);
+    fc->GetOutputSlot().SetTensorInfo(info);
+
+    auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
+
+    //Connect up the layers
+    input->GetOutputSlot().Connect(fc->GetInputSlot(0));
+    fc->GetOutputSlot().Connect(output->GetInputSlot(0));
+
+    //Test the tensor info is correct.
+    BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16);
+
+    // Run the optimizer
+    armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsBFloatToFloat()));
+
+    //Test the tensor info is correct.
+    BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
+
+    // Now test the data matches float32 data
+    float* data = fc->m_Weight->GetTensor<float>();
+    BOOST_CHECK(data[0] == 0.0f);
+    BOOST_CHECK(data[1] == -1.0f);
+    BOOST_CHECK(data[2] == 3.796875f);
+    BOOST_CHECK(data[3] == 3.1072295E29f);
+    BOOST_CHECK(data[4] == 9.131327E-10f);
+    BOOST_CHECK(data[5] == -3.796875f);
+    BOOST_CHECK(data[6] == -3.1072295E29f);
+    BOOST_CHECK(data[7] == -9.131327E-10f);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file