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author | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | 2020-03-20 15:01:01 +0000 |
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committer | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | 2020-03-20 19:09:07 +0000 |
commit | bc7ffb5e9e5f4c86280b20c65772eb12d8bb140e (patch) | |
tree | 5187f34326414e7dfea80e0f4efaae5cbeb05d1d /src/armnn/test/optimizations | |
parent | cf2ad554502830804e991aca2e5b0741623119b2 (diff) | |
download | armnn-bc7ffb5e9e5f4c86280b20c65772eb12d8bb140e.tar.gz |
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
Diffstat (limited to 'src/armnn/test/optimizations')
-rw-r--r-- | src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp | 127 | ||||
-rw-r--r-- | src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp | 45 |
2 files changed, 172 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 diff --git a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp new file mode 100644 index 0000000000..90a15487ac --- /dev/null +++ b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp @@ -0,0 +1,45 @@ +// +// 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(Fp32NetworkToBf16OptimizationTest) +{ + armnn::Graph graph; + + const armnn::TensorInfo infoFP32({ 2, 2, 1, 3 }, armnn::DataType::Float32); + + // Create the simple test network + 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::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::ConvertBf16ToFp32Layer>, &IsLayerOfType<armnn::OutputLayer>)); +} + +BOOST_AUTO_TEST_SUITE_END()
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