diff options
Diffstat (limited to 'src/armnn/test/optimizations')
3 files changed, 0 insertions, 508 deletions
diff --git a/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp b/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp deleted file mode 100644 index 4aacf7f4fe..0000000000 --- a/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp +++ /dev/null @@ -1,128 +0,0 @@ -// -// Copyright © 2020 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <TestUtils.hpp> - -#include <BFloat16.hpp> -#include <Optimizer.hpp> - -#include <doctest/doctest.h> - -using namespace armnn; - -TEST_SUITE("Optimizer") -{ -using namespace armnn::optimizations; - -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, 0.0f, 0, true), 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::ScopedTensorHandle>(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 - 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 - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16); - - // Check whether data matches expected Bf16 data - const BFloat16* data = fc->m_Weight->GetConstTensor<BFloat16>(); - CHECK(data[0] == BFloat16(0.0f)); - CHECK(data[1] == BFloat16(-1.0f)); - CHECK(data[2] == BFloat16(3.796875f)); // 0x4073 - CHECK(data[3] == BFloat16(3.1072295E29f)); // 0x707B - CHECK(data[4] == BFloat16(9.131327E-10f)); // 0x307B - CHECK(data[5] == BFloat16(-3.796875f)); // 0xC073 - CHECK(data[6] == BFloat16(-3.1072295E29f)); // 0xF07B - CHECK(data[7] == BFloat16(-9.131327E-10f)); // 0xB07B -} - -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, 0.0f, 0, true), 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::ScopedTensorHandle>(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. - 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. - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); - - // Now test the data matches float32 data - const float* data = fc->m_Weight->GetConstTensor<float>(); - CHECK(data[0] == 0.0f); - CHECK(data[1] == -1.0f); - CHECK(data[2] == 3.796875f); - CHECK(data[3] == 3.1072295E29f); - CHECK(data[4] == 9.131327E-10f); - CHECK(data[5] == -3.796875f); - CHECK(data[6] == -3.1072295E29f); - CHECK(data[7] == -9.131327E-10f); -} - -}
\ No newline at end of file diff --git a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp deleted file mode 100644 index 66893ce1f5..0000000000 --- a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp +++ /dev/null @@ -1,229 +0,0 @@ -// -// 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, 0.0f, 0, true), 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, 0.0f, 0, true), floatBias); - - // A network with Convolution2d layer - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(infoFP32); - - armnn::Convolution2dDescriptor descriptor; - descriptor.m_BiasEnabled = true; - auto conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d"); - 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::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs(), - Fp32NetworkToBf16Converter())); - - CHECK(7 == graph.GetNumLayers()); - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &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 - 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, 0.0f, 0, true), 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, 0.0f, 0, true), floatBias); - - // A network with FullyConnected layer - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(infoFP32); - - armnn::FullyConnectedDescriptor descriptor; - descriptor.m_BiasEnabled = true; - - auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(descriptor, "fully"); - 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::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::FullyConnectedLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs(), - Fp32NetworkToBf16Converter())); - - CHECK(7 == graph.GetNumLayers()); - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &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 - 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 -} - -}
\ No newline at end of file diff --git a/src/armnn/test/optimizations/FuseConvertF32BF16IntoConstLayerTests.cpp b/src/armnn/test/optimizations/FuseConvertF32BF16IntoConstLayerTests.cpp deleted file mode 100644 index 93d5948d61..0000000000 --- a/src/armnn/test/optimizations/FuseConvertF32BF16IntoConstLayerTests.cpp +++ /dev/null @@ -1,151 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <LayersFwd.hpp> -#include <Network.hpp> -#include <NetworkUtils.hpp> -#include <Optimizer.hpp> -#include <TestUtils.hpp> - -#include <armnn/backends/TensorHandle.hpp> - -#include <doctest/doctest.h> - -TEST_SUITE("Optimizer") -{ -using namespace armnn; -using namespace armnn::optimizations; - -TEST_CASE("FuseConvertFp32Fp16intoConst") -{ - Graph graph; - const unsigned int shape[] = {1, 2, 2, 3}; - - const TensorInfo constTensorInfo(4, shape, DataType::Float32, 1.0, 0, true); - const TensorInfo outputConvertInfo(4, shape, DataType::BFloat16, 1.0, 0, true); - - ConstantLayer* constantLayer = graph.AddLayer<ConstantLayer>("constant"); - std::vector<float> constantValues(constTensorInfo.GetNumElements(), 3.1416f); - ConstTensor constTensor(constTensorInfo, constantValues.data()); - constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor); - constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo); - - ConvertFp32ToBf16Layer* convertLayer = graph.AddLayer<ConvertFp32ToBf16Layer>("convert"); - convertLayer->GetOutputSlot().SetTensorInfo(outputConvertInfo); - - OutputLayer* output = graph.AddLayer<OutputLayer>(0, "output"); - - // Connect up constant -> convert -> output - constantLayer->GetOutputSlot().Connect(convertLayer->GetInputSlot(0)); - convertLayer->GetOutputSlot().Connect(output->GetInputSlot(0)); - - auto checkConstantFloat32 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::Float32); - }; - auto checkConstantBFloat16 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::BFloat16); - }; - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - checkConstantFloat32, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<OutputLayer>)); - - armnn::Optimizer::Pass(graph, MakeOptimizations(FuseConversionLayersIntoConstLayers())); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - checkConstantBFloat16, - &IsLayerOfType<OutputLayer>)); -} - -TEST_CASE("RevertConstantWeightsToFP32") -{ - Graph graph; - const unsigned int shape[] = {1, 2, 2, 3}; - - const TensorInfo constTensorInfo(4, shape, DataType::Float32, 1.0, 0, true); - const TensorInfo outputConvertInfo(4, shape, DataType::BFloat16, 1.0, 0, true); - - TensorInfo inputInfo(4, shape, DataType::Float32); - auto* input = graph.AddLayer<InputLayer>(0, "input0"); - input->GetOutputSlot().SetTensorInfo(inputInfo); - - auto* constantLayer = graph.AddLayer<ConstantLayer>("constant"); - std::vector<float> constantValues(constTensorInfo.GetNumElements(), 3.1416f); - ConstTensor constTensor(constTensorInfo, constantValues.data()); - constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor); - constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo); - - ConvertFp32ToBf16Layer* convertLayerInputs = graph.AddLayer<ConvertFp32ToBf16Layer>("convert"); - convertLayerInputs->GetOutputSlot().SetTensorInfo(outputConvertInfo); - ConvertFp32ToBf16Layer* convertLayerWeights = graph.AddLayer<ConvertFp32ToBf16Layer>("convert2"); - convertLayerWeights->GetOutputSlot().SetTensorInfo(outputConvertInfo); - ConvertFp32ToBf16Layer* convertLayerBiases = graph.AddLayer<ConvertFp32ToBf16Layer>("convert3"); - convertLayerBiases->GetOutputSlot().SetTensorInfo(outputConvertInfo); - - auto* biases = graph.AddLayer<armnn::ConstantLayer>("Biases"); - biases->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(constTensor); - biases->GetOutputSlot().SetTensorInfo(constTensorInfo); - - armnn::Convolution2dDescriptor descriptor; - descriptor.m_BiasEnabled = true; - auto* conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d"); - const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32); - conv->GetOutputSlot().SetTensorInfo(infoFP32); - - auto* output = graph.AddLayer<OutputLayer>(0, "output"); - - // Connect up Input -> Convert -> - // Constant -> Convert -> Conv2d -> Output - // Constant -> Convert -> - input->GetOutputSlot().Connect(convertLayerInputs->GetInputSlot(0)); - constantLayer->GetOutputSlot().Connect(convertLayerWeights->GetInputSlot(0)); - biases->GetOutputSlot().Connect(convertLayerBiases->GetInputSlot(0)); - - convertLayerInputs->GetOutputSlot().Connect(conv->GetInputSlot(0)); - convertLayerWeights->GetOutputSlot().Connect(conv->GetInputSlot(1)); - convertLayerBiases->GetOutputSlot().Connect(conv->GetInputSlot(2)); - - conv->GetOutputSlot().Connect(output->GetInputSlot(0)); - - auto checkConstantFloat32 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::Float32); - }; - auto checkConstantBFloat16 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::BFloat16); - }; - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - checkConstantFloat32, - checkConstantFloat32, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<Convolution2dLayer>, - &IsLayerOfType<OutputLayer>)); - - armnn::Optimizer::Pass(graph, MakeOptimizations(FuseConversionLayersIntoConstLayers())); - - bool revert = RevertConstantWeightsToFP32(conv); - - // Erase unconnected layer as occurs during Topological Sort. - graph.EraseLayer(convertLayerInputs); - - CHECK(revert); - CHECK(constantLayer->GetDataType() == DataType::Float32); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - checkConstantBFloat16, - checkConstantFloat32, - &IsLayerOfType<Convolution2dLayer>, - &IsLayerOfType<OutputLayer>)); -} -} |