From 57ef0088d20dd708ff92222d244ea02f1e1e5216 Mon Sep 17 00:00:00 2001 From: Narumol Prangnawarat Date: Thu, 26 Mar 2020 09:20:43 +0000 Subject: IVGCVSW-4597 Modify BF16 optimizer to Convert only inputs and weights of Convolution2d and FullyConnected layers * Add InsertConvertFp32ToBf16LayersBefore * Add ConvertWeight to ConvertFp32NetworkToBf16Impl for Conv2d and FullyConnected * Allow different input and output when input is BF16 and output is FP32 Conv2d and FullyConnected layers * Unit tests Signed-off-by: Narumol Prangnawarat Change-Id: Ic8f92ff28edcae08a72a3114a28f50c4619f919b --- src/armnn/Network.cpp | 3 +- src/armnn/NetworkUtils.cpp | 39 ++++++ src/armnn/NetworkUtils.hpp | 4 + .../optimizations/ConvertFp32NetworkToBf16.hpp | 78 +++++------ .../Fp32NetworkToBf16ConverterTests.cpp | 148 ++++++++++++++++++++- src/backends/backendsCommon/WorkloadData.cpp | 33 ++++- src/backends/reference/RefLayerSupport.cpp | 32 ++++- 7 files changed, 282 insertions(+), 55 deletions(-) diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 5f7719730b..0272b3da65 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -1020,10 +1020,11 @@ IOptimizedNetworkPtr Optimize(const INetwork& inNetwork, } // If Fp32 to Bf16 optimization is set convert Fp32 network to Bf16 + // Convert input of Convolution2d and FullyConnected from Fp32 to Bf16 + // Only Constant weight of Convolution2d and FullyConnected are converted from Fp32 to Bf16 if (options.m_ReduceFp32ToBf16) { Optimizer::Pass(optGraph, MakeOptimizations(Fp32NetworkToBf16Converter())); - Optimizer::Pass(optGraph, MakeOptimizations(ConvertConstantsFloatToBFloat())); } // Initialize backend settings diff --git a/src/armnn/NetworkUtils.cpp b/src/armnn/NetworkUtils.cpp index 8653a08510..0549a115d4 100644 --- a/src/armnn/NetworkUtils.cpp +++ b/src/armnn/NetworkUtils.cpp @@ -87,6 +87,45 @@ std::vector InsertConvertBf16ToFp32LayersBefore(Graph& return convertLayers; } +std::vector InsertConvertFp32ToBf16LayersBefore(Graph& graph, + Layer& layer, + bool expectCorrectInputType) +{ + std::vector convertLayers; + convertLayers.reserve(layer.GetNumInputSlots()); + + // Insert a ConvertFp32ToBf16Layer before each input slot + for (auto&& inputSlot = layer.BeginInputSlots(); inputSlot != layer.EndInputSlots(); ++inputSlot) + { + bool allowInsert = true; + if (expectCorrectInputType) + { + // Only insert ConvertFp32ToBf16Layer before FP32 input slots + OutputSlot* connectedOutputSlot = inputSlot->GetConnectedOutputSlot(); + allowInsert = + connectedOutputSlot && connectedOutputSlot->GetTensorInfo().GetDataType() == DataType::Float32; + } + + if (allowInsert) + { + const std::string name = + std::string("convert_fp32_to_bf16-" + std::to_string(inputSlot->GetSlotIndex()) + "-") + + layer.GetName(); + ConvertFp32ToBf16Layer* convertLayer = + graph.InsertNewLayer(*inputSlot, name.c_str()); + + TensorInfo convertInfo = convertLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); + convertInfo.SetDataType(DataType::BFloat16); + + convertLayer->GetOutputSlot().SetTensorInfo(convertInfo); + + convertLayers.emplace_back(convertLayer); + } + } + + return convertLayers; +} + std::vector InsertConvertFp16ToFp32LayersBefore(Graph& graph, Layer& layer, bool expectCorrectInputType) diff --git a/src/armnn/NetworkUtils.hpp b/src/armnn/NetworkUtils.hpp index 064545aac5..a922770285 100644 --- a/src/armnn/NetworkUtils.hpp +++ b/src/armnn/NetworkUtils.hpp @@ -15,6 +15,10 @@ std::vector InsertConvertBf16ToFp32LayersBefore(Graph& Layer& layer, bool expectCorrectInputType = true); +std::vector InsertConvertFp32ToBf16LayersBefore(Graph& graph, + Layer& layer, + bool expectCorrectInputType = true); + std::vector InsertConvertFp32ToBf16LayersAfter(Graph& graph, Layer& layer); std::vector InsertConvertFp16ToFp32LayersBefore(Graph& graph, diff --git a/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp b/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp index d6350c3af3..222414c8c5 100644 --- a/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp +++ b/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp @@ -4,68 +4,62 @@ // #pragma once -#include "Optimization.hpp" #include "NetworkUtils.hpp" +#include "Optimization.hpp" namespace armnn { namespace optimizations { +template +inline LayerT* ConvertWeight(Layer* l) +{ + LayerT* layer = boost::polymorphic_downcast(l); + if ((layer->GetType() == LayerType::Convolution2d || layer->GetType() == LayerType::FullyConnected) + && layer->m_Weight) + { + const TensorInfo& info = layer->m_Weight->GetTensorInfo(); + + if (info.GetDataType() == DataType::Float32) + { + std::vector newValues(info.GetNumElements()); + + armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(layer->m_Weight->template GetTensor(), + info.GetNumElements(), + newValues.data()); + + TensorInfo newInfo(info.GetShape(), DataType::BFloat16); + ConstTensor newInput(newInfo, newValues); + layer->m_Weight.reset(new ScopedCpuTensorHandle(newInput)); + } + } + return layer; +} + class ConvertFp32NetworkToBf16Impl { public: + void Run(Graph& graph, Layer& layer) const { - if(layer.GetType() == LayerType::Input) + // Only convert Float32 To BFloat16 for the Input of Convolution2d layer and FullyConnected layer. + // And also convert weight data type from Float32 to Bfloat16. + // Do not convert bias data type. + if (layer.GetType() == LayerType::Convolution2d) { - // if the outputs of this layer are DataType::Float32 - // add a ConvertFloat32ToBFloat16 layer after each of the outputs if (layer.GetDataType() == DataType::Float32) { - InsertConvertFp32ToBf16LayersAfter(graph, layer); + InsertConvertFp32ToBf16LayersBefore(graph,layer); + ConvertWeight(&layer); } } - else if (layer.GetType() == LayerType::Output) + else if (layer.GetType() == LayerType::FullyConnected) { - // if the inputs of this layer are DataType::Float32 - // add a ConvertBFloat16ToFloat32 layer before each of the inputs if (layer.GetDataType() == DataType::Float32) { - // NOTE: We need to call InsertConvertBf16ToFp32LayersBefore with expectCorrectInputType = false - // here, otherwise it will expect the inputs to be DataType::BFloat16 - InsertConvertBf16ToFp32LayersBefore(graph, layer, false); - } - } - else if (layer.GetType() != LayerType::ConvertFp32ToBf16 && layer.GetType() != LayerType::ConvertBf16ToFp32) - { - // if the inputs/outputs of this layer are DataType::Float32 - // change the data type for all inputs and outputs to DataType::BFloat16 - for (auto&& input = layer.BeginInputSlots(); input != layer.EndInputSlots(); ++input) - { - // if it is connected to OutputSlot of the InputLayer do not change the DataType of connection - // InputSlots of the current layer will be updated when conversion layer is inserted after InputLayer - Layer& base = input->GetConnectedOutputSlot()->GetOwningLayer(); - if (base.GetType() != LayerType::Input) - { - TensorInfo convertInfo = input->GetConnection()->GetTensorInfo(); - if (convertInfo.GetDataType() == DataType::Float32) - { - convertInfo.SetDataType(DataType::BFloat16); - input->GetConnection()->SetTensorInfo(convertInfo); - } - } - } - - // change outputs to DataType::BFloat16 - for (auto&& output = layer.BeginOutputSlots(); output != layer.EndOutputSlots(); ++output) - { - TensorInfo convertInfo = output->GetTensorInfo(); - if (convertInfo.GetDataType() == DataType::Float32) - { - convertInfo.SetDataType(DataType::BFloat16); - output->SetTensorInfo(convertInfo); - } + InsertConvertFp32ToBf16LayersBefore(graph,layer); + ConvertWeight(&layer); } } } diff --git a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp index 90a15487ac..b35f983434 100644 --- a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp +++ b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp @@ -12,13 +12,13 @@ BOOST_AUTO_TEST_SUITE(Optimizer) using namespace armnn::optimizations; -BOOST_AUTO_TEST_CASE(Fp32NetworkToBf16OptimizationTest) +BOOST_AUTO_TEST_CASE(Fp32NetworkToBf16OptimizationNoConversionTest) { armnn::Graph graph; const armnn::TensorInfo infoFP32({ 2, 2, 1, 3 }, armnn::DataType::Float32); - // Create the simple test network + // Create the simple test network without Conv2D/FullyConnected. auto input = graph.AddLayer(0, "input"); input->GetOutputSlot().SetTensorInfo(infoFP32); @@ -38,8 +38,148 @@ BOOST_AUTO_TEST_CASE(Fp32NetworkToBf16OptimizationTest) armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType, - &IsLayerOfType, &IsLayerOfType, - &IsLayerOfType, &IsLayerOfType)); + &IsLayerOfType, + &IsLayerOfType)); +} + +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 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 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(0, "input"); + input->GetOutputSlot().SetTensorInfo(infoFP32); + + armnn::Convolution2dDescriptor descriptor; + + auto conv = graph.AddLayer(descriptor, "conv2d"); + conv->m_Weight = std::make_unique(weights); + conv->m_Bias = std::make_unique(bias); + conv->GetOutputSlot().SetTensorInfo(infoFP32); + + auto output = graph.AddLayer(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, + &IsLayerOfType, &IsLayerOfType)); + + // Run the optimizer + armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType, + &IsLayerOfType, &IsLayerOfType, + &IsLayerOfType)); + + 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(); + 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 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 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(0, "input"); + input->GetOutputSlot().SetTensorInfo(infoFP32); + + armnn::FullyConnectedDescriptor descriptor; + + auto fc = graph.AddLayer(descriptor, "fully"); + fc->m_Weight = std::make_unique(weights); + fc->m_Bias = std::make_unique(bias); + fc->GetOutputSlot().SetTensorInfo(infoFP32); + + auto output = graph.AddLayer(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, + &IsLayerOfType, &IsLayerOfType)); + + // Run the optimizer + armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType, + &IsLayerOfType, &IsLayerOfType, + &IsLayerOfType)); + + 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(); + 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() \ No newline at end of file diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp index 85c074a500..f968ad78f7 100644 --- a/src/backends/backendsCommon/WorkloadData.cpp +++ b/src/backends/backendsCommon/WorkloadData.cpp @@ -26,10 +26,9 @@ DataType GetBiasDataType(DataType inputDataType) { switch (inputDataType) { - case DataType::BFloat16: - return DataType::BFloat16; case DataType::Float16: return DataType::Float16; + case DataType::BFloat16: case DataType::Float32: return DataType::Float32; case DataType::QAsymmS8: @@ -1009,7 +1008,20 @@ void FullyConnectedQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) c }; ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName); - ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); + + // For FullyConnected, we allow to have BFloat16 input with Float32 output for optimization. + if (inputTensorInfo.GetDataType() == DataType::BFloat16) + { + if (outputTensorInfo.GetDataType() != DataType::BFloat16 && outputTensorInfo.GetDataType() != DataType::Float32) + { + throw InvalidArgumentException(descriptorName + ": " + " Output tensor type must be BFloat16 or Float32 " + "for BFloat16 input."); + } + } + else + { + ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); + } } void NormalizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const @@ -1206,7 +1218,20 @@ void Convolution2dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) co }; ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName); - ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); + + // For Convolution2d, we allow to have BFloat16 input with Float32 output for optimization. + if (inputTensorInfo.GetDataType() == DataType::BFloat16) + { + if (outputTensorInfo.GetDataType() != DataType::BFloat16 && outputTensorInfo.GetDataType() != DataType::Float32) + { + throw InvalidArgumentException(descriptorName + ": " + " Output tensor type must be BFloat16 or Float32 " + "for BFloat16 input."); + } + } + else + { + ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); + } } void DepthwiseConvolution2dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp index 551a7b5867..7b25a436e9 100644 --- a/src/backends/reference/RefLayerSupport.cpp +++ b/src/backends/reference/RefLayerSupport.cpp @@ -474,8 +474,20 @@ bool RefLayerSupport::IsConvolution2dSupported(const TensorInfo& input, supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, "Reference Convolution2d: output is not a supported type."); - supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, + // For Convolution2d, we allow to have BFloat16 input with Float32 output for optimization. + if (input.GetDataType() == DataType::BFloat16) + { + if (output.GetDataType() != DataType::BFloat16 && output.GetDataType() != DataType::Float32) + { + reasonIfUnsupported.value() += "Output tensor type must be BFloat16 or Float32 for BFloat16 input.\n"; + supported = false; + } + } + else + { + supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, "Reference Convolution2d: input and output types mismatched."); + } const DataType inputType = input.GetDataType(); if (IsQuantized8BitType(inputType)) @@ -882,12 +894,24 @@ bool RefLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, "Reference Fully Connected: output type not supported."); - supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, - "Reference Fully Connected: input and output types mismatched."); - supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, "Reference Fully Connected: weights type not supported."); + // For FullyConnected, we allow to have BFloat16 input with Float32 output for optimization. + if (input.GetDataType() == DataType::BFloat16) + { + if (output.GetDataType() != DataType::BFloat16 && output.GetDataType() != DataType::Float32) + { + reasonIfUnsupported.value() += "Output tensor type must be BFloat16 or Float32 for BFloat16 input.\n"; + supported = false; + } + } + else + { + supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, + "Reference Fully Connected: input and output types mismatched."); + } + ARMNN_NO_DEPRECATE_WARN_BEGIN std::array supportedWeightTypes = { -- cgit v1.2.1