diff options
| -rw-r--r-- | delegate/classic/src/BroadcastTo.hpp | 12 | ||||
| -rw-r--r-- | delegate/common/src/DelegateUtils.hpp | 15 | ||||
| -rw-r--r-- | delegate/common/src/test/DelegateUtilsTest.cpp | 54 | ||||
| -rw-r--r-- | delegate/opaque/src/BroadcastTo.hpp | 12 | ||||
| -rw-r--r-- | src/backends/reference/workloads/Broadcast.cpp | 24 | ||||
| -rw-r--r-- | src/backends/tosaCommon/TosaMappings.cpp | 3 | ||||
| -rw-r--r-- | src/backends/tosaCommon/operatorMappings/ReluOperator.cpp | 53 | ||||
| -rw-r--r-- | src/backends/tosaReference/test/TosaRefEndToEndTests.cpp | 32 |
8 files changed, 188 insertions, 17 deletions
diff --git a/delegate/classic/src/BroadcastTo.hpp b/delegate/classic/src/BroadcastTo.hpp index 92aed79982..2e2b3ab155 100644 --- a/delegate/classic/src/BroadcastTo.hpp +++ b/delegate/classic/src/BroadcastTo.hpp @@ -1,11 +1,12 @@ // -// Copyright © 2023 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2023-2024 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once #include <armnn/utility/IgnoreUnused.hpp> +#include <DelegateUtils.hpp> #include <tensorflow/lite/builtin_ops.h> #include <tensorflow/lite/c/builtin_op_data.h> @@ -83,6 +84,15 @@ namespace armnnDelegate const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + if (ZeroDimPresent({inputTensorInfo, outputTensorInfo})) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Zero dimension tensors are not supported in operator #%d node #%d: ", + broadcastToOperatorCode, nodeIndex); + return kTfLiteError; + } + auto* shapeData = tflite::GetTensorData<int32_t>(&tfLiteShapeTensor); auto shapeTensorNum = tfLiteShapeTensor.dims->data[0]; diff --git a/delegate/common/src/DelegateUtils.hpp b/delegate/common/src/DelegateUtils.hpp index 96767ff78c..245fc9be90 100644 --- a/delegate/common/src/DelegateUtils.hpp +++ b/delegate/common/src/DelegateUtils.hpp @@ -300,4 +300,19 @@ armnn::TensorInfo OutputShapeOfSqueeze(std::vector<uint32_t> squeezeDims, return outTensorInfo; } +bool ZeroDimPresent(std::initializer_list<armnn::TensorInfo> tensorInfoList) +{ + for (armnn::TensorInfo tensorInfo : tensorInfoList) + { + for (unsigned int i = 0; i < tensorInfo.GetNumDimensions(); ++i) + { + if (tensorInfo.GetShape()[i] == 0) + { + return true; + } + } + } + return false; +} + } // namespace anonymous diff --git a/delegate/common/src/test/DelegateUtilsTest.cpp b/delegate/common/src/test/DelegateUtilsTest.cpp new file mode 100644 index 0000000000..5ce470e289 --- /dev/null +++ b/delegate/common/src/test/DelegateUtilsTest.cpp @@ -0,0 +1,54 @@ +// +// Copyright © 2024 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/Tensor.hpp> +#include <doctest/doctest.h> +#include <common/src/DelegateUtils.hpp> + +namespace armnn +{ + +TEST_SUITE("DelegateUtils_Tests") +{ + TEST_CASE("Zero_Dim_In_Input_Test_True") + { + unsigned int inputDimSizes[] = {0, 1, 2, 3}; + TensorInfo inputTensor = armnn::TensorInfo(4, inputDimSizes, DataType::Float32); + + CHECK(ZeroDimPresent({inputTensor}) == true); + } + + TEST_CASE("Zero_Dim_In_Input_Test_False") + { + unsigned int inputDimSizes[] = {1, 2, 3, 4}; + TensorInfo inputTensor = armnn::TensorInfo(4, inputDimSizes, DataType::Float32); + + CHECK(ZeroDimPresent({inputTensor}) == false); + } + + TEST_CASE("Zero_Dim_In_Output_Test_True") + { + unsigned int inputDimSizes[] = {1, 2, 3, 4}; + TensorInfo inputTensor = armnn::TensorInfo(4, inputDimSizes, DataType::Float32); + + unsigned int outputDimSizes[] = {0, 1, 2, 3}; + TensorInfo outputTensor = armnn::TensorInfo(4, outputDimSizes, DataType::Float32); + + CHECK(ZeroDimPresent({inputTensor, outputTensor}) == true); + } + + TEST_CASE("Zero_Dim_In_Output_Test_False") + { + unsigned int inputDimSizes[] = {1, 2, 3, 4}; + TensorInfo inputTensor = armnn::TensorInfo(4, inputDimSizes, DataType::Float32); + + unsigned int outputDimSizes[] = {1, 2, 3, 4}; + TensorInfo outputTensor = armnn::TensorInfo(4, outputDimSizes, DataType::Float32); + + CHECK(ZeroDimPresent({inputTensor, outputTensor}) == false); + } +} + +} // namespace armnn
\ No newline at end of file diff --git a/delegate/opaque/src/BroadcastTo.hpp b/delegate/opaque/src/BroadcastTo.hpp index 379587546f..8fcea9393c 100644 --- a/delegate/opaque/src/BroadcastTo.hpp +++ b/delegate/opaque/src/BroadcastTo.hpp @@ -1,11 +1,12 @@ // -// Copyright © 2023 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2023-2024 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once #include <OpaqueDelegateUtils.hpp> +#include <DelegateUtils.hpp> namespace armnnOpaqueDelegate { @@ -102,6 +103,15 @@ namespace armnnOpaqueDelegate const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + if (ZeroDimPresent({inputTensorInfo, outputTensorInfo})) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Zero dimension tensors are not supported in operator #%d node #%d: ", + broadcastToOperatorCode, nodeIndex); + return kTfLiteError; + } + auto* shapeData = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteShapeTensor)); int32_t shapeTensorNum = TfLiteOpaqueTensorDim(tfLiteShapeTensor, 0); diff --git a/src/backends/reference/workloads/Broadcast.cpp b/src/backends/reference/workloads/Broadcast.cpp index 24af0fc4b1..f17ec6b311 100644 --- a/src/backends/reference/workloads/Broadcast.cpp +++ b/src/backends/reference/workloads/Broadcast.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2019 Arm Ltd. All rights reserved. +// Copyright © 2019,2024 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // @@ -38,13 +38,31 @@ BroadcastLoop::BroadcastLoop(const TensorShape& inShape, const TensorShape& outS unsigned int sIn = 1; unsigned int sOut = 1; + // Get the difference between the output dimension and input dimension + const unsigned int dimDifference = numDims - inShape.GetNumDimensions(); + for (unsigned int j = numDims - 1, k = 0; k < numDims ; k++, j--) { + m_DimData[j].m_DimSize = outShape[j]; - m_DimData[j].m_Stride1 = (inShape[j] > 1) ? sIn : 0; + // Pretend there are extra 1-dimensional tensors prepended + if (dimDifference > 0 && j < dimDifference) + { + m_DimData[j].m_Stride1 = 0; + sIn *= 1; + } + else if (dimDifference > 0) + { + m_DimData[j].m_Stride1 = (inShape[j - dimDifference] > 1) ? sIn : 0; + sIn *= inShape[j - dimDifference]; + } + else + { + m_DimData[j].m_Stride1 = (inShape[j] > 1) ? sIn : 0; + sIn *= inShape[j]; + } m_DimData[j].m_StrideOut = sOut; - sIn *= inShape[j]; sOut *= outShape[j]; } } diff --git a/src/backends/tosaCommon/TosaMappings.cpp b/src/backends/tosaCommon/TosaMappings.cpp index 8608776471..bc1376b9cc 100644 --- a/src/backends/tosaCommon/TosaMappings.cpp +++ b/src/backends/tosaCommon/TosaMappings.cpp @@ -30,7 +30,8 @@ TosaSerializationBasicBlock* GetTosaMapping(const Layer* layer, { return ConvertLeakyReluToTosaOperator(layer, inputs, outputs, activationDesc); } - if (activationDesc->m_Function == ActivationFunction::ReLu) + if (activationDesc->m_Function == ActivationFunction::ReLu || + activationDesc->m_Function == ActivationFunction::BoundedReLu) { return ConvertReluToTosaOperator(layer, inputs, outputs, activationDesc); } diff --git a/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp b/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp index bd1a59670e..541b39cd8d 100644 --- a/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp +++ b/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp @@ -17,7 +17,7 @@ TosaSerializationBasicBlock* ConvertReluToTosaOperator(const Layer* layer, const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - const ActivationDescriptor*) + const ActivationDescriptor* desc) { if (inputs.size() != 1) { @@ -31,7 +31,36 @@ TosaSerializationBasicBlock* ConvertReluToTosaOperator(const Layer* layer, std::string inputName = std::string("input_"); std::string outputName = std::string("output0_"); - std::string blockName = std::string("Op_RELU_block_") + GetUniqueTosaMappingID(); + std::string blockName = ""; + + int32_t clamp_min = 0; + int32_t clamp_max = 0; + float float_max = 0.0f; + switch (desc->m_Function) + { + case ActivationFunction::ReLu: + { + clamp_max = std::numeric_limits<int32_t>::max(); + float_max = std::numeric_limits<float>::max(); + blockName = std::string("Op_RELU_block_") + GetUniqueTosaMappingID(); + break; + } + case ActivationFunction::BoundedReLu: + { + clamp_max = static_cast<int32_t>(desc->m_A); + float_max = desc->m_A; + blockName = std::string("Op_BOUNDED_RELU_block_") + GetUniqueTosaMappingID(); + break; + } + case ActivationFunction::LeakyReLu: + { + throw Exception("LeakyRelu TOSA mappings are performed in ConvertLeakyReluToTosaOperator()."); + } + default: + { + throw Exception("Activation function is not supported in ConvertReluToTosaOperator()."); + } + } // If a layer is present then the block will be used for execution, so input and output names need to be determined // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. @@ -60,8 +89,6 @@ TosaSerializationBasicBlock* ConvertReluToTosaOperator(const Layer* layer, DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); - int32_t clamp_min = 0; - int32_t clamp_max = std::numeric_limits<int32_t>::max(); std::string clampInputNameStr = inputName; if (inputDType0 == tosa::DType::DType_INT8 || inputDType0 == tosa::DType::DType_INT16) { @@ -72,18 +99,26 @@ TosaSerializationBasicBlock* ConvertReluToTosaOperator(const Layer* layer, int32_t input_zp = inputs[0]->GetQuantizationOffset(); int32_t output_zp = outputs[0]->GetQuantizationOffset(); - clamp_min = outputs[0]->GetQuantizationOffset(); + clamp_min = output_zp; + + if (desc->m_Function == ActivationFunction::BoundedReLu) + { + clamp_max = static_cast<int32_t>(std::round(desc->m_A / outputs[0]->GetQuantizationScale())) + output_zp; + } + if (inputDType0 == tosa::DType::DType_INT8) { clamp_min = clamp_min < std::numeric_limits<int8_t>::min() ? std::numeric_limits<int8_t>::min() : clamp_min; - clamp_max = std::numeric_limits<int8_t>::max(); + clamp_max = + clamp_max > std::numeric_limits<int8_t>::max() ? std::numeric_limits<int8_t>::max() : clamp_max; } else { clamp_min = clamp_min < std::numeric_limits<int16_t>::min() ? std::numeric_limits<int16_t>::min() : clamp_min; - clamp_max = std::numeric_limits<int16_t>::max(); + clamp_max = + clamp_max > std::numeric_limits<int16_t>::max() ? std::numeric_limits<int16_t>::max() : clamp_max; } TosaSerializationOperator* rescaleOp = nullptr; @@ -101,8 +136,8 @@ TosaSerializationBasicBlock* ConvertReluToTosaOperator(const Layer* layer, inputDType0, {})); } - - TosaClampAttribute attribute(clamp_min, clamp_max, 0, std::numeric_limits<float>::max()); + + TosaClampAttribute attribute(clamp_min, clamp_max, 0, float_max); auto* clamp_op = new TosaSerializationOperator(Op_CLAMP, Attribute_ClampAttribute, &attribute, diff --git a/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp b/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp index 09a3d44c02..22fd782a1a 100644 --- a/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp +++ b/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp @@ -30,25 +30,28 @@ TEST_SUITE("TosaRefEndToEnd") static std::vector<BackendId> tosaDefaultBackends = { "TosaRef" }; // Activation -//LeakyRelu +// LeakyRelu TEST_CASE("TosaRefLeakyReluActivationFloat32") { ActivationEndToEndTest<DataType::Float32>(tosaDefaultBackends, ActivationFunction::LeakyReLu, 1.f, 0, 0.01f); } + TEST_CASE("TosaRefLeakyReluActivationFloat16") { ActivationEndToEndTest<DataType::Float16>(tosaDefaultBackends, ActivationFunction::LeakyReLu, 0.3f, 5, 0.01f); } + TEST_CASE("TosaRefLeakyReluActivationInt8") { ActivationEndToEndTest<DataType::QAsymmS8>(tosaDefaultBackends, ActivationFunction::LeakyReLu, 0.6f, 7, 0.01f); } + TEST_CASE("TosaRefLeakyReluActivationInt16") { ActivationEndToEndTest<DataType::QSymmS16>(tosaDefaultBackends, ActivationFunction::LeakyReLu, 0.15f, 0, 0.01f); } -//Relu +// Relu TEST_CASE("TosaRefReLuEndToEndTestQAsymmS8") { ActivationEndToEndTest<armnn::DataType::QAsymmS8>(tosaDefaultBackends, ActivationFunction::ReLu); @@ -69,6 +72,31 @@ TEST_CASE("TosaRefReLuEndToEndTestQSymmS16") ActivationEndToEndTest<armnn::DataType::QSymmS16>(tosaDefaultBackends, ActivationFunction::ReLu); } +// BoundedRelu +TEST_CASE("TosaRefBoundedReLuEndToEndTestFloat32") +{ + ActivationEndToEndTest<armnn::DataType::Float32>( + tosaDefaultBackends, ActivationFunction::BoundedReLu, 1.0f, 0, 6.0f, 0.0f); +} + +TEST_CASE("TosaRefBoundedReLuEndToEndTestFloat16") +{ + ActivationEndToEndTest<armnn::DataType::Float16>( + tosaDefaultBackends, ActivationFunction::BoundedReLu, 1.0f, 0, 6.0f, 0.0f); +} + +TEST_CASE("TosaRefBoundedReLuEndToEndTestQAsymmS8") +{ + ActivationEndToEndTest<armnn::DataType::QAsymmS8>( + tosaDefaultBackends, ActivationFunction::BoundedReLu, 1.0f, 0, 6.0f, 0.0f); +} + +TEST_CASE("TosaRefBoundedReLuEndToEndTestQSymmS16") +{ + ActivationEndToEndTest<armnn::DataType::QSymmS16>( + tosaDefaultBackends, ActivationFunction::BoundedReLu, 1.0f, 0, 6.0f, 0.0f); +} + // Addition TEST_CASE("TosaRefAdditionEndtoEndTestFloat32") { |