MLCE-510 Add CpuRef Shape Operator to ArmNN

 * Add TfLiteParser and delegate support

Signed-off-by: Keith Davis <keith.davis@arm.com>
Change-Id: Id3219ba7cc7128b5e73de2c7d8d076a40dcce9c5

5 files changed, 312 insertions, 0 deletions

diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index c7ac4390c5..b43feb7f9c 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -42,6 +42,7 @@ list(APPEND armnnDelegate_sources
         src/Reduce.hpp
         src/Resize.hpp
         src/Round.hpp
+        src/Shape.hpp
         src/Slice.hpp
         src/Softmax.hpp
         src/SpaceDepth.hpp
@@ -170,6 +171,8 @@ if(BUILD_UNIT_TESTS)
         src/test/SoftmaxTestHelper.hpp
         src/test/SpaceDepthTest.cpp
         src/test/SpaceDepthTestHelper.hpp
+        src/test/ShapeTest.cpp
+        src/test/ShapeTestHelper.hpp
         src/test/SliceTest.cpp
         src/test/SliceTestHelper.hpp
         src/test/SplitTest.cpp
diff --git a/delegate/src/Shape.hpp b/delegate/src/Shape.hpp
new file mode 100644
index 0000000000..b173299a62
--- /dev/null
+++ b/delegate/src/Shape.hpp
@@ -0,0 +1,86 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "DelegateUtils.hpp"
+
+#include <tensorflow/lite/builtin_ops.h>
+#include <tensorflow/lite/c/builtin_op_data.h>
+#include <tensorflow/lite/c/common.h>
+#include <tensorflow/lite/minimal_logging.h>
+#include <numeric>
+
+namespace armnnDelegate
+{
+
+TfLiteStatus VisitShapeOperator(DelegateData& delegateData,
+                               TfLiteContext* tfLiteContext,
+                               TfLiteNode* tfLiteNode,
+                               int nodeIndex,
+                               int32_t operatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+    auto* shapeParameters = reinterpret_cast<TfLiteShapeParams*>(tfLiteNode->builtin_data);
+    if ( shapeParameters->out_type != kTfLiteInt32 && shapeParameters->out_type != kTfLiteInt64 )
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ",
+            operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    bool isSupported = false;
+    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC(__func__,
+                                   tfLiteContext,
+                                   IsShapeSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   inputTensorInfo,
+                                   outInfo);
+    };
+
+    // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the
+    // support for the operator
+    // If supported, VisitShapeOperator will be called again to add the layer to the network as seen further below
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Add a Shape layer
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddShapeLayer();
+    ARMNN_ASSERT(layer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // Connect
+    return Connect(layer, tfLiteNode, delegateData);
+}
+
+} // namespace armnnDelegate
diff --git a/delegate/src/armnn_delegate.cpp b/delegate/src/armnn_delegate.cpp
index 0c984ecc82..0ac33808b0 100644
--- a/delegate/src/armnn_delegate.cpp
+++ b/delegate/src/armnn_delegate.cpp
@@ -30,6 +30,7 @@
 #include "Reduce.hpp"
 #include "Resize.hpp"
 #include "Round.hpp"
+#include "Shape.hpp"
 #include "Slice.hpp"
 #include "Softmax.hpp"
 #include "SpaceDepth.hpp"
@@ -805,6 +806,12 @@ TfLiteStatus ArmnnSubgraph::VisitNode(DelegateData& delegateData,
                                                  tfLiteNode,
                                                  nodeIndex,
                                                  armnn::UnaryOperation::Rsqrt);
+        case kTfLiteBuiltinShape:
+            return VisitShapeOperator(delegateData,
+                                      tfLiteContext,
+                                      tfLiteNode,
+                                      nodeIndex,
+                                      kTfLiteBuiltinShape);
         case kTfLiteBuiltinSplit:
             return VisitSplitOperator(delegateData,
                                       tfLiteContext,
diff --git a/delegate/src/test/ShapeTest.cpp b/delegate/src/test/ShapeTest.cpp
new file mode 100644
index 0000000000..b49910adf6
--- /dev/null
+++ b/delegate/src/test/ShapeTest.cpp
@@ -0,0 +1,45 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ShapeTestHelper.hpp"
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+void ShapeSimpleTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> inputShape{ 1, 3, 2, 3 };
+
+    std::vector<int32_t> inputValues{ 1, 1, 1, 1, 1, 1, 1, 1,
+                                      1, 1, 1, 1, 1, 1, 1, 1, };
+
+    std::vector<int32_t> expectedOutputShape{ 4 };
+    std::vector<int32_t> expectedOutputValues{ 1, 3, 2, 3 };
+
+    ShapeTest<int32_t, int32_t>(::tflite::TensorType_INT32,
+                                ::tflite::TensorType_INT32,
+                                backends,
+                                inputShape,
+                                inputValues,
+                                expectedOutputValues,
+                                expectedOutputShape);
+}
+
+// SHAPE Test Suite
+TEST_SUITE("SHAPE_CpuRefTests")
+{
+
+TEST_CASE("SHAPE_Simple_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    ShapeSimpleTest(backends);
+}
+
+}
+// End of SHAPE Test Suite
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/ShapeTestHelper.hpp b/delegate/src/test/ShapeTestHelper.hpp
new file mode 100644
index 0000000000..854c5084aa
--- /dev/null
+++ b/delegate/src/test/ShapeTestHelper.hpp
@@ -0,0 +1,171 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+std::vector<char> CreateShapeTfLiteModel(tflite::TensorType inputTensorType,
+                                         tflite::TensorType outputTensorType,
+                                         const std::vector<int32_t>& inputTensorShape,
+                                         const std::vector<int32_t>& outputTensorShape,
+                                         float quantScale = 1.0f,
+                                         int quantOffset = 0)
+{
+    using namespace tflite;
+    flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+    buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+
+    auto quantizationParameters =
+             CreateQuantizationParameters(flatBufferBuilder,
+                                          0,
+                                          0,
+                                          flatBufferBuilder.CreateVector<float>({ quantScale }),
+                                          flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+    std::array<flatbuffers::Offset<Tensor>, 2> tensors;
+    tensors[0] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+                                                                      inputTensorShape.size()),
+                              inputTensorType,
+                              0,
+                              flatBufferBuilder.CreateString("input"),
+                              quantizationParameters);
+    tensors[1] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                      outputTensorShape.size()),
+                              outputTensorType,
+                              0,
+                              flatBufferBuilder.CreateString("output"),
+                              quantizationParameters);
+
+    const std::vector<int32_t> operatorInputs({ 0 });
+    const std::vector<int32_t> operatorOutputs({ 1 });
+
+    flatbuffers::Offset<Operator> shapeOperator =
+                                      CreateOperator(flatBufferBuilder,
+                                                     0,
+                                                     flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(),
+                                                                                             operatorInputs.size()),
+                                                     flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(),
+                                                                                             operatorOutputs.size()),
+                                                     BuiltinOptions_ShapeOptions,
+                                                     CreateShapeOptions(flatBufferBuilder, outputTensorType).Union());
+
+    flatbuffers::Offset<flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: SHAPE Operator Model");
+
+    flatbuffers::Offset<OperatorCode> operatorCode =
+        CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_SHAPE);
+
+    const std::vector<int32_t>    subgraphInputs({ 0 });
+    const std::vector<int32_t>    subgraphOutputs({ 1 });
+
+    flatbuffers::Offset<SubGraph> subgraph =
+        CreateSubGraph(flatBufferBuilder,
+                       flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(),
+                                                               subgraphInputs.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(),
+                                                               subgraphOutputs.size()),
+                       flatBufferBuilder.CreateVector(&shapeOperator, 1));
+
+    flatbuffers::Offset<Model> flatbufferModel =
+        CreateModel(flatBufferBuilder,
+                    TFLITE_SCHEMA_VERSION,
+                    flatBufferBuilder.CreateVector(&operatorCode, 1),
+                    flatBufferBuilder.CreateVector(&subgraph, 1),
+                    modelDescription,
+                    flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+    flatBufferBuilder.Finish(flatbufferModel);
+
+    return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+                             flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+template<typename T, typename K>
+void ShapeTest(tflite::TensorType inputTensorType,
+               tflite::TensorType outputTensorType,
+               std::vector<armnn::BackendId>& backends,
+               std::vector<int32_t>& inputShape,
+               std::vector<T>& inputValues,
+               std::vector<K>& expectedOutputValues,
+               std::vector<int32_t>& expectedOutputShape,
+               float quantScale = 1.0f,
+               int quantOffset = 0)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateShapeTfLiteModel(inputTensorType,
+                                                           outputTensorType,
+                                                           inputShape,
+                                                           expectedOutputShape,
+                                                           quantScale,
+                                                           quantOffset);
+
+    const Model* tfLiteModel = GetModel(modelBuffer.data());
+
+    // Create TfLite Interpreters
+    std::unique_ptr<Interpreter> armnnDelegate;
+
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+              (&armnnDelegate) == kTfLiteOk);
+    CHECK(armnnDelegate != nullptr);
+    CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk);
+
+    std::unique_ptr<Interpreter> tfLiteDelegate;
+
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+              (&tfLiteDelegate) == kTfLiteOk);
+    CHECK(tfLiteDelegate != nullptr);
+    CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk);
+
+    // Create the ArmNN Delegate
+    armnnDelegate::DelegateOptions delegateOptions(backends);
+
+    std::unique_ptr < TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete) >
+        theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+                         armnnDelegate::TfLiteArmnnDelegateDelete);
+
+    CHECK(theArmnnDelegate != nullptr);
+
+    // Modify armnnDelegateInterpreter to use armnnDelegate
+    CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+    // Set input data
+    armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues);
+    armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues);
+
+    // Run EnqueWorkload
+    CHECK(tfLiteDelegate->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegate->Invoke() == kTfLiteOk);
+
+    // Compare output data
+    armnnDelegate::CompareOutputData<K>(tfLiteDelegate,
+                                        armnnDelegate,
+                                        expectedOutputShape,
+                                        expectedOutputValues,
+                                        0);
+
+    tfLiteDelegate.reset(nullptr);
+    armnnDelegate.reset(nullptr);
+}
+
+} // anonymous namespace