IVGCVSW-8037 Add BROADCAST_TO to tflite classic and opaque delegate.

Signed-off-by: Idriss Chaouch <idriss.chaouch@arm.com>
Change-Id: Ibc145d0ea1ac9414b6a68b5b547bf2ea2852fd36

2 files changed, 247 insertions, 0 deletions

diff --git a/delegate/test/BroadcastToTest.cpp b/delegate/test/BroadcastToTest.cpp
new file mode 100644
index 0000000000..f4692cfb07
--- /dev/null
+++ b/delegate/test/BroadcastToTest.cpp
@@ -0,0 +1,80 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "BroadcastToTestHelper.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 <schema_generated.h>
+#include <tensorflow/lite/version.h>
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+template<typename T>
+void BroadcastToTest(std::vector<armnn::BackendId> &backends, tflite::TensorType inputTensorType)
+{
+    // Set input data
+    std::vector<T> inputValues = {
+                                      0, 1, 2, 3
+                                  };
+    // Set output data
+    std::vector<T> expectedOutputValues = {
+                                               0, 1, 2, 3,
+                                               0, 1, 2, 3,
+                                               0, 1, 2, 3
+                                           };
+
+    // The shape data
+    const std::vector<int32_t> shapeData = {3, 4};
+
+    // Set shapes
+    const std::vector<int32_t> inputShape = {1, 4};
+    const std::vector<int32_t> shapeShape = {2};
+    const std::vector<int32_t> expectedOutputShape = {3, 4};
+
+    BroadcastToTestImpl<T>(inputTensorType,
+                           tflite::BuiltinOperator_BROADCAST_TO,
+                           backends,
+                           inputValues,
+                           inputShape,
+                           shapeShape,
+                           shapeData,
+                           expectedOutputValues,
+                           expectedOutputShape);
+}
+
+TEST_SUITE("BroadcastToTests_CpuRefTests")
+{
+
+    TEST_CASE ("BroadcastTo_int_CpuRef_Test")
+    {
+        std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+        BroadcastToTest<int32_t>(backends, ::tflite::TensorType::TensorType_INT32);
+    }
+
+    TEST_CASE ("BroadcastTo_Float32_CpuRef_Test")
+    {
+        std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+        BroadcastToTest<float>(backends, ::tflite::TensorType::TensorType_FLOAT32);
+    }
+
+    TEST_CASE ("BroadcastTo_Uint8_t_CpuRef_Test")
+    {
+        std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+        BroadcastToTest<uint8_t>(backends, ::tflite::TensorType::TensorType_UINT8);
+    }
+
+    TEST_CASE ("BroadcastTo_Int8_t_CpuRef_Test")
+    {
+        std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+        BroadcastToTest<int8_t>(backends, ::tflite::TensorType::TensorType_INT8);
+    }
+
+} // TEST_SUITE("BroadcastToTests_CpuRefTests")
+}
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diff --git a/delegate/test/BroadcastToTestHelper.hpp b/delegate/test/BroadcastToTestHelper.hpp
new file mode 100644
index 0000000000..630fe3aaf1
--- /dev/null
+++ b/delegate/test/BroadcastToTestHelper.hpp
@@ -0,0 +1,167 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+#include <DelegateTestInterpreter.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/version.h>
+
+#include <schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+    std::vector<char> CreateBroadcastToTfLiteModel(tflite::BuiltinOperator operatorCode,
+                                                   tflite::TensorType inputTensorType,
+                                                   const std::vector<int32_t>& inputTensorShape,
+                                                   const std::vector<int32_t>& shapeTensorShape,
+                                                   const std::vector<int32_t>& shapeTensorData,
+                                                   const std::vector<int32_t>& outputTensorShape)
+    {
+        using namespace tflite;
+        flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+        std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+        buffers.push_back(CreateBuffer(flatBufferBuilder));
+        buffers.push_back(CreateBuffer(flatBufferBuilder));
+        buffers.push_back(CreateBuffer(flatBufferBuilder,
+                                       flatBufferBuilder.CreateVector(
+                                       reinterpret_cast<const uint8_t*>(shapeTensorData.data()),
+                                       sizeof(int32_t) * shapeTensorData.size())));
+        buffers.push_back(CreateBuffer(flatBufferBuilder));
+
+        float   qScale  = 1.0f;
+        int32_t qOffset = 0;
+
+        auto quantizationParameters =
+                CreateQuantizationParameters(flatBufferBuilder,
+                                             0,
+                                             0,
+                                             flatBufferBuilder.CreateVector<float>({ qScale }),
+                                             flatBufferBuilder.CreateVector<int64_t>({ qOffset }));
+
+        std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+        tensors[0] = CreateTensor(flatBufferBuilder,
+                                  flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+                                                                          inputTensorShape.size()),
+                                  inputTensorType,
+                                  1,
+                                  flatBufferBuilder.CreateString("input_tensor"),
+                                  quantizationParameters);
+
+        tensors[1] = CreateTensor(flatBufferBuilder,
+                                  flatBufferBuilder.CreateVector<int32_t>(shapeTensorShape.data(),
+                                                                          shapeTensorShape.size()),
+                                  TensorType_INT32,
+                                  2,
+                                  flatBufferBuilder.CreateString("shape_input_tensor"),
+                                  quantizationParameters);
+
+        tensors[2] = CreateTensor(flatBufferBuilder,
+                                  flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                          outputTensorShape.size()),
+                                  inputTensorType,
+                                  3,
+                                  flatBufferBuilder.CreateString("output_tensor"),
+                                  quantizationParameters);
+
+        // Create Operator
+        tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_BroadcastToOptions;
+        flatbuffers::Offset<void> operatorBuiltinOption = 0;
+
+        const std::vector<int> operatorInputs {0, 1};
+        const std::vector<int> operatorOutputs {2};
+
+        flatbuffers::Offset<Operator> broadcastOperator =
+                CreateOperator(flatBufferBuilder,
+                               0,
+                               flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+                               flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+                               operatorBuiltinOptionsType,
+                               operatorBuiltinOption);
+
+        const std::vector<int> subgraphInputs{0, 1};
+        const std::vector<int> subgraphOutputs{2};
+        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(&broadcastOperator, 1));
+
+        flatbuffers::Offset <flatbuffers::String> modelDescription =
+                flatBufferBuilder.CreateString("ArmnnDelegate: BrodacastTo Operator Model");
+        flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder,0,
+                                                                       0, 2,
+                                                                       tflite::BuiltinOperator_BROADCAST_TO);
+
+        flatbuffers::Offset <Model> flatbufferModel =
+                CreateModel(flatBufferBuilder,
+                            TFLITE_SCHEMA_VERSION,
+                            flatBufferBuilder.CreateVector(&opCode, 1),
+                            flatBufferBuilder.CreateVector(&subgraph, 1),
+                            modelDescription,
+                            flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+        flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
+
+        return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+                                 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+    }
+
+    template<typename T>
+    void BroadcastToTestImpl(tflite::TensorType inputTensorType,
+                             tflite::BuiltinOperator operatorCode,
+                             std::vector<armnn::BackendId>& backends,
+                             std::vector<T>& inputValues,
+                             std::vector<int32_t> inputShape,
+                             std::vector<int32_t> shapeShapes,
+                             std::vector<int32_t> shapeData,
+                             std::vector<T>& expectedOutputValues,
+                             std::vector<int32_t> expectedOutputShape)
+    {
+        using namespace delegateTestInterpreter;
+
+        std::vector<char> modelBuffer = CreateBroadcastToTfLiteModel(operatorCode,
+                                                                     inputTensorType,
+                                                                     inputShape,
+                                                                     shapeShapes,
+                                                                     shapeData,
+                                                                     expectedOutputShape);
+
+
+        // Setup interpreter with just TFLite Runtime.
+        auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
+        CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
+        CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
+        CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(shapeData, 1) == kTfLiteOk);
+        CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
+        std::vector<T>   tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
+        std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(0);
+
+        // Setup interpreter with Arm NN Delegate applied.
+        auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
+        CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
+        CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
+        CHECK(armnnInterpreter.FillInputTensor<int32_t>(shapeData, 1) == kTfLiteOk);
+        CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
+        std::vector<T>   armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
+        std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);
+
+        armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
+        armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);
+
+        tfLiteInterpreter.Cleanup();
+        armnnInterpreter.Cleanup();
+    }
+
+} // anonymous namespace
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