IVGCVSW-5395 TfLiteDelegate: Implement the Softmax operators
Signed-off-by: James Ward <james.ward@arm.com>
Change-Id: I9f098c6b62ebb08e727aa8547e08bddc0b814705
diff --git a/delegate/src/test/SoftmaxTestHelper.hpp b/delegate/src/test/SoftmaxTestHelper.hpp
new file mode 100644
index 0000000..0474561
--- /dev/null
+++ b/delegate/src/test/SoftmaxTestHelper.hpp
@@ -0,0 +1,170 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn_delegate.hpp>
+#include <armnnUtils/FloatingPointComparison.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> CreateSoftmaxTfLiteModel(tflite::BuiltinOperator softmaxOperatorCode,
+ tflite::TensorType tensorType,
+ const std::vector <int32_t>& tensorShape,
+ float beta)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+
+ std::array<flatbuffers::Offset<Tensor>, 2> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
+ tensorShape.size()),
+ tensorType,
+ 0);
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
+ tensorShape.size()),
+ tensorType,
+ 0);
+
+ const std::vector<int32_t> operatorInputs({0});
+ const std::vector<int32_t> operatorOutputs({1});
+
+ flatbuffers::Offset<Operator> softmaxOperator;
+ flatbuffers::Offset<flatbuffers::String> modelDescription;
+ flatbuffers::Offset<OperatorCode> operatorCode;
+
+ switch (softmaxOperatorCode)
+ {
+ case tflite::BuiltinOperator_SOFTMAX:
+ softmaxOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ BuiltinOptions_SoftmaxOptions,
+ CreateSoftmaxOptions(flatBufferBuilder, beta).Union());
+ modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Softmax Operator Model");
+ operatorCode = CreateOperatorCode(flatBufferBuilder,
+ tflite::BuiltinOperator_SOFTMAX);
+ break;
+ case tflite::BuiltinOperator_LOG_SOFTMAX:
+ softmaxOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ BuiltinOptions_LogSoftmaxOptions,
+ CreateLogSoftmaxOptions(flatBufferBuilder).Union());
+ flatBufferBuilder.CreateString("ArmnnDelegate: Log-Softmax Operator Model");
+ operatorCode = CreateOperatorCode(flatBufferBuilder,
+ tflite::BuiltinOperator_LOG_SOFTMAX);
+ break;
+ default:
+ break;
+ }
+ 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(&softmaxOperator, 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());
+}
+
+void SoftmaxTest(tflite::BuiltinOperator softmaxOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& shape,
+ std::vector<float>& inputValues,
+ std::vector<float>& expectedOutputValues,
+ float beta = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateSoftmaxTfLiteModel(softmaxOperatorCode,
+ tensorType,
+ shape,
+ beta);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+ // Create TfLite Interpreters
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&tfLiteInterpreter) == kTfLiteOk);
+ CHECK(tfLiteInterpreter != nullptr);
+ CHECK(tfLiteInterpreter->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(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+ // Set input data
+ auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
+ auto tfLiteInterpreterInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteInterpreterInputData[i] = inputValues[i];
+ }
+
+ auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
+ auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ armnnDelegateInputData[i] = inputValues[i];
+ }
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+
+ for (size_t i = 0; i < inputValues.size(); ++i)
+ {
+ CHECK(armnnUtils::within_percentage_tolerance(expectedOutputValues[i], armnnDelegateOutputData[i], 1e-5));
+ CHECK(armnnUtils::within_percentage_tolerance(tfLiteInterpreterOutputData[i],
+ armnnDelegateOutputData[i], 1e-5));
+ }
+}
+
+} // anonymous namespace