Add logistic kernel for Kelvin

Change-Id: I9e36fea5d4b32d6ac03a3d2f78c7362563bd276e

tensorflow/lite/micro/kernels/kelvin/logistic.cc

diff --git a/tensorflow/lite/micro/kernels/kelvin/logistic.cc b/tensorflow/lite/micro/kernels/kelvin/logistic.cc
new file mode 100644
index 0000000..974ef12
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/kelvin/logistic.cc
@@ -0,0 +1,112 @@
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h"
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/quantization_util.h"
+#include "tensorflow/lite/kernels/internal/reference/logistic.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/logistic.h"
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tflm/opt/opt.h"
+
+namespace tflite {
+namespace {
+
+void* LogisticInit(TfLiteContext* context, const char* buffer, size_t length) {
+  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+  return context->AllocatePersistentBuffer(context, sizeof(OpDataLogistic));
+}
+
+TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) {
+  const TfLiteEvalTensor* input =
+      tflite::micro::GetEvalInput(context, node, kLogisticInputTensor);
+  TfLiteEvalTensor* output =
+      tflite::micro::GetEvalOutput(context, node, kLogisticOutputTensor);
+
+  TFLITE_DCHECK(node->user_data != nullptr);
+  OpDataLogistic* data = static_cast<OpDataLogistic*>(node->user_data);
+
+  if (input->type == kTfLiteFloat32) {
+    switch (output->type) {
+      case kTfLiteFloat32: {
+        reference_ops::Logistic(tflite::micro::GetTensorShape(input),
+                                tflite::micro::GetTensorData<float>(input),
+                                tflite::micro::GetTensorShape(output),
+                                tflite::micro::GetTensorData<float>(output));
+        return kTfLiteOk;
+      }
+      default:
+        MicroPrintf("Input %s, output %s not supported.",
+                    TfLiteTypeGetName(input->type),
+                    TfLiteTypeGetName(output->type));
+        return kTfLiteError;
+    }
+  } else if (input->type == kTfLiteInt16) {
+    switch (output->type) {
+      case kTfLiteInt16: {
+        reference_integer_ops::Logistic(
+            data->input_multiplier, data->input_left_shift,
+            NumElements(input->dims),
+            tflite::micro::GetTensorData<int16_t>(input),
+            tflite::micro::GetTensorData<int16_t>(output));
+        return kTfLiteOk;
+      }
+      default:
+        MicroPrintf("Input %s, output %s not supported.",
+                    TfLiteTypeGetName(input->type),
+                    TfLiteTypeGetName(output->type));
+        return kTfLiteError;
+    }
+  } else if (input->type == kTfLiteInt8) {
+    switch (output->type) {
+      case kTfLiteInt8: {
+        kelvin::opt::LogisticS8(
+            data->input_zero_point, data->input_range_radius,
+            data->input_multiplier, data->input_left_shift,
+            NumElements(input->dims),
+            tflite::micro::GetTensorData<int8_t>(input),
+            tflite::micro::GetTensorData<int8_t>(output));
+        return kTfLiteOk;
+      }
+      default:
+        MicroPrintf("Input %s, output %s not supported.",
+                    TfLiteTypeGetName(input->type),
+                    TfLiteTypeGetName(output->type));
+        return kTfLiteError;
+    }
+  } else {
+    // TODO(b/141211002): Also support other data types once we have supported
+    // temporary tensors in TFLM.
+    MicroPrintf("Input %s, output %s not supported.",
+                TfLiteTypeGetName(input->type),
+                TfLiteTypeGetName(output->type));
+    return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
+}  // namespace
+
+TFLMRegistration Register_LOGISTIC() {
+  return tflite::micro::RegisterOp(LogisticInit, LogisticPrepare, LogisticEval);
+}
+}  // namespace tflite