1 files changed, 106 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/maximum.cc b/tensorflow/contrib/lite/kernels/maximum.cc
new file mode 100644
index 0000000000..9fdf2b47ea
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/maximum.cc
@@ -0,0 +1,106 @@
+/* Copyright 2018 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 <string.h>
+#include <vector>
+#include "tensorflow/contrib/lite/builtin_op_data.h"
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace maximum {
+
+// This file has a reference implemenation of TFMaximum.
+enum KernelType {
+  kReference,
+};
+
+constexpr int kInputTensor1 = 0;
+constexpr int kInputTensor2 = 1;
+constexpr int kOutputTensor = 0;
+
+struct MaximumContext {
+  MaximumContext(TfLiteContext* context, TfLiteNode* node) {
+    input1 = GetInput(context, node, kInputTensor1);
+    input2 = GetInput(context, node, kInputTensor2);
+    output = GetOutput(context, node, kOutputTensor);
+  }
+  TfLiteTensor* input1;
+  TfLiteTensor* input2;
+  TfLiteTensor* output;
+};
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  MaximumContext op_context(context, node);
+  TF_LITE_ENSURE_EQ(context, op_context.input1->type, op_context.input2->type);
+  TfLiteIntArray* output_dims = TfLiteIntArrayCopy(op_context.input2->dims);
+  op_context.output->type = op_context.input2->type;
+  return context->ResizeTensor(context, op_context.output, output_dims);
+}
+
+template <KernelType kernel_type>
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  MaximumContext op_context(context, node);
+
+#define TF_LITE_MAXIMUM(kernel_type, data_type)    \
+  kernel_type::TensorFlowMaximum<data_type>(       \
+      GetTensorData<data_type>(op_context.input1), \
+      GetTensorDims(op_context.input1),            \
+      GetTensorData<data_type>(op_context.input2), \
+      GetTensorDims(op_context.input2),            \
+      GetTensorData<data_type>(op_context.output), \
+      GetTensorDims(op_context.output))
+
+  if (kernel_type == kReference) {
+    switch (op_context.output->type) {
+      case kTfLiteFloat32:
+        TF_LITE_MAXIMUM(reference_ops, float);
+        break;
+      default:
+        context->ReportError(context,
+                             "Type %d is currently not supported by Maximum.",
+                             op_context.output->type);
+        return kTfLiteError;
+    }
+  } else {
+    context->ReportError(context,
+                         "Type %d is currently not supported by Maximum.",
+                         op_context.output->type);
+    return kTfLiteError;
+  }
+#undef TF_LITE_MAXIMUM
+  return kTfLiteOk;
+}
+
+}  // namespace maximum
+
+TfLiteRegistration* Register_MAXIMUM_REF() {
+  static TfLiteRegistration r = {nullptr, nullptr, maximum::Prepare,
+                                 maximum::Eval<maximum::kReference>};
+  return &r;
+}
+
+TfLiteRegistration* Register_MAXIMUM() { return Register_MAXIMUM_REF(); }
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite