tensorflow--tensorflow/tensorflow/lite/kernels/bitwise_xor.cc

/* Copyright 2023 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/core/c/c_api_types.h"
#include "tensorflow/lite/core/c/common.h"
#include "tensorflow/lite/kernels/internal/reference/binary_function.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"

namespace tflite {
namespace ops {
namespace builtin {
namespace bitwise_xor {

// Input/output tensor index.
constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;

// Op data for bitwise xor op.
struct OpData {
  bool requires_broadcast = false;
};

void* Init(TfLiteContext* context, const char* buffer, size_t length) {
  auto* data = new OpData;
  return data;
}

void Free(TfLiteContext* context, void* buffer) {
  delete reinterpret_cast<OpData*>(buffer);
}

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  const TfLiteTensor* input1;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor1, &input1));
  const TfLiteTensor* input2;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor2, &input2));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);

  output->type = input1->type;

  data->requires_broadcast = !HaveSameShapes(input1, input2);

  TfLiteIntArray* output_size = nullptr;
  if (data->requires_broadcast) {
    TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
                                   context, input1, input2, &output_size));
  } else {
    output_size = TfLiteIntArrayCopy(input1->dims);
  }

  return context->ResizeTensor(context, output, output_size);
}

template <typename T>
T BitwiseXor(T x, T y) {
  return x ^ y;
}

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  const TfLiteTensor* input1;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor1, &input1));
  const TfLiteTensor* input2;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor2, &input2));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  const TfLiteType type = output->type;
  switch (type) {
    // The fallthrough is indended. Since bitwise xor function operates on the
    // underlying binary representation of the integers, both integers and
    // unsigned integers will have the same behavior
    case kTfLiteUInt8:
    case kTfLiteInt8: {
      if (data->requires_broadcast) {
        reference_ops::BroadcastBinaryFunction4DSlow<int8_t, int8_t, int8_t>(
            GetTensorShape(input1), GetTensorData<int8_t>(input1),
            GetTensorShape(input2), GetTensorData<int8_t>(input2),
            GetTensorShape(output), GetTensorData<int8_t>(output), BitwiseXor);
      } else {
        reference_ops::BinaryFunction<int8_t, int8_t, int8_t>(
            GetTensorShape(input1), GetTensorData<int8_t>(input1),
            GetTensorShape(input2), GetTensorData<int8_t>(input2),
            GetTensorShape(output), GetTensorData<int8_t>(output), BitwiseXor);
      }
      break;
    }
    case kTfLiteUInt16:
    case kTfLiteInt16: {
      if (data->requires_broadcast) {
        reference_ops::BroadcastBinaryFunction4DSlow<int16_t, int16_t, int16_t>(
            GetTensorShape(input1), GetTensorData<int16_t>(input1),
            GetTensorShape(input2), GetTensorData<int16_t>(input2),
            GetTensorShape(output), GetTensorData<int16_t>(output), BitwiseXor);
      } else {
        reference_ops::BinaryFunction<int16_t, int16_t, int16_t>(
            GetTensorShape(input1), GetTensorData<int16_t>(input1),
            GetTensorShape(input2), GetTensorData<int16_t>(input2),
            GetTensorShape(output), GetTensorData<int16_t>(output), BitwiseXor);
      }
      break;
    }
    case kTfLiteUInt32:
    case kTfLiteInt32: {
      if (data->requires_broadcast) {
        reference_ops::BroadcastBinaryFunction4DSlow<int32_t, int32_t, int32_t>(
            GetTensorShape(input1), GetTensorData<int32_t>(input1),
            GetTensorShape(input2), GetTensorData<int32_t>(input2),
            GetTensorShape(output), GetTensorData<int32_t>(output), BitwiseXor);
      } else {
        reference_ops::BinaryFunction<int32_t, int32_t, int32_t>(
            GetTensorShape(input1), GetTensorData<int32_t>(input1),
            GetTensorShape(input2), GetTensorData<int32_t>(input2),
            GetTensorShape(output), GetTensorData<int32_t>(output), BitwiseXor);
      }
      break;
    }
    default:
      TF_LITE_KERNEL_LOG(context,
                         "BitwiseXor currently only supports "
                         "8-bit/16-bit/32-bit integer/unsigned integer, got %s",
                         TfLiteTypeGetName(type));
      return kTfLiteError;
  }

  return kTfLiteOk;
}

}  // namespace bitwise_xor

TfLiteRegistration* Register_BITWISE_XOR() {
  static TfLiteRegistration r = {bitwise_xor::Init, bitwise_xor::Free,
                                 bitwise_xor::Prepare, bitwise_xor::Eval};
  return &r;
}

}  // namespace builtin
}  // namespace ops
}  // namespace tflite