AI-on-the-edge-device/code/components/tflite-lib/tensorflow/lite/micro/kernels/fill.cc

/* Copyright 2020 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/fill.h"

#include <stdint.h>

#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"

namespace tflite {

namespace {

template <typename T>
TfLiteStatus EnsureEqImpl(TfLiteContext* context, const TfLiteIntArray* array,
                          const TfLiteTensor* tensor) {
  for (int i = 0; i < array->size; ++i) {
    TF_LITE_ENSURE_EQ(context, array->data[i], GetTensorData<T>(tensor)[i]);
  }
  return kTfLiteOk;
}

// Ensure the equality of an int array and a tensor, which must be
// one-dimensional and of an integer type.
TfLiteStatus EnsureEq(TfLiteContext* context, const TfLiteIntArray* array,
                      const TfLiteTensor* tensor) {
  TF_LITE_ENSURE_EQ(context, NumDimensions(tensor), 1);
  const auto tensor_len = tensor->dims->data[0];
  TF_LITE_ENSURE_EQ(context, array->size, tensor_len);

  switch (tensor->type) {
    case kTfLiteInt8:
      return EnsureEqImpl<int8_t>(context, array, tensor);
    case kTfLiteInt16:
      return EnsureEqImpl<int16_t>(context, array, tensor);
    case kTfLiteInt32:
      return EnsureEqImpl<int32_t>(context, array, tensor);
    case kTfLiteInt64:
      return EnsureEqImpl<int64_t>(context, array, tensor);
    default:
      MicroPrintf("cannot compare int array to tensor of type %d.",
                  tensor->type);
      return kTfLiteError;
  }
}

constexpr int kDimsTensor = 0;
constexpr int kValueTensor = 1;
constexpr int kOutputTensor = 0;

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  MicroContext* micro_context = GetMicroContext(context);

  // Ensure inputs and outputs exist.
  TfLiteTensor* dims =
      micro_context->AllocateTempInputTensor(node, kDimsTensor);
  TF_LITE_ENSURE(context, dims != nullptr);
  TfLiteTensor* value =
      micro_context->AllocateTempInputTensor(node, kValueTensor);
  TF_LITE_ENSURE(context, value != nullptr);
  TfLiteTensor* output =
      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
  TF_LITE_ENSURE(context, output != nullptr);

  // The value tensor must be a scalar.
  TF_LITE_ENSURE_EQ(context, NumDimensions(value), 0);

  // The value type and output type must match.
  TF_LITE_ENSURE_EQ(context, value->type, output->type);

  // The dimension of the output tensor is known in model already.
  TFLITE_DCHECK(output->dims != nullptr);

  if (dims->data.data != nullptr) {
    // When the dims tensor is specified in model already (i.e. is not an
    // activation tensor), the dims tensor must match the output tensor shape.
    // As a byproduct, ensures the dims tensor is of an integer type.
    TF_LITE_ENSURE_OK(context, EnsureEq(context, output->dims, dims));
  }

  micro_context->DeallocateTempTfLiteTensor(dims);
  micro_context->DeallocateTempTfLiteTensor(value);
  micro_context->DeallocateTempTfLiteTensor(output);
  return kTfLiteOk;
}

template <typename T>
void FillImpl(const TfLiteEvalTensor* value, TfLiteEvalTensor* output) {
  reference_ops::Fill(
      micro::GetTensorShape(value), micro::GetTensorData<T>(value),
      micro::GetTensorShape(output), micro::GetTensorData<T>(output));
}

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteEvalTensor* value =
      micro::GetEvalInput(context, node, kValueTensor);
  TfLiteEvalTensor* output = micro::GetEvalOutput(context, node, kOutputTensor);

  switch (value->type) {
    case kTfLiteFloat32:
      FillImpl<float>(value, output);
      break;
    case kTfLiteInt32:
      FillImpl<int32_t>(value, output);
      break;
    case kTfLiteInt8:
      FillImpl<int8_t>(value, output);
      break;
    default:
      MicroPrintf("Fill only currently supports float32 for input 1, got %d.",
                  TfLiteTypeGetName(value->type));
      return kTfLiteError;
  }

  return kTfLiteOk;
}

}  // namespace

TfLiteRegistration Register_FILL() {
  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
}

}  // namespace tflite