third_party/hexagon/svdf.cc

/* Copyright 2019 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.
==============================================================================*/

/* Copyright 2020 The Qualcomm Innovation Center, Inc. All Rights Reserved.

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modification, are permitted (subject to the limitations in the disclaimer
below) provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice,
  this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.
* Neither the name of Qualcomm Innovation Center, Inc. nor the names of its
  contributors may be used to endorse or promote products derived from this
  software without specific prior written permission.

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THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
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PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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==============================================================================*/

#include <math.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/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/activation_utils.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/micro_utils.h"
#include "third_party/hexagon/hexagon_svdf.h"
#include "third_party/hexagon/hexagon_tflm_translation_svdf.h"

namespace tflite {

TfLiteStatus SvdfEval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteSVDFParams*>(node->builtin_data);
  TFLITE_DCHECK(node->user_data != nullptr);
  const HexagonOpDataSvdf& data =
      *(static_cast<const HexagonOpDataSvdf*>(node->user_data));

  const TfLiteEvalTensor* input =
      tflite::micro::GetEvalInput(context, node, kSvdfInputTensor);
  const TfLiteEvalTensor* weights_feature =
      tflite::micro::GetEvalInput(context, node, kSvdfWeightsFeatureTensor);
  const TfLiteEvalTensor* weights_time =
      tflite::micro::GetEvalInput(context, node, kSvdfWeightsTimeTensor);
  const TfLiteEvalTensor* bias =
      (NumInputs(node) == 5)
          ? tflite::micro::GetEvalInput(context, node, kSvdfBiasTensor)
          : nullptr;
  TfLiteEvalTensor* activation_state = tflite::micro::GetMutableEvalInput(
      context, node, kSvdfInputActivationStateTensor);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kSvdfOutputTensor);

  switch (weights_feature->type) {
    case kTfLiteFloat32: {
      EvalFloatSvdfReference(context, node, input, weights_feature,
                             weights_time, bias, params,
                             data.reference_op_data.scratch_tensor_index,
                             activation_state, output);
      return kTfLiteOk;
      break;
    }

    case kTfLiteInt8: {
      return HexagonSvdfEvalInt8(context, node);
    }

    default:
      MicroPrintf( "Type %s not currently supported.",
                         TfLiteTypeGetName(weights_feature->type));
      return kTfLiteError;
  }
  return kTfLiteOk;
}

TFLMRegistration Register_SVDF() {
  return tflite::micro::RegisterOp(HexagonSvdfInit, HexagonSvdfPrepare,
                                   SvdfEval);
}

}  // namespace tflite