tensorflow/lite/micro/kernels/xtensa_hifimini_staging/softmax.cc

/*******************************************************************************
 * Copyright (c) 2020 Cadence Design Systems, Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining
 * a copy of this software and associated documentation files (the
 * "Software"), to use this Software with Cadence processor cores only and
 * not with any other processors and platforms, subject to
 * the following conditions:
 *
 * The above copyright notice and this permission notice shall be included
 * in all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
 * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
 * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
 * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
 * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 ******************************************************************************/

/* 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 "tensorflow/lite/kernels/internal/reference/softmax.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/xtensa_hifimini_staging/xtensa_tf_micro_common.h"

namespace tflite {
namespace ops {
namespace micro {
namespace activations {
namespace {

struct OpData {
  int32_t input_multiplier;
  int32_t input_left_shift;
  int32_t diff_min;
  int scratch_tensor_index;
};

}  // namespace

TfLiteStatus CalculateSoftmaxOpData(TfLiteContext* context,
                                    const TfLiteTensor* input,
                                    TfLiteTensor* output,
                                    const TfLiteSoftmaxParams* params,
                                    OpData* op_data) {
  if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) {
    if (input->type == kTfLiteUInt8) {
      TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
    } else {
      if (output->type == kTfLiteInt16) {
        TF_LITE_ENSURE_EQ(context, output->params.zero_point,
                          std::numeric_limits<int16_t>::min());
        // NOTE: Current int16 softmax output does not require symmetric scaling
        // - so no need to verify scale here.
      } else {
        TF_LITE_ENSURE_EQ(context, output->params.zero_point,
                          std::numeric_limits<int8_t>::min());
        TF_LITE_ENSURE(context, output->params.scale == 1.f / 256);
      }
    }

    static const int kScaledDiffIntegerBits = 5;

    int input_left_shift;
    tflite::PreprocessSoftmaxScaling(
        static_cast<double>(params->beta),
        static_cast<double>(input->params.scale), kScaledDiffIntegerBits,
        &op_data->input_multiplier, &input_left_shift);
    op_data->input_left_shift = input_left_shift;
    op_data->diff_min =
        -1.0 * tflite::CalculateInputRadius(kScaledDiffIntegerBits,
                                            op_data->input_left_shift);
  }
  return kTfLiteOk;
}

void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length) {
  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
  return context->AllocatePersistentBuffer(context, sizeof(OpData));
}

TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) {
  auto* params = static_cast<TfLiteSoftmaxParams*>(node->builtin_data);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  const TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TF_LITE_ENSURE(context, NumDimensions(input) >= 1);

  TFLITE_DCHECK(node->user_data != nullptr);
  OpData* op_data = static_cast<OpData*>(node->user_data);

  const RuntimeShape& input_shape = GetTensorShape(input);
  const RuntimeShape& output_shape = GetTensorShape(output);
  const int trailing_dim = input_shape.DimensionsCount() - 1;
  const int depth =
      MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
  int scratch_size =
      xa_nn_get_softmax_scratch_size(PREC_SYM8S, PREC_SYM8S, depth);

  const TfLiteStatus scratch_status = context->RequestScratchBufferInArena(
      context, scratch_size, &(op_data->scratch_tensor_index));
  TF_LITE_ENSURE_OK(context, scratch_status);
  // Allocate an array to precompute exponents over all int8 inputs, applying
  // the scale and beta before calculating exp. It is mandatory to apply beta
  // and scale here, since each softmax op may have different beta and scale
  // values. Beta and scale will remain constant for a given softmax op.

  TF_LITE_ENSURE_STATUS(
      CalculateSoftmaxOpData(context, input, output, params, op_data));

  return kTfLiteOk;
}

TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
  auto* op_data = static_cast<OpData*>(node->user_data);

  const TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);

  if (input->type == kTfLiteInt8 && output->type == kTfLiteInt16) {
    const RuntimeShape& input_shape = GetTensorShape(input);
    const int8_t* input_data = GetTensorData<int8_t>(input);
    const RuntimeShape& output_shape = GetTensorShape(output);
    int16* output_data = GetTensorData<int16>(output);
    const int trailing_dim = input_shape.DimensionsCount() - 1;
    const int outer_size =
        MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
    const int depth =
        MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);

    void* p_scratch = static_cast<void*>(
        context->GetScratchBuffer(context, op_data->scratch_tensor_index));
    TFLITE_DCHECK(p_scratch != nullptr);

    for (int i = 0; i < outer_size; ++i) {
      int err = xa_nn_vec_softmax_asym8s_16(
          &output_data[i * depth], &input_data[i * depth], op_data->diff_min,
          op_data->input_left_shift, op_data->input_multiplier, depth,
          p_scratch);
      CHECK_ERR_HIFI_NNLIB_KER(err, "xa_nn_vec_softmax_asym8s_16 failed");
    }
    return kTfLiteOk;
  } else {
    TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
                       TfLiteTypeGetName(input->type), input->type);
    return kTfLiteError;
  }
}
}  // namespace activations

TfLiteRegistration Register_SOFTMAX() {
  return {/*init=*/activations::SoftmaxInit,
          /*free=*/nullptr,
          /*prepare=*/activations::SoftmaxPrepare,
          /*invoke=*/activations::SoftmaxEval,
          /*profiling_string=*/nullptr,
          /*builtin_code=*/0,
          /*custom_name=*/nullptr,
          /*version=*/0};
}

}  // namespace micro
}  // namespace ops
}  // namespace tflite