87 lines
3.2 KiB
C++
87 lines
3.2 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_
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#include <cmath>
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#include "fixedpoint/fixedpoint.h"
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/cppmath.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/op_macros.h"
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namespace tflite {
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namespace reference_ops {
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inline void Tanh(const RuntimeShape& input_shape, const float* input_data,
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const RuntimeShape& output_shape, float* output_data) {
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const int flat_size = MatchingFlatSize(input_shape, output_shape);
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for (int i = 0; i < flat_size; i++) {
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float val = input_data[i];
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float result = std::tanh(val);
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output_data[i] = result;
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}
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}
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// Convenience version that allows, for example, generated-code calls to be
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// uniform between data types.
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inline void Tanh(const TanhParams&, const RuntimeShape& input_shape,
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const float* input_data, const RuntimeShape& output_shape,
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float* output_data) {
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// Drop params: not needed.
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Tanh(input_shape, input_data, output_shape, output_data);
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}
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inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape,
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const int16* input_data, const RuntimeShape& output_shape,
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int16* output_data) {
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const int input_left_shift = params.input_left_shift;
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// Support for shifts is limited until we have a parameterized version of
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// SaturatingRoundingMultiplyByPOT().
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TFLITE_DCHECK_GE(input_left_shift, 0);
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TFLITE_DCHECK_LE(input_left_shift, 1);
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const int flat_size = MatchingFlatSize(input_shape, output_shape);
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// F0 uses 0 integer bits, range [-1, 1].
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// This is the return type of math functions such as tanh, logistic,
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// whose range is in [-1, 1].
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using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
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// F3 uses 3 integer bits, range [-8, 8], the input range expected here.
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using F3 = gemmlowp::FixedPoint<std::int16_t, 3>;
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if (input_left_shift == 0) {
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for (int i = 0; i < flat_size; i++) {
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F3 input = F3::FromRaw(input_data[i]);
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F0 output = gemmlowp::tanh(input);
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output_data[i] = output.raw();
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}
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} else {
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for (int i = 0; i < flat_size; i++) {
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F3 input = F3::FromRaw(
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gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i]));
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F0 output = gemmlowp::tanh(input);
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output_data[i] = output.raw();
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}
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}
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}
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} // namespace reference_ops
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_
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