133 lines
5.2 KiB
C++
133 lines
5.2 KiB
C++
/* Copyright 2019 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_LOGISTIC_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_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/quantization_util.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 Logistic(const RuntimeShape& input_shape, const float* input_data,
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const RuntimeShape& output_shape, float* output_data) {
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const float cutoff_upper = 16.619047164916992188f;
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const float cutoff_lower = -9.f;
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const int flat_size = MatchingFlatSize(input_shape, output_shape);
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// Rational for using approximation in reference kernel.
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// 0. This approximation gives enough precision for float.
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// 1. This works around an issue on an embedded chipset where exp() does not
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// return correctly as expected - exp(x) should return inf when overflown
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// not 1.701417 IEEE 754 defines representation for inf.
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// 2. This will speed up calculation and is matching the behavior in the
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// optimized kernels. (check the definition of scalar_logistic_op<float>)
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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;
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if (val > cutoff_upper) {
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result = 1.0f;
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} else if (val < cutoff_lower) {
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result = std::exp(val);
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} else {
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result = 1.f / (1.f + std::exp(-val));
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}
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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 Logistic(const LogisticParams&, 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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Logistic(input_shape, input_data, output_shape, output_data);
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}
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inline void Logistic(const LogisticParams& params,
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const RuntimeShape& input_shape, const int16_t* input_data,
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const RuntimeShape& output_shape, int16_t* 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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// 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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const F3 input = F3::FromRaw(input_data[i]);
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F0 output = gemmlowp::logistic(input);
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output_data[i] = output.raw();
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}
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}
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// Quantized int8_t logistic activation. Cheats by dequantizing and
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// requantizing around the floating point logistic method. This implementation
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// is slow on platforms without a floating point unit.
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// TODO(b/141211002): Delete this int8_t implementation once we can reuse the
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// approach used in TFLite for int8_t Logistic.
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inline void Logistic(const RuntimeShape& input_shape, const int8_t* input_data,
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float input_scale, int input_zero_point,
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const RuntimeShape& output_shape, int8_t* output_data,
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float output_scale, int output_zero_point) {
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const float cutoff_upper = 16.619047164916992188f;
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const float cutoff_lower = -9.f;
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const int flat_size = MatchingFlatSize(input_shape, output_shape);
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// Rational for using approximation in reference kernel.
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// 0. This approximation gives enough precision for float.
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// 1. This works around an issue on an embedded chipset where exp() does not
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// return correctly as expected - exp(x) should return inf when overflown
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// not 1.701417 IEEE 754 defines representation for inf.
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// 2. This will speed up calculation and is matching the behavior in the
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// optimized kernels. (check the definition of scalar_logistic_op<float>)
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for (int i = 0; i < flat_size; i++) {
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// Dequantize.
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float val =
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static_cast<float>((input_data[i] - input_zero_point) * input_scale);
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float result;
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if (val > cutoff_upper) {
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result = 1.0f;
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} else if (val < cutoff_lower) {
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result = std::exp(val);
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} else {
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result = 1.f / (1.f + std::exp(-val));
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}
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// Requantize
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int8_t output =
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static_cast<int8_t>(result / output_scale + output_zero_point);
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output_data[i] = output;
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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_LOGISTIC_H_
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