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TFL MCU: Move reference L2Normalization implementation into its own file.

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so that we won't need to import all the dependencies.

This CL simply copies the existing code into the new file.

PiperOrigin-RevId: 307134277
Change-Id: Idf7f9dffe6d6505337caefc736dce372aa014b14
This commit is contained in:
A. Unique TensorFlower 2020-04-17 16:15:38 -07:00 committed by TensorFlower Gardener
parent d8c8b53904
commit f0b228a36d
6 changed files with 95 additions and 60 deletions
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2
tensorflow/lite/kernels/internal/BUILD
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@ -459,6 +459,7 @@ cc_library(
"reference/integer_ops/pooling.h",
"reference/integer_ops/tanh.h",
"reference/integer_ops/transpose_conv.h",
"reference/l2normalization.h",
"reference/logistic.h",
"reference/maximum_minimum.h",
"reference/mul.h",
@ -529,6 +530,7 @@ cc_library(
"reference/dequantize.h",
"reference/floor.h",
"reference/fully_connected.h",
"reference/l2normalization.h",
"reference/legacy_reference_ops.h",
"reference/logistic.h",
"reference/maximum_minimum.h",

2
tensorflow/lite/kernels/internal/common.h
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@ -29,6 +29,8 @@ limitations under the License.
namespace tflite {
constexpr int kReverseShift = -1;
inline void GetActivationMinMax(FusedActivationFunctionType ac,
float* output_activation_min,
float* output_activation_max) {

2
tensorflow/lite/kernels/internal/reference/fully_connected.h
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@ -23,8 +23,6 @@ limitations under the License.
namespace tflite {
namespace reference_ops {
const int kReverseShift = -1;
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& weights_shape,

4
tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h
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@ -41,8 +41,8 @@ inline void L2Normalization(int32_t input_zero_point, int32_t outer_size,
}
int32_t inv_l2norm_multiplier;
int inv_l2norm_shift;
GetInvSqrtQuantizedMultiplierExp(acc, /*reverse_shift*/ -1,
&inv_l2norm_multiplier, &inv_l2norm_shift);
GetInvSqrtQuantizedMultiplierExp(acc, kReverseShift, &inv_l2norm_multiplier,
&inv_l2norm_shift);
for (int inner_index = 0; inner_index < depth; ++inner_index) {
int32_t input =

88
tensorflow/lite/kernels/internal/reference/l2normalization.h Normal file
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@ -0,0 +1,88 @@
/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_
#include <cmath>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
const RuntimeShape& input_shape,
const float* input_data,
const RuntimeShape& output_shape,
float* output_data, float epsilon = 1e-6) {
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);
for (int i = 0; i < outer_size; ++i) {
float squared_l2_norm = 0;
for (int c = 0; c < depth; ++c) {
const float val = input_data[depth * i + c];
squared_l2_norm += val * val;
}
float l2_norm = std::sqrt(squared_l2_norm);
l2_norm = std::max(l2_norm, epsilon);
for (int c = 0; c < depth; ++c) {
output_data[depth * i + c] = input_data[depth * i + c] / l2_norm;
}
}
}
inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
const RuntimeShape& input_shape,
const uint8* input_data,
const RuntimeShape& output_shape,
uint8* output_data) {
const int trailing_dim = input_shape.DimensionsCount() - 1;
const int depth =
MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
const int outer_size =
MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
const int32 input_zero_point = op_params.input_zero_point;
for (int i = 0; i < outer_size; ++i) {
int32 square_l2_norm = 0;
for (int c = 0; c < depth; c++) {
int32 diff = input_data[depth * i + c] - input_zero_point;
square_l2_norm += diff * diff;
}
int32 inv_l2norm_multiplier;
int inv_l2norm_shift;
GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift,
&inv_l2norm_multiplier, &inv_l2norm_shift);
for (int c = 0; c < depth; c++) {
int32 diff = input_data[depth * i + c] - input_zero_point;
int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp(
128 * diff, inv_l2norm_multiplier, inv_l2norm_shift);
int32 unclamped_output_val = 128 + rescaled_diff;
int32 output_val = std::min(255, std::max(0, unclamped_output_val));
output_data[depth * i + c] = static_cast<uint8>(output_val);
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_

57
tensorflow/lite/kernels/internal/reference/reference_ops.h
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@ -42,6 +42,7 @@ limitations under the License.
#include "tensorflow/lite/kernels/internal/reference/dequantize.h"
#include "tensorflow/lite/kernels/internal/reference/floor.h"
#include "tensorflow/lite/kernels/internal/reference/fully_connected.h"
#include "tensorflow/lite/kernels/internal/reference/l2normalization.h"
#include "tensorflow/lite/kernels/internal/reference/logistic.h"
#include "tensorflow/lite/kernels/internal/reference/maximum_minimum.h"
#include "tensorflow/lite/kernels/internal/reference/mul.h"
@ -294,62 +295,6 @@ inline void QuantizeLeakyRelu(const LeakyReluParams& params,
}
}
inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
const RuntimeShape& input_shape,
const float* input_data,
const RuntimeShape& output_shape,
float* output_data, float epsilon = 1e-6) {
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);
for (int i = 0; i < outer_size; ++i) {
float squared_l2_norm = 0;
for (int c = 0; c < depth; ++c) {
const float val = input_data[depth * i + c];
squared_l2_norm += val * val;
}
float l2_norm = std::sqrt(squared_l2_norm);
l2_norm = std::max(l2_norm, epsilon);
for (int c = 0; c < depth; ++c) {
output_data[depth * i + c] = input_data[depth * i + c] / l2_norm;
}
}
}
inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
const RuntimeShape& input_shape,
const uint8* input_data,
const RuntimeShape& output_shape,
uint8* output_data) {
const int trailing_dim = input_shape.DimensionsCount() - 1;
const int depth =
MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
const int outer_size =
MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
const int32 input_zero_point = op_params.input_zero_point;
for (int i = 0; i < outer_size; ++i) {
int32 square_l2_norm = 0;
for (int c = 0; c < depth; c++) {
int32 diff = input_data[depth * i + c] - input_zero_point;
square_l2_norm += diff * diff;
}
int32 inv_l2norm_multiplier;
int inv_l2norm_shift;
GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift,
&inv_l2norm_multiplier, &inv_l2norm_shift);
for (int c = 0; c < depth; c++) {
int32 diff = input_data[depth * i + c] - input_zero_point;
int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp(
128 * diff, inv_l2norm_multiplier, inv_l2norm_shift);
int32 unclamped_output_val = 128 + rescaled_diff;
int32 output_val = std::min(255, std::max(0, unclamped_output_val));
output_data[depth * i + c] = static_cast<uint8>(output_val);
}
}
}
// T is expected to be either float or int.
template <typename T>
inline void AddN(const RuntimeShape& input_shape, const size_t num_inputs,