mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-02 18:27:52 +00:00
174 lines
7.9 KiB
C++
174 lines
7.9 KiB
C++
/* 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.
|
|
==============================================================================*/
|
|
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
|
|
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
|
|
|
|
#include <algorithm>
|
|
#include <cstddef>
|
|
#include <limits>
|
|
|
|
#include "tensorflow/lite/kernels/internal/common.h"
|
|
#include "tensorflow/lite/kernels/internal/reference/broadcast_loop.h"
|
|
#include "tensorflow/lite/kernels/internal/types.h"
|
|
|
|
namespace tflite {
|
|
namespace reference_integer_ops {
|
|
|
|
inline void CheckArithmeticParams(const ArithmeticParams& params) {
|
|
TFLITE_DCHECK_LE(params.quantized_activation_min,
|
|
params.quantized_activation_max);
|
|
// Input offset is negative input zero point. Activation tensors are
|
|
// asymmetric quantized so they span the full int8 range.
|
|
TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits<int8_t>::min());
|
|
TFLITE_DCHECK_GE(-params.input2_offset, std::numeric_limits<int8_t>::min());
|
|
TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits<int8_t>::max());
|
|
TFLITE_DCHECK_LE(-params.input2_offset, std::numeric_limits<int8_t>::max());
|
|
}
|
|
|
|
// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2)
|
|
template <typename T>
|
|
void BroadcastInput1(int size, const ArithmeticParams& params,
|
|
const T* input1_data, const T* input2_data, T* output_data,
|
|
void (*check_arithmetic_params)(const ArithmeticParams&),
|
|
T (*binary_func)(T, T, const ArithmeticParams&)) {
|
|
CheckArithmeticParams(params);
|
|
for (int i = 0; i < size; ++i) {
|
|
output_data[i] = binary_func(input1_data[0], input2_data[i], params);
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
void BroadcastInput2(int size, const ArithmeticParams& params,
|
|
const T* input1_data, const T* input2_data, T* output_data,
|
|
void (*check_arithmetic_params)(const ArithmeticParams&),
|
|
T (*binary_func)(T, T, const ArithmeticParams&)) {
|
|
CheckArithmeticParams(params);
|
|
for (int i = 0; i < size; ++i) {
|
|
output_data[i] = binary_func(input1_data[i], input2_data[0], params);
|
|
}
|
|
}
|
|
|
|
// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2)
|
|
template <typename T>
|
|
void ElementWise(int size, const ArithmeticParams& params, const T* input1_data,
|
|
const T* input2_data, T* output_data,
|
|
void (*check_arithmetic_params)(const ArithmeticParams&),
|
|
T (*binary_func)(T, T, const ArithmeticParams&)) {
|
|
CheckArithmeticParams(params);
|
|
for (int i = 0; i < size; ++i) {
|
|
output_data[i] = binary_func(input1_data[i], input2_data[i], params);
|
|
}
|
|
}
|
|
|
|
// TODO: b/270589088 - move to a more appropriate file. (b/270589088#comment2)
|
|
template <typename T>
|
|
void BroadcastBinaryFunction6DSlow(
|
|
const ArithmeticParams& params, const RuntimeShape& input1_shape,
|
|
const T* input1_data, const RuntimeShape& input2_shape,
|
|
const T* input2_data, const RuntimeShape& output_shape, T* output_data,
|
|
void (*check_arithmetic_params)(const ArithmeticParams&),
|
|
T (*binary_func)(T, T, const ArithmeticParams&)) {
|
|
check_arithmetic_params(params);
|
|
auto op = [¶ms, binary_func](T a, T b) {
|
|
return binary_func(a, b, params);
|
|
};
|
|
reference_ops::BroadcastBinaryOpSimple(input1_shape, input1_data,
|
|
input2_shape, input2_data,
|
|
output_shape, output_data, op);
|
|
}
|
|
|
|
template <typename T>
|
|
void BroadcastBinaryFunction4DSlow(
|
|
const ArithmeticParams& params, const RuntimeShape& input1_shape,
|
|
const T* input1_data, const RuntimeShape& input2_shape,
|
|
const T* input2_data, const RuntimeShape& output_shape, T* output_data,
|
|
void (*check_arithmetic_params)(const ArithmeticParams&),
|
|
T (*binary_func)(T, T, const ArithmeticParams&)) {
|
|
BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape,
|
|
input2_data, output_shape, output_data,
|
|
check_arithmetic_params, binary_func);
|
|
}
|
|
|
|
inline int8_t AddFunc(int8_t x, int8_t y, const ArithmeticParams& params) {
|
|
const int32_t input1_val = params.input1_offset + x;
|
|
const int32_t input2_val = params.input2_offset + y;
|
|
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
|
|
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
|
|
const int32_t scaled_input1_val =
|
|
MultiplyByQuantizedMultiplierSmallerThanOneExp(
|
|
shifted_input1_val, params.input1_multiplier, params.input1_shift);
|
|
const int32_t scaled_input2_val =
|
|
MultiplyByQuantizedMultiplierSmallerThanOneExp(
|
|
shifted_input2_val, params.input2_multiplier, params.input2_shift);
|
|
const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
|
|
const int32_t raw_output =
|
|
MultiplyByQuantizedMultiplierSmallerThanOneExp(
|
|
raw_sum, params.output_multiplier, params.output_shift) +
|
|
params.output_offset;
|
|
const int32_t clamped_output =
|
|
std::min(params.quantized_activation_max,
|
|
std::max(params.quantized_activation_min, raw_output));
|
|
return static_cast<int8_t>(clamped_output);
|
|
}
|
|
|
|
// Element-wise add that can often be used for inner loop of broadcast add as
|
|
// well as the non-broadcast add.
|
|
inline void AddElementwise(int size, const ArithmeticParams& params,
|
|
const int8_t* input1_data, const int8_t* input2_data,
|
|
int8_t* output_data) {
|
|
ElementWise(size, params, input1_data, input2_data, output_data,
|
|
CheckArithmeticParams, AddFunc);
|
|
}
|
|
|
|
inline void Add(const ArithmeticParams& params,
|
|
const RuntimeShape& input1_shape, const int8_t* input1_data,
|
|
const RuntimeShape& input2_shape, const int8_t* input2_data,
|
|
const RuntimeShape& output_shape, int8_t* output_data) {
|
|
CheckArithmeticParams(params);
|
|
|
|
const int flat_size =
|
|
MatchingElementsSize(input1_shape, input2_shape, output_shape);
|
|
|
|
AddElementwise(flat_size, params, input1_data, input2_data, output_data);
|
|
}
|
|
|
|
inline void BroadcastAdd6DSlow(const ArithmeticParams& params,
|
|
const RuntimeShape& input1_shape,
|
|
const int8_t* input1_data,
|
|
const RuntimeShape& input2_shape,
|
|
const int8_t* input2_data,
|
|
const RuntimeShape& output_shape,
|
|
int8_t* output_data) {
|
|
BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape,
|
|
input2_data, output_shape, output_data,
|
|
CheckArithmeticParams, AddFunc);
|
|
}
|
|
|
|
inline void BroadcastAdd4DSlow(const ArithmeticParams& params,
|
|
const RuntimeShape& input1_shape,
|
|
const int8_t* input1_data,
|
|
const RuntimeShape& input2_shape,
|
|
const int8_t* input2_data,
|
|
const RuntimeShape& output_shape,
|
|
int8_t* output_data) {
|
|
BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape,
|
|
input2_data, output_shape, output_data,
|
|
CheckArithmeticParams, AddFunc);
|
|
}
|
|
|
|
} // namespace reference_integer_ops
|
|
} // namespace tflite
|
|
|
|
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
|