wehub-resource-sync
258 lines
11 KiB
C++
258 lines
11 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_SUB_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_
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#include <stdint.h>
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#include <algorithm>
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#include "ruy/profiler/instrumentation.h" // from @ruy
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/compatibility.h"
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#include "tensorflow/lite/kernels/internal/reference/broadcast_loop.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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namespace tflite {
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namespace reference_ops {
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template <typename T, typename F>
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inline void BroadcastSubCommon(const ArithmeticParams& params,
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const RuntimeShape& input1_shape,
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const T* input1_data,
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const RuntimeShape& input2_shape,
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const T* input2_data,
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const RuntimeShape& output_shape, T* output_data,
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F binary_func) {
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auto op = [¶ms, binary_func](T a, T b) {
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return binary_func(a, b, params);
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};
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BroadcastBinaryOpSimple(input1_shape, input1_data, input2_shape, input2_data,
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output_shape, output_data, op);
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}
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template <typename T>
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void BroadcastSubSlow(const ArithmeticParams& params,
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const RuntimeShape& input1_shape, const T* input1_data,
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const RuntimeShape& input2_shape, const T* input2_data,
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const RuntimeShape& output_shape, T* output_data) {
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ruy::profiler::ScopeLabel label("BroadcastSubSlow/T");
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BroadcastSubCommon<T>(
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params, input1_shape, input1_data, input2_shape, input2_data,
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output_shape, output_data,
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[](T input1_val, T input2_val, const ArithmeticParams& params) {
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T activation_min, activation_max;
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GetActivationParams(params, &activation_min, &activation_max);
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return ActivationFunctionWithMinMax(input1_val - input2_val,
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activation_min, activation_max);
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});
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}
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inline void BroadcastSub16POTSlow(const ArithmeticParams& params,
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const RuntimeShape& input1_shape,
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const int16_t* input1_data,
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const RuntimeShape& input2_shape,
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const int16_t* input2_data,
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const RuntimeShape& output_shape,
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int16_t* output_data) {
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ruy::profiler::ScopeLabel label("BroadcastSub16POTSlow/int16_t");
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BroadcastSubCommon<int16_t>(
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params, input1_shape, input1_data, input2_shape, input2_data,
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output_shape, output_data,
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[](int16_t input1_val, int16_t input2_val,
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const ArithmeticParams& params) {
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const int32_t scaled_input1_val =
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gemmlowp::RoundingDivideByPOT(input1_val, -params.input1_shift);
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const int32_t scaled_input2_val =
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gemmlowp::RoundingDivideByPOT(input2_val, -params.input2_shift);
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const int32_t raw_output = scaled_input1_val - scaled_input2_val;
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const int32_t clamped_output =
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std::min(params.quantized_activation_max,
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std::max(params.quantized_activation_min, raw_output));
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return static_cast<int16_t>(clamped_output);
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});
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}
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template <typename T>
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void BroadcastQuantSubSlow(const ArithmeticParams& params,
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const RuntimeShape& input1_shape,
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const T* input1_data,
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const RuntimeShape& input2_shape,
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const T* input2_data,
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const RuntimeShape& output_shape, T* output_data) {
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ruy::profiler::ScopeLabel label("BroadcastQuantSubSlow/T");
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BroadcastSubCommon<T>(
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params, input1_shape, input1_data, input2_shape, input2_data,
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output_shape, output_data,
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[](T input1_val, T input2_val, const ArithmeticParams& params) {
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const int32_t shifted_input1_val =
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(params.input1_offset + input1_val) * (1 << params.left_shift);
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const int32_t shifted_input2_val =
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(params.input2_offset + input2_val) * (1 << params.left_shift);
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const int32_t scaled_input1_val =
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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shifted_input1_val, params.input1_multiplier,
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params.input1_shift);
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const int32_t scaled_input2_val =
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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shifted_input2_val, params.input2_multiplier,
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params.input2_shift);
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const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
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const int32_t raw_output =
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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raw_sub, params.output_multiplier, params.output_shift) +
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params.output_offset;
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const int32_t clamped_output =
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std::min(params.quantized_activation_max,
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std::max(params.quantized_activation_min, raw_output));
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return static_cast<T>(clamped_output);
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});
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}
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// Element-wise add that can often be used for inner loop of broadcast add as
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// well as the non-broadcast add.
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template <typename T>
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inline void SubElementwise(int size, const ArithmeticParams& params,
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const T* input1_data, const T* input2_data,
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T* output_data) {
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for (int i = 0; i < size; ++i) {
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const int32_t input1_val = params.input1_offset + input1_data[i];
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const int32_t input2_val = params.input2_offset + input2_data[i];
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const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
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const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
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const int32_t scaled_input1_val =
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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shifted_input1_val, params.input1_multiplier, params.input1_shift);
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const int32_t scaled_input2_val =
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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shifted_input2_val, params.input2_multiplier, params.input2_shift);
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const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
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const int32_t raw_output =
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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raw_sub, params.output_multiplier, params.output_shift) +
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params.output_offset;
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const int32_t clamped_output =
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std::min(params.quantized_activation_max,
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std::max(params.quantized_activation_min, raw_output));
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output_data[i] = static_cast<T>(clamped_output);
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}
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}
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inline void Sub(const ArithmeticParams& params,
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const RuntimeShape& input1_shape, const uint8_t* input1_data,
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const RuntimeShape& input2_shape, const uint8_t* input2_data,
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const RuntimeShape& output_shape, uint8_t* output_data) {
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TFLITE_DCHECK_LE(params.quantized_activation_min,
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params.quantized_activation_max);
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const int flat_size =
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MatchingElementsSize(input1_shape, input2_shape, output_shape);
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TFLITE_DCHECK_GT(params.input1_offset, -256);
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TFLITE_DCHECK_GT(params.input2_offset, -256);
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TFLITE_DCHECK_LT(params.input1_offset, 256);
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TFLITE_DCHECK_LT(params.input2_offset, 256);
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SubElementwise(flat_size, params, input1_data, input2_data, output_data);
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}
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inline void Sub(const ArithmeticParams& params,
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const RuntimeShape& input1_shape, const int8_t* input1_data,
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const RuntimeShape& input2_shape, const int8_t* input2_data,
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const RuntimeShape& output_shape, int8_t* output_data) {
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TFLITE_DCHECK_LE(params.quantized_activation_min,
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params.quantized_activation_max);
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const int flat_size =
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MatchingElementsSize(input1_shape, input2_shape, output_shape);
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TFLITE_DCHECK_GE(params.input1_offset, -128);
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TFLITE_DCHECK_GE(params.input2_offset, -128);
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// offset = -quantization_params.zero_point in PrepareGeneralSubOp().
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// So it's maximum can be 128 not 127.
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TFLITE_DCHECK_LE(params.input1_offset, 128);
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TFLITE_DCHECK_LE(params.input2_offset, 128);
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SubElementwise(flat_size, params, input1_data, input2_data, output_data);
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}
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inline void Sub(const ArithmeticParams& params,
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const RuntimeShape& input1_shape, const int16_t* input1_data,
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const RuntimeShape& input2_shape, const int16_t* input2_data,
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const RuntimeShape& output_shape, int16_t* output_data) {
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TFLITE_DCHECK_LE(params.quantized_activation_min,
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params.quantized_activation_max);
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const int flat_size =
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MatchingElementsSize(input1_shape, input2_shape, output_shape);
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TFLITE_DCHECK_EQ(params.input1_offset, 0);
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TFLITE_DCHECK_EQ(params.input2_offset, 0);
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SubElementwise(flat_size, params, input1_data, input2_data, output_data);
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}
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template <typename T>
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void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape,
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const T* input1_data, const RuntimeShape& input2_shape,
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const T* input2_data, const RuntimeShape& output_shape,
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T* output_data) {
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BroadcastSubCommon<T>(
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params, input1_shape, input1_data, input2_shape, input2_data,
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output_shape, output_data,
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[](T input1_val, T input2_val, const ArithmeticParams& params) {
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return input1_val - input2_val;
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});
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}
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inline void SetActivationMinMax(const ArithmeticParams& params,
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int32_t* activation_min,
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int32_t* activation_max) {
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*activation_min = params.quantized_activation_min;
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*activation_max = params.quantized_activation_max;
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}
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inline void SetActivationMinMax(const ArithmeticParams& params,
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float* activation_min, float* activation_max) {
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*activation_min = params.float_activation_min;
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*activation_max = params.float_activation_max;
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}
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inline void SetActivationMinMax(const ArithmeticParams& params,
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int64_t* activation_min,
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int64_t* activation_max) {
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*activation_min = params.int64_activation_min;
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*activation_max = params.int64_activation_max;
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}
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template <typename T>
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inline void SubWithActivation(
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const ArithmeticParams& params, const RuntimeShape& input1_shape,
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const T* input1_data, const RuntimeShape& input2_shape,
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const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
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ruy::profiler::ScopeLabel label("SubWithActivation");
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const int flat_size =
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MatchingElementsSize(input1_shape, input2_shape, output_shape);
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T activation_min, activation_max;
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SetActivationMinMax(params, &activation_min, &activation_max);
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for (int i = 0; i < flat_size; ++i) {
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output_data[i] = ActivationFunctionWithMinMax(
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input1_data[i] - input2_data[i], activation_min, activation_max);
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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_SUB_H_
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