wehub-resource-sync
236 lines
10 KiB
C++
236 lines
10 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_DIV_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_
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#include <algorithm>
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/reference/broadcast_loop.h"
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namespace tflite {
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namespace reference_ops {
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template <typename T>
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inline void DivCheckArithmeticParams(const ArithmeticParams& params) {
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TFLITE_DCHECK_LE(params.quantized_activation_min,
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params.quantized_activation_max);
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// Input offset is negative input zero point. Activation tensors are
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// asymmetric quantized so they span the full int8 range.
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constexpr int32_t max_value =
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(static_cast<int32_t>(std::numeric_limits<T>::max()) + 1);
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TFLITE_DCHECK_GE(params.input1_offset, -max_value);
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TFLITE_DCHECK_LE(params.input1_offset, max_value);
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TFLITE_DCHECK_GE(params.input2_offset, -max_value);
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TFLITE_DCHECK_LE(params.input2_offset, max_value);
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TFLITE_DCHECK_GE(params.output_offset, -max_value);
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TFLITE_DCHECK_LE(params.output_offset, max_value);
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}
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// Element-wise div that can often be used for inner loop of broadcast Div as
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// well as the non-broadcast Div.
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template <typename T>
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inline void DivElementwise(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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DivCheckArithmeticParams<T>(params);
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for (int i = 0; i < size; ++i) {
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int32_t input1_val = params.input1_offset + input1_data[i];
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int32_t input2_val = params.input2_offset + input2_data[i];
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TFLITE_DCHECK_NE(input2_val, 0);
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if (input2_val < 0) {
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// Invert signs to avoid a negative input2_val as input2_inv needs to be
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// positive to be used as multiplier of MultiplyByQuantizedMultiplier.
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input1_val = -input1_val;
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input2_val = -input2_val;
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}
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int recip_shift;
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const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift);
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const int headroom = CountLeadingSignBits(input1_val);
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const int32_t unscaled_quotient =
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MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv,
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headroom);
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const int total_shift = params.output_shift - recip_shift - headroom;
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const int32_t unclamped_result =
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params.output_offset +
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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unscaled_quotient, params.output_multiplier, total_shift);
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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, unclamped_result));
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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 Div(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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DivElementwise(flat_size, params, input1_data, input2_data, output_data);
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}
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inline void Div(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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DivElementwise(flat_size, params, input1_data, input2_data, output_data);
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}
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inline void Div(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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DivElementwise(flat_size, params, input1_data, input2_data, output_data);
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}
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template <typename T, int N = 5>
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inline void BroadcastDivSlowQuantized(
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const ArithmeticParams& params, const RuntimeShape& unextended_input1_shape,
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const T* input1_data, const RuntimeShape& unextended_input2_shape,
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const T* input2_data, const RuntimeShape& unextended_output_shape,
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T* output_data) {
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DivCheckArithmeticParams<T>(params);
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auto op = [¶ms](T a, T b) {
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int32_t input1_val = params.input1_offset + a;
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int32_t input2_val = params.input2_offset + b;
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TFLITE_DCHECK_NE(input2_val, 0);
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if (input2_val < 0) {
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input1_val = -input1_val;
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input2_val = -input2_val;
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}
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int recip_shift;
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const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift);
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const int headroom = CountLeadingSignBits(input1_val);
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const int32_t unscaled_quotient =
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MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv,
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headroom);
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const int total_shift = params.output_shift - recip_shift - headroom;
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const int32_t unclamped_result =
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params.output_offset +
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MultiplyByQuantizedMultiplierSmallerThanOneExp(
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unscaled_quotient, params.output_multiplier, total_shift);
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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, unclamped_result));
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return static_cast<T>(clamped_output);
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};
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BroadcastBinaryOpSimple(unextended_input1_shape, input1_data,
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unextended_input2_shape, input2_data,
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unextended_output_shape, output_data, op);
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}
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template <int N = 5>
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inline void BroadcastDivSlow(const ArithmeticParams& params,
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const RuntimeShape& unextended_input1_shape,
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const uint8_t* input1_data,
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const RuntimeShape& unextended_input2_shape,
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const uint8_t* input2_data,
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const RuntimeShape& unextended_output_shape,
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uint8_t* output_data) {
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BroadcastDivSlowQuantized<uint8_t, N>(
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params, unextended_input1_shape, input1_data, unextended_input2_shape,
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input2_data, unextended_output_shape, output_data);
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}
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template <int N = 5>
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inline void BroadcastDivSlow(const ArithmeticParams& params,
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const RuntimeShape& unextended_input1_shape,
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const int8_t* input1_data,
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const RuntimeShape& unextended_input2_shape,
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const int8_t* input2_data,
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const RuntimeShape& unextended_output_shape,
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int8_t* output_data) {
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BroadcastDivSlowQuantized<int8_t, N>(
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params, unextended_input1_shape, input1_data, unextended_input2_shape,
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input2_data, unextended_output_shape, output_data);
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}
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template <int N = 5>
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inline void BroadcastDivSlow(const ArithmeticParams& params,
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const RuntimeShape& unextended_input1_shape,
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const int16_t* input1_data,
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const RuntimeShape& unextended_input2_shape,
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const int16_t* input2_data,
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const RuntimeShape& unextended_output_shape,
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int16_t* output_data) {
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BroadcastDivSlowQuantized<int16_t, N>(
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params, unextended_input1_shape, input1_data, unextended_input2_shape,
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input2_data, unextended_output_shape, output_data);
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}
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template <typename T, int N = 5>
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void BroadcastDivSlow(const ArithmeticParams& params,
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const RuntimeShape& unextended_input1_shape,
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const T* input1_data,
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const RuntimeShape& unextended_input2_shape,
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const T* input2_data,
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const RuntimeShape& unextended_output_shape,
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T* output_data) {
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T output_activation_min;
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T output_activation_max;
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GetActivationParams(params, &output_activation_min, &output_activation_max);
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auto op = [output_activation_min, output_activation_max](T a, T b) {
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return ActivationFunctionWithMinMax(a / b, output_activation_min,
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output_activation_max);
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};
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BroadcastBinaryOpSimple(unextended_input1_shape, input1_data,
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unextended_input2_shape, input2_data,
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unextended_output_shape, output_data, op);
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}
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template <typename T>
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inline void Div(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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T output_activation_min;
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T output_activation_max;
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GetActivationParams(params, &output_activation_min, &output_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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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], output_activation_min,
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output_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_DIV_H_
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