2 * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
3 * Copyright 2019 The TensorFlow Authors. All Rights Reserved.
5 * Licensed under the Apache License, Version 2.0 (the "License");
6 * you may not use this file except in compliance with the License.
7 * You may obtain a copy of the License at
9 * http://www.apache.org/licenses/LICENSE-2.0
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
18 #include "kernels/Sub.h"
19 #include "kernels/Utils.h"
23 #include <tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h>
25 namespace luci_interpreter
30 Sub::Sub(const Tensor *input1, const Tensor *input2, Tensor *output, const SubParams ¶ms)
31 : KernelWithParams<SubParams>({input1, input2}, {output}, params)
37 LUCI_INTERPRETER_CHECK(!(input1()->element_type() != input2()->element_type()))
38 LUCI_INTERPRETER_CHECK(!(input1()->element_type() != output()->element_type()))
39 // TODO: enable it only if kernel with dynamic shapes
40 output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape()));
43 void Sub::execute() const
45 switch (input1()->element_type())
47 case DataType::FLOAT32:
51 evalInteger<int64_t>();
54 evalInteger<int32_t>();
60 assert(false && "Unsupported type.");
64 void Sub::evalFloat() const
66 tflite::ArithmeticParams params{};
67 fillArithmeticActivationRange<float>(params, _params.activation);
69 const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
70 getTensorShape(input1()), getTensorShape(input2()), ¶ms);
74 tflite::reference_ops::BroadcastSubSlow(
75 params, getTensorShape(input1()), getTensorData<float>(input1()), getTensorShape(input2()),
76 getTensorData<float>(input2()), getTensorShape(output()), getTensorData<float>(output()));
80 luci_interpreter_pal::Sub(params, getTensorShape(input1()), getTensorData<float>(input1()),
81 getTensorShape(input2()), getTensorData<float>(input2()),
82 getTensorShape(output()), getTensorData<float>(output()));
86 template <typename T> void Sub::evalInteger() const
88 tflite::ArithmeticParams params{};
89 fillArithmeticActivationRange<T>(params, _params.activation);
91 const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
92 getTensorShape(input1()), getTensorShape(input2()), ¶ms);
96 tflite::reference_ops::BroadcastSubSlow(
97 params, getTensorShape(input1()), getTensorData<T>(input1()), getTensorShape(input2()),
98 getTensorData<T>(input2()), getTensorShape(output()), getTensorData<T>(output()));
102 tflite::reference_ops::Sub(params, getTensorShape(input1()), getTensorData<T>(input1()),
103 getTensorShape(input2()), getTensorData<T>(input2()),
104 getTensorShape(output()), getTensorData<T>(output()));
108 void Sub::evalQuantized() const
110 const auto input1_scale = static_cast<double>(input1()->scale());
111 const auto input2_scale = static_cast<double>(input2()->scale());
112 const auto output_scale = static_cast<double>(output()->scale());
114 const int left_shift = 20;
115 const double twice_max_input_scale = 2 * std::max(input1_scale, input2_scale);
116 const double real_input1_multiplier = input1_scale / twice_max_input_scale;
117 const double real_input2_multiplier = input2_scale / twice_max_input_scale;
118 const double real_output_multiplier = twice_max_input_scale / ((1 << left_shift) * output_scale);
120 int32_t input1_multiplier{}, input2_multiplier{}, output_multiplier{};
121 int input1_shift{}, input2_shift{}, output_shift{};
122 quantizeMultiplierSmallerThanOneExp(real_input1_multiplier, &input1_multiplier, &input1_shift);
123 quantizeMultiplierSmallerThanOneExp(real_input2_multiplier, &input2_multiplier, &input2_shift);
124 quantizeMultiplierSmallerThanOneExp(real_output_multiplier, &output_multiplier, &output_shift);
126 int32_t activation_min{};
127 int32_t activation_max{};
128 calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
130 tflite::ArithmeticParams params{};
131 params.left_shift = left_shift;
132 // The kernel expects inputs' zero points to be negated.
133 params.input1_offset = -input1()->zero_point(); // Note the '-'.
134 params.input1_multiplier = input1_multiplier;
135 params.input1_shift = input1_shift;
136 params.input2_offset = -input2()->zero_point(); // Note the '-'.
137 params.input2_multiplier = input2_multiplier;
138 params.input2_shift = input2_shift;
139 params.output_offset = output()->zero_point();
140 params.output_multiplier = output_multiplier;
141 params.output_shift = output_shift;
142 params.quantized_activation_min = activation_min;
143 params.quantized_activation_max = activation_max;
145 const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
146 getTensorShape(input1()), getTensorShape(input2()), ¶ms);
150 tflite::reference_ops::BroadcastSubSlow(
151 params, getTensorShape(input1()), getTensorData<uint8_t>(input1()), getTensorShape(input2()),
152 getTensorData<uint8_t>(input2()), getTensorShape(output()), getTensorData<uint8_t>(output()));
156 tflite::reference_ops::Sub(params, getTensorShape(input1()), getTensorData<uint8_t>(input1()),
157 getTensorShape(input2()), getTensorData<uint8_t>(input2()),
158 getTensorShape(output()), getTensorData<uint8_t>(output()));
162 } // namespace kernels
163 } // namespace luci_interpreter