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Diffstat (limited to 'compiler/luci-interpreter/src/kernels/Div.cpp')
| -rw-r--r-- | compiler/luci-interpreter/src/kernels/Div.cpp | 152 |
1 files changed, 152 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/Div.cpp b/compiler/luci-interpreter/src/kernels/Div.cpp new file mode 100644 index 000000000..dd1532278 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Div.cpp @@ -0,0 +1,152 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. 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. + */ + +#include "kernels/Div.h" + +#include "kernels/Utils.h" + +#include <tensorflow/lite/kernels/internal/reference/div.h> +#include <tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h> + +namespace luci_interpreter +{ +namespace kernels +{ + +Div::Div(const Tensor *input1, const Tensor *input2, Tensor *output, const DivParams ¶ms) + : KernelWithParams<DivParams>({input1, input2}, {output}, params) +{ +} + +void Div::configure() +{ + LUCI_INTERPRETER_CHECK(input1()->element_type() == input2()->element_type()); + LUCI_INTERPRETER_CHECK(input1()->element_type() == output()->element_type()); + + output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape())); +} + +void Div::execute() const +{ + switch (input1()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::S64: + evalInteger<int64_t>(); + break; + case DataType::S32: + evalInteger<int32_t>(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Div::evalFloat() const +{ + tflite::ArithmeticParams params{}; + fillArithmeticActivationRange<float>(params, _params.activation); + + const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes( + getTensorShape(input1()), getTensorShape(input2()), ¶ms); + + if (need_broadcast) + { + tflite::reference_ops::BroadcastDivSlow( + params, getTensorShape(input1()), getTensorData<float>(input1()), getTensorShape(input2()), + getTensorData<float>(input2()), getTensorShape(output()), getTensorData<float>(output())); + } + else + { + tflite::reference_ops::Div(params, getTensorShape(input1()), getTensorData<float>(input1()), + getTensorShape(input2()), getTensorData<float>(input2()), + getTensorShape(output()), getTensorData<float>(output())); + } +} + +template <typename T> void Div::evalInteger() const +{ + tflite::ArithmeticParams params{}; + fillArithmeticActivationRange<T>(params, _params.activation); + + const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes( + getTensorShape(input1()), getTensorShape(input2()), ¶ms); + + if (need_broadcast) + { + tflite::reference_ops::BroadcastDivSlow( + params, getTensorShape(input1()), getTensorData<T>(input1()), getTensorShape(input2()), + getTensorData<T>(input2()), getTensorShape(output()), getTensorData<T>(output())); + } + else + { + tflite::reference_ops::Div(params, getTensorShape(input1()), getTensorData<T>(input1()), + getTensorShape(input2()), getTensorData<T>(input2()), + getTensorShape(output()), getTensorData<T>(output())); + } +} + +void Div::evalQuantized() const +{ + const auto input1_scale = static_cast<double>(input1()->scale()); + const auto input2_scale = static_cast<double>(input2()->scale()); + const auto output_scale = static_cast<double>(output()->scale()); + + const double real_output_multiplier = input1_scale / (input2_scale * output_scale); + + int32_t output_multiplier{}; + int output_shift{}; + + quantizeMultiplier(real_output_multiplier, &output_multiplier, &output_shift); + + int32_t activation_min{}; + int32_t activation_max{}; + calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max); + + tflite::ArithmeticParams params{}; + + params.input1_offset = -input1()->zero_point(); // Note the '-'. + params.input2_offset = -input2()->zero_point(); // Note the '-'. + params.output_offset = output()->zero_point(); + params.output_multiplier = output_multiplier; + params.output_shift = output_shift; + params.quantized_activation_min = activation_min; + params.quantized_activation_max = activation_max; + + const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes( + getTensorShape(input1()), getTensorShape(input2()), ¶ms); + + if (need_broadcast) + { + tflite::reference_ops::BroadcastDivSlow( + params, getTensorShape(input1()), getTensorData<uint8_t>(input1()), getTensorShape(input2()), + getTensorData<uint8_t>(input2()), getTensorShape(output()), getTensorData<uint8_t>(output())); + } + else + { + tflite::reference_ops::Div(params, getTensorShape(input1()), getTensorData<uint8_t>(input1()), + getTensorShape(input2()), getTensorData<uint8_t>(input2()), + getTensorShape(output()), getTensorData<uint8_t>(output())); + } +} + +} // namespace kernels +} // namespace luci_interpreter |