mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-01 17:57:27 +00:00
e87b788f2d
This mirrors TfLite support for unquantized Int32 add operations. BUG=https://b/273650194
116 lines
4.8 KiB
C++
116 lines
4.8 KiB
C++
/* Copyright 2021 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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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/add.h"
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#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h"
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#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/kernels/op_macros.h"
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#include "tensorflow/lite/micro/kernels/add.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/memory_helpers.h"
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namespace tflite {
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const int kAddInputTensor1 = 0;
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const int kAddInputTensor2 = 1;
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const int kAddOutputTensor = 0;
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TfLiteStatus CalculateOpDataAdd(TfLiteContext* context, TfLiteAddParams* params,
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const TfLiteTensor* input1,
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const TfLiteTensor* input2,
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TfLiteTensor* output, OpDataAdd* data) {
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data->requires_broadcast = !HaveSameShapes(input1, input2);
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if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
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TFLITE_CHECK_NE(output->quantization.type, kTfLiteNoQuantization);
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// 8bit -> 8bit general quantized path, with general rescalings
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data->input1_offset = -input1->params.zero_point;
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data->input2_offset = -input2->params.zero_point;
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data->output_offset = output->params.zero_point;
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data->left_shift = (output->type == kTfLiteInt16) ? 15 : 20;
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const double twice_max_input_scale =
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2 * static_cast<double>(
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std::max(input1->params.scale, input2->params.scale));
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const double real_input1_multiplier =
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static_cast<double>(input1->params.scale) / twice_max_input_scale;
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const double real_input2_multiplier =
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static_cast<double>(input2->params.scale) / twice_max_input_scale;
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const double real_output_multiplier =
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twice_max_input_scale /
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((1 << data->left_shift) * static_cast<double>(output->params.scale));
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QuantizeMultiplierSmallerThanOneExp(
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real_input1_multiplier, &data->input1_multiplier, &data->input1_shift);
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QuantizeMultiplierSmallerThanOneExp(
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real_input2_multiplier, &data->input2_multiplier, &data->input2_shift);
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QuantizeMultiplierSmallerThanOneExp(
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real_output_multiplier, &data->output_multiplier, &data->output_shift);
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TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
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context, params->activation, output, &data->output_activation_min,
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&data->output_activation_max));
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} else if (output->type == kTfLiteFloat32) {
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CalculateActivationRange(params->activation,
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&data->output_activation_min_f32,
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&data->output_activation_max_f32);
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}
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return kTfLiteOk;
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}
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TfLiteStatus AddPrepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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TFLITE_DCHECK(node->builtin_data != nullptr);
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MicroContext* micro_context = GetMicroContext(context);
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TfLiteTensor* input1 =
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micro_context->AllocateTempInputTensor(node, kAddInputTensor1);
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TF_LITE_ENSURE(context, input1 != nullptr);
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TfLiteTensor* input2 =
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micro_context->AllocateTempInputTensor(node, kAddInputTensor2);
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TF_LITE_ENSURE(context, input2 != nullptr);
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TfLiteTensor* output =
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micro_context->AllocateTempOutputTensor(node, kAddOutputTensor);
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TF_LITE_ENSURE(context, output != nullptr);
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OpDataAdd* data = static_cast<OpDataAdd*>(node->user_data);
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auto* params = reinterpret_cast<TfLiteAddParams*>(node->builtin_data);
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TF_LITE_ENSURE_STATUS(
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CalculateOpDataAdd(context, params, input1, input2, output, data));
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if (output->type == kTfLiteInt32) {
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// Only support int32 unquantized add for now.
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TF_LITE_ENSURE_EQ(context, input1->quantization.type,
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kTfLiteNoQuantization);
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TF_LITE_ENSURE_EQ(context, input2->quantization.type,
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kTfLiteNoQuantization);
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}
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micro_context->DeallocateTempTfLiteTensor(input1);
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micro_context->DeallocateTempTfLiteTensor(input2);
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micro_context->DeallocateTempTfLiteTensor(output);
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return kTfLiteOk;
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}
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} // namespace tflite
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