fff2c83262
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages. We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`). PiperOrigin-RevId: 332520146 Change-Id: I405d986cfc653aaafcfdf4162c0acbd46220b921
257 lines
10 KiB
C++
257 lines
10 KiB
C++
/* Copyright 2019 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/kernels/internal/reference/sub.h"
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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/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.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/internal/types.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/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace sub {
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constexpr int kInputTensor1 = 0;
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constexpr int kInputTensor2 = 1;
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constexpr int kOutputTensor = 0;
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struct OpData {
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bool requires_broadcast;
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// These fields are used in both the general 8-bit -> 8bit quantized path,
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// and the special 16-bit -> 16bit quantized path
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int input1_shift;
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int input2_shift;
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int32_t output_activation_min;
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int32_t output_activation_max;
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// These fields are used only in the general 8-bit -> 8bit quantized path
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int32_t input1_multiplier;
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int32_t input2_multiplier;
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int32_t output_multiplier;
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int output_shift;
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int left_shift;
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int32_t input1_offset;
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int32_t input2_offset;
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int32_t output_offset;
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};
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TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteSubParams* params,
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const TfLiteTensor* input1,
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const TfLiteTensor* input2, TfLiteTensor* output,
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OpData* data) {
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data->requires_broadcast = !HaveSameShapes(input1, input2);
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if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
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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 = 20;
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const float twice_max_input_scale =
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2 * 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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static_cast<double>(twice_max_input_scale /
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((1 << data->left_shift) * 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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}
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return kTfLiteOk;
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}
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(OpData));
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}
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TfLiteStatus Prepare(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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OpData* data = static_cast<OpData*>(node->user_data);
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auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_data);
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const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
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TF_LITE_ENSURE(context, input1 != nullptr);
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const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
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TF_LITE_ENSURE(context, input2 != nullptr);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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TF_LITE_ENSURE(context, output != nullptr);
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TF_LITE_ENSURE_STATUS(
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CalculateOpData(context, params, input1, input2, output, data));
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return kTfLiteOk;
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}
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void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params,
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const OpData* data, const TfLiteEvalTensor* input1,
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const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
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float output_activation_min, output_activation_max;
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CalculateActivationRange(params->activation, &output_activation_min,
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&output_activation_max);
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tflite::ArithmeticParams op_params;
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SetActivationParams(output_activation_min, output_activation_max, &op_params);
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if (data->requires_broadcast) {
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tflite::reference_ops::BroadcastSubSlow(
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op_params, tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<float>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<float>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output));
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} else {
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tflite::reference_ops::SubWithActivation(
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op_params, tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<float>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<float>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output));
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}
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}
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TfLiteStatus EvalSubQuantized(TfLiteContext* context, TfLiteNode* node,
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TfLiteSubParams* params, const OpData* data,
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const TfLiteEvalTensor* input1,
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const TfLiteEvalTensor* input2,
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TfLiteEvalTensor* output) {
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if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
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tflite::ArithmeticParams op_params;
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op_params.left_shift = data->left_shift;
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op_params.input1_offset = data->input1_offset;
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op_params.input1_multiplier = data->input1_multiplier;
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op_params.input1_shift = data->input1_shift;
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op_params.input2_offset = data->input2_offset;
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op_params.input2_multiplier = data->input2_multiplier;
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op_params.input2_shift = data->input2_shift;
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op_params.output_offset = data->output_offset;
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op_params.output_multiplier = data->output_multiplier;
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op_params.output_shift = data->output_shift;
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SetActivationParams(data->output_activation_min,
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data->output_activation_max, &op_params);
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bool need_broadcast = reference_ops::ProcessBroadcastShapes(
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tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorShape(input2), &op_params);
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if (output->type == kTfLiteInt8) {
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if (need_broadcast) {
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tflite::reference_ops::BroadcastSubSlow(
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op_params, tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<int8_t>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<int8_t>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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} else {
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tflite::reference_ops::Sub(
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op_params, tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<int8_t>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<int8_t>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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}
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} else {
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if (need_broadcast) {
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tflite::reference_ops::BroadcastSubSlow(
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op_params, tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<uint8_t>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<uint8_t>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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} else {
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tflite::reference_ops::Sub(
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op_params, tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<uint8_t>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<uint8_t>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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}
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}
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_data);
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const TfLiteEvalTensor* input1 =
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tflite::micro::GetEvalInput(context, node, kInputTensor1);
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const TfLiteEvalTensor* input2 =
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tflite::micro::GetEvalInput(context, node, kInputTensor2);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData& data = *(static_cast<const OpData*>(node->user_data));
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if (output->type == kTfLiteFloat32) {
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EvalSub(context, node, params, &data, input1, input2, output);
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} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
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TF_LITE_ENSURE_OK(context, EvalSubQuantized(context, node, params, &data,
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input1, input2, output));
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} else {
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TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
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TfLiteTypeGetName(output->type), output->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace sub
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TfLiteRegistration Register_SUB() {
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return {/*init=*/sub::Init,
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/*free=*/nullptr,
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/*prepare=*/sub::Prepare,
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/*invoke=*/sub::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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