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.
|
|
|
|
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 "tensorflow/lite/kernels/internal/reference/sub.h"
|
|
|
|
#include "tensorflow/lite/c/builtin_op_data.h"
|
|
#include "tensorflow/lite/c/common.h"
|
|
#include "tensorflow/lite/kernels/internal/common.h"
|
|
#include "tensorflow/lite/kernels/internal/quantization_util.h"
|
|
#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
|
|
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
|
|
#include "tensorflow/lite/kernels/internal/types.h"
|
|
#include "tensorflow/lite/kernels/kernel_util.h"
|
|
#include "tensorflow/lite/kernels/op_macros.h"
|
|
#include "tensorflow/lite/micro/kernels/kernel_util.h"
|
|
|
|
namespace tflite {
|
|
namespace ops {
|
|
namespace micro {
|
|
namespace sub {
|
|
|
|
constexpr int kInputTensor1 = 0;
|
|
constexpr int kInputTensor2 = 1;
|
|
constexpr int kOutputTensor = 0;
|
|
|
|
struct OpData {
|
|
bool requires_broadcast;
|
|
|
|
// These fields are used in both the general 8-bit -> 8bit quantized path,
|
|
// and the special 16-bit -> 16bit quantized path
|
|
int input1_shift;
|
|
int input2_shift;
|
|
int32_t output_activation_min;
|
|
int32_t output_activation_max;
|
|
|
|
// These fields are used only in the general 8-bit -> 8bit quantized path
|
|
int32_t input1_multiplier;
|
|
int32_t input2_multiplier;
|
|
int32_t output_multiplier;
|
|
int output_shift;
|
|
int left_shift;
|
|
int32_t input1_offset;
|
|
int32_t input2_offset;
|
|
int32_t output_offset;
|
|
};
|
|
|
|
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteSubParams* params,
|
|
const TfLiteTensor* input1,
|
|
const TfLiteTensor* input2, TfLiteTensor* output,
|
|
OpData* data) {
|
|
data->requires_broadcast = !HaveSameShapes(input1, input2);
|
|
|
|
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
|
|
// 8bit -> 8bit general quantized path, with general rescalings
|
|
data->input1_offset = -input1->params.zero_point;
|
|
data->input2_offset = -input2->params.zero_point;
|
|
data->output_offset = output->params.zero_point;
|
|
data->left_shift = 20;
|
|
const float twice_max_input_scale =
|
|
2 * std::max(input1->params.scale, input2->params.scale);
|
|
const double real_input1_multiplier =
|
|
static_cast<double>(input1->params.scale / twice_max_input_scale);
|
|
const double real_input2_multiplier =
|
|
static_cast<double>(input2->params.scale / twice_max_input_scale);
|
|
const double real_output_multiplier =
|
|
static_cast<double>(twice_max_input_scale /
|
|
((1 << data->left_shift) * output->params.scale));
|
|
|
|
QuantizeMultiplierSmallerThanOneExp(
|
|
real_input1_multiplier, &data->input1_multiplier, &data->input1_shift);
|
|
|
|
QuantizeMultiplierSmallerThanOneExp(
|
|
real_input2_multiplier, &data->input2_multiplier, &data->input2_shift);
|
|
|
|
QuantizeMultiplierSmallerThanOneExp(
|
|
real_output_multiplier, &data->output_multiplier, &data->output_shift);
|
|
|
|
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
|
|
context, params->activation, output, &data->output_activation_min,
|
|
&data->output_activation_max));
|
|
}
|
|
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
|
|
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
|
|
return context->AllocatePersistentBuffer(context, sizeof(OpData));
|
|
}
|
|
|
|
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
|
TFLITE_DCHECK(node->user_data != nullptr);
|
|
TFLITE_DCHECK(node->builtin_data != nullptr);
|
|
|
|
OpData* data = static_cast<OpData*>(node->user_data);
|
|
auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_data);
|
|
|
|
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
|
|
TF_LITE_ENSURE(context, input1 != nullptr);
|
|
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
|
|
TF_LITE_ENSURE(context, input2 != nullptr);
|
|
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
|
|
TF_LITE_ENSURE(context, output != nullptr);
|
|
|
|
TF_LITE_ENSURE_STATUS(
|
|
CalculateOpData(context, params, input1, input2, output, data));
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params,
|
|
const OpData* data, const TfLiteEvalTensor* input1,
|
|
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
|
|
float output_activation_min, output_activation_max;
|
|
CalculateActivationRange(params->activation, &output_activation_min,
|
|
&output_activation_max);
|
|
tflite::ArithmeticParams op_params;
|
|
SetActivationParams(output_activation_min, output_activation_max, &op_params);
|
|
if (data->requires_broadcast) {
|
|
tflite::reference_ops::BroadcastSubSlow(
|
|
op_params, tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorData<float>(input1),
|
|
tflite::micro::GetTensorShape(input2),
|
|
tflite::micro::GetTensorData<float>(input2),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<float>(output));
|
|
} else {
|
|
tflite::reference_ops::SubWithActivation(
|
|
op_params, tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorData<float>(input1),
|
|
tflite::micro::GetTensorShape(input2),
|
|
tflite::micro::GetTensorData<float>(input2),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<float>(output));
|
|
}
|
|
}
|
|
|
|
TfLiteStatus EvalSubQuantized(TfLiteContext* context, TfLiteNode* node,
|
|
TfLiteSubParams* params, const OpData* data,
|
|
const TfLiteEvalTensor* input1,
|
|
const TfLiteEvalTensor* input2,
|
|
TfLiteEvalTensor* output) {
|
|
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
|
|
tflite::ArithmeticParams op_params;
|
|
op_params.left_shift = data->left_shift;
|
|
op_params.input1_offset = data->input1_offset;
|
|
op_params.input1_multiplier = data->input1_multiplier;
|
|
op_params.input1_shift = data->input1_shift;
|
|
op_params.input2_offset = data->input2_offset;
|
|
op_params.input2_multiplier = data->input2_multiplier;
|
|
op_params.input2_shift = data->input2_shift;
|
|
op_params.output_offset = data->output_offset;
|
|
op_params.output_multiplier = data->output_multiplier;
|
|
op_params.output_shift = data->output_shift;
|
|
SetActivationParams(data->output_activation_min,
|
|
data->output_activation_max, &op_params);
|
|
bool need_broadcast = reference_ops::ProcessBroadcastShapes(
|
|
tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorShape(input2), &op_params);
|
|
|
|
if (output->type == kTfLiteInt8) {
|
|
if (need_broadcast) {
|
|
tflite::reference_ops::BroadcastSubSlow(
|
|
op_params, tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorData<int8_t>(input1),
|
|
tflite::micro::GetTensorShape(input2),
|
|
tflite::micro::GetTensorData<int8_t>(input2),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
} else {
|
|
tflite::reference_ops::Sub(
|
|
op_params, tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorData<int8_t>(input1),
|
|
tflite::micro::GetTensorShape(input2),
|
|
tflite::micro::GetTensorData<int8_t>(input2),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
}
|
|
} else {
|
|
if (need_broadcast) {
|
|
tflite::reference_ops::BroadcastSubSlow(
|
|
op_params, tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorData<uint8_t>(input1),
|
|
tflite::micro::GetTensorShape(input2),
|
|
tflite::micro::GetTensorData<uint8_t>(input2),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<uint8_t>(output));
|
|
} else {
|
|
tflite::reference_ops::Sub(
|
|
op_params, tflite::micro::GetTensorShape(input1),
|
|
tflite::micro::GetTensorData<uint8_t>(input1),
|
|
tflite::micro::GetTensorShape(input2),
|
|
tflite::micro::GetTensorData<uint8_t>(input2),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<uint8_t>(output));
|
|
}
|
|
}
|
|
}
|
|
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
|
|
auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_data);
|
|
|
|
const TfLiteEvalTensor* input1 =
|
|
tflite::micro::GetEvalInput(context, node, kInputTensor1);
|
|
const TfLiteEvalTensor* input2 =
|
|
tflite::micro::GetEvalInput(context, node, kInputTensor2);
|
|
TfLiteEvalTensor* output =
|
|
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
|
|
|
|
TFLITE_DCHECK(node->user_data != nullptr);
|
|
const OpData& data = *(static_cast<const OpData*>(node->user_data));
|
|
|
|
if (output->type == kTfLiteFloat32) {
|
|
EvalSub(context, node, params, &data, input1, input2, output);
|
|
} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) {
|
|
TF_LITE_ENSURE_OK(context, EvalSubQuantized(context, node, params, &data,
|
|
input1, input2, output));
|
|
} else {
|
|
TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
|
|
TfLiteTypeGetName(output->type), output->type);
|
|
return kTfLiteError;
|
|
}
|
|
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
} // namespace sub
|
|
|
|
TfLiteRegistration Register_SUB() {
|
|
return {/*init=*/sub::Init,
|
|
/*free=*/nullptr,
|
|
/*prepare=*/sub::Prepare,
|
|
/*invoke=*/sub::Eval,
|
|
/*profiling_string=*/nullptr,
|
|
/*builtin_code=*/0,
|
|
/*custom_name=*/nullptr,
|
|
/*version=*/0};
|
|
}
|
|
|
|
} // namespace micro
|
|
} // namespace ops
|
|
} // namespace tflite
|