Mihai Maruseac fff2c83262 [tflite]: Insert nullptr checks when obtaining tensors.
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
2020-09-18 14:10:11 -07:00

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