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tflite-micro/tensorflow/lite/micro/kernels/sub.cc
Hemanth Sai Garladinne 686df2db9f
Add INT32 support to SUB (#3037) 
- Add INT32 support in sub
- Add Tflite tests in sub_test.cc

bug=fixes #2720
2025-06-04 16:34:13 +00:00

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/* Copyright 2025 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/micro/kernels/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/add.h"
#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
#include "tensorflow/lite/kernels/internal/reference/sub.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"
#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
void* SubInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataSub));
}
TfLiteStatus EvalSub(TfLiteContext* context, TfLiteNode* node,
TfLiteSubParams* params, const OpDataSub* data,
const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
switch (output->type) {
case kTfLiteFloat32: {
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));
}
} break;
case kTfLiteInt32: {
int32_t 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<int32_t>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<int32_t>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int32_t>(output));
} else {
tflite::reference_ops::SubWithActivation(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<int32_t>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<int32_t>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int32_t>(output));
}
} break;
default:
MicroPrintf("Type %s (%d) not supported.",
TfLiteTypeGetName(output->type), output->type);
return kTfLiteError;
}
return kTfLiteOk;
}
TfLiteStatus EvalSubQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteSubParams* params, const OpDataSub* data,
const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2,
TfLiteEvalTensor* output) {
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);
switch (output->type) {
case kTfLiteInt8: {
if (need_broadcast) {
tflite::reference_ops::BroadcastQuantSubSlow(
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));
}
break;
}
case kTfLiteInt16: {
if (need_broadcast) {
tflite::reference_ops::BroadcastQuantSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<int16_t>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<int16_t>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
} else {
tflite::reference_ops::Sub(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<int16_t>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<int16_t>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
}
break;
}
default:
MicroPrintf("Quantized type %s not currently supported.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
}
TfLiteStatus SubEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSubParams*>(node->builtin_data);
const TfLiteEvalTensor* input1 =
tflite::micro::GetEvalInput(context, node, kSubInputTensor1);
const TfLiteEvalTensor* input2 =
tflite::micro::GetEvalInput(context, node, kSubInputTensor2);
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kSubOutputTensor);
TFLITE_DCHECK(node->user_data != nullptr);
const OpDataSub& data = *(static_cast<const OpDataSub*>(node->user_data));
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
TF_LITE_ENSURE_OK(
context, EvalSub(context, node, params, &data, input1, input2, output));
} else if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
TF_LITE_ENSURE_OK(context, EvalSubQuantized(context, node, params, &data,
input1, input2, output));
} else {
MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(output->type),
output->type);
return kTfLiteError;
}
return kTfLiteOk;
}
TFLMRegistration Register_SUB() {
return tflite::micro::RegisterOp(SubInit, SubPrepare, SubEval);
}
} // namespace tflite
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