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Add HiFi-optimized Xtensa Logistic and Tanh kernels #3728

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33f2a91
Add HiFi-optimized Xtensa Logistic and Tanh kernels
unmeshna017 Sep 16, 2026
9cfbddd
Include NNLib headers for HIFI_IQ target in xtensa.h
unmeshna017 Sep 21, 2026
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39 changes: 25 additions & 14 deletions tensorflow/lite/micro/kernels/xtensa/logistic.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -26,14 +26,15 @@ limitations under the License.
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/logistic.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa_logistic.h"
#include "tensorflow/lite/micro/micro_log.h"

namespace tflite {
namespace {

void* LogisticInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataLogistic));
return context->AllocatePersistentBuffer(context, sizeof(OpDataLogisticXtensa));
}

TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) {
Expand All @@ -43,7 +44,9 @@ TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) {
tflite::micro::GetEvalOutput(context, node, kLogisticOutputTensor);

TFLITE_DCHECK(node->user_data != nullptr);
OpDataLogistic* data = static_cast<OpDataLogistic*>(node->user_data);
OpDataLogisticXtensa* xtensa_data =
static_cast<OpDataLogisticXtensa*>(node->user_data);
OpDataLogistic* data = &xtensa_data->reference_op_data;

if (input->type != output->type) {
MicroPrintf(
Expand All @@ -54,7 +57,7 @@ TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) {

switch (input->type) {
case kTfLiteFloat32: {
#if HIFI_VFPU && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
#if defined(INCLUDE_FLOAT_OPT) && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const int flat_size = MatchingFlatSize(input_shape, output_shape);
Expand All @@ -70,44 +73,52 @@ TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) {
tflite::micro::GetTensorData<float>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
#endif // HIFI_VFPU && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
#endif // defined(INCLUDE_FLOAT_OPT) && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
break;
}
case kTfLiteInt8: {
#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) || defined(HIFI_IQ)
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const int flat_size = MatchingFlatSize(input_shape, output_shape);

const int8_t* input_data_ptr =
tflite::micro::GetTensorData<int8_t>(input);
const int8_t* input_data_ptr = tflite::micro::GetTensorData<int8_t>(input);
int8_t* output_data_ptr = tflite::micro::GetTensorData<int8_t>(output);

TF_LITE_ENSURE_EQ(
context,
xa_nn_vec_sigmoid_asym8s_asym8s(
output_data_ptr, input_data_ptr, data->input_zero_point,
data->input_range_radius, data->input_multiplier,
data->input_left_shift, flat_size),
xa_nn_vec_apply_lut_asym8s_asym8s(
output_data_ptr, input_data_ptr,
static_cast<int8_t*>(xtensa_data->sigmoid_lut),
256, flat_size),
0);
#else
reference_integer_ops::Logistic(
data->input_zero_point, data->input_range_radius,
data->input_multiplier, data->input_left_shift,
NumElements(input->dims), tflite::micro::GetTensorData<int8_t>(input),
tflite::micro::GetTensorData<int8_t>(output));
#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) || defined(HIFI_IQ)
break;
}
case kTfLiteInt16: {
switch (output->type) {
case kTfLiteInt16:
case kTfLiteInt16 : {
#if defined(HIFI4) || defined(HIFI5) || defined(HIFI_IQ)
TF_LITE_ENSURE_EQ(context, xa_nn_vec_sigmoid_sym16s_sym16s(tflite::micro::GetTensorData<int16_t>(output),
tflite::micro::GetTensorData<int16_t>(input),
data->input_multiplier,
data->input_left_shift,
NumElements(input->dims)), 0);
#else
reference_integer_ops::Logistic(
data->input_multiplier, data->input_left_shift,
NumElements(input->dims),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorData<int16_t>(output));
break;
#endif
return kTfLiteOk;
} break;
default:
MicroPrintf("Input %s, output %s not supported.",
TfLiteTypeGetName(input->type),
Expand Down
156 changes: 156 additions & 0 deletions tensorflow/lite/micro/kernels/xtensa/logistic_common.cc
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Original file line number Diff line number Diff line change
@@ -0,0 +1,156 @@
/* Copyright 2021 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/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/integer_ops/logistic.h"
#include "tensorflow/lite/kernels/internal/reference/logistic.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.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/kernels/logistic.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa_logistic.h"
#if defined(USE_HIFI_ACT_TIE)
#include <xtensa/tie/xt_hifi2.h>
#endif

namespace tflite {
const int kLogisticInputTensor = 0;
const int kLogisticOutputTensor = 0;

TfLiteStatus CalculateArithmeticOpDataLogistic(TfLiteContext* context,
TfLiteNode* node,
OpDataLogistic* data) {
MicroContext* micro_context = GetMicroContext(context);

TfLiteTensor* input =
micro_context->AllocateTempInputTensor(node, kLogisticInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kLogisticOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);

TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (input->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, output->params.zero_point,
std::numeric_limits<int8_t>::min());

static constexpr int kInputIntegerBits = 4;
const double input_real_multiplier =
static_cast<double>(input->params.scale) *
static_cast<double>(1 << (31 - kInputIntegerBits));

data->input_zero_point = input->params.zero_point;

const double q = std::frexp(input_real_multiplier, &data->input_left_shift);
data->input_multiplier = static_cast<int32_t>(TfLiteRound(q * (1ll << 31)));

data->input_range_radius =
CalculateInputRadius(kInputIntegerBits, data->input_left_shift, 31);
}

if (input->type == kTfLiteInt16) {
static constexpr int kInputIntegerBits = 3;
static constexpr int kOutputFractionalBits = 15;

// See comments in TanhPrepare about requiring zero_point==0
// and a power-of-two ("POT") scale.

TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);

int input_scale_log2_rounded;
bool param_scale_pot =
CheckedLog2(input->params.scale, &input_scale_log2_rounded);

data->input_left_shift =
(15 - kInputIntegerBits) + input_scale_log2_rounded;
param_scale_pot &= (data->input_left_shift == 0);

if (param_scale_pot) {
data->input_multiplier = 0;
} else {
// Calculate multiplier to change input scale to 1/(3*4096)
// as required by the table lookup.
// In this scaling +/-2^17 represents +/-10.7
#if (defined(USE_HIFI_ACT_TIE) && (defined(AE_SIGMOID16X4) || defined(AE_SIGMOID16X4X2)))
double multiplier =
static_cast<double>(input->params.scale) * 4096.0;
#else
double multiplier =
static_cast<double>(input->params.scale) * 4096.0 * 3.0;
#endif
data->input_left_shift = 0;

while (multiplier <= 32767.0 / 2.0 && data->input_left_shift <= 30) {
data->input_left_shift++;
multiplier = multiplier * 2.0;
}

data->input_multiplier = static_cast<int32_t>(multiplier);
}

int output_scale_log2_rounded;
TF_LITE_ENSURE(
context, CheckedLog2(output->params.scale, &output_scale_log2_rounded));
TF_LITE_ENSURE_EQ(context, output_scale_log2_rounded,
-kOutputFractionalBits);
}

micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(output);
return kTfLiteOk;
}

TfLiteStatus LogisticPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
auto* xtensa_data = static_cast<OpDataLogisticXtensa*>(node->user_data);
TF_LITE_ENSURE_OK(context, CalculateArithmeticOpDataLogistic(
context, node, &xtensa_data->reference_op_data));

#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) || defined(HIFI_IQ)
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* input =
micro_context->AllocateTempInputTensor(node, kLogisticInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
if (input->type == kTfLiteInt8)
{
void* raw = context->AllocatePersistentBuffer(
context, sizeof(int8_t) * 256);
TF_LITE_ENSURE(context, raw != nullptr);
xtensa_data->sigmoid_lut = raw;
TF_LITE_ENSURE_EQ(
context,
xa_nn_init_lut_asym8s_sigmoid(
static_cast<int8_t*>(xtensa_data->sigmoid_lut),
xtensa_data->reference_op_data.input_zero_point,
xtensa_data->reference_op_data.input_range_radius,
xtensa_data->reference_op_data.input_multiplier,
xtensa_data->reference_op_data.input_left_shift),
0);
} else {
xtensa_data->sigmoid_lut = nullptr;
}

micro_context->DeallocateTempTfLiteTensor(input);
#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) || defined(HIFI_IQ)
return kTfLiteOk;
}

} // namespace tflite
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