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/*
* Copyright (C) 2020 The Android Open Source Project
*
* 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.
*/
#define LOG_TAG "Operations"
#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
#include <algorithm>
#include <vector>
#include "CpuOperationUtils.h"
#include "OperationResolver.h"
#include "Tracing.h"
namespace android {
namespace nn {
namespace local_response_norm {
constexpr char kOperationName[] = "LOCAL_RESPONSE_NORMALIZATION";
constexpr uint32_t kNumInputs = 6;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kRadiusScalar = 1;
constexpr uint32_t kBiasScalar = 2;
constexpr uint32_t kAlphaScalar = 3;
constexpr uint32_t kBetaScalar = 4;
constexpr uint32_t kAxisScalar = 5;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape,
int32_t radius, float bias, float alpha, float beta,
int32_t axis, float* outputData,
const Shape& outputShape) {
NNTRACE_TRANS("localResponseNormFloat32");
const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
const uint32_t innerSize =
getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
for (uint32_t outer = 0; outer < outerSize; ++outer) {
const float* inputBase = inputData + outer * axisSize * innerSize;
float* outputBase = outputData + outer * axisSize * innerSize;
for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBase, ++outputBase) {
for (int32_t i = 0; i < axisSize; i++) {
const int32_t dBegin = std::max(0, i - radius);
// Add 1 on dEnd to comply with optimized_ops in TFLite
const int32_t dEnd = std::min(static_cast<int32_t>(axisSize), i + radius + 1);
float sum = 0.0f;
for (int32_t d = dBegin; d < dEnd; d++) {
float val = inputBase[d * innerSize];
sum += val * val;
}
float multiplier = std::pow(bias + alpha * sum, -beta);
outputBase[i * innerSize] = inputBase[i * innerSize] * multiplier;
}
}
}
return true;
}
template <typename T>
bool localResponseNorm(const T* inputData, const Shape& inputShape, int32_t radius, T bias, T alpha,
T beta, int32_t axis, T* outputData, const Shape& outputShape);
template <>
bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius,
float bias, float alpha, float beta, int32_t axis, float* outputData,
const Shape& outputShape) {
int32_t ndim = getNumberOfDimensions(inputShape);
NN_CHECK(handleNegativeAxis(inputShape, &axis));
// TFLite optimized implementation only supports computation along the last axis
if (axis == ndim - 1) {
NNTRACE_COMP("optimized_ops::LocalResponseNormalization::float");
tflite::LocalResponseNormalizationParams param = {
.range = radius, .bias = bias, .alpha = alpha, .beta = beta};
tflite::optimized_ops::LocalResponseNormalization(
param, convertShapeToTflshape(inputShape), inputData,
convertShapeToTflshape(outputShape), outputData);
return true;
} else {
return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis,
outputData, outputShape);
}
}
template <>
bool localResponseNorm<_Float16>(const _Float16* inputData, const Shape& inputShape, int32_t radius,
_Float16 bias, _Float16 alpha, _Float16 beta, int32_t axis,
_Float16* outputData, const Shape& outputShape) {
NNTRACE_TRANS("localResponseNormFloat16");
std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
convertFloat16ToFloat32(inputData, &inputDataFloat32);
std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
localResponseNorm<float>(inputDataFloat32.data(), inputShape, radius, bias, alpha, beta, axis,
outputDataFloat32.data(), outputShape);
convertFloat32ToFloat16(outputDataFloat32, outputData);
return true;
}
template <typename T>
bool executeTyped(IOperationExecutionContext* context) {
int32_t axis = context->getNumInputs() == kNumInputs
? context->getInputValue<int32_t>(kAxisScalar)
: -1;
NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
return localResponseNorm<T>(
context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor),
context->getInputValue<int32_t>(kRadiusScalar), context->getInputValue<T>(kBiasScalar),
context->getInputValue<T>(kAlphaScalar), context->getInputValue<T>(kBetaScalar), axis,
context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor));
}
} // namespace
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK(context->getNumInputs() == kNumInputs ||
context->getNumInputs() == kNumInputs - 1);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
const OperandType inputType = context->getInputType(kInputTensor);
std::vector<OperandType> inExpectedTypes;
std::vector<OperandType> outExpectedTypes;
auto minSupportedVersion = Version::ANDROID_OC_MR1;
if (inputType == OperandType::TENSOR_FLOAT32) {
minSupportedVersion = Version::ANDROID_OC_MR1;
inExpectedTypes = {
OperandType::TENSOR_FLOAT32, OperandType::INT32, OperandType::FLOAT32,
OperandType::FLOAT32, OperandType::FLOAT32,
};
outExpectedTypes = {OperandType::TENSOR_FLOAT32};
} else if (inputType == OperandType::TENSOR_FLOAT16) {
minSupportedVersion = Version::ANDROID_Q;
inExpectedTypes = {
OperandType::TENSOR_FLOAT16, OperandType::INT32, OperandType::FLOAT16,
OperandType::FLOAT16, OperandType::FLOAT16,
};
outExpectedTypes = {OperandType::TENSOR_FLOAT16};
} else {
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
if (context->getNumInputs() == kNumInputs) {
inExpectedTypes.push_back(OperandType::INT32);
minSupportedVersion = Version::ANDROID_Q;
} else if (context->getInputShape(kInputTensor).dimensions.size() != 4) {
minSupportedVersion = Version::ANDROID_Q;
}
const Shape& input = context->getInputShape(kInputTensor);
if (hasKnownRank(input)) {
NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
}
NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
return validateVersion(context, minSupportedVersion);
}
bool prepare(IOperationExecutionContext* context) {
const Shape& input = context->getInputShape(kInputTensor);
int32_t numDimensions = getNumberOfDimensions(input);
int32_t axis = context->getNumInputs() == kNumInputs
? context->getInputValue<int32_t>(kAxisScalar)
: -1;
NN_RET_CHECK_LE(numDimensions, 4);
NN_RET_CHECK_GE(axis, -numDimensions);
NN_RET_CHECK_LT(axis, numDimensions);
return context->setOutputShape(kOutputTensor, input);
}
bool execute(IOperationExecutionContext* context) {
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT32:
return executeTyped<float>(context);
case OperandType::TENSOR_FLOAT16:
return executeTyped<_Float16>(context);
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace local_response_norm
NN_REGISTER_OPERATION(LOCAL_RESPONSE_NORMALIZATION, local_response_norm::kOperationName,
local_response_norm::validate, local_response_norm::prepare,
local_response_norm::execute);
} // namespace nn
} // namespace android
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