/*
* Copyright (C) 2019 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.
*/
#include "CpuOperationUtils.h"
#include "OperationResolver.h"
#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
#include "Tracing.h"
namespace android {
namespace nn {
namespace softmax {
constexpr char kOperationName[] = "SOFTMAX";
constexpr uint32_t kNumInputs = 3;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kBetaScalar = 1;
constexpr uint32_t kAxisScalar = 2;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
inline bool softmaxSlowFloat32(const float* inputData, const Shape& inputShape, const float beta,
int32_t axis, float* outputData, const Shape& outputShape) {
NNTRACE_TRANS("softmaxFloatSlow32");
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* inputBeg = inputData + outer * axisSize * innerSize;
const float* inputEnd = inputBeg + axisSize * innerSize;
float* outputBeg = outputData + outer * axisSize * innerSize;
for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBeg, ++inputEnd, ++outputBeg) {
// Find max
float maxValue = -FLT_MAX;
for (const float* p = inputBeg; p < inputEnd; p += innerSize) {
maxValue = std::max(maxValue, *p);
}
// Compute sum
float sum = 0.0f;
for (const float* p = inputBeg; p < inputEnd; p += innerSize) {
sum += std::exp((*p - maxValue) * beta);
}
// Compute result
float* pOut = outputBeg;
for (const float* p = inputBeg; p < inputEnd; p += innerSize, pOut += innerSize) {
*pOut = std::exp((*p - maxValue) * beta) / sum;
}
}
}
return true;
}
bool softmaxFloat32(const float* inputData, const Shape& inputShape, const 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::Softmax::float");
tflite::SoftmaxParams param = {.beta = beta};
tflite::optimized_ops::Softmax(param, convertShapeToTflshape(inputShape), inputData,
convertShapeToTflshape(outputShape), outputData);
return true;
} else {
return softmaxSlowFloat32(inputData, inputShape, beta, axis, outputData, outputShape);
}
}
bool softmaxFloat16(const _Float16* inputData, const Shape& inputShape, const float beta,
int32_t axis, _Float16* outputData, const Shape& outputShape) {
NNTRACE_TRANS("softmaxFloat16");
std::vector<float> inputData_float32(getNumberOfElements(inputShape));
convertFloat16ToFloat32(inputData, &inputData_float32);
std::vector<float> outputData_float32(getNumberOfElements(outputShape));
softmaxFloat32(inputData_float32.data(), inputShape, beta, axis, outputData_float32.data(),
outputShape);
convertFloat32ToFloat16(outputData_float32, outputData);
return true;
}
bool softmaxQuant8Impl(const uint8_t* inputData, const Shape& inputShape, const float beta,
int32_t axis, int32_t inputMultiplier, int32_t inputLeftShift, float diffMin,
uint8_t* outputData, const Shape& outputShape) {
NNTRACE_TRANS("softmaxQuant8");
// The representation chosen for the input to the exp() function is Q5.26.
// We need to leave extra space since values that we skip might be as large as
// -32 before multiplying by input_beta_multiplier, and therefore as large as
// -16 afterwards. Note that exp(-8) is definitely not insignificant to
// accumulation, but exp(-16) definitely is.
static const int32_t kScaledDiffIntegerBits = 5;
static const int kAccumulationIntegerBits = 12;
using FixedPointScaledDiff = gemmlowp::FixedPoint<int32_t, kScaledDiffIntegerBits>;
using FixedPointAccum = gemmlowp::FixedPoint<int32_t, kAccumulationIntegerBits>;
using FixedPoint0 = gemmlowp::FixedPoint<int32_t, 0>;
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 uint8_t* inputBeg = inputData + outer * axisSize * innerSize;
const uint8_t* inputEnd = inputBeg + axisSize * innerSize;
uint8_t* outputBeg = outputData + outer * axisSize * innerSize;
for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBeg, ++inputEnd, ++outputBeg) {
// Find max
uint8_t maxValue = 0;
for (const uint8_t* p = inputBeg; p < inputEnd; p += innerSize) {
maxValue = std::max(maxValue, *p);
}
// Compute sum
FixedPointAccum sum_of_exps = FixedPointAccum::Zero();
for (const uint8_t* p = inputBeg; p < inputEnd; p += innerSize) {
int32_t input_diff = static_cast<int32_t>(*p) - maxValue;
if (input_diff >= diffMin) {
const int32_t input_diff_rescaled =
tflite::MultiplyByQuantizedMultiplierGreaterThanOne(
input_diff, inputMultiplier, inputLeftShift);
const auto scaled_diff_f8 = FixedPointScaledDiff::FromRaw(input_diff_rescaled);
sum_of_exps = sum_of_exps + gemmlowp::Rescale<kAccumulationIntegerBits>(
exp_on_negative_values(scaled_diff_f8));
}
}
uint32_t fixed_sum_of_exps = static_cast<uint32_t>(sum_of_exps.raw());
int32_t headroom_plus_one = tflite::CountLeadingZeros(fixed_sum_of_exps);
// This is the number of bits to the left of the binary point above 1.0.
// Consider fixed_sum_of_exps=1.25. In that case shifted_scale=0.8 and
// no later adjustment will be needed.
int32_t num_bits_over_unit = kAccumulationIntegerBits - headroom_plus_one;
int32_t shifted_sum_minus_one = static_cast<int32_t>(
(fixed_sum_of_exps << headroom_plus_one) - (static_cast<uint32_t>(1) << 31));
FixedPoint0 shifted_scale = gemmlowp::one_over_one_plus_x_for_x_in_0_1(
FixedPoint0::FromRaw(shifted_sum_minus_one));
// Compute result
uint8_t* pOut = outputBeg;
for (const uint8_t* p = inputBeg; p < inputEnd; p += innerSize, pOut += innerSize) {
int32_t input_diff = static_cast<int32_t>(*p) - maxValue;
if (input_diff >= diffMin) {
const int32_t input_diff_rescaled =
tflite::MultiplyByQuantizedMultiplierGreaterThanOne(
input_diff, inputMultiplier, inputLeftShift);
const auto scaled_diff_f8 = FixedPointScaledDiff::FromRaw(input_diff_rescaled);
FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8);
int32_t unsat_output = gemmlowp::RoundingDivideByPOT(
(shifted_scale * exp_in_0).raw(), num_bits_over_unit + 31 - 8);
*pOut = static_cast<uint8_t>(
std::max(std::min(unsat_output, static_cast<int32_t>(255)), 0));
} else {
*pOut = 0;
}
}
}
}
return true;
}
bool softmaxQuant8(const uint8_t* inputData, const Shape& inputShape, const float beta,
int32_t axis, uint8_t* outputData, const Shape& outputShape) {
int32_t ndim = getNumberOfDimensions(inputShape);
NN_CHECK(handleNegativeAxis(inputShape, &axis));
if (outputShape.offset != 0 || outputShape.scale != 1.f / 256) {
LOG(ERROR) << "incorrect scale / offset for output";
return false;
}
static const int32_t kScaledDiffIntegerBits = 5;
const double input_beta_real_multiplier =
std::min(1.0 * beta * inputShape.scale * (1 << (31 - kScaledDiffIntegerBits)),
(1LL << 31) - 1.0);
int32_t inputMultiplier = 0, inputLeftShift = 0;
if (!QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier, &inputMultiplier,
&inputLeftShift)) {
return false;
}
int32_t diffMin = -CalculateInputRadius(kScaledDiffIntegerBits, inputLeftShift);
// TFLite optimized implementation only supports computation along the last axis
if (axis == ndim - 1) {
NNTRACE_COMP("optimized_ops::Softmax::uint8");
tflite::SoftmaxParams param = {.beta = beta,
.input_multiplier = inputMultiplier,
.input_left_shift = inputLeftShift,
.diff_min = diffMin};
tflite::optimized_ops::Softmax(param, convertShapeToTflshape(inputShape), inputData,
convertShapeToTflshape(outputShape), outputData);
return true;
} else {
return softmaxQuant8Impl(inputData, inputShape, beta, axis, inputMultiplier, inputLeftShift,
diffMin, outputData, outputShape);
}
}
} // namespace
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK(context->getNumInputs() == kNumInputs ||
context->getNumInputs() == kNumInputs - 1);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
auto inputType = context->getInputType(kInputTensor);
std::vector<OperandType> inExpectedTypes;
if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_QUANT8_ASYMM) {
NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_0));
inExpectedTypes = {inputType, OperandType::FLOAT32};
} else if (inputType == OperandType::TENSOR_FLOAT16) {
NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
inExpectedTypes = {inputType, OperandType::FLOAT16};
} else {
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
if (context->getNumInputs() == kNumInputs) {
NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
inExpectedTypes.push_back(OperandType::INT32);
} else {
const size_t ndim = context->getInputShape(kInputTensor).dimensions.size();
if (ndim != 2 && ndim != 4 && ndim != 0) {
NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
}
}
return validateInputTypes(context, inExpectedTypes) &&
validateOutputTypes(context, {inputType});
}
bool prepare(IOperationExecutionContext* context) {
Shape input = context->getInputShape(kInputTensor);
float beta = (input.type == OperandType::TENSOR_FLOAT16)
? context->getInputValue<_Float16>(kBetaScalar)
: context->getInputValue<float>(kBetaScalar);
NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
NN_RET_CHECK_GT(beta, 0.0f);
Shape output = context->getOutputShape(kOutputTensor);
output.dimensions = input.dimensions;
return context->setOutputShape(kOutputTensor, output);
}
bool execute(IOperationExecutionContext* context) {
// Bypass execution in the case of zero-sized input.
if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
int32_t axis = (context->getNumInputs() == kNumInputs)
? context->getInputValue<int32_t>(kAxisScalar)
: -1;
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return softmaxFloat16(context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<_Float16>(kBetaScalar), axis,
context->getOutputBuffer<_Float16>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return softmaxFloat32(context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<float>(kBetaScalar), axis,
context->getOutputBuffer<float>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_QUANT8_ASYMM:
return softmaxQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<float>(kBetaScalar), axis,
context->getOutputBuffer<uint8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace softmax
NN_REGISTER_OPERATION(SOFTMAX, "SOFTMAX", softmax::validate, softmax::prepare, softmax::execute,
.allowZeroSizedInput = true);
} // namespace nn
} // namespace android