/*
* Copyright (C) 2018 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 "Multinomial.h"
#include "CpuExecutor.h"
#include "CpuOperationUtils.h"
#include "HalInterfaces.h"
#include "Tracing.h"
#include "guarded_philox_random.h"
#include "philox_random.h"
#include "simple_philox.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace android {
namespace nn {
namespace {
template <typename T>
inline T* GetBuffer(RunTimeOperandInfo* operand) {
return reinterpret_cast<T*>(operand->buffer);
}
template <typename T>
inline const T* GetBuffer(const RunTimeOperandInfo* operand) {
return reinterpret_cast<const T*>(operand->buffer);
}
} // namespace
Multinomial::Multinomial(const Operation& operation, std::vector<RunTimeOperandInfo>& operands) {
NNTRACE_TRANS("Multinomial::Multinomial");
input_ = GetInput(operation, operands, kInputTensor);
sample_count_ = getScalarData<int>(*GetInput(operation, operands, kSampleCountParam));
random_seeds_ = GetInput(operation, operands, kRandomSeedsTensor);
output_ = GetOutput(operation, operands, kOutputTensor);
}
bool Multinomial::Prepare(const Operation& operation, std::vector<RunTimeOperandInfo>& operands,
Shape* outputShape) {
NNTRACE_TRANS("Multinomial::Prepare");
NN_CHECK_EQ(NumInputsWithValues(operation, operands), 3);
NN_CHECK_EQ(NumOutputs(operation), 1);
const RunTimeOperandInfo* input = GetInput(operation, operands, Multinomial::kInputTensor);
const Shape& inputShape = input->shape();
const uint32_t batch_size = SizeOfDimension(input, 0);
const uint32_t sample_count =
getScalarData<int>(*GetInput(operation, operands, kSampleCountParam));
outputShape->type = OperandType::TENSOR_INT32;
outputShape->dimensions = {batch_size, sample_count};
outputShape->offset = inputShape.offset;
outputShape->scale = inputShape.scale;
return true;
}
bool Multinomial::Eval() {
NNTRACE_COMP("Multinomial::Eval");
switch (input_->type) {
case OperandType::TENSOR_FLOAT16: {
std::vector<float> inputDataFloat32(getNumberOfElements(input_->shape()));
convertFloat16ToFloat32(GetBuffer<_Float16>(input_), &inputDataFloat32);
EvalFloat32(inputDataFloat32.data());
break;
}
case OperandType::TENSOR_FLOAT32: {
EvalFloat32(GetBuffer<float>(input_));
break;
}
default: {
LOG(ERROR) << "Unsupported data type: " << static_cast<int>(input_->type);
return false;
}
}
return true;
}
void Multinomial::EvalFloat32(const float* inputData) {
const int batch_size = SizeOfDimension(input_, 0);
const int class_size = SizeOfDimension(input_, 1);
tensorflow::GuardedPhiloxRandom random_generator;
int32_t* seeds = GetBuffer<int32_t>(random_seeds_);
random_generator.Init(seeds[0], seeds[1]);
// PhiloxRandom produces results as 4 32-bit integers.
int sample_count_aligned = (sample_count_ + 3) / 4 * 4;
// The CPU operation uses 64-bit double values, so two results per sample.
sample_count_aligned *= 2;
auto random_generator_reserved =
random_generator.ReserveRandomOutputs(batch_size * sample_count_aligned, 256);
tensorflow::random::SimplePhilox simple_philox(&random_generator_reserved);
for (uint64_t b = 0; b < batch_size; ++b) {
const float* input_ptr_batch = inputData + b * class_size;
float max = std::numeric_limits<float>::lowest();
for (uint64_t j = 0; j < class_size; ++j) {
if (Eigen::numext::isfinite(input_ptr_batch[j])) {
max = std::max(max, input_ptr_batch[j]);
}
}
const double batch_max = static_cast<double>(max);
double total = 0;
std::vector<double> cdf;
cdf.resize(class_size);
for (uint64_t j = 0; j < class_size; ++j) {
if (Eigen::numext::isfinite(static_cast<float>(input_ptr_batch[j]))) {
total += exp(static_cast<double>(input_ptr_batch[j]) - batch_max);
}
cdf[j] = total;
}
auto* output_ptr_batch = GetBuffer<int32_t>(output_) + b * sample_count_;
for (uint64_t j = 0; j < sample_count_; ++j) {
const double target = simple_philox.RandDouble() * total;
auto found_iter = std::upper_bound(cdf.begin(), cdf.end(), target);
output_ptr_batch[j] = std::distance(cdf.begin(), found_iter);
}
}
}
} // namespace nn
} // namespace android