1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
|
/*
* 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.
*/
#define LOG_TAG "Operations"
#include "Multinomial.h"
#include "CpuExecutor.h"
#include "CpuOperationUtils.h"
#include "Tracing.h"
#include "guarded_philox_random.h"
#include "philox_random.h"
#include "simple_philox.h"
#include <algorithm>
#include <limits>
#include <unsupported/Eigen/CXX11/Tensor>
#include <vector>
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, 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, 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
|
