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
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
|
#pragma once
#include "caffe2/core/operator.h"
#include "c10/util/irange.h"
#include <algorithm>
#include <cmath>
#include <limits>
namespace caffe2 {
template <class Context>
class SelfBinningHistogramOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit SelfBinningHistogramOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
num_bins_(this->template GetSingleArgument<int>("num_bins", 0)),
num_edges_(num_bins_ + 1),
bin_spacing_(this->template GetSingleArgument<std::string>(
"bin_spacing",
"linear")),
logspace_start_(this->template GetSingleArgument<float>("logspace_start", 1e-24)),
abs_(this->template GetSingleArgument<bool>("abs", false))
{
CAFFE_ENFORCE_GE(
num_bins_, 1, "Number of bins must be greater than or equal to 1.");
CAFFE_ENFORCE(
bin_spacing_ == "linear" || bin_spacing_ == "logarithmic",
"Bin spacing can be one of 'linear' or 'logarithmic'."
);
CAFFE_ENFORCE_GT(
logspace_start_, 0,
"Logarithmic spacing base is a multiplier and is expected to be >1.");
}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<float, double>>::call(this, Input(0));
}
template <typename T>
bool DoRunWithType() {
CheckInputs();
// Scale the range so that the last count is always 0.
const T RANGE_SCALING = 1.0001;
const auto* histogram_values = Output(HISTOGRAM_VALUES);
histogram_values->Resize(num_edges_);
auto* histogram_values_data = histogram_values->template mutable_data<T>();
const auto* histogram_counts = Output(HISTOGRAM_COUNTS);
histogram_counts->Resize(num_edges_);
auto* histogram_counts_data =
histogram_counts->template mutable_data<int64_t>();
// Calculate the max and min.
bool first_seen = false;
// 0 initialization is arbitrary here to suppress linter warnings.
// The actual initialization check happens through the first_seen variable.
T max = 0;
T min = 0;
int64_t total_count = 0;
for (const auto input_idx : c10::irange(InputSize())) {
const auto& x = Input(input_idx);
const int64_t N = x.numel();
total_count += N;
const auto* x_data = x.template data<T>();
for (const auto data_idx : c10::irange(N)) {
const T val = this->abs_ ? std::abs(x_data[data_idx]) : x_data[data_idx];
if (!first_seen) {
max = val;
min = val;
first_seen = true;
} else {
max = std::max(val, max);
min = std::min(val, min);
}
}
}
if (!first_seen) {
math::Set<T, Context>(num_edges_, 0, histogram_values_data, &context_);
math::Set<int64_t, Context>(
num_edges_, 0, histogram_counts_data, &context_);
return true;
}
CAFFE_ENFORCE(min <= max, "Incorrect min-max computation min=", min, " max=", max);
T scaled_max = 0; // this is set in both branches
if (bin_spacing_ == "linear") {
// Let's scale the range so that the last count is 0.
scaled_max = min + (max - min) * RANGE_SCALING;
T scaled_range = (scaled_max - min);
// Avoid underflow by calculating advancement through multiplication.
for (const auto i : c10::irange(num_edges_)) {
T advancement_ratio = T(i) / num_bins_;
histogram_values_data[i] = min + advancement_ratio * scaled_range;
}
} else if (bin_spacing_ == "logarithmic") {
// First, we need to sanitize the range.
if (min < logspace_start_) {
min = logspace_start_;
}
if (max < logspace_start_) {
max = logspace_start_;
}
T linear_range = max - min;
linear_range = linear_range * RANGE_SCALING;
scaled_max = min + linear_range;
// Calculate base interval using geometric sum.
// Simply: multiplier = exp((log(max) - log(min))/N)
// Avoid underflow by delaying division and exp.
T log_multiplier_numerator =log(scaled_max) - log(min);
// Avoid underflow by:
// - Calculating each advancement separately for each i.
for (const auto i : c10::irange(num_edges_)) {
T advancement_ratio = T(i)/num_bins_;
histogram_values_data[i] = min * exp(log_multiplier_numerator * advancement_ratio);
}
}
math::Set<int64_t, Context>(
num_edges_, 0, histogram_counts_data, &context_);
if (histogram_values_data[num_edges_-1] <= max) {
// In cases of min&max being equal (or any unexpected numerical underflow) we
// may not have a final edge larger than the max.
histogram_values_data[num_edges_-1] = scaled_max;
histogram_counts_data[0] = total_count;
}
else {
for (const auto input_idx : c10::irange(InputSize())) {
const auto& x = Input(input_idx);
const int64_t N = x.numel();
const auto* x_data = x.template data<T>();
for (const auto data_idx : c10::irange(N)) {
const T val = this->abs_ ? std::abs(x_data[data_idx]) : x_data[data_idx];
const auto bisection_it = std::upper_bound(
histogram_values_data,
histogram_values_data + num_edges_,
val);
const int bisection_idx = bisection_it - histogram_values_data;
if (bisection_idx > 0 && bisection_idx < num_edges_) {
histogram_counts_data[bisection_idx - 1]++;
}
if (bisection_idx == 0) {
histogram_counts_data[0]++;
}
}
}
}
return true;
}
protected:
OUTPUT_TAGS(HISTOGRAM_VALUES, HISTOGRAM_COUNTS);
private:
int num_bins_;
int num_edges_;
std::string bin_spacing_;
float logspace_start_;
bool abs_; // automatically apply abs() on the input values
void CheckInputs() {
const auto& input_zero = Input(0);
for (const auto i : c10::irange(1, InputSize())) {
CAFFE_ENFORCE_EQ(
Input(i).dtype(),
input_zero.dtype(),
"All inputs must have the same type; expected ",
input_zero.dtype().name(),
" but got ",
Input(i).dtype().name(),
" for input ",
i);
}
}
};
} // namespace caffe2
|
