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
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
|
#include <memory>
#include <string>
#include <vector>
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
namespace caffe2 {
namespace {
template <class Context>
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
class LastNWindowCollectorOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit LastNWindowCollectorOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
numToCollect_(
OperatorBase::GetSingleArgument<int>("num_to_collect", -1)) {
CAFFE_ENFORCE_GT(numToCollect_, 0);
}
bool RunOnDevice() override {
if (InputSize() > MUTEX) {
auto& mutex = OperatorBase::Input<std::unique_ptr<std::mutex>>(MUTEX);
std::lock_guard<std::mutex> guard(*mutex);
return collect();
} else {
return collect();
}
}
private:
const int32_t numToCollect_;
bool collect() {
auto* output = Output(LAST_N);
const auto& input = Input(DATA);
CAFFE_ENFORCE_GE(input.dim(), 1);
bool output_initialized = output->numel() > 0 &&
(static_cast<std::shared_ptr<std::vector<TensorCPU>>*>(
output->raw_mutable_data(input.dtype()))[0] != nullptr);
if (output_initialized) {
CAFFE_ENFORCE_EQ(output->dim(), input.dim());
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
for (size_t i = 1; i < input.dim(); ++i) {
CAFFE_ENFORCE_EQ(output->size(i), input.size(i));
}
}
auto num_entries = input.sizes()[0];
if (OutputSize() > NUM_VISITED) {
auto* num_visited_tensor = Output(NUM_VISITED);
CAFFE_ENFORCE_EQ(1, num_visited_tensor->numel());
auto* num_visited = num_visited_tensor->template mutable_data<int64_t>();
if (!output_initialized) {
*num_visited = 0;
}
CAFFE_ENFORCE_GE(*num_visited, 0);
*num_visited += num_entries;
}
if (!output_initialized) {
auto dims = input.sizes().vec();
dims[0] = 0;
output->Resize(dims);
// pass meta to output
output->raw_mutable_data(input.dtype());
output->ReserveSpace(numToCollect_);
}
if (num_entries == 0) {
if (!output_initialized) {
// Get both shape and meta
output->CopyFrom(input, true /*async*/);
}
return true;
}
auto num_to_copy = std::min<int32_t>(num_entries, numToCollect_);
auto output_batch_size = output_initialized ? output->size(0) : 0;
auto output_num =
std::min<size_t>(numToCollect_, output_batch_size + num_to_copy);
// output_num is >= output_batch_size
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
if (output_num > output_batch_size) {
output->ExtendTo(output_num, 50);
}
auto* output_data =
static_cast<char*>(output->raw_mutable_data(input.dtype()));
auto* next = Output(NEXT);
CAFFE_ENFORCE_EQ(0, next->dim());
auto* next_data = next->template mutable_data<int32_t>();
if (!output_initialized) {
*next_data = 0;
}
CAFFE_ENFORCE_LT(*next_data, output->size(0));
auto block_size = input.size_from_dim(1);
auto block_bytesize = block_size * input.itemsize();
const auto* input_data = static_cast<const char*>(input.raw_data());
if (num_entries > numToCollect_) {
// just copy the last N rows
context_.CopyItemsSameDevice(
input.dtype(),
num_to_copy * block_size,
input_data + (num_entries - numToCollect_) * block_bytesize,
output_data);
*next_data = 0;
return true;
}
auto start = *next_data;
auto first_chunk_size =
std::min<size_t>(num_to_copy + start, numToCollect_) - start;
context_.CopyItemsSameDevice(
input.dtype(),
first_chunk_size * block_size,
input_data,
output_data + start * block_bytesize);
context_.CopyItemsSameDevice(
input.dtype(),
(num_to_copy - first_chunk_size) * block_size,
input_data + first_chunk_size * block_bytesize,
output_data);
*next_data = (start + num_to_copy) % numToCollect_;
return true;
}
INPUT_TAGS(LAST_N_IN, NEXT_IN, DATA, MUTEX, NUM_VISITED_IN);
OUTPUT_TAGS(LAST_N, NEXT, NUM_VISITED);
};
REGISTER_CPU_OPERATOR(LastNWindowCollector, LastNWindowCollectorOp<CPUContext>);
OPERATOR_SCHEMA(LastNWindowCollector)
.NumInputs({3, 4, 5})
.NumOutputs(2, 3)
.EnforceInplace({{0, 0}, {1, 1}, {4, 2}})
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
auto output_size = def.output_size();
vector<TensorShape> out(output_size);
const ArgumentHelper helper(def);
const auto num_to_collect =
helper.GetSingleArgument<int>("num_to_collect", -1);
const auto data_dims = GetDimsVector(in[2]);
vector<int64_t> last_n_shape(data_dims.size());
last_n_shape[0] = num_to_collect;
std::copy(data_dims.begin() + 1, data_dims.end(), last_n_shape.begin() + 1);
out[0] = CreateTensorShape(last_n_shape, in[2].data_type());
out[1] = in[1];
if (output_size > 2) {
vector<int64_t> num_visited_shape(1);
num_visited_shape[0] = 1;
out[2] = CreateTensorShape(num_visited_shape, TensorProto::INT64);
}
return out;
})
.SetDoc(R"DOC(
Collect the last N rows from input data. The purpose is to keep track of data
accross batches, so for example suppose the LastNWindowCollector is called
successively with the following input data
[1, 2, 3, 4]
[5, 6, 7]
[8, 9, 10, 11]
And the number of items is set to 6, then the output after the 3rd call
will contain the following elements:
[6, 7, 8, 9, 10, 11]
No guarantee is made on the ordering of elements in input. So a valid value for
output could have been
[11, 10, 9, 8, 7, 6]
Also, this method works for any order tensor, treating the first dimension as
input rows and keeping the last N rows seen as input. So for instance:
[[1, 2], [2, 3], [3, 4], [4, 5]]
[[5, 6], [6, 7], [7, 8]]
[[8, 9], [9, 10], [10, 11], [11, 12]]
A possible output would be
[[6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12]]
This is not thread safe unless a mutex is given.
)DOC")
.Arg(
"num_to_collect",
"The number of random samples to append for each positive samples")
.Input(
0,
"last-N buffer",
"The buffer for last-N record. Should be initialized to empty tensor")
.Input(
1,
"next cursor",
"The cursor pointing to the next position that should be replaced. "
"Should be initialized to 0.")
.Input(2, "DATA", "tensor to collect from")
.Input(3, "MUTEX", "(optional) mutex to use to make this thread-safe")
.Input(4, "NUM_VISITED", "")
.Output(0, "last-N buffer", "Data stored in sessions")
.Output(1, "next cursor", "Updated input cursor")
.Output(2, "NUM_VISITED", "number of records seen so far");
SHOULD_NOT_DO_GRADIENT(LastNWindowCollector);
} // namespace
} // namespace caffe2
|
