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reservoir_sampling.cc
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reservoir_sampling.cc
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#include <memory>
#include <string>
#include <vector>
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
#include "caffe2/operators/map_ops.h"
namespace caffe2 {
namespace {
template <class Context>
class ReservoirSamplingOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
ReservoirSamplingOp(const OperatorDef operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
numToCollect_(
OperatorBase::GetSingleArgument<int>("num_to_collect", -1)) {
CAFFE_ENFORCE(numToCollect_ > 0);
}
bool RunOnDevice() override {
auto& mutex = OperatorBase::Input<std::unique_ptr<std::mutex>>(MUTEX);
std::lock_guard<std::mutex> guard(*mutex);
auto* output = Output(RESERVOIR);
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());
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 (!output_initialized) {
// IMPORTANT: Force the output to have the right type before reserving,
// so that the output gets the right capacity
auto dims = input.sizes().vec();
dims[0] = 0;
output->Resize(dims);
output->raw_mutable_data(input.dtype());
output->ReserveSpace(numToCollect_);
}
auto* pos_to_object =
OutputSize() > POS_TO_OBJECT ? Output(POS_TO_OBJECT) : nullptr;
if (pos_to_object) {
if (!output_initialized) {
// Cleaning up in case the reservoir got reset.
pos_to_object->Resize(0);
pos_to_object->template mutable_data<int64_t>();
pos_to_object->ReserveSpace(numToCollect_);
}
}
auto* object_to_pos_map = OutputSize() > OBJECT_TO_POS_MAP
? OperatorBase::Output<MapType64To32>(OBJECT_TO_POS_MAP)
: nullptr;
if (object_to_pos_map && !output_initialized) {
object_to_pos_map->clear();
}
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);
if (num_entries == 0) {
if (!output_initialized) {
// Get both shape and meta
output->CopyFrom(input, /* async */ true);
}
return true;
}
const int64_t* object_id_data = nullptr;
std::set<int64_t> unique_object_ids;
if (InputSize() > OBJECT_ID) {
const auto& object_id = Input(OBJECT_ID);
CAFFE_ENFORCE_EQ(object_id.dim(), 1);
CAFFE_ENFORCE_EQ(object_id.numel(), num_entries);
object_id_data = object_id.template data<int64_t>();
unique_object_ids.insert(
object_id_data, object_id_data + object_id.numel());
}
const auto num_new_entries = countNewEntries(unique_object_ids);
auto num_to_copy = std::min<int32_t>(num_new_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
output->ExtendTo(output_num, 50);
if (pos_to_object) {
pos_to_object->ExtendTo(output_num, 50);
// ExtendTo doesn't zero-initialize tensors any more, explicitly clear
// the memory
memset(
pos_to_object->template mutable_data<int64_t>() +
output_batch_size * sizeof(int64_t),
0,
(output_num - output_batch_size) * sizeof(int64_t));
}
auto* output_data =
static_cast<char*>(output->raw_mutable_data(input.dtype()));
auto* pos_to_object_data = pos_to_object
? pos_to_object->template mutable_data<int64_t>()
: nullptr;
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());
const auto start_num_visited = *num_visited;
std::set<int64_t> eligible_object_ids;
if (object_to_pos_map) {
for (auto oid : unique_object_ids) {
if (!object_to_pos_map->count(oid)) {
eligible_object_ids.insert(oid);
}
}
}
for (int i = 0; i < num_entries; ++i) {
if (object_id_data && object_to_pos_map &&
!eligible_object_ids.count(object_id_data[i])) {
// Already in the pool or processed
continue;
}
if (object_id_data) {
eligible_object_ids.erase(object_id_data[i]);
}
int64_t pos = -1;
if (*num_visited < numToCollect_) {
// append
pos = *num_visited;
} else {
// uniform between [0, num_visited]
at::uniform_int_from_to_distribution<int64_t> uniformDist(*num_visited+1, 0);
pos = uniformDist(context_.RandGenerator());
if (pos >= numToCollect_) {
// discard
pos = -1;
}
}
if (pos < 0) {
// discard
CAFFE_ENFORCE_GE(*num_visited, numToCollect_);
} else {
// replace
context_.CopyItemsSameDevice(
input.dtype(),
block_size,
input_data + i * block_bytesize,
output_data + pos * block_bytesize);
if (object_id_data && pos_to_object_data && object_to_pos_map) {
auto old_oid = pos_to_object_data[pos];
auto new_oid = object_id_data[i];
pos_to_object_data[pos] = new_oid;
object_to_pos_map->erase(old_oid);
object_to_pos_map->emplace(new_oid, pos);
}
}
++(*num_visited);
}
// Sanity check
CAFFE_ENFORCE_EQ(*num_visited, start_num_visited + num_new_entries);
return true;
}
private:
// number of tensors to collect
int numToCollect_;
INPUT_TAGS(
RESERVOIR_IN,
NUM_VISITED_IN,
DATA,
MUTEX,
OBJECT_ID,
OBJECT_TO_POS_MAP_IN,
POS_TO_OBJECT_IN);
OUTPUT_TAGS(RESERVOIR, NUM_VISITED, OBJECT_TO_POS_MAP, POS_TO_OBJECT);
int32_t countNewEntries(const std::set<int64_t>& unique_object_ids) {
const auto& input = Input(DATA);
if (InputSize() <= OBJECT_ID) {
return input.size(0);
}
const auto& object_to_pos_map =
OperatorBase::Input<MapType64To32>(OBJECT_TO_POS_MAP_IN);
return std::count_if(
unique_object_ids.begin(),
unique_object_ids.end(),
[&object_to_pos_map](int64_t oid) {
return !object_to_pos_map.count(oid);
});
}
};
REGISTER_CPU_OPERATOR(ReservoirSampling, ReservoirSamplingOp<CPUContext>);
OPERATOR_SCHEMA(ReservoirSampling)
.NumInputs({4, 7})
.NumOutputs({2, 4})
.NumInputsOutputs([](int in, int out) { return in / 3 == out / 2; })
.EnforceInplace({{0, 0}, {1, 1}, {5, 2}, {6, 3}})
.SetDoc(R"DOC(
Collect `DATA` tensor into `RESERVOIR` of size `num_to_collect`. `DATA` is
assumed to be a batch.
In case where 'objects' may be repeated in data and you only want at most one
instance of each 'object' in the reservoir, `OBJECT_ID` can be given for
deduplication. If `OBJECT_ID` is given, then you also need to supply additional
book-keeping tensors. See input blob documentation for details.
This operator is thread-safe.
)DOC")
.Arg(
"num_to_collect",
"The number of random samples to append for each positive samples")
.Input(
0,
"RESERVOIR",
"The reservoir; should be initialized to empty tensor")
.Input(
1,
"NUM_VISITED",
"Number of examples seen so far; should be initialized to 0")
.Input(
2,
"DATA",
"Tensor to collect from. The first dimension is assumed to be batch "
"size. If the object to be collected is represented by multiple "
"tensors, use `PackRecords` to pack them into single tensor.")
.Input(3, "MUTEX", "Mutex to prevent data race")
.Input(
4,
"OBJECT_ID",
"(Optional, int64) If provided, used for deduplicating object in the "
"reservoir")
.Input(
5,
"OBJECT_TO_POS_MAP_IN",
"(Optional) Auxiliary bookkeeping map. This should be created from "
" `CreateMap` with keys of type int64 and values of type int32")
.Input(
6,
"POS_TO_OBJECT_IN",
"(Optional) Tensor of type int64 used for bookkeeping in deduplication")
.Output(0, "RESERVOIR", "Same as the input")
.Output(1, "NUM_VISITED", "Same as the input")
.Output(2, "OBJECT_TO_POS_MAP", "(Optional) Same as the input")
.Output(3, "POS_TO_OBJECT", "(Optional) Same as the input");
SHOULD_NOT_DO_GRADIENT(ReservoirSampling);
} // namespace
} // namespace caffe2